Sebastian Kozerke: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Sebastian Kozerke |
| Field | Biomedical Imaging |
| Address | Professur für Biomed. Bildgebung ETH Zürich, ETZ F 94 Gloriastrasse 35 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 35 49 |
| kozerke@biomed.ee.ethz.ch | |
| Department | Information Technology and Electrical Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 227-0085-05L | Projects & Seminars: Fast Signal Acquisition and Processing for Quantum Experiments using FPGA  Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 2 credits | 2P | S. Kozerke | |
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | ||||
| Objective | FPGAs 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. | ||||
| 227-0085-26L | Projects & Seminars: Biosignal Acquisition and Processing for IoT Low Power Wearable Sensing... Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 3 credits | 3P | S. Kozerke | |
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | ||||
| Objective | Biosignal 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. | ||||
| 227-0085-27L | Projects & Seminars: Android Application Development (AAD) Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 4 credits | 3P | S. Kozerke | |
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | ||||
| Objective | Android 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. | ||||
| 227-0085-29L | Projects & Seminars: Practical Embedded Deep Neural Networks with Special Hardware Accelerator Only for Electrical Engineering and Information Technology BSc. The course unit can only be taken once. Repeated enrollment in a later semester is not creditable. | 3 credits | 3P | S. Kozerke | |
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | ||||
| Objective | Deep 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. | ||||
| 227-0385-10L | Biomedical Imaging | 6 credits | 5G | S. Kozerke, K. P. Prüssmann | |
| Abstract | Introduction 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. | ||||
| Objective | To 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. | ||||
| Content | - X-ray imaging - Computed tomography - Single photon emission tomography - Positron emission tomography - Magnetic resonance imaging - Ultrasound/Doppler imaging | ||||
| Lecture notes | Lecture notes and handouts | ||||
| Literature | Webb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011 | ||||
| Prerequisites / Notice | Analysis, Linear Algebra, Physics, Basics of Signal Theory, Basic skills in Matlab programming | ||||
| 227-0386-00L | Biomedical Engineering  | 4 credits | 3G | J. Vörös, S. J. Ferguson, S. Kozerke, M. P. Wolf, M. Zenobi-Wong | |
| Abstract | Introduction 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. | ||||
| Objective | Introduction 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. | ||||
| Content | Introduction 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. | ||||
| Lecture notes | Introduction to Biomedical Engineering by Enderle, Banchard, and Bronzino AND https://lbb.ethz.ch/education/biomedical-engineering.html | ||||
| 227-0970-00L | Research Topics in Biomedical Engineering | 0 credits | 2K | K. P. Prüssmann, S. Kozerke, M. Stampanoni, K. Stephan, J. Vörös | |
| Abstract | Current topics in Biomedical Engineering presented by speakers from academia and industry. | ||||
| Objective | Getting insight into actual areas and problems of Biomedical Engineering an Health Care. | ||||
| 227-0980-00L | Seminar on Biomedical Magnetic Resonance | 0 credits | 1S | K. P. Prüssmann, S. Kozerke | |
| Abstract | Actuel developments and problems of magnetic resonance imaging (MRI) | ||||
| Objective | Getting insight to advanced topics in Magnetic Resonance Imaging | ||||