Difference between revisions of "Master Level Course Projects"

Project phases:

• Design and implementation of the game (it is possible to start form avaliable open source game)
• Design of experimental protocol used to stimulate particolar emotions.
• Data acquisition by usign biological sensors during the playing experience.
• Off-line classification of data with avaliable tools.
• Desing and develop of on-line classifier sistem for emotion recognition
• Closed loop control: the game reacts to the user emotional state changing its behaviour.

These projects allow to experiment with biological-data acquisition tools and videogames design.

Each project consists on the realization of one or more phases depending on the difficulty/cfu to be achieved and to the competences of the candidate(s)

Project phases:

• Design and implementation of the multimedia application.
• Design of experimental protocol used to stimulate particolar emotions.
• Data acquisition by usign biological sensors during the multimedia experience.
• Off-line classification of data with avaliable tools.
• Desing and develop of on-line classifier sistem for emotion recognition
• Closed loop control: the multimedia application will provide contents according to your enjoyment .

These projects allow to experiment with biological-data acquisition tools and multimedia application design.

The project consists on the realization of one or more phases depending on the difficulty/cfu to be achieved and to the competences of the candidate(s)

Project phases:

• Design and implementation of the robotic game on the avaliable robot.
• Design of experimental protocol used to stimulate particolar emotions.
• Data acquisition by usign biological sensors during the interaction with the robot.
• Off-line classification of data with avaliable tools.
• Desing and develop of on-line classifier sistem for emotion recognition
• Closed loop control: the thrapy will be adapted to the patient's needs.

These projects allow to experiment with biological-data acquisition tools, robots and videogame design.

The project consists on the realization of one or more phases depending on the difficulty/cfu to be achieved and to the competences of the candidate(s)

Project phases:

• Design of experimental protocol used to stimulate particolar emotions.
• Data acquisition by usign biological sensors while driving in different conditions (city, highway, country ..)
• Off-line classification of data with avaliable tools.
• Desing and develop of on-line classifier sistem for emotion recognition
• Closed loop control: the car will give audio/visual feedbacks to the user letting him know its phisiological state

These projects allow to experiment with biological-data acquisition tools, robots and videogame design.

The project consists on the realization of one or more phases depending on the difficulty/cfu to be achieved and to the competences of the candidate(s)

A genetic algorithm (GA) for ERP feature extraction has been developed at the Airlab. The GA has been proved to work, but there different ways that can explored to further develop this algorithm and expand its application field.

Tools and instruments

C++, Matlab

Good programming skill required

Bibliography

B. Dal Seno, M. Matteucci, L. Mainardi. A Genetic Algorithm for Automatic Feature Extraction in P300 Detection [1]

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.

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

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.

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.

The goal of this project is to apply machine learning techniques for the generation of customized tracks in 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.

The goal of this project is to apply any machine learning technique to develop a successfull controller following the competition rules (available here)

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 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.

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.