Search result: Catalogue data in Autumn Semester 2014
|Electrical Engineering and Information Technology Master|
| Major Courses|
A total of 42 CP must be achieved during the Master Program. The individual study plan is subject to the tutor's approval.
|Systems and Control|
| Recommended Subjects|
These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
|151-0563-01L||Dynamic Programming and Optimal Control||W||4 credits||3G||R. D'Andrea|
|Abstract||Introduction to Dynamic Programming and Optimal Control.|
|Objective||Covers the fundamental concepts of Dynamic Programming & Optimal Control.|
|Content||Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.|
|Literature||Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.|
|Prerequisites / Notice||Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.|
|151-0607-00L||Optimal & Learning Control for Autonomous Robots |
Does not take place this semester.
|W||4 credits||3G||J. Buchli|
|Abstract||The students will learn the fundamentals of optimal and learning control. They will learn how these fundamental ideas can be applied to real world problems encountered in autonomous and articulated robots.|
|Objective||After this lecture the students will have the understanding and tools to apply learning and optimal control to problems encountered in robotics and other fields.|
|Lecture notes||slide handouts|
|Literature||Literature will be given in the lecture|
|Prerequisites / Notice||Calculus, Basic Control Theory|
|401-0647-00L||Introduction to Mathematical Optimization||W||5 credits||2V + 1U||U.‑U. Haus, R. Zenklusen|
|Abstract||Introduction to basic techniques and problems of mathematical optimization.|
|Objective||The goal is to get a good understanding of some of the most important mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems.|
|Content||Topics covered in this course include:|
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, network flows, knapsack problem, ...).
|Literature||Information about relevant literature will be given in the lecture.|
|Prerequisites / Notice||This course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics and more.|
|401-3901-00L||Mathematical Optimization||W||11 credits||4V + 2U||R. Weismantel|
|Abstract||Mathematical treatment of diverse optimization techniques.|
|Objective||Advanced optimization theory and algorithms.|
|Content||1. Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming.|
2. Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization.
3. Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory.
4. Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings and, more generally, independence systems.
|636-0007-00L||Computational Systems Biology||W||6 credits||3V + 2U||J. Stelling|
|Abstract||Study of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).|
|Objective||The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.|
|Content||Biology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.|
|Literature||U. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.|
Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006.
|Subjects of General Interest|
|227-0377-00L||Physics of Failure and Failure Analysis of Electronic Devices and Equipment||W||3 credits||2V||U. Sennhauser|
|Abstract||Failures have to be avoided by proper design, material selection and manufacturing. Properties, degradation mechanisms, and expected lifetime of materials are introduced and the basics of failure analysis and analysis equipment are presented. Failures will be demonstrated experimentally and the opportunity is offered to perform a failure analysis with advanced equipment in the laboratory.|
|Objective||Introduction to the degradation and failure mechanisms and causes of electronic components, devices and systems as well as to methods and tools of reliability testing, characterization and failure analysis.|
|Content||Summary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis of ICs, PCBs, opto-electronics, discrete and other components and devices; basics and properties of instruments; application in circuit design and reliability analysis|
|Lecture notes||Comprehensive copy of transparencies|
|363-0790-00L||Technology Entrepreneurship||W||2 credits||2V||U. Claesson, P. Baschera, F. Hacklin|
|Abstract||Technology ventures are significantly changing the global economic picture. Technological skills increasingly need to be complemented by entrepreneurial understanding. |
This course offers the fundamentals in theory and practice of entrepreneurship in new technology ventures. Main topics covered are success factors in the creation of new firms, including founding, financing and growing a venture.
|Objective||This course provides theory-grounded knowledge and practice-driven skills for founding, financing, and growing new technology ventures. A critical understanding of dos and don'ts is provided through highlighting and discussing real life examples and cases.|
|Content||See course website|
|Lecture notes||Lecture slides and case material|
|151-0317-00L||Visualization, Simulation and Interaction - Virtual Reality II||W||4 credits||3G||A. Kunz|
|Abstract||This lecture provides deeper knowledge on the possible applications of virtual reality, its basic technolgy, and future research fields. The goal is to provide a strong knowledge on Virtual Reality for a possible future use in business processes.|
|Objective||Virtual Reality can not only be used for the visualization of 3D objects, but also offers a wide application field for small and medium enterprises (SME). This could be for instance an enabling technolgy for net-based collaboration, the transmission of images and other data, the interaction of the human user with the digital environment, or the use of augmented reality systems.|
The goal of the lecture is to provide a deeper knowledge of today's VR environments that are used in business processes. The technical background, the algorithms, and the applied methods are explained more in detail. Finally, future tasks of VR will be discussed and an outlook on ongoing international research is given.
|Content||Introduction into Virtual Reality; basisc of augmented reality; interaction with digital data, tangible user interfaces (TUI); basics of simulation; compression procedures of image-, audio-, and video signals; new materials for force feedback devices; intorduction into data security; cryptography; definition of free-form surfaces; digital factory; new research fields of virtual reality|
|Lecture notes||The handout is available in German and English.|
|Prerequisites / Notice||Prerequisites:|
"Visualization, Simulation and Interaction - Virtual Reality I" is recommended.
The course consists of lectures and exercises.
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