Search result: Catalogue data in Spring Semester 2018
|Computer Science Bachelor|
|Bachelor Studies (Programme Regulations 2016)|
|First Year Examinations|
| First Year Examination Block 1|
Offered in the autumn semester.
|First Year Examination Block 2|
|401-0212-16L||Analysis I||O||7 credits||4V + 2U||E. Kowalski|
|Abstract||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Objective||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Content||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Literature||Michael Struwe: Analysis für Informatik|
Christian Blatter: Ingenieur-analysis
Tom Apostol: Mathematical Analysis
Teaching materials and further information is available through the course website (https://metaphor.ethz.ch/x/2018/fs/401-0212-16L/)
|252-0028-00L||Design of Digital Circuits||O||7 credits||4V + 2U||O. Mutlu|
|Abstract||The class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.|
|Objective||The class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.|
|Content||The class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.|
|252-0029-00L||Parallel Programming||O||7 credits||4V + 2U||T. Hoefler, M. Vechev|
|Abstract||Introduction to parallel programming: deterministic and non-deterministic programs, models for parallel computation, synchronization, communication, and fairness.|
|Objective||The student should learn how to write a correct parallel program, how to measure its efficiency, and how to reason about a parallel program. Student should become familiar with issues, problems, pitfalls, and solutions related to the construction of parallel programs. Labs provide an opportunity to gain experience with threads, libraries for thread management in modern programming lanugages (e.g., Java, C#) and with the execution of parallel programs on multi-processor/multi-core computers.|
|252-0030-00L||Algorithms and Probability||O||7 credits||4V + 2U||A. Steger, E. Welzl|
|Abstract||Fortsetzung der Vorlesung Algorithmen und Datenstrukturen des ersten Semesters. Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt.|
|Objective||Verständnis des Entwurfs und der Analyse von Algorithmen. Grundlagen der diskreten Wahrscheinlichkeitstheorie und ihrer Anwendung in der Algorithmik.|
|252-0058-00L||Formal Methods and Functional Programming||O||7 credits||4V + 2U||D. Basin, P. Müller|
|Abstract||In this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. The first half will focus on using functional programs to express and reason about computation. The second half presents methods for developing and verifying programs represented as discrete transition systems.|
|Objective||In this course, participants will learn about new ways of specifying,|
reasoning about, and developing programs and computer systems. Our objective is to help students raise their level of abstraction in modeling and implementing systems.
|Content||The first part of the course will focus on designing and reasoning|
about functional programs. Functional programs are mathematical
expressions that are evaluated and reasoned about much like ordinary
mathematical functions. As a result, these expressions are simple to
analyze and compose to implement large-scale programs. We will cover the mathematical foundations of functional programming, the lambda calculus, as well as higher-order programming, typing, and proofs of correctness.
The second part of the course will focus on deductive and algorithmic validation of programs modeled as transition systems. As an example of deductive verification, students will learn how to formalize the semantics of imperative programming languages and how to use a formal semantics to prove properties of languages and programs. As an example of algorithmic validation, the course will introduce model checking and apply it to programs and program designs.
|252-0063-00L||Data Modelling and Databases||O||7 credits||4V + 2U||G. Alonso, C. Zhang|
|Abstract||Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines|
|Objective||Introduction to relational databases and data management. Basics of SQL programming and transaction management.|
|Content||The course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.|
|Literature||Kemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.|
Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
|252-0064-00L||Computer Networks||O||7 credits||4V + 2U||A. Perrig, A. Singla|
|Abstract||This introductory course on computer networking takes a top-down view from networked applications all through the physical layer.|
|Objective||Students will get a comprehensive overview of the key protocols and the architecture of the Internet, as one example of more general principles in network design. Students will also acquire hands-on experience in programming different aspects of a computer networks. Apart from the state-of-the-art in networking practice, students will explore the rationale for the design choices that networks in the past have made, and where applicable, why these choices may no longer be ideal.|
|Lecture notes||The slides for each lecture will be made available through the course Web page, along with additional reference material.|
|Literature||Computer Networking: A Top-Down Approach, James F. Kurose and Keith W. Ross. Pearson; 7th edition (May 6, 2016)|
|Prerequisites / Notice||The course includes 2-4 graded programming assignments, which together will enable students to obtain a bonus of up to 15% of the final grade.|
|401-0614-00L||Probability and Statistics||O||5 credits||2V + 2U||P. Cheridito|
|Abstract||Einführung in die Wahrscheinlichkeitstheorie und Statistik|
|Objective||a) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden|
b) Probabilistisches Denken und stochastische Modellierung
c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
|Content||Wahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle, lineare Regression|
|Lecture notes||Lernmaterialien sind erhältlich auf https://metaphor.ethz.ch/x/2018/fs/401-0614-00L/|
|Major: Systems and Software Engineering|
|252-0210-00L||Compiler Design||O||8 credits||4V + 3U||T. Gross|
|Abstract||This course uses compilers as example to expose modern software development techniques.|
Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: conditionals, loops, procedure calls, simple register allocation techniques.
|Objective||Learn principles of compiler design, gain practical experience designing and implementing a medium-scale software system.|
|Content||This course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project.|
Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling, practical application of the techniques and principles presented in the lecture in the context of the OpenJDK HotSpot Java Virtual Machine.
|Literature||Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition)|
Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997
|Prerequisites / Notice||Prerequisites: |
Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level.
|252-0216-00L||Software Architecture and Engineering||O||8 credits||4V + 3U||P. Müller, M. Vechev|
|Abstract||This course introduces both theoretical and applied aspects of software engineering and analysis. It covers:|
- Software Architecture
- Informal and formal Modeling
- Design Patterns
- Code Refactoring
- Program Testing
- Dynamic Program Analysis
- Static Program Analysis
|Objective||The course has two main objectives:|
- Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software.
- Understand how to apply these techniques in practice.
|Content||Some of the core technical topics covered will be:|
- modeling and mapping of models to code
- common code design patterns
- functional and structural testing
- dynamic and static analysis
|Literature||Will be announced in the lecture.|
|Major: Information and Data Processing|
|252-0220-00L||Introduction to Machine Learning |
Previously called Learning and Intelligent Systems
Prof. Krause approves that students take distance exams, also if the exam will take place at a later time due to a different time zone of the alternative exam place.
To get Prof. Krause's signature on the distance exam form please send it to Rita Klute, email@example.com.
|O||8 credits||4V + 2U + 1A||A. Krause|
|Abstract||The course introduces the foundations of learning and making predictions based on data.|
|Objective||The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.|
|Content||- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)|
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression; k-NN
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian networks and exact inference (conditional independence; variable elimination; TANs)
- Approximate inference (sum/max product; Gibbs sampling)
- Latent variable models (Gaussian Misture Models, EM Algorithm)
- Temporal models (Bayesian filtering, Hidden Markov Models)
- Sequential decision making (MDPs, value and policy iteration)
- Reinforcement learning (model-based RL, Q-learning)
|Literature||Textbook: Kevin Murphy: A Probabilistic Perspective, MIT Press|
|Prerequisites / Notice||Designed to provide basis for following courses:|
- Advanced Machine Learning
- Data Mining: Learning from Large Data Sets
- Probabilistic Artificial Intelligence
- Probabilistic Graphical Models
- Seminar "Advanced Topics in Machine Learning"
|Major: Theoretical Computer Science|
|252-0211-00L||Information Security||O||8 credits||4V + 3U||D. Basin, S. Capkun|
|Abstract||This course provides an introduction to Information Security. The focus|
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
|Objective||Master fundamental concepts in Information Security and their|
application to system building. (See objectives listed below for more details).
|Content||1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to |
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
|252-0055-00L||Information Theory||W||4 credits||2V + 1U||L. Haug|
|Abstract||The course covers the fundamental concepts of Shannon's information theory. |
The most important topics are: Entropy, information, data compression, channel coding, codes.
|Objective||The goal of the course is to familiarize with the theoretical fundamentals of information theory and to illustrate the practical use of the theory with the help of selected examples of data compression and coding.|
|Content||Introduction and motivation, basics of probability theory, entropy and information, Kraft inequality, bounds on expected length of source codes, Huffman coding, asymptotic equipartition property and typical sequences, Shannon's source coding theorem, channel capacity and channel coding, Shannon's noisy channel coding theorem, examples|
|Literature||T. Cover, J. Thomas: Elements of Information Theory, John Wiley, 1991.|
D. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
C. Shannon, The Mathematical Theory of Communication, 1948.
|252-0341-01L||Information Retrieval||W||4 credits||2V + 1U||G. Fourny|
|Abstract||Introduction to information retrieval with a focus on text documents and images.|
Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
|Objective||In depth understanding of how to model, index and query unstructured data (text), the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision.|
Knowledge on how to make queries faster and how to make queries work on very large datasets. Knowledge on how to evaluate the quality of an information retrieval engine.
Knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank.
|Content||Tentative plan (subject to change). The lecture structure will follow the pedagogical approach of the book (see below).|
The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture.
2. The basics of how to index and query unstructured data
3. Pre-processing the data prior to indexing: building the term vocabulary, posting lists
4. Dealing with spelling errors: tolerant retrieval
5. Scaling up to large datasets
6. How to improve performance by compressing the index
7. Ranking the results: scores and the vector space model
8. Evaluating the quality of information retrieval: relevance
9. Query expansion
10. Structured retrieval: when the data is not quite unstructured (XML or HTML)
11. Alternate approach: Probabilistic information retrieval
12. Alternate approach: Language models
13. Crawling the Web
14. Link analysis (PageRank)
|Literature||C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press.|
|Prerequisites / Notice||Prior knowledge in linear algebra, data structures and algorithms, and probability theory (at the Bachelor's level) is required.|
|252-0820-00L||Case Studies from Practice||W||4 credits||2V + 1U||M. Brandis|
|Abstract||The course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these.|
|Objective||By using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice.|
A particular focus is to make the often imprecise and fuzzy problems in practice accessible to factual analysis and reasoning, and to challenge "common wisdom" and hearsay.
|Content||The course consists of multiple lectures on methods to systematically analyze problems in a business setting and communicate about them as well as IT management and IT economics, presented by the lecturer, and a number of case studies provided by guest lecturers from either IT companies or IT departments of a diverse range of companies. Students will obtain insights into both established and startup companies, small and big, and different industries.|
Presenting companies have included avaloq, Accenture, AdNovum, Bank Julius Bär, Credit Suisse, Deloitte, HP, Hotelcard, IBM Research, McKinsey & Company, Open Web Technology, SAP Research, Selfnation, SIX Group, Teralytics, 28msec, Zühlke and dormakaba, and Marc Brandis Strategic Consulting. The participating companies in spring 2017 will be announced at course start.
|151-0116-10L||High Performance Computing for Science and Engineering (HPCSE) for Engineers II||W||4 credits||4G||P. Koumoutsakos, P. Chatzidoukas|
|Abstract||This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures.|
|Objective||The course will teach |
- programming models and tools for multi and many-core architectures
- fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences
|Content||High Performance Computing:|
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation
Class notes, handouts
|Literature||- Class notes|
- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein
- CUDA by example, J. Sanders and E. Kandrot
- Data Analysis: A Bayesian Tutorial, Devinderjit Sivia
|227-0124-00L||Embedded Systems||W||6 credits||4G||L. Thiele|
|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.|
| Minor Courses|
In gewissen Fächern werden Vorbedingungen verlangt. Es liegt in der Verantwortung der Studierenden, sicherzustellen, dass diese Voraussetzungen erfüllt sind.
|151-0854-00L||Autonomous Mobile Robots||W||5 credits||4G||R. Siegwart, M. Chli, J. Nieto|
|Abstract||The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.|
|Objective||The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation.|
|Lecture notes||This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.|
|Literature||This lecture is based on the Textbook: |
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
|227-0075-00L||Electrical Engineering I||W||3 credits||2V + 2U||J. Biela|
|Abstract||Basic course in electrical engineering with the following topics: Concepts of voltage and currents; Analyses of dc and ac networks; Series and parallel resistive circuits, circuits including capacitors and inductors; Kirchhoff's laws and other network theorems; Transient responses; Basics of electrical and magnetic fields;|
|Objective||Understanding of the basic concepts in electrical engineering with focus on network theory. The successful student knows the basic components of electrical circuits and the network theorems after attending the course.|
|Content||Diese Vorlesung vermittelt Grundlagenkenntnisse im Fachgebiet Elektrotechnik. Ausgehend von den grundlegenden Konzepten der Spannung und des Stroms wird die Analyse von Netzwerken bei Gleich- und Wechselstrom behandelt. Dies schliesst Serie- und Parallelschaltungen von Widerstandsnetzwerken und Netzwerken mit Kapazitäten und Induktivitäten, wie auch die Kirchhoff'schen Gesetze zur Behandlung solcher Schaltungen und anderer Netzwerktheoreme mit ein. Weiterhin werden transiente Vorgänge in einfachen Netzwerken untersucht und grundlegende Konzepte von leistungselektronischen Konvertersystemen betrachtet.|
|Lecture notes||Vorlesungskript/-folien Elektrotechnik I über SPOD und als PDF im Moodle verfügbar|
|Literature||Für das weitergehende Studium werden in der Vorlesung verschiedene Bücher vorgestellt.|
|227-0123-00L||Mechatronics||W||6 credits||4G||T. M. Gempp|
|Abstract||Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.|
|Objective||Introduction into the basics and technology of mechatronical devices. Theoretical and practical know-how of the basic elements of a mechatronical system.|
|Content||Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.|
|Lecture notes||Recommendation of textbook. Additional documentation to the individual topics. Documentation from industrial companies.|
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