# Search result: Catalogue data in Spring Semester 2021

Biomedical Engineering Master | ||||||

Major Courses | ||||||

Bioelectronics | ||||||

Recommended Elective Courses These courses are particularly recommended for the Bioelectronics track. Please consult your track adviser if you wish to select other subjects. | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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151-0172-00L | Microsystems II: Devices and Applications | W | 6 credits | 3V + 3U | C. Hierold, C. I. Roman | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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 | |||||

Lecture notes | Handouts (on-line) | |||||

151-0622-00L | Measuring on the Nanometer Scale | W | 2 credits | 2G | A. Stemmer | |

Abstract | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||

Objective | Introduction to theory and practical application of measuring techniques suitable for the nano domain. | |||||

Content | 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. | |||||

Lecture notes | Slides and recordings available via Moodle (registered participants only). | |||||

151-0630-00L | Nanorobotics | W | 4 credits | 2V + 1U | S. Pané Vidal | |

Abstract | 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. | |||||

Objective | 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. | |||||

151-0980-00L | Biofluiddynamics | W | 4 credits | 2V + 1U | D. Obrist, P. Jenny | |

Abstract | Introduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics). | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | Lecture notes are provided electronically. | |||||

Literature | 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 | W | 3 credits | 3G | T. Haslwanter | |

Abstract | 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. | |||||

Objective | 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!! | |||||

Content | 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). | |||||

Lecture notes | 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 | |||||

Literature | 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. | |||||

Prerequisites / Notice | • 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 credits | 2V + 2U | J. Leuthold | |

Abstract | This lecture covers both - the fundamentals of "Optics" such as e.g. "ray optics", "coherence", the "Planck law" or the "Einstein relations" but also the fundamentals of "Photonics" on the generation, processing, transmission and detection of photons. | |||||

Objective | A sound base for work in the field of optics and photonics will be given. | |||||

Content | 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 | |||||

Lecture notes | Lecture notes will be handed out. | |||||

Prerequisites / Notice | Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics. | |||||

227-0395-00L | Neural Systems | W | 6 credits | 2V + 1U + 1A | R. Hahnloser, M. F. Yanik, B. Grewe | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Prerequisites / Notice | 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 credits | 3G | M. Stampanoni, G. Csúcs, A. Sologubenko | |

Abstract | 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. | |||||

Objective | Solid introduction to the basics of microscopy, either with visible light, electrons or X-rays. | |||||

Content | 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. | |||||

Literature | Available Online. | |||||

227-0622-00L | Thermal Modeling: From Semiconductor to Medical Devices and Personalized Therapy Planning | W | 4 credits | 2V + 1U | E. Neufeld, M. Luisier | |

Abstract | The course introduces computational techniques to model electromagnetic heating across many orders of magnitudes, from the atomic to the macroscopic scale. Both desired and undesired thermal effects will be covered, e.g. thermal cancer therapies based on tissue heating or Joule heating in semiconductor devices. A wide range of simulation approaches and numerical methods will be introduced. | |||||

Objective | During this course the students will: - learn the physics governing and computational models describing electromagnetic-induced heating; - get familiar with computational simulation techniques across a wide range of spatial scales, incl. methods to simulate in vivo heating, considering thermoregulation and perfusion, or quantum mechanical approaches considering 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, as well as a framework for computer-aided design of semiconductor devices; - learn about remaining challenges in this field | |||||

Content | The following topics will be discussed during the semester: - Introduction about electromagnetic heating (from its historical perspective to its application in biology); - Microscopic/Macroscopic thermal transport (governing equations, numerical methods, examples); - Numerical algorithms and their implementation in python and/or C++, parallelisation approaches, and high performance computing solutions; - Practical examples: thermal therapy planning with Sim4Life and technology computer aided design with OMEN; - Model verification and validation. | |||||

Lecture notes | Lecture slides are distributed every week and can be found at https://iis-students.ee.ethz.ch/lectures/thermal-modeling/ | |||||

Prerequisites / Notice | The course requires an open attitude towards interdisciplinarity, basic python scripting and C++ coding skills, undergraduate entry-level familiarity with electric & magnetic fields/forces, differential equations, calculus, and basic knowledge of biology and quantum mechanics. | |||||

227-0669-00L | Chemistry of Devices and Technologies Limited to 30 participants. | W | 4 credits | 1V + 2U | M. Yarema | |

Abstract | The course covers basics of chemistry and material science, relevant for modern devices and technologies. The course consists from lecture, laboratory, and individual components. Students accomplish individual projects, in which they study and evaluate a chosen technology from chemistry and materials viewpoints. | |||||

Objective | 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 a chemist's intuition through hands-on workshops that combine tutorials and laboratory 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. | |||||

Content | Students will spend 3h per week in the tutorials and practical 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 (to be chosen from the list of projects). To ensure continued learning throughout the semester, individual projects are evaluated by three interim project reports and by 10 min final presentation. | |||||

Literature | Lecture notes will be made available on the website. | |||||

227-0690-11L | Large-Scale Convex Optimization | W | 4 credits | 2V + 2U | G. Banjac | |

Abstract | 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. | |||||

Objective | Students should be able to apply the fundamental results in convex analysis and numerical methods to analyze and solve large-scale convex optimization problems. | |||||

Content | 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. | |||||

Lecture notes | Available on the course Moodle platform. | |||||

Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra and analysis. | |||||

227-0690-12L | Advanced Topics in Control (Spring 2021)New topics are introduced every year. | W | 4 credits | 2V + 2U | F. Dörfler, M. Hudoba de Badyn, W. Mei | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. | |||||

Lecture notes | A complete set of lecture notes and slides will be provided. | |||||

Literature | 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/ | |||||

Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra and dynamical systems. | |||||

227-0966-00L | Quantitative Big Imaging: From Images to Statistics | W | 4 credits | 2V + 1U | P. A. Kaestner, M. Stampanoni | |

Abstract | 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. | |||||

Objective | 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 | |||||

Content | 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. | |||||

Lecture notes | Available online. https://imaginglectures.github.io/Quantitative-Big-Imaging-2021/weeklyplan.html | |||||

Literature | Will be indicated during the lecture. | |||||

Prerequisites / Notice | 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 credits | 3V + 2U + 1A | K. Stephan | |

Abstract | 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. | |||||

Objective | 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. | |||||

Content | 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. 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 use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required. | |||||

Literature | See TNU website: https://www.tnu.ethz.ch/en/teaching | |||||

Prerequisites / Notice | Good knowledge of principles of statistics, good programming skills (MATLAB, Julia, or Python) | |||||

227-0976-00L | Computational Psychiatry & Computational Psychosomatics Number of participants limited to 24. Information for UZH students: Enrolment to this course unit only possible at ETH Zurich. No enrolment to module BMT20002. Please mind the ETH enrolment deadlines for UZH students: Link | W | 2 credits | 4S | K. Stephan | |

Abstract | This seminar deals with the development of clinically relevant computational tools and/or their application to psychiatry and psychosomatics. Complementary to the annual Computational Psychiatry Course, it serves to build bridges between computational scientists and clinicians and is designed to foster in-depth exchange, with ample time for discussion | |||||

Objective | Understanding strengths and weaknesses of current trends in the development of clinically relevant computational tools and their application to problems in psychiatry and psychosomatics. | |||||

Content | This seminar deals with the development of computational tools (e.g. generative models, machine learning) and/or their application to psychiatry and psychosomatics. The seminar includes (i) presentations by computational scientists and clinicians, (ii) group discussion with focus on methodology and clinical utility, (iii) self-study based on literature provided by presenters. | |||||

Literature | Literature for additional self-study of the topics presented in this seminar will be provided by the presenters and will be available online at https://www.tnu.ethz.ch/en/teaching.html | |||||

Prerequisites / Notice | Participants are expected to be familiar with general principles of statistics (including Bayesian statistics) and have successfully completed the course “Computational Psychiatry” (Course number 227-0971-00L). | |||||

252-0220-00L | Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 credits | 4V + 2U + 1A | A. Krause, F. Yang | |

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-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - 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 approaches to unsupervised learning (Gaussian mixtures, EM) | |||||

Literature | Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press | |||||

Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||

327-2225-00L | MaP Distinguished Lecture Series on Soft RoboticsThis course is primarily designed for MSc and doctoral students. Guests are welcome. | W | 1 credit | 2S | R. Katzschmann, L. Schefer | |

Abstract | This course is an interdisciplinary colloquium on Soft Robotics involving different internationally renowned speakers from academia and industry giving lectures about their cutting-edge research, which highlights the state-of-the-art and frontiers in the Soft Robotics field. | |||||

Objective | Participants become acquainted with the state-of-the-art and frontiers in Soft Robotics, which is a topic of global and future relevance from the field of materials and process engineering. The self-study of relevant literature and active participation in discussions following presentations by internationally renowned speakers stimulate critical thinking and allow participants to deliberately discuss challenges and opportunities with leading academics and industrial experts and to exchange ideas within an interdisciplinary community. | |||||

Content | This course is a colloquium involving a selected mix of internationally renowned speaker from academia and industry who present their cutting-edge research in the field of Soft Robotics. The self-study of relevant pre-read literature provided in advance to each lecture serves as a basis for active participation in the critical discussions following each presentation. | |||||

Lecture notes | Selected scientific pre-read literature (max. three articles per lecture) relevant for and discussed during the lectures is posted in advance on the course web page. | |||||

Prerequisites / Notice | Participants should have a solid background in materials science and/or engineering. | |||||

376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 4 credits | 2V + 1U | S. Raspopovic, M. Xiloyannis | |

Abstract | “Rehabilitation engineering” is the application of science and technology to ameliorate the handicaps of individuals with disabilities in order to reintegrate them into society. The goal of this lecture is to present classical and new rehabilitation engineering principles and examples applied to compensate or enhance especially motor deficits. | |||||

Objective | Provide theoretical and practical knowledge of principles and applications used to rehabilitate individuals with motor disabilities. | |||||

Content | “Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive (also communicational) disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. Orthoses support existing but affected body functions (e.g., glasses, crutches), while prostheses compensate for lost body functions (e.g., cochlea implant, artificial limbs). In case of sensory disorders, the lost function can also be substituted by other modalities (e.g. tactile Braille display for vision impaired persons). The goal of this lecture is to present classical and new technical principles as well as specific examples applied to compensate or enhance mainly motor deficits. Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited by displaying the patient with a maximum amount of information in order to compensate his/her impairment. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. Multi-modality and interactivity have the potential to increase the therapeutical outcome compared to classical rehabilitation strategies. In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics. | |||||

Literature | Introductory Books Neural prostheses - replacing motor function after desease or disability. Eds.: R. Stein, H. Peckham, D. Popovic. New York and Oxford: Oxford University Press. Advances in Rehabilitation Robotics – Human-Friendly Technologies on Movement Assistance and Restoration for People with Disabilities. Eds: Z.Z. Bien, D. Stefanov (Lecture Notes in Control and Information Science, No. 306). Springer Verlag Berlin 2004. Intelligent Systems and Technologies in Rehabilitation Engineering. Eds: H.N.L. Teodorescu, L.C. Jain (International Series on Computational Intelligence). CRC Press Boca Raton, 2001. Control of Movement for the Physically Disabled. Eds.: D. Popovic, T. Sinkjaer. Springer Verlag London, 2000. Interaktive und autonome Systeme der Medizintechnik - Funktionswiederherstellung und Organersatz. Herausgeber: J. Werner, Oldenbourg Wissenschaftsverlag 2005. Biomechanics and Neural Control of Posture and Movement. Eds.: J.M. Winters, P.E. Crago. Springer New York, 2000. Selected Journal Articles Abbas, J., Riener, R. (2001) Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function. Neuromodulation 4, pp. 187-195. Burdea, G., Popescu, V., Hentz, V., and Colbert, K. (2000): Virtual reality-based orthopedic telerehabilitation, IEEE Trans. Rehab. Eng., 8, pp. 430-432 Colombo, G., Jörg, M., Schreier, R., Dietz, V. (2000) Treadmill training of paraplegic patients using a robotic orthosis. Journal of Rehabilitation Research and Development, vol. 37, pp. 693-700. Colombo, G., Jörg, M., Jezernik, S. (2002) Automatisiertes Lokomotionstraining auf dem Laufband. Automatisierungstechnik at, vol. 50, pp. 287-295. Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206. Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T. (1998): Robot-aided neurorehabilitation, IEEE Trans. Rehab. Eng., 6, pp. 75-87 Leifer, L. (1981): Rehabilitive robotics, Robot Age, pp. 4-11 Platz, T. (2003): Evidenzbasierte Armrehabilitation: Eine systematische Literaturübersicht, Nervenarzt, 74, pp. 841-849 Quintern, J. (1998) Application of functional electrical stimulation in paraplegic patients. NeuroRehabilitation 10, pp. 205-250. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Riener, R., Fuhr, T., Schneider, J. (2002) On the complexity of biomechanical models used for neuroprosthesis development. International Journal of Mechanics in Medicine and Biology 2, pp. 389-404. Riener, R. (1999) Model-based development of neuroprostheses for paraplegic patients. Royal Philosophical Transactions: Biological Sciences 354, pp. 877-894. | |||||

Prerequisites / Notice | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK - Biomedical Engineering - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome | |||||

376-1308-00L | Development Strategies for Medical Implants Number of participants limited to 25 - 30. Assignments will be considered in chronological order. | W | 3 credits | 2V + 1U | J. Mayer-Spetzler | |

Abstract | Introduction to development strategies for implantable devices considering the interdependecies of biocompatibility, clinical, regulatory and economical requirements ; discussion of the state of the art and actual trends in in orthopedics, sports medicine and cardio-vascular surgery as well as regenerative medicine (tissue engineering). | |||||

Objective | Basic considerations in implant development Concept of structural and surface biocompatiblity and its relevance for the design of implant and surgical technique Understanding of conflicting factors, e.g. clinical need, economics and regulatory requirements Concepts of tissue engineering, its strengths and weaknesses as current and future clinical solution | |||||

Content | Understanding of clinical and economical needs as guide lines for the development of medical implants; implant and implantation related tissue reactions, biocompatible materials and material processing technologies; implant testing and regulatory procedures; discussion of the state of the art and actual trends in implant development in sports medicine, spinal and cardio-vascular surgery; introduction to tissue engineering. Selected topics will be further illustrated by commented movies from surgeries. Seminar: Group seminars on selected controversial topics in implant development. Participation is mandatory Planned excursions (limited availability, not mandatory, to be confirmed): 1. Participation (as visitor) on a life surgery (travel at own expense) | |||||

Lecture notes | Scribt (electronically available): - presented slides - selected scientific papers for further reading | |||||

Literature | Reference to key papers will be provided during the lectures | |||||

Prerequisites / Notice | Only Master students, achieved Bachelor degree is a pre-condition The number of participants in the course is limited to 30 students in total. Students will be exposed to surgical movies which may cause emotional reactions. The viewing of the surgical movies is voluntary and is on the student's own responsability. | |||||

376-1397-00L | Orthopaedic Biomechanics Number of participants limited to 48. | W | 3 credits | 2G | R. Müller, J. Schwiedrzik | |

Abstract | This course is aimed at studying the mechanical and structural engineering of the musculoskeletal system alongside the analysis and design of orthopaedic solutions to musculoskeletal failure. | |||||

Objective | To apply engineering and design principles to orthopaedic biomechanics, to quantitatively assess the musculoskeletal system and model it, and to review rigid-body dynamics in an interesting context. | |||||

Content | Engineering principles are very important in the development and application of quantitative approaches in biology and medicine. This course includes a general introduction to structure and function of the musculoskeletal system: anatomy and physiology of musculoskeletal tissues and joints; biomechanical methods to assess and quantify tissues and large joint systems. These methods will also be applied to musculoskeletal failure, joint replacement and reconstruction; implants; biomaterials and tissue engineering. | |||||

Lecture notes | Stored on Moodle. | |||||

Literature | Orthopaedic Biomechanics: Mechanics and Design in Musculoskeletal Systems Authors: Donald L. Bartel, Dwight T. Davy, Tony M. Keaveny Publisher: Prentice Hall; Copyright: 2007 ISBN-10: 0130089095; ISBN-13: 9780130089090 | |||||

Prerequisites / Notice | Lectures will be given in English. |

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