Revision as of 14:22, 16 July 2009

Curriculum

Core Courses

The following courses are mandatory:

Introduction to Self-Organizing Networked Systems

• Part I (30h): What is a self-organizing system? Methodology and theory. Giving the big picture. Few mathematics, non-technical. History. Evolution, cybernetics, chaos, emergence, entropy. Links to following courses. Similar to course "complexity and evolution" in Brussels.
• Part II: Case studies. A ring lecture (colloquium) with speakers from different universities and different fields (which can also attended by all university members):
  • Self-Organization in Communication and Computer Networks
  • Social insects
  • Immune system
  • Social group dynamics
  • Self-Organization in Biology
  • Brain dynamics
  • Robotics
  • Chemical reactions
  • Self-organization in linguistics

Dynamical Systems

Lecture and Lab

Content: similar to "Nonlinear Dynamics and Chaos" by Steven H. Strogatz

Textbooks:

• Nonlinear Dynamics and Chaos by Steven H. Strogatz

Network Theory

Lecture and Exercises

Content: Networks from the real world. Network topology: Graph theory basics, random graphs, phenomena small wold and scale-freeness. Network functions/processes/algorithms: E.g. search, percolation.

Textbooks:

• [The structure and function of complex networks http://arxiv.org/PS_cache/cond-mat/pdf/0303/0303516v1.pdf ]
• [Dynamical Processes on Complex Networks http://www.amazon.de/Dynamical-Processes-Complex-Networks-Barrat/dp/0521879507/ref=sr_1_1?ie=UTF8&s=books-intl-de&qid=1247749733&sr=8-1]

Information Theory and Coding

Lecture and Exercises

Advanced Stochastics

Lecture and Exercises

Content: Selected topics from the following fields: Stochastic Processes. Statistical Physics.

Intelligent Systems (?)

Game theory: Cooperation (Prisoneer's Dilemma), economic behavior

Neural networks: introduction to biological neural networks, Artificial Neural Networks, topologies (Multilayer, recurrent, fully meshed), Self-Organizing Feature Maps, spiking neural networks, emergent patterns in ANN

Machine learning: Genetic algorithms, simulated anneahling, Sintflut algorithm, rule-based learning, bayesian networks

Modeling and Simulation (?)

Algorithms and Data Structures

Lecture and Lab.

Content: Sorting and searching, tree-based structures, graph algorithms (over), recursive algorithms, complexity classes and computational effort.

Textbooks:

• The Algorithm Design Manual by Steven S. Skiena
• Introduction to Algorithms by Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Control Theory

control loop, stability, distributed control, event-based control, MIMO control systems

Catch-up Courses

Depending on the background of the student, she or he attends a subset of the following courses:

Specialization 1: Communication Networks

Mobile and Wireless Systems

Contents: Introduction and overview. Radio propagation. Coding, modulation, and duplexing. Multiple access and cellular concept. Medium access control (MAC) protocols. Wireless LAN 802.11. Network architecture and mobility protocols. Security in mobile networks. Multihop networks. Economic, health, and social aspects.

Sensor Networks

Peer-to-Peer Networks

Protocol Engineering

Group Work

Curriculum table

Group 1: Bauschert, Bettstetter, Pletzer, Quaritsch, Yanmaz

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Group 2: Anton, Manfred, Felix, Johannes,Alain

All courses should specifically deal with applications towards SOS.