Suchergebnis: Katalogdaten im Frühjahrssemester 2022
Biomedical Engineering Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Vertiefungsfächer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Bioelectronics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kernfächer der Vertiefung Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-1032-00L | Neuromorphic Engineering II Information für UZH Studierende: Die Lerneinheit kann nur an der ETH belegt werden. Die Belegung des Moduls INI405 ist an der UZH nicht möglich. Beachten Sie die Einschreibungstermine an der ETH für UZH Studierende: Link | W | 6 KP | 5G | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Design of a neuromorphic circuit for implementation with CMOS technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Neuromorphic Engineering I strongly recommended | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0427-10L | Advanced Signal Analysis, Modeling, and Machine Learning | W | 6 KP | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course develops a selection of topics pivoting around state space methods, factor graphs, and pertinent algorithms: - hidden-Markov models - factor graphs and message passing algorithms - linear state space models, Kalman filtering, and recursive least squares - Gibbs sampling, particle filter - recursive local polynomial fitting for signal analysis - parameter learning by expectation maximization - linear-model fitting beyond least squares: sparsity, Lp-fitting and regularization, jumps - binary, M-level, and half-plane constraints in control and communications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Solid mathematical foundations (especially in probability, estimation, and linear algebra) as provided by the course "Introduction to Estimation and Machine Learning". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0973-00L | Translational Neuromodeling  | W | 8 KP | 3V + 2U + 1A | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Generative embedding: Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | See TNU website: https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Good knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia or Python can also be used) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Wahlfächer der Vertiefung Diese Fächer sind für die Vertiefung in Bioelectronics besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0172-00L | Microsystems II: Devices and Applications | W | 6 KP | 3V + 3U | C. Hierold, C. I. Roman | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The students are introduced to the fundamentals and physics of microelectronic devices as well as to microsystems in general (MEMS). They will be able to apply this knowledge for system research and development and to assess and apply principles, concepts and methods from a broad range of technical and scientific disciplines for innovative products. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The students are introduced to the fundamentals and physics of microelectronic devices as well as to microsystems in general (MEMS), basic electronic circuits for sensors, RF-MEMS, chemical microsystems, BioMEMS and microfluidics, magnetic sensors and optical devices, and in particular to the concepts of Nanosystems (focus on carbon nanotubes), based on the respective state-of-research in the field. They will be able to apply this knowledge for system research and development and to assess and apply principles, concepts and methods from a broad range of technical and scientific disciplines for innovative products. During the weekly 3 hour module on Mondays dedicated to Übungen the students will learn the basics of Comsol Multiphysics and utilize this software to simulate MEMS devices to understand their operation more deeply and optimize their designs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Transducer fundamentals and test structures Pressure sensors and accelerometers Resonators and gyroscopes RF MEMS Acoustic transducers and energy harvesters Thermal transducers and energy harvesters Optical and magnetic transducers Chemical sensors and biosensors, microfluidics and bioMEMS Nanosystem concepts Basic electronic circuits for sensors and microsystems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts (on-line) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0566-00L | Recursive Estimation | W | 4 KP | 2V + 1U | R. D'Andrea | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Learn the basic recursive estimation methods and their underlying principles. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Requirements: Introductory probability theory and matrix-vector algebra. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0622-00L | Measuring on the Nanometer Scale | W | 2 KP | 2G | A. Stemmer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Conventional techniques to analyze nano structures using photons and electrons: light microscopy with dark field and differential interference contrast; scanning electron microscopy, transmission electron microscopy. Interferometric and other techniques to measure distances. Optical traps. Foundations of scanning probe microscopy: tunneling, atomic force, optical near-field. Interactions between specimen and probe. Current trends, including spectroscopy of material parameters. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Slides and recordings available via Moodle (registered participants only). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0630-00L | Nanorobotics | W | 4 KP | 2V + 1U | S. Pané Vidal | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Nanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0636-00L | Soft and Biohybrid Robotics | W | 4 KP | 3G | R. Katzschmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Soft and biohybrid robots are emerging fields taking inspiration from Nature to create integrated robots that are inherently safer to interact with. You will be able to create the structures, actuators, sensors, models, controllers, and machine learning architectures exploiting the deformable nature of these robots. You will apply the learned principles to challenges of your research domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Learning Objective 1: Convert any robotics challenge into a functional soft robotic physical prototype Step 1: Formulate suitable functional requirements Step 2: Select actuator material Step 3: Design + fabricate suitable for the task Step 4: Controller for basic functionality Step 5: Learning Approach for complex robotic skills Learning Objective 2: Formulate control and learning frameworks to highly articulated robots in real life scenarios Step 1: Formulate the dynamic skills needed for the real life scenario Step 2: Pick or combine suitable control and learning frameworks given the robot at hand Step 3: Evaluate the control approach for a real life scenario Step 4: Modify and enhance the control approach and repeat the evaluation Learning Objective 3: Apply the principle of mechanical impedance and embodied intelligence to any research challenge within any domain Step 1: Identify the moving aspects of the problem Step 2: Choose and design the passive and actively-controlled degrees of freedom Step 3: Pick the actuation material based on suitability to your challenge Step 4: Design in detail multiple combinations of body and brain Step 5: Simulate, build, test, fail, and repeat this often and quickly until the soft robot works for simple settings Step 6: Upgrade and validate the robot for performances in real world conditions Learning Objective 4: Rethink approaches to robotics by moving towards designs made of living materials Step 1: Identify what problems could be easier to solve with a complex living material Step 2: Scout for available works that have potentially tackled the problem with a living material Step 3: Formulate a hypothesis for your new approach with a living material Step 4: Design a minimum viable prototype (MVP) that properly highlights your new approach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Students will cover a range of latest research insights on materials, fabrication technologies, and modeling approaches to design, simulate, and build soft and biohybrid robots. Part 1: Functional and intelligent materials for use in soft and biohybrid robotic applications Part 2: Design and design morphologies of soft robotic actuators and sensors Part 3: Fabrication techniques including 3D printing, casting, roll-to-roll, tissue engineering Part 4: Biohybrid robotics including microrobots and macrorobots; tissue engineering Part 5: Mechanical modeling including minimal parameter models, finite-element models and ML-based models Part 6: Closed-loop controllers of soft robots that exploit the robot's impedance and dynamics for locomotion and manipulation tasks Part 7: Machine Learning approaches to soft robotics, for design synthesis, modeling, and control A mandatory semester-long project will teach the participants to implement the skills and knowledge learned during the class by building their own soft robotic prototype or simulation. There is a mandatory pass/fail assignment to be submitted within the first two weeks of class to get a spot in the project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | All class materials including slides, recordings, class challenges infos, pre-reads, and tutorial summaries can be found on Moodle: https://moodle-app2.let.ethz.ch/course/view.php?id=14501 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | 1) Wang, Liyu, Surya G. Nurzaman, and Fumiya Iida. "Soft-material robotics." (2017). 2) Polygerinos, Panagiotis, et al. "Soft robotics: Review of fluid‐driven intrinsically soft devices; manufacturing, sensing, control, and applications in human‐robot interaction." Advanced Engineering Materials 19.12 (2017): 1700016. 3) Verl, Alexander, et al. Soft Robotics. Berlin, Germany:: Springer, 2015. 4) Cianchetti, Matteo, et al. "Biomedical applications of soft robotics." Nature Reviews Materials 3.6 (2018): 143-153. 5) Ricotti, Leonardo, et al. "Biohybrid actuators for robotics: A review of devices actuated by living cells." Science Robotics 2.12 (2017). 6) Sun, Lingyu, et al. "Biohybrid robotics with living cell actuation." Chemical Society Reviews 49.12 (2020): 4043-4069. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | dynamics, controls, intro to robotics Only for students at master or PhD level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0641-00L | Introduction to Robotics and Mechatronics Number of participants limited to 60. Enrollment is only valid through registration on the MSRL website (www.msrl.ethz.ch). Registrations per e-mail is no longer accepted! | W | 4 KP | 2V + 2U | B. Nelson | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | An ever-increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this course is to practically and theoretically expose students to the fundamentals of mechatronic and robotic systems. Over the course of the semester, the lecture topics will include an overview of robotics, an introduction to different types of sensors and their use, the programming of microcontrollers and interfacing these embedded computers with the real world, signal filtering and processing, an introduction to different types of actuators and their use, an overview of computer vision, and forward and inverse kinematics. Throughout the course, students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems. By the end of the course, you will be able to independently choose, design and integrate these different building blocks into a working mechatronic system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course consists of weekly lectures and lab sessions. The weekly topics are the following: 0. Course Introduction 1. C Programming 2. Sensors 3. Data Acquisition 4. Signal Processing 5. Digital Filtering 6. Actuators 7. Computer Vision and Kinematics 8. Modeling and Control 9. Review and Outlook The lecture schedule can be found on our course page on the MSRL website (www.msrl.ethz.ch) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The students are expected to be familiar with C programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0980-00L | Biofluiddynamics | W | 4 KP | 2V + 1U | D. Obrist, P. Jenny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A basic understanding of fluid dynamical processes in the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes are provided electronically. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | A list of books on selected topics of biofluiddynamics can be found on the course web page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-1046-00L | Computer Simulations of Sensory Systems Findet dieses Semester nicht statt. | W | 3 KP | 3G | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system. The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses. In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The following topics will be covered: • Introduction into the signal processing in nerve cells. • Introduction into Python. • Simplified simulation of nerve cells (Hodgkins-Huxley model). • Description of the auditory system, including the application of Fourier transforms on recorded sounds. • Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing. • Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | For each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Sensory_Systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Open source information is available as wikibook http://en.wikibooks.org/wiki/Sensory_Systems For good overviews of the neuroscience, I recommend: • Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth ISBN 0071390111 / 9780071390118 THE standard textbook on neuroscience. NOTE: The 6th edition will be released on February 5, 2021! • L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702]. This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. • G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)] A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception. • The best place to get started with Python programming are the https://scipy-lectures.org/ On signal processing with Python, my upcoming book • Hands-on Signal Analysis with Python (Due: January 13, 2021 ISBN 978-3-030-57902-9, https://www.springer.com/gp/book/9783030579029) will contain an explanation to all the required programming tools and packages. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | • Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week). • In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0125-00L | Optics and Photonics | W | 6 KP | 2V + 2U | J. Leuthold | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This lecture covers both - the fundamentals of "Optics" such as e.g. "ray optics", "coherence", the "Planck law", the "reciprocity theorem" or the "Einstein relations" but also the fundamentals of "Photonics" on the generation (the laser), processing, transmission and detection of photons. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A sound base for work in the field of optics and photonics will be conveyed. Key principles of optics will the thaught. The lecture passes on the essentials for work with free-space optics or waveguide optics. In addition important optical devices will be discussed. Among them are e.g. optical filters, copulers (MMI-couplers,...), Holograms,... . | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Chapter 1: Ray Optics Chapter 2: Electromagnetic Optics Chapter 3: Polarization Chapter 4: Coherence and Interference Chapter 5: Fourier Optics and Diffraction Chapter 6: Guided Wave Optics Chapter 7: Optical Fibers Chapter 8: The Laser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes will be handed out. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0395-00L | Neural Systems | W | 6 KP | 2V + 1U + 1A | R. Hahnloser, M. F. Yanik, B. Grewe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | This course introduces - Basic neurophysiology and mathematical descriptions of neurons - Methods for dissecting animal behavior - Neural recordings in intact nervous systems and information decoding principles - Methods for manipulating the state and activity in selective neuron types - Neuromodulatory systems and their computational roles - Reward circuits and reinforcement learning - Imaging methods for reconstructing the synaptic networks among neurons - Birdsong and language - Neurobiological principles for machine learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L). As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0390-00L | Elements of Microscopy | W | 4 KP | 3G | M. Stampanoni, G. Csúcs, A. Sologubenko | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Solid introduction to the basics of microscopy, either with visible light, electrons or X-rays. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | It would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level. The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and X-ray microscopy. During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Available Online. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0622-00L | Applications of Thermal Modeling: From Hot Atoms to Heated Tissues | W | 4 KP | 3G | E. Neufeld, M. Luisier | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | How about leveraging heat to cure cancer or to solve today’s energy crisis? Computational simulation of heat-related phenomena from the atomic-scale to living organisms is key to achieve these goals and will be at the core of this multidisciplinary course. The necessary physics, modeling, and computing background will be covered, from theory to practical implementations in concrete applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | During this course students will: - learn the physics governing the formation and propagation of heat in solids and living human tissues; - discover how heat can be used in personalised cancer therapies or in thermoelectric applications to produce reusable energy; - develop computational models describing electromagnetically-induced heating; - get familiar with computational simulation techniques across a wide range of spatial scales, incl. methods for simulating in vivo heating, considering thermoregulation and perfusion, or more fundamental approaches that consider heat at the level of atomic vibrations; - implement and apply simulation techniques within a state-of-the-art open-source simulation platform for computational life sciences, and a framework for computer-aided design of nanoscale electronic devices; - learn about practical aspects related to performance-critical coding and numerics for computational simulations; - work on two small projects applying the theoretical concepts presented during the lectures to two specific real-world applications where heat modeling is required; - learn about current challenges of high social relevance associated with heat modeling. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The following topics will be covered: - introduction to electromagnetic heating, from its social relevance and history to its application in biology and electronics; - personalised therapies relying on local heating; - thermoelectricity (production of electricity from heat gradients); - microscopic/macroscopic thermal transport including governing equations, numerical methods to solve them, and applications; - numerical algorithms and their implementation, shared and distributed parallelization approaches and pitfalls, use of graphics processing units (GPUs) for hardware acceleration, and solutions for high performance computing; - usage of the Sim4Life simulation platform (therapy planning) and of the OMEN technology computer aided design tool (device simulation) as practical examples; - odel verification and validation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture slides are distributed every week and can be found at https://iis-students.ee.ethz.ch/lectures/thermal-modeling/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This course is ideal for students who have an interest in computational sciences, a passion for interdisciplinarity, and generally enjoy problem-solving. The course requires a basic knowledge of Python scripting and C/C++ coding skills, undergraduate entry-level familiarity with electric and magnetic fields/forces, differential equations, calculus, and basic knowledge of biology and physics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0669-00L | Chemistry of Devices and Technologies Limited to 30 participants. | W | 4 KP | 1V + 2U | M. Yarema | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course covers basics of chemistry and material science, relevant for modern devices and technologies. The course consists of interactive classroom activities (lectures, workshops, laboratory sessions) and individual component. For the latter, students accomplish individual projects to study, evaluate, and present a chosen technology from a viewpoint of chemistry and materials science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course brings relevant chemistry knowledge, tailored to the needs of electrical engineering students. Students will gain understanding of the basic concepts of chemistry and materials science, acquire technology-related practical and analytic skills through the small group activities, laboratory experiments, workshops, and conference sessions as well as guidance through individual projects that require interdisciplinary and critical thinking. Students will learn which materials, reactions, and device fabrication processes are important for nowadays technologies and products. They will gain important knowledge of state-of-the-art technologies from materials and fabrication viewpoints. Finally, students will choose selected technologies or devices and study them in details in order to establish and understand the link between the structure, properties, and performance of functional materials. By doing this, students will also improve important soft skills, such as academic text writing, presenting, and active learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Students will spend 3h per week in the interactive classroom activities (lectures, workshops, laboratory and conference sessions) and additional 4-6h per week working on individual projects. The goal of the individual student's project is to understand the chemistry related to the manufacture and operation of a specific device or technology and how the structure and properties of materials relate to the performance of devices/technologies (students will be able to choose which technology they want to study). To ensure project-based continued learning throughout the semester, students will receive a matching information during the classroom activities. Individual projects will be evaluated by three interim project reports and by a final presentation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Lecture notes will be made available on the website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0690-11L | Large-Scale Convex Optimization Findet dieses Semester nicht statt. | W | 4 KP | 2V + 2U | Noch nicht bekannt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Convex optimization has revolutionized modern decision making and underpins many scientific and engineering disciplines. To enable its use in modern large-scale applications, we require new analytical methods that address limitations of existing solutions. This course is intended to provide a comprehensive overview of convex analysis and numerical methods for large-scale optimization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students should be able to apply the fundamental results in convex analysis and numerical methods to analyze and solve large-scale convex optimization problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Convex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Available on the course Moodle platform. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Sufficient mathematical maturity, in particular in linear algebra and analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0690-12L | Advanced Topics in Control (Spring 2022) This course offers similar content as the last time it was offered, students who were enroled in spring 2021 cannot enrol in this course. | W | 4 KP | 2V + 2U | F. Dörfler, M. Hudoba de Badyn, M. Mamduhi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Advanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor. During the spring of 2020, the course will cover a range of topics in distributed systems control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | By the end of this course you will have developed a sound and versatile toolkit to tackle a range of problems in network systems and distributed systems control. In particular, we will develop the methodological foundations of algebraic graph theory, consensus algorithms, and multi-agent systems. Building on top of these foundations we cover a range of problems in epidemic spreading over networks, swarm robotics, sensor networks, opinion dynamics, distributed optimization, and electrical network theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Distributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are electric power grids, swarm robotics, sensor networks, and epidemic spreading over networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, computing, and control capabilities. This course covers algebraic graph theory, consensus algorithms, stability of network systems, distributed optimization, and applications in various domains. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | A complete set of lecture notes and slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | The course will be largely based on the following set of lecture notes co-authored by one of the instructors: http://motion.me.ucsb.edu/book-lns/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Sufficient mathematical maturity, in particular in linear algebra and dynamical systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0966-00L | Quantitative Big Imaging: From Images to Statistics | W | 4 KP | 2V + 1U | P. A. Kaestner, M. Stampanoni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The lecture focuses on the challenging task of extracting robust, quantitative metrics from imaging data and is intended to bridge the gap between pure signal processing and the experimental science of imaging. The course will focus on techniques, scalability, and science-driven analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 1. Introduction of applied image processing for research science covering basic image processing, quantitative methods, and statistics. 2. Understanding of imaging as a means to accomplish a scientific goal. 3. Ability to apply quantitative methods to complex 3D data to determine the validity of a hypothesis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Imaging is a well established field and is rapidly growing as technological improvements push the limits of resolution in space, time, material and functional sensitivity. These improvements have meant bigger, more diverse datasets being acquired at an ever increasing rate. With methods varying from focused ion beams to X-rays to magnetic resonance, the sources for these images are exceptionally heterogeneous; however, the tools and techniques for processing these images and transforming them into quantitative, biologically or materially meaningful information are similar. The course consists of equal parts theory and practical analysis of first synthetic and then real imaging datasets. Basic aspects of image processing are covered such as filtering, thresholding, and morphology. From these concepts a series of tools will be developed for analyzing arbitrary images in a very generic manner. Specifically a series of methods will be covered, e.g. characterizing shape, thickness, tortuosity, alignment, and spatial distribution of material features like pores. From these metrics the statistics aspect of the course will be developed where reproducibility, robustness, and sensitivity will be investigated in order to accurately determine the precision and accuracy of these quantitative measurements. A major emphasis of the course will be scalability and the tools of the 'Big Data' trend will be discussed and how cluster, cloud, and new high-performance large dataset techniques can be applied to analyze imaging datasets. In addition, given the importance of multi-scale systems, a data-management and analysis approach based on modern databases will be presented for storing complex hierarchical information in a flexible manner. Finally as a concluding project the students will apply the learned methods on real experimental data from the latest 3D experiments taken from either their own work / research or partnered with an experimental imaging group. The course provides the necessary background to perform the quantitative evaluation of complicated 3D imaging data in a minimally subjective or arbitrary manner to answer questions coming from the fields of physics, biology, medicine, material science, and paleontology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Available online. https://imaginglectures.github.io/Quantitative-Big-Imaging-2021/weeklyplan.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Will be indicated during the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Ideally, students will have some familiarity with basic manipulation and programming in languages like Python, Matlab, or R. Interested students who are worried about their skill level in this regard are encouraged to contact Anders Kaestner directly (anders.kaestner@psi.ch). More advanced students who are familiar with Python, C++, (or in some cases Java) will have to opportunity to develop more of their own tools. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0973-00L | Translational Neuromodeling | W | 8 KP | 3V + 2U + 1A | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Generative embedding: Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | See TNU website: https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Good knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia or Python can also be used) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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