AIRWiki - User contributions [en]

Master Level Course Projects

2009-04-01T14:34:13Z

FisherAvril:

Here you can find a list of project proposals for the courses of "Laboratorio di Intelligenza Artificiale e Robotica" (5 CFU for each student) and "Soft Computing" (1 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:FisherAvril|Fisher Avril]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=90-60-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of existing algorithms able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}

==== Evolutionary Computation ====

{{Project template
|title=Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms [1,2,3,4], a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers [4]. The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Picture taken from http://www.genetic-programming.org

;Bibliography
*[1] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[2] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[3] Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
*[4] Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.

|start=Anytime
|number=1-2
|cfu=5-10
|image=genetic.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=5-20
|image=CAP_Sleep_Staging.jpg}}



==== BioSignal Analysis ====

{{Project template
|title=Human-computer interaction via voice recognition system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=We want develop a system to allow a voice interaction between the user and the wheelchair.
This project consists in develop one of the solutions proposed in literature and extended the LURCH software to include this kind of interface.
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
* Phinx project [http://cmusphinx.org/]

|start= Anytime
|number=1-2
|cfu=2.5-10
|image=LURCH_wheelchair.jpg
}}

===== Brain-Computer Interface =====

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000]
:Linux

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000]

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}

{{Project template
|title=Reproduction of an algorithm for the recognition of error potentials
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Error potentials (ErrPs) are [http://en.wikipedia.org/wiki/Event-related_potential event-related potentials] present in the EEG (electroencephalogram) when a subject makes a mistake or when the machine a subject is interacting with works in an expected way. They could be used in the [[Brain-Computer Interface|BCI]] field to improve the performance of a BCI by automatically detecting classification errors.
The project aims at reproducing algorithms for ErrP detection from the literature.

;Tools and instruments
:Matlab

;Bibliography
:P.W. Ferrez, J. Millán. ''You Are Wrong! Automatic Detection of Interaction Errors from Brain Waves'' [ftp://ftp.idiap.ch/pub/reports/2005/ferrez_2005_ijcai.pdf]
:G. Schalk et al. ''EEG-based communication: presence of an error potential'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=13882457&volume=111&issue=12&firstpage=2138&form=html]
|start=This project has already been assigned
|number=1
|cfu=5-15
|image=Bci_arch.png}}

==== Computer Vision and Image Analysis ====

{{Project template
|title=Environment Monitoring
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a video surveillance system to track in 3D vehicles or people.
The idea is to use one or more calibrated camera to estimate the position and the trajectories of the moving objects in the scene.
The skills required for this project are:
* C/C++ and OpenCV library
* Linux o.s.
* Geometry/Image processing
* Probabilistic robotics/IMAD

The project can be turned into a thesis extending the algorithm for a generic outdoor environment.
|start=Anytime
|number=2-3
|cfu=10-15
|image=Danch4.png
}}

{{Project template
|title=Visual Merchandising
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop algorithms to count the number of products on the shelves of a market.
The idea is to use a calibrated camera to recognize the shelves, estimate the scale and improve the image quality.
The skills required for this project are:
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=VisualM.jpg
}}

{{Project template
|title=Analysis of patch recognition algorithms
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Extract distinctive features from images is very important in computer vision application.
It can be used in algorithms for tasks like matching different views of an object or scene (e.g. for stereo vision) and object recognition.
The aim of this work is to integrate in an existent framework the existing solution proposed in literature.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Oxford website [http://www.robots.ox.ac.uk/~vgg/research/affine/index.html]
*Hess website [http://web.engr.oregonstate.edu/~hess/index.html]
*Feature FAST [http://mi.eng.cam.ac.uk/~er258/work/fast.html]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Object.jpg
}}

{{Project template
|title=Catadioptric MonoSLAM
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this work is to investigate a SLAM solutions based on catadioptric camera, integrating the solution presented in literature into an existing frameword.
Improvements could be the basis for a tesi.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Visual SLAM by Single Catadioptric Stereo [http://cv2.kaist.ac.kr/VisualSLAMBySingleCameraCatadioptricStereo.pdf]
*Catadioptric reconstruction [http://citeseer.ist.psu.edu/cache/papers/cs/23657/http:zSzzSzwww.cis.upenn.eduzSz~cgeyerzSzsfm_tr.pdf/geyer01structure.pdf]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Photo.jpg
}}

{{Project template
|title=Trinocular Vision System (SUGR)
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A Trinocular Vision System is a device composed by three cameras that allows to measure 3D data (in this case segments) directly from images.
The aim of this tesina/project is to implement a trinocular algorithm based on SUGR, a library for Uncertain Projective Geometry.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=Trinoex.jpg
}}

{{Project template
|title=GIFT and features extraction and description
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The idea is to improve and optimize the solution proposed by Campari et al. in their paper, who propose to estimate invariant descriptor using geodesic features descriptor based on color information.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= Anytime
|number=1-3
|cfu=10-20
|image=Palla_GIFT.jpg
}}

{{Project template
|title=Multimedia Indexing Framework
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a framework for multimedia indexing.
The idea is create an images database indexer that allows to make query using images or strings.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
*CBIR system definition [http://en.wikipedia.org/wiki/CBIR]
*Image database [http://www.cs.washington.edu/research/imagedatabase/]

|start= Anytime
|number=1-3
|cfu=2.5-15
|image=CIR.gif
}}


==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2, 3] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning Competition
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=This project has the goal of participating to (and possibly winning ;)) the 2009 Reinforcement Learning competition. To have an idea of what participate to such a competition means you can have a look at the website of the [http://rl-competition.org/content/view/51/79/ 2008 RL competition].
The problems that will be proposed are still unknown. As soon as the domains will be published, the work will start by analyzing their main characteristics and, then we will identify which RL algorithms are most suited for solving such problems. After an implementation phase, the project will required a long experimental period to tune the parameters of the learning algorithms in order to improve the performance as much as possible.
|start=January, 2009
|number=2-4
|cfu=10-20
|image=keepaway.gif}}

{{Project template
|title= Learning API for TORCS
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. The goal of this project is to extend the existing C++ API (available [http://cig.dei.polimi.it/ here]) to simplify the development of controller using a learning framework.
Such an extension can be partially developed by porting an existing Java API for TORCS that already provides a lot of functionalities for machine learning approaches.
|start=Anytime
|number=1 to 2
|cfu=5 to 12.5
|image=TORCS.jpg}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS3.jpg}}

{{Project template
|title= TORCS competition
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques.
The goal of this project is to apply any machine learning technique to develop a successfull controller following the competition rules (available [http://cig.dei.polimi.it/?page_id=67 here])
|start=Anytime
|number=1
|cfu=5
|image=TORCS.jpg}}



==== Robotics ====

{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots
* Design of the game and a new suitable robot
* Implementation/setting of a suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

The project can be turned into a thesis by producing a new game and robot.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=Robowii_robot.jpg}}

{{Project template
|title= Robocup: soccer robots
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it), Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to finalize the team of robots that will participate to the robocup world championship in Graz next summer (see the [http://www.robocup.org Robocup page] and the [http://robocup.elet.polimi.it MRT Team page])
Projects are available in different areas:
* Implementation of mechanical and electronical parts of the robots for the management of the ball and kicking
* Design of robot behaviors (fuzzy systems)
* Coordination of robots
* New sensors

These projects allow to experiment with real mobile robots. Participation to the championships is a unique experience (2000 people, with 800 robots playing all sort of games...)

The project can be turned into a thesis by facing different problems in depth.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=RIeRO.jpg}}

{{Project template
|title=Calibration of IMU-camera system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This work is about the problem to calibrate a system composed by an XSense
Inertial Measurement Unit and a Fire-i Camera. The pro ject will be focus on
the problem to estimate both unknown rotation between the two devices and the
extrinsic/intrinsic parameters of the camera. This algorithm allows to use the
system for SLAM or robotics applications, like a wereable device for autonomous
navigation or augmented reality.

;Tools and instruments
:Matlab/C++

;Links
:Matlab Toolbox for mutual calibration [http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Toolbox.html]
:List of pubblications[http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Pubs.php]

|start=Anytime
|number=1
|cfu=5-20
|image=Imu_cam_big_sphere.gif}}

{{Project template
|title=MonoSLAM system implementation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The aim of this proposal is to investigate the different monocamera SLAM solution proposed in literature.
After a deepen bibliography research, the work will be focused on developing one of these algorithms into an existing framework and, only for tesi option, investigate possible improvements.

The algorithms interested are based on [http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm]:
*Extended Kalman Filter [http://www.doc.ic.ac.uk/~ajd/publications.html]
*Unscented Kalman Filter [http://www.cs.unc.edu/~welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf]
*FastSLAM [http://robots.stanford.edu/papers.html]
*GraphSLAM [http://mi.eng.cam.ac.uk/~ee231/]

;Tools and instruments
:Matlab/C++

|start=Anytime
|number=1-2
|cfu=5-20
|image=KC_jc_third.jpg}}

Master Level Theses

2009-04-01T14:33:54Z

FisherAvril:

Here you can find proposals for master thesis (20 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:FisherAvril|Fisher Avril]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=90-60-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}



==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====

{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
*[4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
*[5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}



==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}

Master Level Course Projects

2009-04-01T14:17:05Z

FisherAvril:

Here you can find a list of project proposals for the courses of "Laboratorio di Intelligenza Artificiale e Robotica" (5 CFU for each student) and "Soft Computing" (1 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:FisherAvril|Fisher Avril]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of existing algorithms able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}

==== Evolutionary Computation ====

{{Project template
|title=Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms [1,2,3,4], a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers [4]. The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Picture taken from http://www.genetic-programming.org

;Bibliography
*[1] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[2] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[3] Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
*[4] Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.

|start=Anytime
|number=1-2
|cfu=5-10
|image=genetic.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=5-20
|image=CAP_Sleep_Staging.jpg}}



==== BioSignal Analysis ====

{{Project template
|title=Human-computer interaction via voice recognition system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=We want develop a system to allow a voice interaction between the user and the wheelchair.
This project consists in develop one of the solutions proposed in literature and extended the LURCH software to include this kind of interface.
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
* Phinx project [http://cmusphinx.org/]

|start= Anytime
|number=1-2
|cfu=2.5-10
|image=LURCH_wheelchair.jpg
}}

===== Brain-Computer Interface =====

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000]
:Linux

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000]

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}

{{Project template
|title=Reproduction of an algorithm for the recognition of error potentials
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Error potentials (ErrPs) are [http://en.wikipedia.org/wiki/Event-related_potential event-related potentials] present in the EEG (electroencephalogram) when a subject makes a mistake or when the machine a subject is interacting with works in an expected way. They could be used in the [[Brain-Computer Interface|BCI]] field to improve the performance of a BCI by automatically detecting classification errors.
The project aims at reproducing algorithms for ErrP detection from the literature.

;Tools and instruments
:Matlab

;Bibliography
:P.W. Ferrez, J. Millán. ''You Are Wrong! Automatic Detection of Interaction Errors from Brain Waves'' [ftp://ftp.idiap.ch/pub/reports/2005/ferrez_2005_ijcai.pdf]
:G. Schalk et al. ''EEG-based communication: presence of an error potential'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=13882457&volume=111&issue=12&firstpage=2138&form=html]
|start=This project has already been assigned
|number=1
|cfu=5-15
|image=Bci_arch.png}}

==== Computer Vision and Image Analysis ====

{{Project template
|title=Environment Monitoring
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a video surveillance system to track in 3D vehicles or people.
The idea is to use one or more calibrated camera to estimate the position and the trajectories of the moving objects in the scene.
The skills required for this project are:
* C/C++ and OpenCV library
* Linux o.s.
* Geometry/Image processing
* Probabilistic robotics/IMAD

The project can be turned into a thesis extending the algorithm for a generic outdoor environment.
|start=Anytime
|number=2-3
|cfu=10-15
|image=Danch4.png
}}

{{Project template
|title=Visual Merchandising
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop algorithms to count the number of products on the shelves of a market.
The idea is to use a calibrated camera to recognize the shelves, estimate the scale and improve the image quality.
The skills required for this project are:
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=VisualM.jpg
}}

{{Project template
|title=Analysis of patch recognition algorithms
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Extract distinctive features from images is very important in computer vision application.
It can be used in algorithms for tasks like matching different views of an object or scene (e.g. for stereo vision) and object recognition.
The aim of this work is to integrate in an existent framework the existing solution proposed in literature.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Oxford website [http://www.robots.ox.ac.uk/~vgg/research/affine/index.html]
*Hess website [http://web.engr.oregonstate.edu/~hess/index.html]
*Feature FAST [http://mi.eng.cam.ac.uk/~er258/work/fast.html]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Object.jpg
}}

{{Project template
|title=Catadioptric MonoSLAM
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this work is to investigate a SLAM solutions based on catadioptric camera, integrating the solution presented in literature into an existing frameword.
Improvements could be the basis for a tesi.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Visual SLAM by Single Catadioptric Stereo [http://cv2.kaist.ac.kr/VisualSLAMBySingleCameraCatadioptricStereo.pdf]
*Catadioptric reconstruction [http://citeseer.ist.psu.edu/cache/papers/cs/23657/http:zSzzSzwww.cis.upenn.eduzSz~cgeyerzSzsfm_tr.pdf/geyer01structure.pdf]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Photo.jpg
}}

{{Project template
|title=Trinocular Vision System (SUGR)
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A Trinocular Vision System is a device composed by three cameras that allows to measure 3D data (in this case segments) directly from images.
The aim of this tesina/project is to implement a trinocular algorithm based on SUGR, a library for Uncertain Projective Geometry.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=Trinoex.jpg
}}

{{Project template
|title=GIFT and features extraction and description
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The idea is to improve and optimize the solution proposed by Campari et al. in their paper, who propose to estimate invariant descriptor using geodesic features descriptor based on color information.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= Anytime
|number=1-3
|cfu=10-20
|image=Palla_GIFT.jpg
}}

{{Project template
|title=Multimedia Indexing Framework
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a framework for multimedia indexing.
The idea is create an images database indexer that allows to make query using images or strings.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
*CBIR system definition [http://en.wikipedia.org/wiki/CBIR]
*Image database [http://www.cs.washington.edu/research/imagedatabase/]

|start= Anytime
|number=1-3
|cfu=2.5-15
|image=CIR.gif
}}


==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2, 3] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning Competition
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=This project has the goal of participating to (and possibly winning ;)) the 2009 Reinforcement Learning competition. To have an idea of what participate to such a competition means you can have a look at the website of the [http://rl-competition.org/content/view/51/79/ 2008 RL competition].
The problems that will be proposed are still unknown. As soon as the domains will be published, the work will start by analyzing their main characteristics and, then we will identify which RL algorithms are most suited for solving such problems. After an implementation phase, the project will required a long experimental period to tune the parameters of the learning algorithms in order to improve the performance as much as possible.
|start=January, 2009
|number=2-4
|cfu=10-20
|image=keepaway.gif}}

{{Project template
|title= Learning API for TORCS
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. The goal of this project is to extend the existing C++ API (available [http://cig.dei.polimi.it/ here]) to simplify the development of controller using a learning framework.
Such an extension can be partially developed by porting an existing Java API for TORCS that already provides a lot of functionalities for machine learning approaches.
|start=Anytime
|number=1 to 2
|cfu=5 to 12.5
|image=TORCS.jpg}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS3.jpg}}

{{Project template
|title= TORCS competition
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques.
The goal of this project is to apply any machine learning technique to develop a successfull controller following the competition rules (available [http://cig.dei.polimi.it/?page_id=67 here])
|start=Anytime
|number=1
|cfu=5
|image=TORCS.jpg}}



==== Robotics ====

{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots
* Design of the game and a new suitable robot
* Implementation/setting of a suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

The project can be turned into a thesis by producing a new game and robot.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=Robowii_robot.jpg}}

{{Project template
|title= Robocup: soccer robots
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it), Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to finalize the team of robots that will participate to the robocup world championship in Graz next summer (see the [http://www.robocup.org Robocup page] and the [http://robocup.elet.polimi.it MRT Team page])
Projects are available in different areas:
* Implementation of mechanical and electronical parts of the robots for the management of the ball and kicking
* Design of robot behaviors (fuzzy systems)
* Coordination of robots
* New sensors

These projects allow to experiment with real mobile robots. Participation to the championships is a unique experience (2000 people, with 800 robots playing all sort of games...)

The project can be turned into a thesis by facing different problems in depth.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=RIeRO.jpg}}

{{Project template
|title=Calibration of IMU-camera system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This work is about the problem to calibrate a system composed by an XSense
Inertial Measurement Unit and a Fire-i Camera. The pro ject will be focus on
the problem to estimate both unknown rotation between the two devices and the
extrinsic/intrinsic parameters of the camera. This algorithm allows to use the
system for SLAM or robotics applications, like a wereable device for autonomous
navigation or augmented reality.

;Tools and instruments
:Matlab/C++

;Links
:Matlab Toolbox for mutual calibration [http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Toolbox.html]
:List of pubblications[http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Pubs.php]

|start=Anytime
|number=1
|cfu=5-20
|image=Imu_cam_big_sphere.gif}}

{{Project template
|title=MonoSLAM system implementation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The aim of this proposal is to investigate the different monocamera SLAM solution proposed in literature.
After a deepen bibliography research, the work will be focused on developing one of these algorithms into an existing framework and, only for tesi option, investigate possible improvements.

The algorithms interested are based on [http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm]:
*Extended Kalman Filter [http://www.doc.ic.ac.uk/~ajd/publications.html]
*Unscented Kalman Filter [http://www.cs.unc.edu/~welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf]
*FastSLAM [http://robots.stanford.edu/papers.html]
*GraphSLAM [http://mi.eng.cam.ac.uk/~ee231/]

;Tools and instruments
:Matlab/C++

|start=Anytime
|number=1-2
|cfu=5-20
|image=KC_jc_third.jpg}}

Master Level Theses

2009-04-01T14:16:45Z

FisherAvril:

Here you can find proposals for master thesis (20 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:FisherAvril|Fisher Avril]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}



==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====

{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
*[4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
*[5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}



==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}

Master Level Theses

2009-04-01T14:16:06Z

FisherAvril:

Here you can find proposals for master thesis (20 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:FisherAvril]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}



==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====

{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
*[4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
*[5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}



==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}

Master Level Course Projects

2009-04-01T14:10:36Z

FisherAvril:

Here you can find a list of project proposals for the courses of "Laboratorio di Intelligenza Artificiale e Robotica" (5 CFU for each student) and "Soft Computing" (1 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:Admin]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of existing algorithms able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}

==== Evolutionary Computation ====

{{Project template
|title=Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms [1,2,3,4], a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, where classical crossover and mutation operators, used in genetic algorithms, are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers [4]. The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Picture taken from http://www.genetic-programming.org

;Bibliography
*[1] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[2] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[3] Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
*[4] Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.

|start=Anytime
|number=1-2
|cfu=5-10
|image=genetic.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=5-20
|image=CAP_Sleep_Staging.jpg}}



==== BioSignal Analysis ====

{{Project template
|title=Human-computer interaction via voice recognition system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=We want develop a system to allow a voice interaction between the user and the wheelchair.
This project consists in develop one of the solutions proposed in literature and extended the LURCH software to include this kind of interface.
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
* Phinx project [http://cmusphinx.org/]

|start= Anytime
|number=1-2
|cfu=2.5-10
|image=LURCH_wheelchair.jpg
}}

===== Brain-Computer Interface =====

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000]
:Linux

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
Depending on the effort the student is willing to put into it, the project can grow to a full experimental thesis.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000]

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}

{{Project template
|title=Reproduction of an algorithm for the recognition of error potentials
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Error potentials (ErrPs) are [http://en.wikipedia.org/wiki/Event-related_potential event-related potentials] present in the EEG (electroencephalogram) when a subject makes a mistake or when the machine a subject is interacting with works in an expected way. They could be used in the [[Brain-Computer Interface|BCI]] field to improve the performance of a BCI by automatically detecting classification errors.
The project aims at reproducing algorithms for ErrP detection from the literature.

;Tools and instruments
:Matlab

;Bibliography
:P.W. Ferrez, J. Millán. ''You Are Wrong! Automatic Detection of Interaction Errors from Brain Waves'' [ftp://ftp.idiap.ch/pub/reports/2005/ferrez_2005_ijcai.pdf]
:G. Schalk et al. ''EEG-based communication: presence of an error potential'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=13882457&volume=111&issue=12&firstpage=2138&form=html]
|start=This project has already been assigned
|number=1
|cfu=5-15
|image=Bci_arch.png}}

==== Computer Vision and Image Analysis ====

{{Project template
|title=Environment Monitoring
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a video surveillance system to track in 3D vehicles or people.
The idea is to use one or more calibrated camera to estimate the position and the trajectories of the moving objects in the scene.
The skills required for this project are:
* C/C++ and OpenCV library
* Linux o.s.
* Geometry/Image processing
* Probabilistic robotics/IMAD

The project can be turned into a thesis extending the algorithm for a generic outdoor environment.
|start=Anytime
|number=2-3
|cfu=10-15
|image=Danch4.png
}}

{{Project template
|title=Visual Merchandising
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop algorithms to count the number of products on the shelves of a market.
The idea is to use a calibrated camera to recognize the shelves, estimate the scale and improve the image quality.
The skills required for this project are:
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=VisualM.jpg
}}

{{Project template
|title=Analysis of patch recognition algorithms
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=Extract distinctive features from images is very important in computer vision application.
It can be used in algorithms for tasks like matching different views of an object or scene (e.g. for stereo vision) and object recognition.
The aim of this work is to integrate in an existent framework the existing solution proposed in literature.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Oxford website [http://www.robots.ox.ac.uk/~vgg/research/affine/index.html]
*Hess website [http://web.engr.oregonstate.edu/~hess/index.html]
*Feature FAST [http://mi.eng.cam.ac.uk/~er258/work/fast.html]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Object.jpg
}}

{{Project template
|title=Catadioptric MonoSLAM
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this work is to investigate a SLAM solutions based on catadioptric camera, integrating the solution presented in literature into an existing frameword.
Improvements could be the basis for a tesi.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

References:
*Visual SLAM by Single Catadioptric Stereo [http://cv2.kaist.ac.kr/VisualSLAMBySingleCameraCatadioptricStereo.pdf]
*Catadioptric reconstruction [http://citeseer.ist.psu.edu/cache/papers/cs/23657/http:zSzzSzwww.cis.upenn.eduzSz~cgeyerzSzsfm_tr.pdf/geyer01structure.pdf]

|start= Anytime
|number=2-3
|cfu=2.5-15
|image=Photo.jpg
}}

{{Project template
|title=Trinocular Vision System (SUGR)
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A Trinocular Vision System is a device composed by three cameras that allows to measure 3D data (in this case segments) directly from images.
The aim of this tesina/project is to implement a trinocular algorithm based on SUGR, a library for Uncertain Projective Geometry.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= As soon as possible
|number=2-3
|cfu=2.5-15
|image=Trinoex.jpg
}}

{{Project template
|title=GIFT and features extraction and description
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The idea is to improve and optimize the solution proposed by Campari et al. in their paper, who propose to estimate invariant descriptor using geodesic features descriptor based on color information.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux
* Geometry/Image processing

|start= Anytime
|number=1-3
|cfu=10-20
|image=Palla_GIFT.jpg
}}

{{Project template
|title=Multimedia Indexing Framework
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The goal of this project is to develop a framework for multimedia indexing.
The idea is create an images database indexer that allows to make query using images or strings.

Skills
* C/C++ and OpenCV library
* Matlab (optionally)
* Linux

References:
*CBIR system definition [http://en.wikipedia.org/wiki/CBIR]
*Image database [http://www.cs.washington.edu/research/imagedatabase/]

|start= Anytime
|number=1-3
|cfu=2.5-15
|image=CIR.gif
}}


==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2, 3] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning Competition
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=This project has the goal of participating to (and possibly winning ;)) the 2009 Reinforcement Learning competition. To have an idea of what participate to such a competition means you can have a look at the website of the [http://rl-competition.org/content/view/51/79/ 2008 RL competition].
The problems that will be proposed are still unknown. As soon as the domains will be published, the work will start by analyzing their main characteristics and, then we will identify which RL algorithms are most suited for solving such problems. After an implementation phase, the project will required a long experimental period to tune the parameters of the learning algorithms in order to improve the performance as much as possible.
|start=January, 2009
|number=2-4
|cfu=10-20
|image=keepaway.gif}}

{{Project template
|title= Learning API for TORCS
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. The goal of this project is to extend the existing C++ API (available [http://cig.dei.polimi.it/ here]) to simplify the development of controller using a learning framework.
Such an extension can be partially developed by porting an existing Java API for TORCS that already provides a lot of functionalities for machine learning approaches.
|start=Anytime
|number=1 to 2
|cfu=5 to 12.5
|image=TORCS.jpg}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=5 to 20
|image=TORCS3.jpg}}

{{Project template
|title= TORCS competition
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques.
The goal of this project is to apply any machine learning technique to develop a successfull controller following the competition rules (available [http://cig.dei.polimi.it/?page_id=67 here])
|start=Anytime
|number=1
|cfu=5
|image=TORCS.jpg}}



==== Robotics ====

{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots
* Design of the game and a new suitable robot
* Implementation/setting of a suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

The project can be turned into a thesis by producing a new game and robot.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=Robowii_robot.jpg}}

{{Project template
|title= Robocup: soccer robots
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it), Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to finalize the team of robots that will participate to the robocup world championship in Graz next summer (see the [http://www.robocup.org Robocup page] and the [http://robocup.elet.polimi.it MRT Team page])
Projects are available in different areas:
* Implementation of mechanical and electronical parts of the robots for the management of the ball and kicking
* Design of robot behaviors (fuzzy systems)
* Coordination of robots
* New sensors

These projects allow to experiment with real mobile robots. Participation to the championships is a unique experience (2000 people, with 800 robots playing all sort of games...)

The project can be turned into a thesis by facing different problems in depth.
|start=Anytime
|number=1-2
|cfu=5-12.5
|image=RIeRO.jpg}}

{{Project template
|title=Calibration of IMU-camera system
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This work is about the problem to calibrate a system composed by an XSense
Inertial Measurement Unit and a Fire-i Camera. The pro ject will be focus on
the problem to estimate both unknown rotation between the two devices and the
extrinsic/intrinsic parameters of the camera. This algorithm allows to use the
system for SLAM or robotics applications, like a wereable device for autonomous
navigation or augmented reality.

;Tools and instruments
:Matlab/C++

;Links
:Matlab Toolbox for mutual calibration [http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Toolbox.html]
:List of pubblications[http://www.deec.uc.pt/~jlobo/InerVis_WebIndex/InerVis_Pubs.php]

|start=Anytime
|number=1
|cfu=5-20
|image=Imu_cam_big_sphere.gif}}

{{Project template
|title=MonoSLAM system implementation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:DavideMigliore|Davide Migliore]] ([mailto:migliore%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=The aim of this proposal is to investigate the different monocamera SLAM solution proposed in literature.
After a deepen bibliography research, the work will be focused on developing one of these algorithms into an existing framework and, only for tesi option, investigate possible improvements.

The algorithms interested are based on [http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm]:
*Extended Kalman Filter [http://www.doc.ic.ac.uk/~ajd/publications.html]
*Unscented Kalman Filter [http://www.cs.unc.edu/~welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf]
*FastSLAM [http://robots.stanford.edu/papers.html]
*GraphSLAM [http://mi.eng.cam.ac.uk/~ee231/]

;Tools and instruments
:Matlab/C++

|start=Anytime
|number=1-2
|cfu=5-20
|image=KC_jc_third.jpg}}

File:Bra.jpg

2009-04-01T14:09:57Z

FisherAvril:

Master Level Theses

2009-04-01T14:07:38Z

FisherAvril:

Here you can find proposals for master thesis (20 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:Admin]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}



==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====

{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
*[4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
*[5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}



==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}

Master Level Theses

2009-04-01T14:07:15Z

FisherAvril:

Here you can find proposals for master thesis (20 CFU for each student). See [[Project Proposals]] for other kinds of projects and theses.

{{Project template
|title=Genetic Algorithms for the optimization of the structural design of push-up bras
|tutor=[[User:Admin]]
|description=

The project will focus on the application of techniques from evolutionary optimization, such as Genetic Algorithms or Evolutionary Strategies, for the optimization of design of push-up bras for women. In particular the student will focus on the identification of the best shape for extra large breast, in order to propose new comfortable and safe designs, to be worn in different contexts, such as gym or disco-pub.

The project is divided into three parts. First a review of the existing literature, both in the engineering community and the fashion literature. In particular students will be asked to create a statistical model of the shape of breasts of women, using techniques that come image processing, starting from a dataset of pictures, taken from last 20 years of the Playboy magazine.
Starting from the result of the first phase, genetic algorithms and other techniques will be used to improve the shape of current state-of-the -art bras. Finally, the last phase will consist of a validation of the model. For the purpose we have a partnership with Victoria Secrets, Milan, that will provide some models, in order to experimentally validate the results.

Successful design will be presented at the next Lingerie & Swimwear exhibition in Milan. Good coding techniques are required, and good spoken English language is a plus, in order to interact with other people involved in the project.

Picture taken from www.victoriassecret.com

;Bibliography
*[1] Genetic Algorithm Handbook
*[2] Playboy, magazine, from January 1989 to April 2009

|start=Starting from 1st of April
|number=1-2
|cfu=69-90
|image=bra.jpg}}

==== Evolutionary Optimization and Stochastic Optimization ====

{{Project template
|title=Combinatorial optimization based on stochastic relaxation
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

;Bibliography
*[1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
*[2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
*[3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
*[4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods

|start=Anytime
|number=1-2
|cfu=20-40
|image=stochastic.jpg}}



==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====

{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====

{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Statistical inference for phylogenetic trees
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc-AT-elet-DOT-polimi-DOT-it]), [[User:LuigiMalago|Luigi Malagò]] ([mailto:malago-AT-elet-DOT-polimi-DOT-it email])
|description=The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/

;Bibliography
*[1] Felsenstein 2003: Inferring Phylogenies
*[2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
*[3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
*[4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
*[5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.

|start=Anytime
|number=1-2
|cfu=20-40
|image=toloverview.jpg}}

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}



==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}