Gerhard Tröster: Catalogue data in Autumn Semester 2017

Name	Prof. Dr. Gerhard Tröster
Address	Ombudsstelle der ETH Zürich 8092 Zürich SWITZERLAND
Telephone	+41 44 632 39 64
E-mail	gerhard.troester@ethz.ch
URL	https://www.ethz.ch/services/de/organisation/ombuds-und-vertrauenspersonen.html
Department	Information Technology and Electrical Engineering
Relationship	Professor Emeritus

Number	Title	ECTS	Hours	Lecturers
227-0003-00L	Digital Circuits	4 credits	2V + 2U	G. Tröster
Abstract	Digital and analogue signals and their representation. Combinational and sequential circuits and systems, boolean algebra, K-maps. Finite state machines. Memory and computing building blocks in CMOS technology, programmable logic circuits.
Objective	Provide basic knowledge and methods to understand and to design digital circuits and systems.
Content	Digital and analogue signals and their representation. Boolean Algebra, circuit analysis and synthesis, the MOS transistor, CMOS logic, static and dynamic behaviour, tristate logic, Karnough-Maps, hazards, binary nuber systems, coding. Combinational and sequential circuits and systems (boolean algebra, K-maps, etc.). Memory building blocks and memory structures, programmable logic circuits. Finite state machines, architetcure of microprocessors.
Lecture notes	Lecture notes for all lessons, assignments and solutions. http://www.ife.ee.ethz.ch/education/digital-technology.html
Literature	Literature will be announced during the lessons.
Prerequisites / Notice	No special prerequisites
227-0005-10L	Digital Circuits Laboratory	1 credit	1P	G. Tröster
Abstract	Digital and analogue signals and their representation. Combinational and sequential circuits and systems, boolean algebra, K-maps. Finite state machines. Memory and computing building blocks in CMOS technology, programmable logic circuits.
Objective	Deepen and extend the knowledge from lecture and exercises, usage of design software Quartus II as well as an oscilloscope
Content	The contents of the digital circuits laboratory will deepen and extend the knowledge of the correspondent lecture and exercises. With the help of the logic device design software Quartus II different circuits will be designed and then tested on an evaluation board. You will build up the control for a 7-digit display as well as an adder and you will create different types of latches and flip-flops. At the end of the laboratory a small synthesizer will be programmed that is able to play self-created melodies. At the same time the usage of a modern oscilloscope will be taught in order to analyse the programmed circuits through the digital and analogue inputs.
Lecture notes	Lecture notes for all experiments. http://www.ife.ee.ethz.ch/education/digital-technology-internship.html
Prerequisites / Notice	No special prerequisites
227-0197-00L	Wearable Systems I	6 credits	4G	G. Tröster, U. Blanke
Abstract	Context recognition in mobile communication systems like mobile phone, smart watches and wearable computer will be studied using advanced methods from sensor data fusion, pattern recognition, statistics, data mining and machine learning. Context comprises the behavior of individuals and of groups, their activites as well as the local and social environment.
Objective	Using internal sensors and sensors in our environment including data from the wristwatch, bracelet or internet (crowd sourcing), our 'smart phone' detects our context continuously, e.g. where we are, what we are doing, with whom we are together, what is our constitution, what are our needs. Based on this information our 'smart phone' offers us the appropriate services like a personal assistant.Context comprises user's behavior, his activities, his local and social environment. In the data path from the sensor level to signal segmentation to the classification of the context, advanced methods of signal processing, pattern recognition and machine learning will be applied. Sensor data generated by crowdsouring methods are integrated. The validation using MATLAB is followed by implementation and testing on a smart phone. Context recognition as the crucial function of mobile systems is the main focus of the course. Using MatLab the participants implement and verify the discussed methods also using a smart phone.
Content	Using internal sensors and sensors in our environment including data from the wristwatch, bracelet or internet (crowd sourcing), our 'smart phone' detects our context continuously, e.g. where we are, what we are doing, with whom we are together, what is our constitution, what are our needs. Based on this information our 'smart phone' offers us the appropriate services like a personal assistant. Context recognition - what is the situation of the user, his activity, his environment, how is he doing, what are his needs - as the central functionality of mobile systems constitutes the focus of the course. The main topics of the course include Sensor nets, sensor signal processing, data fusion, time series (segmentation, similariy measures), supervised learning (Bayes Decision Theory, Decision Trees, Random Forest, kNN-Methods, Support Vector Machine, Adaboost, Deep Learning), clustering (k-means, dbscan, topic models), Recommender Systems, Collaborative Filtering, Crowdsourcing. The exercises show concrete design problems like motion and gesture recognition using distributed sensors, detection of activity patterns and identification of the local environment. Presentations of the PhD students and the visit at the Wearable Computing Lab introduce in current research topics and international research projects. Language: german/english (depending on the participants)
Lecture notes	Lecture notes for all lessons, assignments and solutions. http://www.ife.ee.ethz.ch/education/wearable-systems-i.html
Literature	Literature will be announced during the lessons.
Prerequisites / Notice	No special prerequisites