Search result: Catalogue data in Autumn Semester 2019
Mechanical Engineering Master  | ||||||
 Core Courses | ||||||
| Robotics, Systems and Control The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 151-0107-20L | High Performance Computing for Science and Engineering (HPCSE) I | W | 4 credits | 4G | P. Koumoutsakos | |
| Abstract | This course gives an introduction into algorithms and numerical methods for parallel computing on shared and distributed memory architectures. The algorithms and methods are supported with problems that appear frequently in science and engineering. | |||||
| Objective | With manufacturing processes reaching its limits in terms of transistor density on today’s computing architectures, efficient utilization of computing resources must include parallel execution to maintain scaling. The use of computers in academia, industry and society is a fundamental tool for problem solving today while the “think parallel” mind-set of developers is still lagging behind. The aim of the course is to introduce the student to the fundamentals of parallel programming using shared and distributed memory programming models. The goal is on learning to apply these techniques with the help of examples frequently found in science and engineering and to deploy them on large scale high performance computing (HPC) architectures. | |||||
| Content | 1. Hardware and Architecture: Moore’s Law, Instruction set architectures (MIPS, RISC, CISC), Instruction pipelines, Caches, Flynn’s taxonomy, Vector instructions (for Intel x86) 2. Shared memory parallelism: Threads, Memory models, Cache coherency, Mutual exclusion, Uniform and Non-Uniform memory access, Open Multi-Processing (OpenMP) 3. Distributed memory parallelism: Message Passing Interface (MPI), Point-to-Point and collective communication, Blocking and non-blocking methods, Parallel file I/O, Hybrid programming models 4. Performance and parallel efficiency analysis: Performance analysis of algorithms, Roofline model, Amdahl’s Law, Strong and weak scaling analysis 5. Applications: HPC Math libraries, Linear Algebra and matrix/vector operations, Singular value decomposition, Neural Networks and linear autoencoders, Solving partial differential equations (PDEs) using grid-based and particle methods | |||||
| Lecture notes | https://www.cse-lab.ethz.ch/teaching/hpcse-i_hs19/ Class notes, handouts | |||||
| Literature | • An Introduction to Parallel Programming, P. Pacheco, Morgan Kaufmann • Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press • Computer Organization and Design, D.H. Patterson and J.L. Hennessy, Morgan Kaufmann • Vortex Methods, G.H. Cottet and P. Koumoutsakos, Cambridge University Press • Lecture notes | |||||
| Prerequisites / Notice | Students should be familiar with a compiled programming language (C, C++ or Fortran). Exercises and exams will be designed using C++. The course will not teach basics of programming. Some familiarity using the command line is assumed. Students should also have a basic understanding of diffusion and advection processes, as well as their underlying partial differential equations. | |||||
| 151-0323-00L | Autonomous Mobility on Demand: From Car to Fleet  Number of participants limited to 30. | W | 4 credits | 4G | J. Tani, A. Censi | |
| Abstract | Autonomous Mobility on Demand systems based on self-driving cars will make a huge impact in the world. This class describes the basics of modeling, perception, planning, control and learning for self-driving cars. The focus is on integration and co-design of components and behaviors. The course has a heavy experimental component based on the Duckietown platform. | |||||
| Objective | The students will learn how to design and implement all parts of an architecture for a complex multi-robot system performing nontrivial tasks. | |||||
| Content | Development tools and best practices for software development of open source projects; single autonomous car functionalities (perception, planning, modeling and control, based on vision data, complemented by learning based approaches); Multi agent behaviors (platooning, coordination, fleet-level policy optimization) focus in group projects. | |||||
| Lecture notes | Course notes will be provided for free in an electronic form. | |||||
| Literature | Course notes will be provided for free in an electronic form. These are some books that can be used to provide background information or consulted as references: (1) Siegwart, Nourbakhsh, Scaramuzza - Introduction to autonomous mobile robots; (2) Norvig, Russell - Artificial Intelligent, a modern approach. (3) Peter Corke - Robotics Vision and Control (4) Oussama Khatib, Bruno Siciliano - Handbook of Robotics | |||||
| Prerequisites / Notice | This course is also known as Duckietown. Students should have taken a basic course in probability theory, computer vision, control systems, and should be familiar with basic programming (Python) and Linux use. | |||||
| 151-0532-00L | Nonlinear Dynamics and Chaos I | W | 4 credits | 2V + 2U | 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. | |||||
| 151-0563-01L | Dynamic Programming and Optimal Control | W | 4 credits | 2V + 1U | R. D'Andrea | |
| Abstract | Introduction to Dynamic Programming and Optimal Control. | |||||
| Objective | Covers the fundamental concepts of Dynamic Programming & Optimal Control. | |||||
| Content | Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control. | |||||
| Literature | Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover. | |||||
| Prerequisites / Notice | Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra. | |||||
| 151-0567-00L | Engine Systems | W | 4 credits | 3G | C. Onder | |
| Abstract | Introduction to current and future engine systems and their control systems | |||||
| Objective | Introduction to methods of control and optimization of dynamic systems. Application to real engines. Understand the structure and behavior of drive train systems and their quantitative descriptions. | |||||
| Content | Physical description and mathematical models of components and subsystems (mixture formation, load control, supercharging, emissions, drive train components, etc.). Case studies of model-based optimal design and control of engine systems with the goal of minimizing fuel consumption and emissions. | |||||
| Lecture notes | Introduction to Modeling and Control of Internal Combustion Engine Systems Guzzella Lino, Onder Christopher H. 2010, Second Edition, 354 p., hardbound ISBN: 978-3-642-10774-0 | |||||
| Prerequisites / Notice | Combined homework and testbench exercise (air-to-fuel-ratio control or idle-speed control) in groups | |||||
| 151-0569-00L | Vehicle Propulsion Systems | W | 4 credits | 3G | C. Onder, P. Elbert | |
| Abstract | Introduction to current and future propulsion systems and the electronic control of their longitudinal behavior | |||||
| Objective | Introduction to methods of system optimization and controller design for vehicles. Understanding the structure and working principles of conventional and new propulsion systems. Quantitative descriptions of propulsion systems | |||||
| Content | Understanding of physical phenomena and mathematical models of components and subsystems (manual, automatic and continuously variable transmissions, energy storage systems, electric drive trains, batteries, hybrid systems, fuel cells, road/wheel interaction, automatic braking systems, etc.). Presentation of mathematical methods, CAE tools and case studies for the model-based design and control of propulsion systems with the goal of minimizing fuel consumption and emissions. | |||||
| Lecture notes | Vehicle Propulsion Systems -- Introduction to Modeling and Optimization Guzzella Lino, Sciarretta Antonio 2013, X, 409 p. 202 illus., Geb. ISBN: 978-3-642-35912-5 | |||||
| Prerequisites / Notice | Lectures of Prof. Dr. Ch. Onder and Dr. Ph. Elbert are also possible to be held in German. | |||||
| 151-0573-00L | System Modeling | W | 4 credits | 2V + 2U | L. Guzzella | |
| Abstract | Introduction to system modeling for control. Generic modeling approaches based on first principles, Lagrangian formalism, energy approaches and experimental data. Model parametrization and parameter estimation. Basic analysis of linear and nonlinear systems. | |||||
| Objective | Learn how to mathematically describe a physical system or a process in the form of a model usable for analysis and control purposes. | |||||
| Content | This class introduces generic system-modeling approaches for control-oriented models based on first principles and experimental data. The class will span numerous examples related to mechatronic, thermodynamic, chemistry, fluid dynamic, energy, and process engineering systems. Model scaling, linearization, order reduction, and balancing. Parameter estimation with least-squares methods. Various case studies: loud-speaker, turbines, water-propelled rocket, geostationary satellites, etc. The exercises address practical examples. | |||||
| Lecture notes | The handouts in English will be sold in the first lecture. | |||||
| Literature | A list of references is included in the handouts. | |||||
| 151-0593-00L | Embedded Control Systems | W | 4 credits | 6G | J. S. Freudenberg, M. Schmid Daners | |
| Abstract | This course provides a comprehensive overview of embedded control systems. The concepts introduced are implemented and verified on a microprocessor-controlled haptic device. | |||||
| Objective | Familiarize students with main architectural principles and concepts of embedded control systems. | |||||
| Content | An embedded system is a microprocessor used as a component in another piece of technology, such as cell phones or automobiles. In this intensive two-week block course the students are presented the principles of embedded digital control systems using a haptic device as an example for a mechatronic system. A haptic interface allows for a human to interact with a computer through the sense of touch. Subjects covered in lectures and practical lab exercises include: - The application of C-programming on a microprocessor - Digital I/O and serial communication - Quadrature decoding for wheel position sensing - Queued analog-to-digital conversion to interface with the analog world - Pulse width modulation - Timer interrupts to create sampling time intervals - System dynamics and virtual worlds with haptic feedback - Introduction to rapid prototyping | |||||
| Lecture notes | Lecture notes, lab instructions, supplemental material | |||||
| Prerequisites / Notice | Prerequisite courses are Control Systems I and Informatics I. This course is restricted to 33 students due to limited lab infrastructure. Interested students please contact Marianne Schmid (E-Mail: marischm@ethz.ch) After your reservation has been confirmed please register online at www.mystudies.ethz.ch. Detailed information can be found on the course website http://www.idsc.ethz.ch/education/lectures/embedded-control-systems.html | |||||
| 151-0601-00L | Theory of Robotics and Mechatronics | W | 4 credits | 3G | P. Korba, S. Stoeter | |
| Abstract | This 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. | |||||
| Objective | Robotics 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. | |||||
| Content | 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. | |||||
| Lecture notes | available. | |||||
| 151-0604-00L | Microrobotics | W | 4 credits | 3G | B. Nelson, N. Shamsudhin | |
| Abstract | Microrobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination. | |||||
| Objective | The objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field. | |||||
| Content | Main topics of the course include: - Scaling laws at micro/nano scales - Electrostatics - Electromagnetism - Low Reynolds number flows - Observation tools - Materials and fabrication methods - Applications of biomedical microrobots | |||||
| Lecture notes | The powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically. | |||||
| Prerequisites / Notice | The lecture will be taught in English. | |||||
| 151-0632-00L | Vision Algorithms for Mobile Robotics Number of participants limited to 55 Registration is on a first come, first served basis and SPACE IS LIMITED! | W | 4 credits | 2V + 2U | D. Scaramuzza | |
| Abstract | For a robot to be autonomous, it has to perceive and understand the world around it. This course introduces you to the key computer vision algorithms used in mobile robotics, such as feature extraction, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Google Tango, Ms Hololens, and the Mars rovers). | |||||
| Objective | Learn the fundamental computer vision algorithms used in mobile robotics, in particular: feature extraction, multiple view geometry, dense reconstruction, object tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithm behind Google Tango). | |||||
| Content | Each lecture will be followed by a lab session where you will learn to implement the building block of a visual odometry algorithm in Matlab. By the end of the course, you will integrate all these building blocks into a working visual odometry algorithm. | |||||
| Lecture notes | Lecture slides will be made available on the course official website: http://rpg.ifi.uzh.ch/teaching.html | |||||
| Literature | [1] Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010. [2] Robotics Vision and Control: Fundamental Algorithms, by Peter Corke 2011. [3] An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry. [4] Multiple view Geometry, by R. Hartley and A. Zisserman. [5] Introduction to autonomous mobile robots 2nd Edition, by R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, February, 2011 | |||||
| Prerequisites / Notice | Fundamentals of algebra, geomertry, matrix calculus, and Matlab programming. | |||||
| 151-0655-00L | Skills for Creativity and Innovation | W | 4 credits | 3G | I. Goller, C. Kobe | |
| Abstract | This lecture aims to enhance the knowledge and competency of students regarding their innovation capability. An overview on prerequisites of and different skills for creativity and innovation in individual & team settings is given. The focus of this lecture is clearly on building competencies - not just acquiring knowledge. | |||||
| Objective | - Basic knowledge about creativity and skills - Knowledge about individual prerequisites for creativity - Development of individual skills for creativity - Knowledge about teams - Development of team-oriented skills for creativity - Knowledge and know-how about transfer to idea generation teams | |||||
| Content | Basic knowledge about creativity and skills: - Introduction into creativity & innovation: definitions and models Knowledge about individual prerequisites for creativity: - Personality, motivation, intelligence Development of individual skills for creativity: - Focus on creativity as problem analysis & solving - Individual skills in theoretical models - Individual competencies: exercises and reflection Knowledge about teams: - Definitions and models - Roles in innovation processes Development of team-oriented skills for creativity: - Idea generation and development in teams - Cooperation & communication in innovation teams Knowledge and know-how about transfer to idea generation teams: - Self-reflection & development planning - Methods of knowledge transfer | |||||
| Lecture notes | Slides, script and other documents will be distributed via moodle.ethz.ch (access only for students registered to this course) | |||||
| Literature | Goller, I. & Bessant, J. (2017). Creativity for Innovation Management. Routledge. (ISBN-13: 978-1138641327) As well as material handed out in the lecture | |||||
| 151-0727-00L | Colloquium on Manufacturing Technology | W | 4 credits | 3K | K. Wegener, A. Kunz | |
| Abstract | Future training on selected current topics of the manufacturing technology. Per afternoon a selected topic is presented in several lectures, by the majority by experts from the industry. The students prepare a summary of the lectures given and prepare themselves on the basis of these lectures and own information search. | |||||
| Objective | Contious further training to current topics of the manufacturing technique. Exchange of experience and knowledge with the industry and other universities. | |||||
| Content | Selected actual topics on manufacturing methods and tools, machine tools, NC-control and drives, components and measuring methods and devices. Topics are changing every year. | |||||
| Lecture notes | no Script | |||||
| Prerequisites / Notice | - Students must have participated and passed the courses Manufacturing, Production Machines I and Forming Technology III - Forming Processes. - Further training with specialized lectures and large participation from the industry. Language: Help for English speaking students on request. | |||||
| 151-0851-00L | Robot Dynamics | W | 4 credits | 2V + 2U | M. Hutter, R. Siegwart | |
| Abstract | We will provide an overview on how to kinematically and dynamically model typical robotic systems such as robot arms, legged robots, rotary wing systems, or fixed wing. | |||||
| Objective | The primary objective of this course is that the student deepens an applied understanding of how to model the most common robotic systems. The student receives a solid background in kinematics, dynamics, and rotations of multi-body systems. On the basis of state of the art applications, he/she will learn all necessary tools to work in the field of design or control of robotic systems. | |||||
| Content | The course consists of three parts: First, we will refresh and deepen the student's knowledge in kinematics, dynamics, and rotations of multi-body systems. In this context, the learning material will build upon the courses for mechanics and dynamics available at ETH, with the particular focus on their application to robotic systems. The goal is to foster the conceptual understanding of similarities and differences among the various types of robots. In the second part, we will apply the learned material to classical robotic arms as well as legged systems and discuss kinematic constraints and interaction forces. In the third part, focus is put on modeling fixed wing aircraft, along with related design and control concepts. In this context, we also touch aerodynamics and flight mechanics to an extent typically required in robotics. The last part finally covers different helicopter types, with a focus on quadrotors and the coaxial configuration which we see today in many UAV applications. Case studies on all main topics provide the link to real applications and to the state of the art in robotics. | |||||
| Prerequisites / Notice | The contents of the following ETH Bachelor lectures or equivalent are assumed to be known: Mechanics and Dynamics, Control, Basics in Fluid Dynamics. | |||||
| 151-0917-00L | Mass Transfer | W | 4 credits | 2V + 2U | G. Kelesidis, S. E. Pratsinis, A. Güntner, V. Mavrantzas | |
| Abstract | This course presents the fundamentals of transport phenomena with emphasis on mass transfer. The physical significance of basic principles is elucidated and quantitatively described. Furthermore the application of these principles to important engineering problems is demonstrated. | |||||
| Objective | This course presents the fundamentals of transport phenomena with emphasis on mass transfer. The physical significance of basic principles is elucidated and quantitatively described. Furthermore the application of these principles to important engineering problems is demonstrated. | |||||
| Content | Fick's laws; application and significance of mass transfer; comparison of Fick's laws with Newton's and Fourier's laws; derivation of Fick's 2nd law; diffusion in dilute and concentrated solutions; rotating disk; dispersion; diffusion coefficients, viscosity and heat conduction (Pr and Sc numbers); Brownian motion; Stokes-Einstein equation; mass transfer coefficients (Nu and Sh numbers); mass transfer across interfaces; Reynolds- and Chilton-Colburn analogies for mass-, heat-, and momentum transfer in turbulent flows; film-, penetration-, and surface renewal theories; simultaneous mass, heat and momentum transfer (boundary layers); homogenous and heterogenous reversible and irreversible reactions; diffusion-controlled reactions; mass transfer and first order heterogenous reaction. Applications. | |||||
| Literature | Cussler, E.L.: "Diffusion", 3nd edition, Cambridge University Press, 2009. | |||||
| Prerequisites / Notice | Students attending this highly-demanding course are expected to allocate sufficient time within their weekly schedule to successfully conduct the exercises. | |||||
| 151-1116-00L | Introduction to Aircraft and Car Aerodynamics | W | 4 credits | 3G | J. Wildi | |
| Abstract | Aircraft aerodynamics: Atmosphere; aerodynamic forces (lift, drag); thrust. Vehicle aerodynamics: Aerodynamic and mass forces, drag, lift, car aerodynamics and performence. Passenger cars, trucks, racing cars. | |||||
| Objective | An introduction to the basic principles and interrelationships of aircraft and automotive aerodynamics. To understand the basic relations of the origin of aerodynamic forces (ie lift, drag). To quantify the aerodynamic forces for basic configurations of aercraft and car components. Illustration of the intrinsic problems and results using examples. Using experimental and theoretical methods to illustrate possibilities and limits. | |||||
| Content | Aircraft aerodynamics: atmosphere, aerodynamic forces (ascending force: profile, wings. Resistance, residual resistance, induced resistance); thrust (overview of the propulsion system, aerodynamics of the propellers), introduction to static longitudinal stability. Automobile aerodynamics: Basic principles: aerodynamic force and the force of inertia, resistance, drive, aerodynamic and driving performance. Cars commercial vehicles, racing cars. | |||||
| Lecture notes | 1.) Grundlagen der Flugtechnik (Basics of flight science, script in german language) 2.) Einführung in die Fahrzeugaerodynamik (Introduction in car aerodynamics, script in german language) | |||||
| Literature | English literature covering the content of the course: - Anderson Jr, John D: Introduction to Flight, Mc Graw Hill, Ed 06, 2007; ISBN: 9780073529394 - Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics, John Wiley and Sons, 1979 - Hucho, Wolf-Heinrich: Aerodynamics of Road Vehicles, SAE International, 1998 | |||||
| 227-0124-00L | Embedded Systems | W | 6 credits | 4G | J. Beutel | |
| Abstract | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions. | |||||
| Objective | Understanding specific requirements and problems arising in embedded system applications. Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis. Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system FreeRTOS, a commercial embedded system platform and the associated design environment. | |||||
| Content | An embedded system is some combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices. The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides, the lecture is complemented by laboratory sessions where students learn to program in C, to base their design on the embedded operating systems FreeRTOS, to use a commercial embedded system platform including sensors, and to edit/debug via an integrated development environment. Specifically the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, hardware architecture synthesis. More information is available at https://www.tec.ee.ethz.ch/education/lectures/embedded-systems.html . | |||||
| Lecture notes | The following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at https://www.tec.ee.ethz.ch/education/lectures/embedded-systems.html . | |||||
| Literature | P. Marwedel: Embedded System Design, Springer, ISBN 978-3-319-56045-8, 2018. G.C. Buttazzo: Hard Real-Time Computing Systems. Springer Verlag, ISBN 978-1-4614-0676-1, 2011. Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press, ISBN 978-0-262-53381-2, 2017. M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 978-0-128-05387-4, 2016. | |||||
| Prerequisites / Notice | Prerequisites: Basic knowledge in computer architectures and programming. | |||||
| 227-0225-00L | Linear System Theory | W | 6 credits | 5G | J. Lygeros | |
| Abstract | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems. | |||||
| Objective | Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems. | |||||
| Content | - Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. | |||||
| Lecture notes | Available on the course Moodle platform. | |||||
| Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra, analysis. | |||||
| 227-0447-00L | Image Analysis and Computer Vision | W | 6 credits | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
| Abstract | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||
| Objective | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
| Content | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with 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 the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, 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 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||
| Lecture notes | Course material Script, computer demonstrations, exercises and problem solutions | |||||
| Prerequisites / Notice | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||
| 227-0517-00L | Electrical Drive Systems II Does not take place this semester. This course will be replaced by 227-0518-10L "Design and Control of Electric Machines" as of spring semester 2020. | W | 6 credits | 4G | to be announced | |
| Abstract | In the course "Drive System II" the power semiconductors are repeated. The creation of converters based on the combination of switches/cells and based topologies is explained. Another main focus is on the 3-level inverter with its switching and transfer functions. Further topics are the control of the synchronous machine, of line-side converters and issues with converter-fed machines | |||||
| Objective | The students establish a deeper understanding in regards of the design of the main components of an electrical drive system. They establish knowledge on the most important interaction with the grid and the machine and their related high dynamic control. | |||||
| Content | Converter topologies (switch or cell based), multi-pulse diode rectifiers, system aspects of transfomer and electrical machines, 3-level inverter with its switching and transfer functions, grid side harmonics, modeling and control of synchronous machines (including permanent magnet machines), control of line-side converters, reflection effects with power cables, winding isolation and bearing stress. Field trip to ABB Semionductors. | |||||
| Lecture notes | Skript can be downloaded from Ilias | |||||
| Literature | Skript of lecture; References in skript to related technical publications and books | |||||
| Prerequisites / Notice | Prerequisites: Electrical Drive Systems I (recommended), Basics in electrical engineering, power electronics, automation and mechatronics | |||||
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