Search result: Catalogue data in Spring Semester 2015
|Electrical Engineering and Information Technology Master|
| Major Courses|
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.
|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.
|376-1217-00L||Rehabilitation Engineering I: Motor Functions||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 especially motor deficits.|
|Objective||Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor 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 mainly motor 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)|
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
|151-0104-00L||Uncertainty Quantification for Engineering & Life Sciences |
Does not take place this semester.
Number of participants limited to 40.
|W||4 credits||3G||P. Koumoutsakos|
|Abstract||Quantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology.|
|Objective||The course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures.|
|Content||Topics that will be covered include: Uncertainty quantification under|
parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis.
|Lecture notes||The class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class.|
|Literature||1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia |
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
|Prerequisites / Notice||Fundamentals of Probability, Fundamentals of Computational Modeling|
|151-0532-00L||Nonlinear Dynamics and Chaos I||W||4 credits||2V + 1U||D. Karrasch, G. Haller|
|Abstract||Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.|
|Objective||This course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.|
|Content||(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.|
(2) Near equilibrium dynamics: Linear and Lyapunov stability
(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations
(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.
(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
|Lecture notes||The class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.|
|Prerequisites / Notice||- Prerequisites: Analysis, linear algebra and a basic course in differential equations.|
- Exam: two-hour written exam in English.
- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
|151-0641-00L||Introduction to Robotics and Mechatronics |
Number of participants limited to 60. COURSE IS FULLY BOOKED!
The enrollment is only valid if an e-mail is sent to email@example.com with "IRM participation" in the subject. Enrollment is valid starting from September 2014. The order of enrollment will be considered according to the time your e-mail is sent.
|W||4 credits||2V + 2U||B. Nelson|
|Abstract||The aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use.|
|Objective||The aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.|
|Content||An ever increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this lecture is to expose students to the fundamentals of these systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use, and forward and inverse kinematics. Throughout the course students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems.|
|Prerequisites / Notice||The registration is limited to 60 students.|
There are 4 credit points for this lecture.
The lecture will be held in English.
The students are expected to be familiar with C programming.
|151-0854-00L||Autonomous Mobile Robots||W||5 credits||4G||P. Furgale, M. Hutter, M. Rufli, D. Scaramuzza, R. Siegwart|
|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||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.|
|Lecture notes||This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.|
|Literature||This lecture is based on the Textbook: |
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
|227-0529-00L||SmartGrids: System Optimization of Smart and Liberalized Electric Power Systems||W||6 credits||4G||R. Bacher|
|Abstract||Model based optimization of SmartGrids systems considering Physics, Economics and Legislation; Optimality conditions and solutions; Lagrange-Multipliers and market prices; Price incentives in case of restrictions and grid constraints; Transmission grid congestions and implicit auctions; Security of supply with high variability + market requirements; Electricity market and SmartGrids system models.|
|Objective||- Understanding the legal, physical and market based framework for Smart Grid based electric power systems. |
- Understanding the theory of mathematical optimization models and algorithms for a secure and market based operation of Smart Power Systems.
- Gaining experience with the formulation, implementation and computation of constrained optimization problems for Smart Grid and market 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 analyzing network based electric power systems.
- Types of optimization problems, optimality conditions and optimization methods.
- Various electricity market models, their advantages and disadvantages.
- SmartGrids: The new energy system and compatibility issues with traditional market models.
|Lecture notes||Text book is continuously updated and distributed to students.|
|Literature||Class text book contains active hyperlinks related to back ground material.|
|Prerequisites / Notice||Motivation, Active participation (discussions). Numerical analysis, power system basics and modeling, optimization basics|
|252-0526-00L||Statistical Learning Theory||W||4 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 Statistical Learning Theory can be followed without the introductory course.
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