Suchergebnis: Katalogdaten im Herbstsemester 2016

Elektrotechnik und Informationstechnologie Master Information
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Systems and Control
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227-0102-00LDiskrete Ereignissysteme Information W6 KP4GL. Thiele, L. Vanbever, R. Wattenhofer
KurzbeschreibungEinführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra.
LernzielOver the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss).

The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems.

In this lecture we give an introduction to discrete event systems. We start out the course by studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queuing.
Inhalt1. Introduction
2. Automata and Languages
3. Smarter Automata
4. Specification Models
5. Stochastic Discrete Event Systems
6. Worst-Case Event Systems
7. Network Calculus
Literatur[bertsekas] Data Networks
Dimitri Bersekas, Robert Gallager
Prentice Hall, 1991, ISBN: 0132009161

[borodin] Online Computation and Competitive Analysis
Allan Borodin, Ran El-Yaniv.
Cambridge University Press, 1998

[boudec] Network Calculus
J.-Y. Le Boudec, P. Thiran
Springer, 2001

[cassandras] Introduction to Discrete Event Systems
Christos Cassandras, Stéphane Lafortune.
Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4

[fiat] Online Algorithms: The State of the Art
A. Fiat and G. Woeginger

[hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin)
D. Hochbaum

[schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik)
T. Schickinger, A. Steger
Springer, Berlin, 2001

[sipser] Introduction to the Theory of Computation
Michael Sipser.
PWS Publishing Company, 1996, ISBN 053494728X
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
227-0526-00LPower System Analysis Information W6 KP4GG. Hug
KurzbeschreibungZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
LernzielZiel dieser Vorlesung ist das Verständnis der stationären und dynamischen, bei der elektrischen Energieübertragung auftretenden Vorgänge. Die Herleitung der stationären Modelle der Komponenten des elektrischen Netzes, die Aufstellung der mathematischen Gleichungssysteme, deren spezielle Charakteristiken und Lösungsmethoden stehen im Vordergrund.
InhaltDas elektrische Energieübertragungssystem, das netzleittechnische System, Anforderungen an elektrische Energieübertragungsnetze (Versorgungstechnisch, betrieblich, wirtschaftlich), Netzplanung und Betriebsführung, Modelle der N-Tor-Netz- komponenten (Leitung, Kabel, Shunts, Transformator), Bezogene Grössen (p.u.), Modelldarstellung der N-Tor-Komponenten, Lineare Darstellung des Netzes, Lineare und nicht-lineare Netzberechnung (Newton- Raphson), Nicht-lineare Lastflussrechnung (Problemformulierung, Problemlösungsmethoden), Dreiphasige und verallgemeinerte Kurzschlussberechnung, Weiterführende Anwendungen der Lastflussrechnung. Einführung in die Neztstabilität.
SkriptVorlesung wird durch WWW-Autorensystem unterstützt.
227-0689-00LSystem IdentificationW4 KP2V + 1UR. Smith
KurzbeschreibungTheory and techniques for the identification of dynamic models from experimentally obtained system input-output data.
LernzielTo provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.
InhaltIntroduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models.

Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods.

Optimal experimental design, Cramer-Rao bounds, input signal design.

Parametric identification methods. On-line and batch approaches.

Closed-loop identification strategies. Trade-off between controller performance and information available for identification.
Literatur"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.

"Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977.
Voraussetzungen / BesonderesControl systems (227-0216-00L) or equivalent.
227-0945-00LCell and Molecular Biology for Engineers I
This course is part I of a two-semester course.
W3 KP3GC. Frei
KurzbeschreibungThe course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology.
LernzielAfter completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested.
InhaltLectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells.

In addition, three journal clubs will be held, where one/two publictions will be discussed (part I: 1 Journal club, part II: 2 Journal Clubs). For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded and count as 25% for the final grade.
SkriptScripts of all lectures will be available.
Literatur"Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Raff, Roberts, and Walter.
151-0104-00LUncertainty Quantification for Engineering & Life Sciences Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 60.
W4 KP3GP. Koumoutsakos
KurzbeschreibungQuantification 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.
LernzielThe 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.
InhaltTopics 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.
SkriptThe 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.
Literatur1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
Voraussetzungen / BesonderesFundamentals of Probability, Fundamentals of Computational Modeling
151-0532-00LNonlinear Dynamics and Chaos I Information W4 KP2V + 2UG. Haller, F. Kogelbauer
KurzbeschreibungBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
LernzielThis 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.
Inhalt(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
SkriptThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Voraussetzungen / Besonderes- 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.
151-0573-00LSystemmodellierung Information W4 KP2V + 2UG. Ducard, C. Onder
KurzbeschreibungMethoden der theoretischen und experimentellen Modellbildung für regelungstechnische Zwecke. Modellparametrierung und Parameteridentifikationsmethoden. Analyse von linearen Systemen, Modellskalierung, Linearisierung, Ordnungsreduktion und Balancing. Grundlegende Analysemöglichkeiten für nichtlineare Systeme.
LernzielVermitteln der Grundkenntnisse der Modellbildung in der Regelungstechnik. Analyse und Optimierung linearer und nichtlinearer Systeme. Parameteridentifikation. Erfahrungen sammeln an konkreten Fallstudien.
InhaltMethoden der theoretischen und experimentellen Modellbildung für regelungstechnische Zwecke.

Beispiele: mechatronische, thermodynamische, chemische, fluiddynamische, energie- und verfahrenstechnische Systeme. Modellskalierung, Linearisierung, Ordnungsreduktion und Balancing. Identifikationstechniken (Methode der kleinsten Quadrate).

Fallstudien in der Vorlesung: Lautsprecher, Wasserrakete, geostationäre Satelliten, etc.
SkriptDas Skript in englischer Sprache wird in der ersten Lektion verkauft.
LiteraturEine Literaturliste ist im Skript enthalten.
151-0601-00LTheory of Robotics and Mechatronics Information W4 KP3GP. Korba, S. Stoeter, B. Nelson
KurzbeschreibungThis course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. It’s a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
LernzielRobotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. This course is a requirement for the Robotics Vertiefung and for the Masters in Mechatronics and Microsystems.
InhaltAn introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
Voraussetzungen / BesonderesThe course will be taught in English.
151-0563-01LDynamic Programming and Optimal Control Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungIntroduction to Dynamic Programming and Optimal Control.
LernzielCovers the fundamental concepts of Dynamic Programming & Optimal Control.
InhaltDynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
LiteraturDynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Voraussetzungen / BesonderesRequirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
376-1219-00LRehabilitation Engineering II: Rehabilitation of Sensory and Vegetative FunctionsW3 KP2VR. Riener, R. Gassert, L. Marchal Crespo
KurzbeschreibungRehabilitation Engng is the application of science and technology to ameliorate the handicaps of individuals with disabilities to reintegrate them into society.The goal is to present classical and new rehabilitation engineering principles applied to compensate or enhance motor, sensory, and cognitive deficits. Focus is on the restoration and treatment of the human sensory and vegetative system.
LernzielProvide knowledge on the anatomy and physiology of the human sensory system, related dysfunctions and pathologies, and how rehabilitation engineering can provide sensory restoration and substitution.

This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order.
InhaltIntroduction, problem definition, overview
Rehabilitation of visual function
- Anatomy and physiology of the visual sense
- Technical aids (glasses, sensor substitution)
- Retina and cortex implants
Rehabilitation of hearing function
- Anatomy and physiology of the auditory sense
- Hearing aids
- Cochlea Implants
Rehabilitation and use of kinesthetic and tactile function
- Anatomy and physiology of the kinesthetic and tactile sense
- Tactile/haptic displays for motion therapy (incl. electrical stimulation)
- Role of displays in motor learning
Rehabilitation of vestibular function
- Anatomy and physiology of the vestibular sense
- Rehabilitation strategies and devices (e.g. BrainPort)
Rehabilitation of vegetative Functions
- Cardiac Pacemaker
- Phrenic stimulation, artificial breathing aids
- Bladder stimulation, artificial sphincter
Brain stimulation and recording
- Deep brain stimulation for patients with Parkinson, epilepsy, depression
- Brain-Computer Interfaces
LiteraturIntroductory Books:

An Introduction to Rehabilitation Engineering. R. A. Cooper, H. Ohnabe, D. A. Hobson (Eds.). Taylor & Francis, 2007.

Principles of Neural Science. E. R. Kandel, J. H. Schwartz, T. M Jessell (Eds.). Mc Graw Hill, New York, 2000.

Force and Touch Feedback for Virtual Reality. G. C. Burdea (Ed.). Wiley, New York, 1996 (available on NEBIS).

Human Haptic Perception, Basics and Applications. M. Grunwald (Ed.). Birkhäuser, Basel, 2008.

The Sense of Touch and Its Rendering, Springer Tracts in Advanced Robotics 45, A. Bicchi et al.(Eds). Springer-Verlag Berlin, 2008.

Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005.

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.

Selected Journal Articles and Web Links:

Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195.

Bach-y-Rita P., Tyler M., and Kaczmarek K (2003). Seeing with the brain. International journal of human-computer-interaction, 15(2):285-295.

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.

Hayward, V. (2008): A Brief Taxonomy of Tactile Illusions and
Demonstrations That Can Be Done In a Hardware Store. Brain Research Bulletin, Vol 75, No 6, pp 742-752

Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87

Levesque. V. (2005). Blindness, technology and haptics. Technical report, McGill University. Available at:

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. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894.

The vOICe.

VideoTact, ForeThought Development, LLC.
Voraussetzungen / BesonderesTarget Group:
Students of higher semesters and PhD students of
- Biomedical Engineering, Robotics, Systems and Control
- Medical Faculty, University of Zurich
Students of other departments, faculties, courses are also welcome
This lecture is independent from Rehabilitation Engineering I. Thus, both lectures can be visited in arbitrary order.
401-0647-00LIntroduction to Mathematical Optimization Information W5 KP2V + 1UD. Adjiashvili
KurzbeschreibungIntroduction to basic techniques and problems in mathematical optimization, and their applications to problems in engineering.
LernzielThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
InhaltTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, network flows, knapsack problem, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteraturInformation about relevant literature will be given in the lecture.
Voraussetzungen / BesonderesThis 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.
401-3901-00LMathematical Optimization Information W11 KP4V + 2UR. Weismantel
KurzbeschreibungMathematical treatment of diverse optimization techniques.
LernzielAdvanced optimization theory and algorithms.
Inhalt1. 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.
636-0007-00LComputational Systems Biology Information W6 KP3V + 2UJ. Stelling
KurzbeschreibungStudy 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).
LernzielThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
InhaltBiology 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.
LiteraturU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006.
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