Search result: Catalogue data in Autumn Semester 2022
Electrical Engineering and Information Technology Bachelor  
5th Semester: Third Year Core Courses Can be freely combined, a list of recommendations is available under Link  
Number  Title  Type  ECTS  Hours  Lecturers  

227010100L  DiscreteTime and Statistical Signal Processing  W  6 credits  4G  H.‑A. Loeliger  
Abstract  The course is about some fundamental topics of digital signal processing with a bias towards applications in communications: discretetime linear filters, inverse filters and equalization, DFT, discretetime stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.  
Objective  The course is about some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discretetime linear filters. In the second part of the course, we review the basics of probability theory and discretetime stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.  
Content  1. Discretetime linear systems and filters: statespace realizations, ztransform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discretetime stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm.  
Lecture notes  Lecture Notes  
227010200L  Discrete Event Systems  W  6 credits  4G  L. Josipovic, L. Vanbever, R. Wattenhofer  
Abstract  Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an averagecase and from a worstcase perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, WorstCase Event Systems, Verification, Network Calculus.  
Objective  Over 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 averagecase 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 worstcase perspective using the theory of online algorithms and adversarial queuing.  
Content  1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. WorstCase Event Systems 7. Network Calculus  
Lecture notes  Available  
Literature  [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran ElYaniv. 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 0792386094 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NPhard 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  
227010300L  Control Systems  W  6 credits  2V + 2U  F. Dörfler  
Abstract  Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input  single output and multivariable systems.  
Objective  Study of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input  single output and multivariable systems.  
Content  Process automation, concept of control. Modelling of dynamical systems  examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems  effect of additional poles and zeros. Closedloop control  idea of feedback. PID control, Ziegler  Nichols tuning. Stability, RouthHurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multiloop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement  choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.  
Literature  K. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010. R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007. G. F. Franklin, J. D. Powell, and A. EmamiNaeini. Feedback Control of Dynamic Systems. AddisonWesley, 2010. J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014. J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.  
Prerequisites / Notice  Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation.  
227011300L  Power Electronics  W  6 credits  4G  J. W. Kolar  
Abstract  Fields of application of power electronic converters; basic concept of switchmode voltage and current conversion; derivation of circuit structures of nonisolated and isolated DC/DC converters, AC/DC and DC/AC converter structures; analysis procedure and analysis of the operating behaviour and operating range; design criteria and design of main power components.  
Objective  Fields of application of power electronic converters; basic concept of switchmode voltage and current conversion; derivation of circuit structures of nonisolated and isolated DC/DC converters, AC/DC and DC/AC converter structures; analysis procedure and analysis of the operating behaviour and operating range; design criteria and design of main power components.  
Content  Fields of application and application examples of power electronic converters, basic concept of switchmode voltage and current conversion, pulsewidth modulation (PWM); derivation and operating modes (continuous and discontinuous current mode) of DC/DC converter topologies, buck / boost / buckboost converter; extension to DC/AC conversion using differences of unipolar output voltages varying over time; singlephase diode rectifier; boosttype PWM rectifier featuring sinusoidal input current; tolerance band AC current control and cascaded output voltage control with inner constant switching frequency current control; local and global averaging of switching frequency discontinuous quantities for calculation of component stresses; threephase AC/DC conversion, centertap rectifier with impressed output current, thyristor function, thyristor centertap and fullbridge converter, rectifier and inverter operation, control angle and recovery time, inverter operation limit; basics of inductors and singlephase transformers, design based on scaling laws; Isolated DCDC converter, flyback and forward converter, singleswitch and twoswitch circuit; singlephase DC/AC conversion, fourquadrant converter, unipolar and bipolar modulation, fundamental frequency model of ACside operating behaviour; threephase DC/AC converter with starconnected threephase load, zero sequence (commonmode) and current forming differentialmode output voltage components, fundamental frequency modulation and PWM with singe triangular carrier and individual carrier signals of the phases.  
Lecture notes  Lecture notes and associated exercises including correct answers, simulation program for interactive selflearning including visualization/animation features.  
Prerequisites / Notice  Prerequisites: Basic knowledge of electrical engineering / electric circuit analysis and signal theory.  
Competencies 
 
227011600L  VLSI 1: HDL Based Design for FPGAs  W  6 credits  5G  F. K. Gürkaynak, L. Benini  
Abstract  This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrialquality circuits.  
Objective  Understand VeryLargeScale Integrated Circuits (VLSI chips), ApplicationSpecific Integrated Circuits (ASIC), and FieldProgrammable GateArrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in frontend design from architectural conception to gatelevel netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrialquality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools.  
Content  This course is concerned with systemlevel issues of VLSI design and FPGA implementations. Topics include:  Overview on design methodologies and fabrication depths.  Levels of abstraction for circuit modeling.  Organization and configuration of commercial fieldprogrammable components.  FPGA design flows.  Dedicated and general purpose architectures compared.  How to obtain an architecture for a given processing algorithm.  Meeting throughput, area, and power goals by way of architectural transformations.  Hardware Description Languages (HDL) and the underlying concepts.  SystemVerilog  Register Transfer Level (RTL) synthesis and its limitations.  Building blocks of digital VLSI circuits.  Functional verification techniques and their limitations.  Modular and largely reusable testbenches.  Assertionbased verification.  Synchronous versus asynchronous circuits.  The case for synchronous circuits.  Periodic events and the Anceau diagram.  Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gatelevel netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout.  
Lecture notes  Textbook and all further documents in English.  
Literature  H. Kaeslin: "TopDown Digital VLSI Design, from Architectures to GateLevel Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.  
Prerequisites / Notice  Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: Link  
227012100L  Communication Systems Does not take place this semester.  W  6 credits  4G  to be announced  
Abstract  Information Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet  
Objective  Introduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems  
Content  Covered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated. The application of the basic methods will be extensively explained using existing and future wireless and wired systems.  
Lecture notes  Lecture Slides  
Literature  [1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001 [2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003 [3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999  
227012400L  Embedded Systems  W  6 credits  4G  M. Magno, 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 hardwaresoftware interfaces, the memory architecture, communication between components, embedded operating systems, realtime scheduling theory, shared resources, lowpower and lowenergy 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 ThreadX, 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, internetofthings, 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 computerbased 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 ThreadX, 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, hardwaresoftware interfaces and memory architecture, software design methodology, communication, embedded operating systems, realtime scheduling, shared resources, lowpower and lowenergy design, hardware architecture synthesis. More information is available at Link .  
Lecture notes  The following information will be available: Lecture material, publications, exercise sheets and laboratory documentation at Link .  
Literature  P. Marwedel: Embedded System Design, Springer, ISBN 9783319560458, 2018. G.C. Buttazzo: Hard RealTime Computing Systems. Springer Verlag, ISBN 9781461406761, 2011. Edward A. Lee and Sanjit A. Seshia: Introduction to Embedded Systems, A CyberPhysical Systems Approach, Second Edition, MIT Press, ISBN 9780262533812, 2017. M. Wolf: Computers as Components – Principles of Embedded System Design. Morgan Kaufman Publishers, ISBN 9780128053874, 2016.  
Prerequisites / Notice  Prerequisites: Basic knowledge in computer architectures and programming.  
227014500L  Solid State Electronics and Optics  W  6 credits  4G  N. Yazdani, V. Wood  
Abstract  "Solid State Electronics" is an introductory condensed matter physics course covering crystal structure, electron models, classification of metals, semiconductors, and insulators, band structure engineering, thermal and electronic transport in solids, magnetoresistance, and optical properties of solids.  
Objective  Understand the fundamental physics behind the mechanical, thermal, electric, magnetic, and optical properties of materials.  
Prerequisites / Notice  Recommended background: Undergraduate physics, mathematics, semiconductor devices  
227016600L  Analog Integrated Circuits  W  6 credits  2V + 2U  T. Jang  
Abstract  This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.  
Objective  Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course.  
Content  Review of bipolar and MOS devices and their smallsignal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of opamps; stability; comparators; secondorder effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided stepbystep design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on opamp measurements.  
Lecture notes  Handouts of presented slides. No script but an accompanying textbook is recommended.  
Literature  Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGrawHill Education  
227031100L  Qubits, Electrons, Photons  W  6 credits  3V + 2U  T. Zambelli  
Abstract  Indepth analysis of the quantum mechanics origin of nuclear magnetic resonance (qubits, twolevel systems), of LASER (quantization of the electromagnetic field, photons), and of electron transfer (from electrochemistry to photosynthesis).  
Objective  Beside electronics nanodevices, DITET is pushing its research in the fields of NMR (MRI), electrochemistry, bioelectronics, nanooptics, and quantum information, which are all rationalized in terms of quantum mechanics. Starting from the axioms of quantum mechanics, we will derive the fascinating theory describing spin and qubits, electron transitions and transfer, photons and LASER: quantum mechanics is different because it mocks our daily Euclidean intuition! In this way, students will work out a robust quantum mechanics (theoretical!!!) basis which will help them in their advanced studies of the following masters: EEIT (batteries), Biomedical Engineering (NMR, bioelectronics), Quantum Engineering, Micro and Nanosystems. IMPORTANT: "qubits" from the point of view of NMR (and NOT from that of quantum computing!).  
Content  • Lagrangian and Hamiltonian: Symmetries and Poisson Brackets • Postulates of QM: Hilbert Spaces and Operators • Heisenberg’s Matrix Mechanics: Hamiltonian and Time Evolution Operator • Density Operator • Spin: Qubits, Bloch Equations, and NMR • Entanglement • Symmetries and Corresponding Operators • Schrödinger's Wave Mechanics: Electrons in a Periodic Potential and Energy Bands • Harmonic Oscillator: Creation and Annihilation Operators • Identical Particles: Bosons and Fermions • Quantization of the Electromagnetic Field: Photons, Absorption and Emission, LASER • Electron Transfer: Marcus Theory via BornOppenheimer, FranckCondon, LandauZener  
Lecture notes  No lecture notes because the proposed textbooks together with the provided supplementary material are more than exhaustive! !!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!!  
Literature  • J.S. Townsend, "A Modern Approach to Quantum Mechanics", Second Edition, 2012, University Science Books • M. Le Bellac, "Quantum Physics", 2011, Cambridge University Press • (Lagrangian and Hamiltonian) L. Susskind, G. Hrabovsky, "Theoretical Minimum: What You Need to Know to Start Doing Physics", 2014, Hachette Book Group USA Supplementary material will be uploaded in Moodle. _ _ _ _ _ _ _ + (as rigorous and profound presentation of the mathematical framework) G. Dell'Antonio, "Lectures on the Mathematics of Quantum Mechanics I", 2015, Springer + (as account of those formidable years) G. Gamow, "Thirty Years that Shook Physics", 1985, Dover Publications Inc.  
Prerequisites / Notice  The course has been intentionally conceived to be selfconsistent with respect to QM for those master students not having encountered it in their track yet. Therefore, a presumably large overlapping has to be expected with a (welcome!) QM introduction course like the DITET "Physics II". A solid base of Analysis I & II as well as of Linear Algebra is really helpful.  
Competencies 
 
227038510L  Biomedical Imaging  W  6 credits  5G  S. Kozerke, K. P. Prüssmann  
Abstract  Introduction to diagnostic medical imaging based on electromagnetic and acoustic fields including Xray planar and tomographic imaging, radiotracer based nuclear imaging techniques, magnetic resonance imaging and ultrasoundbased procedures.  
Objective  Upon completion of the course students are able to: • Explain the physical and mathematical foundations of diagnostic medical imaging systems • Characterize system performance based on signaltonoise ratio, contrasttonoise ratio and transfer function • Design a basic diagnostic imaging system chain including data acquisition and data reconstruction • Identify advantages and limitations of different imaging methods in relation to medical diagnostic applications  
Content  • Introduction (intro, overview, history) • Signal theory and processing (foundations, transforms, filtering, signaltonoise ratio) • Xrays (production, tissue interaction, contrast, modular transfer function) • Xrays (resolution, detection, digital subtraction angiography, Radon transform) • Xrays (filtered backprojection, spiral computed tomography, image quality, dose) • Nuclear imaging (radioactive tracer, collimation, point spread function, SPECT/PET) • Nuclear imaging (detection principles, image reconstruction, kinetic modelling) • Magnetic Resonance (magnetic moment, spin transitions, excitation, relaxation, detection) • Magnetic Resonance (plane wave encoding, Fourier reconstruction, pulse sequences) • Magnetic Resonance (contrast mechanisms, gradient and spinecho, applications) • Ultrasound (mechanical wave generation, propagation in tissue, reflection, transmission) • Ultrasound (spatial and temporal resolution, phased arrays) • Ultrasound (Doppler shift, implementations, applications) • Summary, example exam questions  
Lecture notes  Lecture notes and handouts  
Literature  Webb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011  
Prerequisites / Notice  Analysis, Linear algebra, Physics, Basics of signal theory, Basic skills in Matlab/Python programming  
Competencies 
 
227039310L  Bioelectronics and Biosensors  W  6 credits  2V + 2U  J. Vörös, M. F. Yanik  
Abstract  The course introduces bioelectricity and the sensing concepts that enable obtaining information about neurons and their networks. The sources of electrical fields and currents in the context of biological systems are discussed. The fundamental concepts and challenges of measuring bioelectronic signals and the basic concepts to record optogenetically modified organisms are introduced.  
Objective  During this course the students will:  learn the basic concepts in bioelectronics including the sources of bioelectronic signals and the methods to measure them  be able to solve typical problems in bioelectronics  learn about the remaining challenges in this field  
Content  Lecture topics: 1. Introduction Sources of bioelectronic signals 2. Membrane and Transport 34. Action potential and HodgkinHuxley Measuring bioelectronic signals 5. Detection and Noise 6. Measuring currents in solutions, nanopore sensing and patch clamp pipettes 7. Measuring potentials in solution and core conductance model 8. Measuring electronic signals with wearable electronics, ECG, EEG 9. Measuring mechanical signals with bioelectronics In vivo stimulation and recording 10. Functional electric stimulation 11. In vivo electrophysiology Optical recording and control of neurons (optogenetics) 12. Measuring neurons optically, fundamentals of optical microscopy 13. Fluorescent probes and scanning microscopy, optogenetics, in vivo microscopy 14. Measuring biochemical signals  
Lecture notes  A detailed script is provided to each lecture including the exercises and their solutions.  
Literature  Plonsey and Barr, Bioelectricity: A Quantitative Approach (Third edition)  
Prerequisites / Notice  The course requires an open attitude to the interdisciplinary approach of bioelectronics. In addition, it requires undergraduate entrylevel familiarity with electric & magnetic fields/forces, resistors, capacitors, electric circuits, differential equations, calculus, probability calculus, Fourier transformation & frequency domain, lenses / light propagation / refractive index, pressure, diffusion AND basic knowledge of biology and chemistry (e.g. understanding the concepts of concentration, valence, reactantsproducts, etc.).  
Competencies 

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