Search result: Catalogue data in Spring Semester 2015

Electrical Engineering and Information Technology Master Information
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.
Energy and Power Electronics
Recommended Subjects
These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
NumberTitleTypeECTSHoursLecturers
151-0160-00LNuclear Energy SystemsW4 credits2V + 1US. Hirschberg, H.‑M. Prasser, I. Günther-Leopold, W. Hummel, T. Williams, P. K. Zuidema
AbstractNuclear energy and sustainability, Nuclear fuel production, energy and materials balance of Nuclear Power Plants, Fuel and spent fuel handling, Fuel reprocessing, Radioactive waste disposal, Environmental impact of radiation releases.
ObjectiveStudents get an overview on the physical fundamentals, the technological processes and the environmental impact of the full energy conversion chain of nuclear power generation. The are enabled to assess to potentials and risks arising from embedding nuclear power in a complex energy system.
ContentMethods to measure the sustainability of energy systems will be presented, nuclear energy is analysed concerning its sustainability and compared to other energy sources. The environmental impact of the nuclear energy system as a whole is discussed, including the question of CO2 emissions, CO2 reduction costs, radioactive releases from the power plant, the fuel chain and the final disposal. The material balance of different fuel cycles with thermal and fast reactors is examined. A survey on the geological origin of nuclear fuel, uranium mining, refinement, enrichment and fuel rod fabrication processes is given. Methods of fuel reprocessing including modern developments of deep partitioning as well as methods to treat and minimize the amount and radiotoxicity of nuclear waste are described. The project of final disposals for radioactive waste in Switzerland is presented.
Lecture notesThe script will be handed out
376-1217-00LRehabilitation Engineering I: Motor Functions Information W3 credits2V + 1UR. Riener
AbstractRehabilitation 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.
ObjectiveProvide 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 notesLecture notes will be distributed at the beginning of the lecture (1st session)
LiteratureIntroductory 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 / NoticeTarget 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
227-0117-00LHigh Voltage Technology Information W6 credits4GC. Franck, U. Straumann
AbstractUnderstanding of the fundamental phenomena and principles connected with the occurrence of extensive electric field strengths. This knowledge is applied to the dimensioning of equipment of electric power systems. Methods of computer-modeling in use today are presented and applied within the framework of the exercises.
ObjectiveThe students know the fundamental phenomena and principles connected with the occurrence of extensive electric field strengths. They comprehend the different mechanisms leading to the failure of insulation systems and are able to apply failure criteria on the dimensioning of high voltage components. They have the ability to identify of weak spots in insulation systems and to name possibilities for improvement. Further they know the different insulation systems and their dimensioning in practice.
Content- discussion of the field equations relevant for high voltage engineering.
- analytical and numerical solutions/solving of this equations, as well as the derivation of the important equivalent circuits for the description of the fields and losses in insulations
- introduction to kinetic theory of gases
- mechanisms of the breakdown in gaseous, liquid and solid insulations, as well as insulation systems
- methods for the mathematical determination of the electric withstand of gaseous, liquid and solid insulations
- application of the expertise on high voltage components
- excursions to manufacturers of high voltage components
- workshop to learn on computer-modeling in high voltage engineering
Lecture notesHandouts
LiteratureA. Küchler, Hochspannungstechnik, Springer Berlin, 3. Auflage, 2009 (ISBN: 978-3540784128)
227-0524-00LRailway Systems II Information W6 credits4GM. Meyer
AbstractConcepts, characteristics and interaction of the railway subsystems with a focus on the integration of vehicles, and infrastructure:
- traction chain and auxiliary supply
- railway power supply
- signaling systems
- communication and train control systems
- electrical system compatibility
Objectiverefer to the german version
ContentET II (Frühjahrsemester) - Traktion, Bahnstrom, Signalisierung und Zugsicherung, Elektrische Systemkompatibilität

Traktionsausrüstung
1.1 Systemkonzepte, Topologien, Auswahlkriterien
1.2 Traktionsstromrichter, Steuerung, Regelung und Schutz
1.3 Fahrmotor, Getriebe
1.4 Hochspannungsausrüstung, inkl. Störstromfilter und Haupttransformator, Erdkonzepte
1.5 Hilfsbetriebe, Kühlung,
1.6 Energieverbrauch

Kommunikations- und Zugsicherungssysteme
2.1 Zugbeeinflussung
2.2 European Train Control System (ETCS)
2.3 Automatisierung

Systemintegration
3.1 Bahnstromversorgung: Konzepte, Merkmale, Ausführungsbeispiele
3.2 Störstrom, Stabilität, Elektrische Systemkompatibilität


Exkursionen
Bombardier Transportation, Zürich
Grosse Bahnexkursion (2 Tage), u.a.:
- Energieversorgung
- Unterhalt
- Führerstandsfahrten
Prerequisites / NoticeGrosse Exkursion zu Herstellern und Betreibern

Referenten:
Dr. Christian Gerster, Bombardier Transportation (Switzerland) AG
Dr. Rolf Gutzwiller, EduRail GmbH
Dr. Markus Meyer, Emkamatik GmbH

Voraussetzungen (empfohlen):
- Eisenbahn-Systemtechnik I
- Grundlagen Elektrotechnik
- Grundlagen Leistungselektronik
- Grundlagen Elektrische Maschinen
Systems and Control
Core Subjects
These core subjects are particularly recommended for the field of "Systems and Control".
NumberTitleTypeECTSHoursLecturers
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
227-0207-00LNonlinear Systems and Control Information
Prerequisite: Control Systems (227-0103-00L)
W6 credits4GE. Gallestey Alvarez, P. F. Al Hokayem
AbstractTo introduce students to the area of nonlinear systems and control, to familiarize them with tools for modelling and analysing nonlinear systems and to provide an overview of the various nonlinear controller design methods.
ObjectiveOn completion of the course, students understand the difference between linear and nonlinear systems, know the the mathematical techniques for modeling and analysing these systems, and have learnt various methods for designing controllers for these systems.
Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of virtues and drawbacks in the different methods.
ContentVirtually all practical control problems are of nonlinear nature. In some
cases the application of linear control methods will lead to satisfying controller performance. In many other cases only application of nonlinear analysis and synthesis methods will guarantee achievement of the desired objectives. During the past decades a number of practically applicable and mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for modelling and analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons, and the students will be familiarized with the basic concepts of nonlinear control theory.

This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control.
Lecture notesAn english manuscript will be made available on the course homepage during the course.
LiteratureH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Prerequisites / NoticePrerequisites: Linear Control Systems, or equivalent.
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
Lecture notesThe slides of the lecture are available to download
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0221-00LModel Predictive Control Information Restricted registration - show details
Enrolling necessary (see "Notice").
W6 credits4GM. Morari
AbstractSystem complexity and demanding performance render traditional control inadequate. Applications from the process industry to the communications sector increasingly use MPC. The last years saw tremendous progress in this interdisciplinary area. The course first gives an overview of basic concepts and then uses them to derive MPC algorithms. There are exercises and invited speakers from industry.
ObjectiveIncreased system complexity and more demanding performance requirements have rendered traditional control laws inadequate regardless if simple PID loops are considered or robust feedback controllers designed according to some H2/infinity criterion. Applications ranging from the process industries to the automotive and the communications sector are making increased use of Model Predictive Control (MPC), where a fixed control law is replaced by on-line optimization performed over a receding horizon. The advantage is that MPC can deal with almost any time-varying process and specifications, limited only by the availability of real-time computer power.
In the last few years we have seen tremendous progress in this interdisciplinary area where fundamentals of systems theory, computation and optimization interact. For example, methods have emerged to handle hybrid systems, i.e. systems comprising both continuous and discrete components. Also, it is now possible to perform most of the computations off-line thus reducing the control law to a simple look-up table.
The first part of the course is an overview of basic concepts of system theory and optimization, including hybrid systems and multi-parametric programming. In the second part we show how these concepts are utilized to derive MPC algorithms and to establish their properties. On the last day, speakers from various industries talk about a wide range of applications where MPC was used with great benefit.
There will be exercise sessions throughout the course where the students can test their understanding of the material. We will make use of the MPC Toolbox for Matlab that is distributed by MathWorks.
ContentTentative Program

Day 1: Linear Systems I
Fundamentals of linear system theory – Review (system representations, poles, zeros, stability, controllability & observability, stochastic system descriptions, modeling of noise).

Day 2: Linear Systems II
Optimal control and filtering for linear systems (linear quadratic regulator, linear observer, Kalman Filter, separation principle, Riccati Difference Equation).

Days 3 and 4: Basics on Optimization
Fundamentals of optimization (linear programming, quadratic programming, mixed integer linear/quadratic programming, duality theory, KKT conditions, constrained optimization solvers).
Exercises.

Day 5: Introduction to MPC
MPC – concept and formulation, finite horizon optimal control, receding horizon control, stability and feasibility, computation.
Exercises.

Day 6: Numerical methods for MPC
Unconstrained Optimization, Constrained Optimization, Software applications

Day 7: Practical Aspects, Explicit & Hybrid MPC
- Reference tracking and soft constraints
- Explicit solution to MPC for linear constrained systems. Motivation. Introduction to (multi)-parametric programming through a simple example. Multi-parametric linear and quadratic programming: geometric algorithm. Formulation of MPC for linear constrained systems as a multi-parametric linear/quadratic program. A brief introduction to Multi-parametric Toolbox.
- MPC for discrete-time hybrid systems. Introduction to hybrid systems. Models of hybrid systems (MLD, DHA, PWA, etc.). Equivalence between different models. Modelling using HYSDEL. MLD systems. MPC based on MILP/MIQP. Explicit solution: mpMILP. Short introduction into dynamic programming (DP). Computation of the explicit MPC for PWA systems based on DP. Exercises.

Day 8: Applications
Invited speakers from industry and academia, different case studies

Day 9
Design exercise
Lecture notesScript / lecture notes will be provided.
Prerequisites / NoticePrerequisites:
One semester course on automatic control, Matlab, linear algebra.

ETH students:
As participation is limited, a reservation (e-mail: Link) is required. Please give information on your "Studienrichtung", semester, institute, etc.
After your reservation has been confirmed, please register online at Link.

Interested persons from outside ETH:
It is not possible/needed to enrol as external auditor for this course. Please contact Alain Bolle to register for the course (Link).

We have only a limited number of places in the course, it is "first come, first served"!
227-0224-00LStochastic Systems Information W4 credits2V + 1UF. Herzog
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Content- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
Lecture notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0690-06LAdvanced Topics in Control (Spring 2015) Information
New topics are introduced every year.
W4 credits2V + 2UF. Dörfler
AbstractThis class will introduce students to advanced, research level topics in the area of automatic control. Coverage varies from semester to semester, repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will concentrate on distributed systems and control.
ObjectiveThe intent is to introduce students to advanced research level topics in the area of automatic control. The course is jointly organized by Prof. R. D'Andrea, L. Guzzella, J. Lygeros, M. Morari, R. Smith, and F. Dörfler. Coverage and instructor varies from semester to semester. Repetition for credit is possible, upon consent of the instructor. During the Spring Semester 2015 the class will be taught by F. Dörfler and will focus on distributed systems and control.
ContentDistributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are the electric power grid, camera networks, and robotic sensor networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, and control capabilities. This course covers modeling, analysis, and design of distributed control systems.

Topics covered in the course include:
- the theory of graphs (with an emphasis on algebraic and spectral graph theory);
- basic models of multi-agent and interconnected dynamical systems;
- continuous-time and discrete-time distributed averaging algorithms (consensus);
- coordination algorithms for rendezvous, formation, flocking, and deployment;
- applications in robotic coordination, coupled oscillators, social networks, sensor networks, electric power grids, epidemics, and positive systems.
Lecture notesA set of self-contained set of lecture nodes will be made available on the course website.
LiteratureRelevant papers and books will be made available through the course website.
Prerequisites / NoticeControl systems (227-0216-00L), Linear system theory (227-0225-00L), or equivalents, as well as sufficient mathematical maturity.
Recommended Subjects
These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
NumberTitleTypeECTSHoursLecturers
376-1217-00LRehabilitation Engineering I: Motor Functions Information W3 credits2V + 1UR. Riener
AbstractRehabilitation 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.
ObjectiveProvide 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 notesLecture notes will be distributed at the beginning of the lecture (1st session)
LiteratureIntroductory 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 / NoticeTarget 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
151-0104-00LUncertainty Quantification for Engineering & Life Sciences Restricted registration - show details
Does not take place this semester.
Number of participants limited to 40.
W4 credits3GP. Koumoutsakos
AbstractQuantification 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.
ObjectiveThe 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.
ContentTopics 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 notesThe 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.
Literature1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
Prerequisites / NoticeFundamentals of Probability, Fundamentals of Computational Modeling
151-0532-00LNonlinear Dynamics and Chaos I Information W4 credits2V + 1UD. Karrasch, G. Haller
AbstractBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
ObjectiveThis 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 notesThe 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.
151-0641-00LIntroduction to Robotics and Mechatronics Information Restricted registration - show details
Number of participants limited to 60. COURSE IS FULLY BOOKED!

The enrollment is only valid if an e-mail is sent to Link 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.
W4 credits2V + 2UB. Nelson
AbstractThe 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.
ObjectiveThe 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.
ContentAn 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 / NoticeThe 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.
151-0854-00LAutonomous Mobile Robots Information W5 credits4GP. Furgale, M. Hutter, M. Rufli, D. Scaramuzza, R. Siegwart
AbstractThe 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.
ObjectiveThe 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 notesThis 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.
LiteratureThis 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
227-0529-00LSmartGrids: System Optimization of Smart and Liberalized Electric Power Systems Information W6 credits4GR. Bacher
AbstractModel 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 notesText book is continuously updated and distributed to students.
LiteratureClass text book contains active hyperlinks related to back ground material.
Prerequisites / NoticeMotivation, Active participation (discussions). Numerical analysis, power system basics and modeling, optimization basics
252-0526-00LStatistical Learning Theory Information W4 credits2V + 1UJ. M. Buhmann
AbstractThe 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.
ObjectiveThe 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 notesno script; transparencies of the lectures will be made available.
LiteratureDuda, 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 / NoticeRequirements:

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.
Subjects of General Interest
These courses are suitable for several special fields. Please consult your tutor.
NumberTitleTypeECTSHoursLecturers
227-0708-00LDiagnostics, Measurement and Testing Technology in High Voltage Technology Information Z0 credits2SH.‑J. Weber
AbstractDiscussion of various diagnostic methods to evaluate the electrical insulation of the components and subsystems of high-voltage networks. Independent performance of experiments in the laboratory using high and low voltages. Acquaintance with the most important testing methods and international standards. Calibration methods and maintenance of high-voltage measuring devices.
Objectivesee above
Lecture notesHandouts
Literature- M. Beyer, W. Boeck, K. Möller, W. Zaengl: Hochspannungstechnik, Springer-Verlag, 1986
- A. Küchler: Hochspannungstechnik, Springer, Berlin, 3. Auflage, 2009
151-0306-00LVisualization, Simulation and Interaction - Virtual Reality I Information W4 credits4GA. Kunz
AbstractTechnology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).
ObjectiveThe product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.
ContentIntroduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.
Lecture notesA complete version of the handout is also available in English.
Prerequisites / NoticeVoraussetzungen:
keine
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF

Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
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