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  • ESE 572: Analog Integrated Circuits

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 570 and ESE 319 (for undergraduates) or permission of the instructor. Design of analog circuits and subsystems using bipolar and MOS technologies at the transistor and higher levels. Transistor level design of building block circuits such as op amps, comparators, sample and hold circuits, voltage and current references, capacitors and resistor arrays, and class AB output stages. This graduate course relies heavily on Spice simulation and will require some use of CAD systems to generate integrated circuit layouts as part of a capstone project.
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  • ESE 573: Building Brains in Silicon

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Students with advanced knowledge in neurobiology but rudimentary knowledge in electrical engineering or vice versa are welcome. Biology students should have a course in Cellular Neurobiology and BIOL 451, Systems Neuroscience. Engineering students should have ESE 218, Physics and Models of Semiconductor Devices and ESE 319, Fundamentals of Solid-State Circuits. We model the stucture and function of neural systems in silicon using very large scale integration (VLSI) complimentary metal-oxide-semiconductor (CMOS) technology. To build these neuromorphic systems, we proceed from the device level, through the circuit level, to the system level. At the device level, we mimic electrodiffusion of ions through membrane channels with electrodiffusion of electrons through transistor channels. At the circuit level, we derive minimal implementation of synaptic interaction, dendritic integration, and active membrane behavior. At the system level, we synthesize the spatiotemporal dynamics of the cochlea, the retina, and early stages of cortical processing.
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  • ESE 574: The Principles and Practice of Microfabrication Technology

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Any of the following courses: ESE 218, MSE 321, MEAM 333, CBE 351, CHEM 321/322, PHYS 250 or permission of the instructor. A laboratory-based course on fabricating microelectronic and micromechanical devices using photolithographic processing and related fabrication technologies. Lectures discuss: clean room procedures; microelectronic and microstructural materials; photolithography; diffusion, oxidation; materials deposition; etching and plasma processes. Basic laboratory processes are covered for the first two thirds of the course with students completing structures appropriate to their major in the final third. Students registering for ESE 574 will be expected to do extra work (including term paper and additional project).
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  • ESE 575: Introduction to Wireless Systems

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Undergraduate linear systems and elementary probability theory. System/Network Design, cellular concepts, resource management, radio management, radio channel propagation fundamentals, modulation, fading countermeasure, diversity, coding, spread spectrum, multiple access techniques.
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  • ESE 576: Digital Communication Systems

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Undergraduate linear systems, probability, random processes. Sampling, source coding, and capacity. Quantization and coding of speech and video. Baseband data transmission: line coding, intersymbol interference, equalization, digital modulation schemes, spectral efficiency. Error control coding; block and convolutional codes, Viterbi Algorithm.
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  • ESE 601: Hybrid Systems

    3.00 Credits
    University of Pennsylvania

    Hybrid systems combine discrete state-machines and continuous differential equations, and have been used as models of a large number of applications in areas such as real-time software, embedded systems, robotics, mechatronics, aeronautics, process control, and biological systems. The course will cover state-of-the-art modeling, design, and analysis of hybrid systems. The course is interdisciplinary, and is aimed at bringing together concepts in control theory and computer science. Specific topics include modeling, simulation, stability, reachability,and controller design for hybrid systems. Computational tools for the simulation and verification of hybrid systems will be emphasized with applications to robotics, avionics, air traffic management systems, and biological systems. The course consists of lectures, homeworks, and a final project.
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  • ESE 603: Simulation Modeling and Analysis

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Probability (undergraduate level) and one computer language. This course provides a study of discrete-event systems simulation. Some areas of application include: queuing systems, inventory systems, reliability systems Markov Chains, Random-Walks and Monte-Carlo systems. The course examines many of the discrete and continuous probability distributions used in simulation studies as well as the Poisson process. Long-run measurements of performances of queuing systems, steady-state behavior of infinite and finite-population queuing systems and network of queues are also examined. Fundamental to most simulation studies is the ability to generate reliable random numbers. The course investigates the basic properties of random numbers and techniques used for the generation of pseudo-random numbers. In addition, the course examines techniques used to test pseudorandom numbers for uniformity and independence. These include the Kolmogorov-Smirnov and chi-squared tests, runs tests, gap tests, and poker tests. Random numbers are used to generate random samples and the course examines the inverse-transform, convolution, composition and acceptance/rejection methods for the generation of random samples for many different types of probability distributions. Finally, since most inputs to simulation are probabilistic instead of deterministic in nature, the course examines some techniques used for identifying the probabilistic nature of input data. These include identifying distributional families with sample data, then using maximum-likelihood methods for parameter estimating within a given family and then testing the final choice of distribution using chi-squared goodness-of-fit tests.
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  • ESE 605: Modern Convex Optimization

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Knowledge of linear algebra and willingness to do programming. Exposure to numerical computing, optimization, and application fields is helpful but not required. This course concentrates on recognizing and solving convex optimization problems that arise in engineeering. Topics include: convex sets, functions, and optimization problems. Basis of convex analysis. Linear, quadratic, geometric, and semidefinite programming. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods, ellipsoid algorithm and barrier methods, self-concordance. Applications to signal processing, control, digital and analog circuit design, computation geometry, statistics, and mechanical engineering.
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  • ESE 608: Intelligent and Animated Software Agents

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Undergraduate courses in probability (ESE 301 or equivalent), optimization (ESE 304 or equivalent), knowledge of one computer programming language (Fortran, Pascal, or C), or permission of the instructor. This course will begin with an introduction to virtual reality personas and web-based agents, including their usage to assist, train, and entertain people wherever digital interfaces exist (on the Web, in e-commerce, in games, in kitchen appliances, on your dashboard, etc.). What makes an agent rational Emotionally appealing Entertaining We will explore mathematical theories of rationality and behavior, including those from cognitive, behavioral and decision science. We will then progress into human behavior, literature, personality and individual differences studies, and integlligent and emotive agent designs. We will examine various types of agents such as web shopping agents, emotive agents, personal support agents, chatterbots, mobile agents, virtual reality personas, game-based adversaries, pedagogical agent coaches, and multi-agent societies. Finally, students will learn principles about animation, simulated social interaction and speech generation, knowledge representation, agent planning and reasoning, agent communication languages, testing of the use of agent based systems, and methodologies/toolbenches for engineering of systems of intelligent and emotive agents.
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  • ESE 610: Electromagnetic and Optical Theory II

    3.00 Credits
    University of Pennsylvania

    This course covers exact, approximate and numerical methods of wave propagation, radiation, diffraction and scattering with an emphasis on bringing students to a point of contributing to the current research literature. Topics are chosen from a list including analytical and numerical techniques, waves in complex media and metamaterials, photonic bandgap structures, imaging, miniaturized antennas, high-impedance ground plans, and fractal electrodynamics.
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