Sebastian Kozerke: Katalogdaten im Herbstsemester 2020

NameHerr Prof. Dr. Sebastian Kozerke
LehrgebietBiomedizinische Bildgebung
Adresse
Professur für Biomed. Bildgebung
ETH Zürich, ETZ F 94
Gloriastrasse 35
8092 Zürich
SWITZERLAND
Telefon+41 44 632 35 49
E-Mailkozerke@biomed.ee.ethz.ch
DepartementInformationstechnologie und Elektrotechnik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
227-0085-05LProjekte & Seminare: Fast Signal Acquisition and Processing for Quantum Experiments using FPGA Belegung eingeschränkt - Details anzeigen
Nur für Elektrotechnik und Informationstechnologie BSc.

Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar.
2 KP2PS. Kozerke
KurzbeschreibungDer Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden.
LernzielFPGAs are used in wide range of applications including video processing, machine learning, cryptography and radar signal processing, thanks to their flexibility and massive parallel processing power. Recently FPGAs have become important in quantum signal processing where high amount of data should be analyzed in a short time to use quantum setups most efficiently. In addition, FPGAs are used for quantum state detection and feedback generation, which have to be performed in the scale of hundreds of nanoseconds. The goal of this course is to understand the FPGA based signal processing for superconducting circuits based quantum experiments. The course participants will learn the implementation techniques of the modules for fast quantum signal acquisition and processing, the electronics supporting quantum experiments, and FPGA programming. You will implement quantum signal processing and quantum state detection modules using Xilinx FPGA, Verilog HDL, and high speed ADC. The course will be taught in English. No prior knowledge in quantum physics or FPGA is required, still a good knowledge in any coding language (for example C or Java) is required.
227-0085-26LProjekte & Seminare: Biosignal Acquisition and Processing for IoT Low Power Wearable Sensing... Belegung eingeschränkt - Details anzeigen
Nur für Elektrotechnik und Informationstechnologie BSc.

Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar.
3 KP3PS. Kozerke
KurzbeschreibungDer Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden.
LernzielBiosignal acquisition and processing – Wearable sensor node design and analysis for bio-impedance sensor using an Arm Cortex-M (Nordic nrf52838) Microcontroller
Wearable smart sensor electronics has the potential to revolutionize the medical field. Various body conformal flexible sensors have been used to monitor motion and physiological electrical signals such as electrocardiography (ECG), electroencephalography (EEG) and body composition analysis via body bio-impedance measurements. Smart sensor nodes not only provide accurate and continuous data in time but also automate the process of maintaining medical records, thereby lowering the workload oft he health worker or clinician. This course offers an avenue for the students to understand the interdisciplinary principles that make it possible to interpret human physiology by utilizing discreet electronic components. Most importantly, participants will get a chance to do hands-on system design specific to electronically tracking a particular physiological phenomenon. In particular, the focus will be laid on programming of micro controllers, interfacing with sensors, acquisition of data and utilizing discreet analog elements for bio-signal processing. The programming will be performed in C.


The course will be taught in English and by the ITET center for project based learning.
227-0085-27LProjekte & Seminare: Android Application Development (AAD) Belegung eingeschränkt - Details anzeigen
Nur für Elektrotechnik und Informationstechnologie BSc.

Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar.
4 KP3PS. Kozerke
KurzbeschreibungDer Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden.
LernzielAndroid Applications – Programming and development of Application - Android Studio – Smart Phone Sensors – GPS and Google Maps.

Although the App-Industry is dominated by the giant Apps right now, it is still crucial that one knows how those Apps function and how those Apps are communicating with their hardware. This course offers the opportunity for the participants to understand the development of application using Android Studio. Most importantly, participants will get a chance to do hands-on software design specific to Android smartphone and the data acquisition from sensors, GPS, google maps and other internal devices. The main goal of the course if providing the students with the basic principle and software programming for build up every android application. The course include 4-5 weeks project were the students alone or in group will build up a working demo of a target application. The course will conclude with the presentation of the students work. Previous experience in C/Java or other languages is preferable but not mandatory. The students will program their own Android Smartphone.

The course will be taught in English by the new Project-based learning centre.
227-0085-29LProjekte & Seminare: Practical Embedded Deep Neural Networks with Special Hardware Accelerator Belegung eingeschränkt - Details anzeigen
Nur für Elektrotechnik und Informationstechnologie BSc.

Die Lerneinheit kann nur einmal belegt werden. Eine wiederholte Belegung in einem späteren Semester ist nicht anrechenbar.
3 KP3PS. Kozerke
KurzbeschreibungDer Bereich Praktika, Projekte, Seminare umfasst Lehrveranstaltungen in unterschiedlichen Formaten zum Erwerb von praktischen Kenntnissen und Fertigkeiten. Ausserdem soll selbstständiges Experimentieren und Gestalten gefördert, exploratives Lernen ermöglicht und die Methodik von Projektarbeiten vermittelt werden.
LernzielDeep neural networks (DNNs) have become the leading method for a wide range of data analytics tasks, after a series of major victories at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). For ILSVRC, the task was to classify images into 1000 different classes, many of which are difficult to distinguish (e.g. many classes are different breeds of dogs). All that was given were 1.2 million labelled images. Meanwhile, this recipe for success has taken over many more areas, from image-based tasks like segmenting objects in images, detecting objects, enhancing images using super-resolution and compression artifact reduction, to robotics and reinforcement learning, and a wide range of industrial applications.
DNNs and their subtype convolutional neural networks (CNNs) have not been new in the 2013 when the wave of success has started, but they got this huge boost through the new availability of large-scale dataset and—at least as importantly—the availability of the necessary compute resources by using GPUs to perform the computations required during training.
While GPUs were then also used to stem the high computation effort of DNNs during inference (e.g. classifying images directly using a trained DNN rather than training the DNN itself). The high demand, the need for cost efficiency, and the goal of deploying DNNs not just in data centers but pervasively in everyday devices, wearables, and low-latency industrial or interactive applications, has triggered the development of various application-specific processors which are much faster, vastly more energy efficient, and cheaper at the same time—such as the Google TPU, Graphcore, …, and Huawei’s Ascend/Atlas platforms.

In this course, you will learn:
1) the basics of deep neural networks, how they work, and what challenges there are for inference,
2) how platforms with specialized hardware accelerators, specifically the Huawei Atlas 200, can be used for running DNN inference and getting a practical application running, and
3) work on your own project using DNNs and hardware accelerators based on your own ideas or on some of our proposals.

The course will be taught in English by the new D-ITET center for Project-Based Learning and a special guest lecturer from Huawei. Individual interactions/help can also be in (Swiss) German.
Most sessions will be around 1 hour of lecture and 2 hours of practical computer exercises. We will start an introduction and then you will have ca. 8 weeks to work on your project, which will concluded with a final presentation of your results.
227-0385-10LBiomedical Imaging6 KP5GS. Kozerke, K. P. Prüssmann
KurzbeschreibungIntroduction and analysis of medical imaging technology including X-ray procedures, computed tomography, nuclear imaging techniques using single photon and positron emission tomography, magnetic resonance imaging and ultrasound imaging techniques.
LernzielTo understand the physical and technical principles underlying X-ray imaging, computed tomography, single photon and positron emission tomography, magnetic resonance imaging, ultrasound and Doppler imaging techniques. The mathematical framework is developed to describe image encoding/decoding, point-spread function/modular transfer function, signal-to-noise ratio, contrast behavior for each of the methods. Matlab exercises are used to implement and study basic concepts.
Inhalt- X-ray imaging
- Computed tomography
- Single photon emission tomography
- Positron emission tomography
- Magnetic resonance imaging
- Ultrasound/Doppler imaging
SkriptLecture notes and handouts
LiteraturWebb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011
Voraussetzungen / BesonderesAnalysis, Linear Algebra, Physics, Basics of Signal Theory, Basic skills in Matlab programming
227-0386-00LBiomedical Engineering Information 4 KP3GJ. Vörös, S. J. Ferguson, S. Kozerke, M. P. Wolf, M. Zenobi-Wong
KurzbeschreibungIntroduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The focus is on learning the concepts that govern common medical instruments and the most important organs from an engineering point of view. In addition, the most recent achievements and trends of the field of biomedical engineering are also outlined.
LernzielIntroduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The course provides an overview of the various topics of the different tracks of the biomedical engineering master course and helps orienting the students in selecting their specialized classes and project locations.
InhaltIntroduction into neuro- and electrophysiology. Functional analysis of peripheral nerves, muscles, sensory organs and the central nervous system. Electrograms, evoked potentials. Audiometry, optometry. Functional electrostimulation: Cardiac pacemakers. Function of the heart and the circulatory system, transport and exchange of substances in the human body, pharmacokinetics. Endoscopy, medical television technology. Lithotripsy. Electrical Safety. Orthopaedic biomechanics. Lung function. Bioinformatics and Bioelectronics. Biomaterials. Biosensors. Microcirculation.Metabolism.
Practical and theoretical exercises in small groups in the laboratory.
SkriptIntroduction to Biomedical Engineering
by Enderle, Banchard, and Bronzino

AND

https://lbb.ethz.ch/education/biomedical-engineering.html
227-0970-00LResearch Topics in Biomedical Engineering0 KP2KK. P. Prüssmann, S. Kozerke, M. Stampanoni, K. Stephan, J. Vörös
KurzbeschreibungCurrent topics in Biomedical Engineering presented by speakers from academia and industry.
LernzielGetting insight into actual areas and problems of Biomedical Engineering an Health Care.
227-0980-00LSeminar on Biomedical Magnetic Resonance0 KP1SK. P. Prüssmann, S. Kozerke
KurzbeschreibungActuel developments and problems of magnetic resonance imaging (MRI)
LernzielGetting insight to advanced topics in Magnetic Resonance Imaging