# Search result: Catalogue data in Autumn Semester 2018

Biomedical Engineering Master | ||||||

Track Courses | ||||||

Bioimaging | ||||||

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

Number | Title | Type | ECTS | Hours | Lecturers | |
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227-0967-00L | Computational Neuroimaging Clinic Prerequisite: Successful completion of course "Methods & Models for fMRI Data Analysis", "Translational Neuromodeling" or "Computational Psychiatry" | W | 3 credits | 2V | K. Stephan | |

Abstract | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||

Objective | 1. Consolidation of theoretical knowledge (obtained in the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry') in a practical setting. 2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data. | |||||

Content | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | |||||

Prerequisites / Notice | The participants are expected to have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry' | |||||

227-0969-00L | Methods & Models for fMRI Data Analysis | W | 6 credits | 4V | K. Stephan | |

Abstract | This course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. | |||||

Objective | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their application to empirical fMRI data. | |||||

Content | This course teaches state-of-the-art methods and models for fMRI data analysis. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of studies in psychiatry, neurology and neuroeconomics. | |||||

227-0971-00L | Computational Psychiatry | W | 3 credits | 4S | K. Stephan | |

Abstract | This five-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | |||||

Objective | This course aims at bridging the gap between mathematical modelers and clinical neuroscientists by teaching computational techniques in the context of clinical applications. The hope is that the acquisition of a joint language and tool-kit will enable more effective communication and joint translational research between fields that are usually worlds apart. | |||||

Content | This five-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | |||||

227-2037-00L | Physical Modelling and Simulation | W | 6 credits | 4G | J. Smajic | |

Abstract | This module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects. | |||||

Objective | Basic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained. | |||||

Content | The module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS. In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers. | |||||

151-0105-00L | Quantitative Flow Visualization | W | 4 credits | 2V + 1U | T. Rösgen | |

Abstract | The course provides an introduction to digital image analysis in modern flow diagnostics. Different techniques which are discussed include image velocimetry, laser induced fluorescence, liquid crystal thermography and interferometry. The physical foundations and measurement configurations are explained. Image analysis algorithms are presented in detail and programmed during the exercises. | |||||

Objective | Introduction to modern imaging techniques and post processing algorithms with special emphasis on flow analysis and visualization. Understanding of hardware and software requirements and solutions. Development of basic programming skills for (generic) imaging applications. | |||||

Content | Fundamentals of optics, flow visualization and electronic image acquisition. Frequently used mage processing techniques (filtering, correlation processing, FFTs, color space transforms). Image Velocimetry (tracking, pattern matching, Doppler imaging). Surface pressure and temperature measurements (fluorescent paints, liquid crystal imaging, infrared thermography). Laser induced fluorescence. (Digital) Schlieren techniques, phase contrast imaging, interferometry, phase unwrapping. Wall shear and heat transfer measurements. Pattern recognition and feature extraction, proper orthogonal decomposition. | |||||

Lecture notes | Handouts will be made available. | |||||

Prerequisites / Notice | Prerequisites: Fluiddynamics I, Numerical Mathematics, programming skills. Language: German on request. | |||||

376-1279-00L | Virtual and Augmented Reality in Medicine | W | 3 credits | 2V | R. Riener, O. Göksel, M. Harders | |

Abstract | Virtual and Augmented Reality can support applications in medicine, e.g. for training, planning or therapy. This lecture derives the technical principles of multimodal (audiovisual, haptic, etc.) input devices, displays, and rendering techniques. Examples are presented in the fields of surgical training, intra-operative support, and rehabilitation. The lecture is accompanied by lab demonstrations. | |||||

Objective | Provide theoretical and practical knowledge of new principles and applications of multi-modal simulation and interface technologies in medical education, therapy, and rehabilitation. | |||||

Content | Virtual and Augmented Reality have the potential to provide descriptive and practical information for medical applications, while relieving the patient and/or the physician. Multi-modal interactions between the user and the virtual environment facilitate the generation of high-fidelity sensory impressions, by using visual, haptic, and auditory modalities. On the basis of the existing physiological constraints, this lecture derives the technical requirements and principles of multi-modal input devices, displays, and rendering techniques. Several examples are presented that are currently being developed or already applied, for instance in surgical training, intra-operative augmentation, and rehabilitation. The lecture will be accompanied by visits to facilities equipped with current VR and AR equipment. | |||||

Literature | Recommended readings will be announced in the lecture. Selected books covering some of the presented topics are: • Virtual Reality in Medicine. Riener, Robert; Harders, Matthias; 2012 Springer. • Augmented Reality: Principles and Practice (Usability). Schmalstieg, Dieter; Hollerer, Tobias; 2016 Pearson. • Real-Time Volume Graphics. Rezk-Salama, Christof; Engel, Klaus; Hadwiger, Markus; Kniss, Joe; Weiskopf, Daniel; 2006 Taylor & Francis. • Haptic Rendering: Foundations, Algorithms, and Applications. Lin, Ming; Otaduy, Miguel; 2008 CRC Press. • Developing Virtual Reality Applications: Foundations of Effective Design. Craig , Alan; Sherman, William; Will, Jeffrey; 2009 Morgan Kaufmann. | |||||

Prerequisites / Notice | Notice The course language is English. Any further details will be announced in the first lecture. The general target group is students of higher semesters as well as PhD students of D-HEST, D-MAVT, D-ITET, D-INFK, D-PHYS. Students of other departments, faculties, and courses are also welcome. | |||||

151-0605-00L | Nanosystems | W | 4 credits | 4G | A. Stemmer | |

Abstract | From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. Special emphasis on the emerging field of molecular electronic devices. | |||||

Objective | Familiarize students with basic science and engineering principles governing the nano domain. | |||||

Content | The course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected. Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled. Topics are treated in 2 blocks: (I) From Quantum to Continuum From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. (II) Interaction Forces on the Micro and Nano Scale Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. | |||||

Literature | - Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2 - Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4 - Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9 - Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4 - Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0 - Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0 - Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7 | |||||

Prerequisites / Notice | Course format: Lectures and Mini-Review presentations: Thursday 10-13, ML F 36 Homework: Mini-Review (compulsory continuous performance assessment) Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper. | |||||

252-0543-01L | Computer Graphics | W | 6 credits | 3V + 2U | M. Gross, J. Novak | |

Abstract | This course covers some of the fundamental concepts of computer graphics, namely 3D object representations and generation of photorealistic images from digital representations of 3D scenes. | |||||

Objective | At the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own. | |||||

Content | This course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling and representation, texture mapping and ray-tracing, we will move on to acceleration structures, the physics of light transport, appearance modeling and global illumination principles and algorithms. We will end with an overview of modern image-based image synthesis techniques, covering topics such as lightfields and depth-image based rendering. | |||||

Lecture notes | no | |||||

Prerequisites / Notice | Prerequisites: Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended. The programming assignments will be in C++. This will not be taught in the class. | |||||

402-0674-00L | Physics in Medical Research: From Atoms to Cells | W | 6 credits | 2V + 1U | B. K. R. Müller | |

Abstract | Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells. | |||||

Objective | The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. These characterization techniques are vital for optimizing the preparation of medical implants and the determination of tissue's anisotropies within the human body. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. 3D scaffolds are important for tissue augmentation and engineering. Design, preparation methods, and characterization of these highly porous 3D microstructures are also presented. Visiting clinical research in a leading university hospital will show the usefulness of the lecture series. | |||||

227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. | W | 6 credits | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |

Abstract | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||

Objective | Understanding of the characteristics of neuromorphic circuit elements. | |||||

Content | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||

Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||

Prerequisites / Notice | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||

227-1037-00L | Introduction to Neuroinformatics | W | 6 credits | 2V + 1U | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |

Abstract | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||

Objective | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||

Content | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||

465-0953-00L | Biostatistics | W | 4 credits | 2V + 1U | B. Sick | |

Abstract | The course deals with simple quantitative and graphical as well as more complex methods of biostatistics. Contents: Descriptive statistics, probability theory and design of experiments, testing hypotheses, confidence intervals, correlation, simple and multiple linear regression, classification and prediction, diagnostic tests, measurement of agreement. | |||||

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