Search result: Catalogue data in Spring Semester 2009
Electrical Engineering and Information Technology Master  | ||||||
 Courses of the specialisation | ||||||
| Systems and Control A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval. | ||||||
| Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor. | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 151-0217-00L | Rehabilitation Engineering | W | 3 credits | 2V + 1U | R. Riener | |
| Abstract | “Rehabilitation engineering” is the application of science and technology to ameliorate the handicaps of individuals with disabilities in order to reintegrate them into society. The goal of this lecture is to present classical and new rehabilitation engineering principles and examples applied to compensate or enhance motor, sensor, and cognitive (communicational) deficits. | |||||
| Objective | Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor, sensor, and cognitive disabilities. | |||||
| Content | “Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive (also communicational) disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. Orthoses support existing but affected body functions (e.g., glasses, crutches), while prostheses compensate for lost body functions (e.g., cochlea implant, artificial limbs). In case of sensory disorders, the lost function can also be substituted by other modalities (e.g. tactile Braille display for vision impaired persons). The goal of this lecture is to present classical and new technical principles as well as specific examples applied to compensate or enhance motor, sensor, and cognitive deficits. Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited by displaying the patient with a maximum amount of information in order to compensate his/her impairment. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. Multi-modality and interactivity have the potential to increase the therapeutical outcome compared to classical rehabilitation strategies. In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics. | |||||
| Lecture notes | Lecture notes will be distributed at the beginning of the lecture (1st session) | |||||
| Literature | Introductory Books Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press. Advances in Rehabilitation Robotics – Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004. Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001. Control of Movement for the Physically Disabled. Eds.: D. Popovic, T. Sinkjaer. Springer Verlag London, 2000. Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005. Biomechanics and Neural Control of Posture and Movement. Eds.: J.M. Winters, P.E. Crago. Springer New York, 2000. Selected Journal Articles Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195. Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432 Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700. Colombo, G., Jörg, M., Jezernik, S. (2002) Automatisiertes Lokomotionstraining auf dem Laufband. Automatisierungstechnik at, vol. 50, pp. 287-295. Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206. Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87 Leifer, L. (1981): Rehabilitive robotics, Robot Age, pp. 4-11 Platz, T. (2003): Evidenzbasierte Armrehabilitation: Eine systematische Literaturübersicht, Nervenarzt, 74, pp. 841-849 Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Riener, R., Fuhr, T., Schneider, J. (2002) On the complexity of biomechanical models used for neuroprosthesis development. International Journal of Mechanics in Medicine and Biology 2, pp. 389-404. Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894. | |||||
| Prerequisites / Notice | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK - Biomedical Engineering - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome | |||||
| 151-0608-00L | Advanced Robotics and Mechatronic Systems | W | 4 credits | 3G | B. Nelson | |
| Abstract | Based on our successful microrobotic platform, the students are given tasks involving the (re)design of magneto-mechanical microrobots (dim. < 300um). The lecture culminates in a competition between the teams and the potential participation of the winning team at the final international competition at RoboCup 2009 in Graz, Austria. | |||||
| Objective | This lecture exposes students to these challenges by presenting them with a complex mechatronic problem to be solved in a semester time frame. The students will be given the chance to test and improve both their professional and social skills in a real-world engineering project from concept to competition. The project includes insights into the microfabrication process, but focuses on the development of robust real-time strategies and algorithms to track and control these robots in a fully automated fashion. | |||||
| Content | Microrobotics is the study of robotics at the micron scale, and includes robots that are microscale in size and large robots capable of manipulating objects that have dimensions in the microscale range. Key challenges in microrobotics are power, actuation, localization and control. This project course is based on state-of-the-art microrobots which are wirelessly powered and controlled with external oscillating magnetic and electrostatic field. The students will be organized in 2-3 competing multidisciplinary teams. The students can develop their own robots and systems in the framework of our MagMite platform. These tasks are open-ended and require skills of creativity, teamwork, organization, and firm theoretical and practical backgrounds for the students to succeed. Strong personal commitment and determination as well as good teamwork will be key aspects to success. | |||||
| Lecture notes | no script, but technical papers and other guidelines. | |||||
| Literature | Link | |||||
| Prerequisites / Notice | For this lecture, students are getting 4 credit points The course is held in English and German. The operating systems will be Linux-based. The students are expected to form multidisciplinary teams involving a) multiple students with a strong background in C++ programming and algorithms, b) multiple students with a suitable background for the overall design and modeling of magneto-mechanical systems (CAD, FEM, analytical). The project work will be exceptionally demanding and time consuming. | |||||
| 151-0854-00L | Autonomous Mobile Robots | W | 4 credits | 2V + 1U | R. Siegwart, D. Scaramuzza | |
| Abstract | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples. | |||||
| Objective | ||||||
| Lecture notes | Introduction to Autonomous Mobile Robots. Siegwart, R. and Nourbakhsh, I. (2004), A Bradford Book, The MIT Press, Cambridge, Massachusetts, London, England | |||||
| 227-0529-00L | Optimization of Liberalized Electric Power Systems | W | 6 credits | 4G | R. Bacher | |
| Abstract | Legal framework for regulated, network based electrical systems; Physical laws; Contrained network elements; Differences to regular market based systems; Optimization for the solution of goal conflicts: Network based security of supply versus market requirements; (Non-linear) Optimization problems; Optimality conditions and solutions; Different electricity market models | |||||
| Objective | 1) Understanding the legal and physical framework for the efficient regulation of electic transmission and distribution systems. 2) Understanding the theory of mathematical optimization models and algorithms for a secure and economic operation of power systems. 3) Gaining experience with the formulation, implementation and computation of non-linear constrained optimization problems of regulated network based electricity systems. | |||||
| Content | Legal conditions for the regulation and operation of electric power systems (CH, EU). Physical laws and constraints in electric power systems. Special characteristics of the good "electricity". Optimization as mathematical tool for analysing network based electric power systems. Types of optimization problems, optimality conditions and optimization methods). Various electricity market models, their advantages and disadvantages. | |||||
| Lecture notes | Text book is continuously updated and distributed to students., | |||||
| Literature | Class text book contains active hyperlinks related to back ground material. | |||||
| 251-0526-00L | Statistical Learning Theory | W | 5 credits | 2V + 1U | J. M. Buhmann | |
| Abstract | The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models. | |||||
| Objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed. | |||||
| Content | # Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future. | |||||
| Lecture notes | no script; transparencies of the lectures will be made available. | |||||
| Literature | Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||
| Prerequisites / Notice | Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Advanced Topics in Machine Learning can be followed without the introductory course. | |||||
| 251-0532-00L | Bio-Inspired Optimization and Design | W | 5 credits | 2V + 1U | E. Zitzler, P. Koumoutsakos | |
| Abstract | This lecture focuses on the foundations of bio-inspired optimization. The exercises will be oriented towards the implementation of these concepts to design applications. | |||||
| Objective | You are familiar with the foundations of optimization and with different randomized search algorithms, in particular bio-inspired ones. You will be able to design, implement, and tune basic and advanced bio-inspired optimization techniques for tackling complex, large-scale applications. You will be able to evaluate different search algorithms and implementations. You are aware of the theoretical foundations of bio-inspired optimization, know the limitations as well as potential advantages and disadvantages of specific design concepts. | |||||
| Content | Biologically-inspired computation is an umbrella term for different computational approaches that are based on principles or models of biological systems. This class of methods such as evolutionary algorithms, ant colony optimization, and swarm intelligence complements traditional techniques in the sense that the former can be applied to large-scale applications where little is known about the underlying problem and where the latter approaches encounter difficulties. Therefore, bio-inspired methods are becoming increasingly important in face of the complexity of today's demanding applications, and accordingly they have been successfully used in various fields ranging from computer engineering and mechanical engineering to chemical engineering and molecular biology. This lecture focuses on the foundations of bio-inspired computation with an emphasis on their application to optimization. The exercises will be oriented towards the implementation of these concepts to realistic applications. | |||||
| Lecture notes | Lecture notes will be provided in the course of the semester. | |||||
| 251-0574-00L | Spatiotemporal Modeling and Simulation | W | 6 credits | 2V + 2U | I. Sbalzarini | |
| Abstract | This course teaches modeling techniques for spatially resolved systems. You will learn to account for the geometry of a system and for transport in space. After repetition of the basics from mathematics and physics, you will model processes such as diffusion, waves, and flow, and simulate them in the computer. | |||||
| Objective | - Analysis of the dynamic behavior of biological or physical systems with spatial structure - Formulation of model of the system behavior - Computer simulation of the model using numerical methods We focus on biological systems. The taught methods and concepts are, however, applicable in a much broader sense. | |||||
| Content | Dimensionality analysis, causality diagrams, vector fields, governing equations for diffusion, flow, and waves, hybrid particle-mesh methods for computer simulations, student project: simulation of a biological system. See course web page for complete syllabus: Link | |||||
| Lecture notes | Lecture notes (Skript) written in English are available and will be handed out chapter-wise during the semester. | |||||
| Prerequisites / Notice | Core course in the specialized Master in Computational Biology and Bioinformatics (Link) | |||||
| 401-3904-00L | Convex Optimization | W | 6 credits | 2V + 1U | M. Baes | |
| Abstract | Convex optimization encompasses in a balanced manner theory (convex analysis, optimality conditions, duality theory) and algorithms for convex optimization. In particular the recent theory of semidefinite programming is discussed. | |||||
| Objective | Introduction to convex analysis from the viewpoint of optimization. Derivation of first order optimailty conditions for convex optimization problems. Subgradients and conjugate functions. Lagrange duality theory and minmax theorems. Classes of convex optimization: quadratic, conic and semi-definite optimization problems. Efficent algorithms for convex optimization based on self-concordant barrier functions and Newton's method. Applications from various domains. | |||||
| Content | Convexity plays a central role in the design and analysis of modern and highly successful algorithms for solving real-world optimization problems. The lecture (in English) on convex optimization will treat in a balanced manner theory (convex analysis, optimality conditions) and algorithms for convex optimization. Beginning with basic concepts and results about the structure of convex sets, continuity and differentiability of convex functions (including conjugate functions), the lecture will cover systems of inequalities, the minimum (or maximum) of a convex function over a convex set, Lagrange multipliers, duality theory and mini-may theorems. On the algorithmic part, we will cover efficient algorithms based on interior-point methods in the framework of self-concordant functions. In this way, we will obtain a simple algorithm for semi-definite optimization. Thus, we will be discussing one of the most challenging research areas of nonlinear optimization for which there are many interesting open questions both in theory and practice. The lecture will follow the textbook by S. Boyd, Convex Optimization, made available on the net. - Review of linear and convex quadratic programming. - Convexity of sets and functions. - Duality: weak and strong, complementary slackness. Certification of solutions. - Second-order cones and semidefinite programming, geometric programming. - Algorithms: penalty and barrier functions, ellipsoid method, outer approximations and cutting planes, interior point. - Applications: Statistics, control systems analysis and design, signal processing, geometry, combinatorics, etc. | |||||
| Lecture notes | The lecture will follow the textbook by S. Boyd "Convex Optimization" made available on the net. | |||||
| Literature | * A. Barvinok, A Course in Convexity. American Mathematical Society, 2003. * A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization - Analysis, Algorithms, and Engineering Applications, MPS-SIAM Series on Optimization, MPS-SIAM. * D. P. Bertsekas, A. Nedic and A. E. Ozdaglar, Convex Analysis and Optimization, Athena Scientific, 2003. * D. Bertsimas and J. N. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific, 1997. * S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. * S. Boyd, L. El Ghaoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. SIAM, 1994. * E. de Klerk, Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, Book Series: APPLIED OPTIMIZATION, Vol. 65. Kluwer Academic Publishers. * Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Book Series: APPLIED OPTIMIZATION, Vol. 87. Kluwer Academic Publishers, * R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge University Press, 1985. * J. Renegar, A Mathematical View of Interior-Point Methods in Convex Optimization, MPS-SIAM Series on Optimization. * H. Wolkowicz, R. Saigal and L. Vandenberghe, Handbook of Semidefinite Programming: Theory, Algorithms, and Applications, Kluwer Academic Publishers. * A. Nemirovski and D. Yudin, Problem complexity and method efficiency in optimization, Wiley, 1983. | |||||
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