Master of Science - Communications Systems Theory

Outline of Courses:

Coding Theory

Back to top

Advanced Digital Image Processing and Analysis

• The main acquisition and display systems for digital imaging.
• The mathematical tools involved (multi-dimensional Fourier analysis, M-D linear system, colour representation, stochastic models, vision-based models, human visual models).
• The main processing methods (filtering, enhancement, restoration, analysis and compression).
• As an example, the design of a complete end-to-end image communication system will be discussed

Advanced Computer Graphics and Virtual Reality

• Basics in Computer Graphics, algebra and geometry.
• Fundamental of Virtual Reality Systems, telepresence and teleoperation.
• 3D graphics and animation for VR.
• Tracking technology, sensors, immersion, presence.
• Haptic interfaces and tactile feedback for VE applications.
• Audio space and auditory systems.
• Networking Virtual Environments.
• Collaborative virtual environments and telecooperative work.
• Spatial model of interaction: aura and nimbus.
• Case studies if networking VR systems: DIVE, NPSNET, BRICKNET, VLNET.
• Modelling of virtual bodies and clones.
• Facial communication and 3D teleconferencing.
• Multisensory augmented reality and computer-augmented environments.

Networking and Traffic Control

D1:     TCP/IP and ATM, Ethernet, IP, TCP and Application Layers, Routing, Promela. Homework on DLC, Bridging, ATM, IP over ATM, RSVP. Exercises on Unix commands, socket programming and Promela.

D2:     Traffic Control:
        I. Reserved Services: Guranteed service, Arrival and service curves, effective bandwidth, buffering, Statistical service, Large deviations and effective bandwidth, Statistical Multiplexing.
        II. Best effort services: Feedback based congestion control.
        III. Routing: Routing in the Internet, Routing in circuit switched networks.
        IV. Network Design: Simulated Annealing, Tabu search, Flow deviation algorithm
        V. Traffic Modelling: Traffic models, Self-similarity, Long Term dependence.

Neural Networks

• Description. Learning algorithms, knowledge, learning laws. Habbian Learning.
• Supervised & Unsupervised Learning.
• McCullock-Pitts Model.
• Universal Approximation Theorem.
• Kohomen Network.
• Counterpropagation Networks (CPN).
• Computer implementations of neural networks.
• Applications.