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  • ESE 303: Stochastic Systems Analysis and Simulation

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 301 or equivalent 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 pseudo-random 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 304: Optimization of Systems

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): MATH 240. Model Building and Linear Programming: Graphical Methods and The Simplex Method, the LINDO and LINGO Computer Packages, Degeneracies, Minimization and the BigM and the Two-Phase Methods, and Goal Programming. Sensitivity Analysis: Geometric and Algebraic Approaches, The Computer and Sensitivity Analysis, The Dual of An LP Problem, The Dual Theorem, Shadow Prices, Complementary Slackness, The Dual Simplex Method, and The Revised Simplex Method. Integer Programming: The Branch and Bound Method, Enumeration Methods, and the Cutting Plane Method. Nonlinear Programming: Review of Differential Calculus, Convex and Concave Functions, Solving NLP Problems with One Variable, Uncontraint Nonlinear Optimization with Several Variables, Lagrange Multipliers and Constraint Nonlinear Optimization with Several Variables, The Kuhn-Tucker Conditions and Quadratic Programming.
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  • ESE 308: Agent Based Modeling and Simulation

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Probability, Java or C programming, or equivalent. Agents are a new technique for trying to model, simulate, and understand systems that are ill-structured and whose mathematics is initially unknown and possibly unknowable. This approach allows the analyst to assemble models of agents and components where micro-decision rules may be understood; to bring the agents and components together as a system where macro-behavior then emerges; and to use that to empirically probe and improve understanding of the whole, the interrelations of the components, and synergies. This approach helps one explore parametrics, causality, and what-ifs about socio-technical systems (technologies that must support people, groups, crowds, organizations, and societies). It is applicable when trying to model and understand human behavior -- consumers, investors , passengers, plant operatoars, patients, voters, political leaders, terrorists, and so on. This course will allow students to investigate and compare increasingly complex agent based paradigms along three lines - math foundations, heuristic algorithms/knowledge representations, and empirical science. The student will gain a toolbox and methodology for attempting to represent and study complex socio-technical systems.
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  • ESE 310: Electric and Magnetic Fields I

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): PHYS 151 and MATH 241. This course examines concepts of electromagnetism, vector analysis, electrostatic fields, Coulomb's Law, Gauss's Law, magnetostatic fields, Biot-Savart Law, Ampere's Law, electromagnetic induction, Faraday's Law, transformers, Maxwell equations and time-varying fields, wave equations, wave propagation, dipole antenna, polarization, energy flow, and applications.
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  • ESE 313: Robotics and Bioinspired Systems

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 116 (or equivalent) and MATH 240 or by instructor permission. This is a 1.0 cu research-patterned, open-ended, laboratory-focused course addressing the interface between robotics and integrative biology. The goal is to identify and then explore and possibly add to a specific corner of the scientific literature wherein it is possible to reach the horizons of knowledge quickly because the relevant empirical tools have only recently become available for broad use. We will focus attention on the development of complex adaptive behavior in a legged robotic system with emphasis on such modalities as locomotion, manipulation, situational awareness, localization and mapping and so on.
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  • ESE 319: Fundamentals of Solid-State Circuits

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 216. Analysis and design of basic active circuits involving semiconductor devices including diodes, bipolar and field effect transistors. Single stage, differential, multi-stage, and operational amplifiers will be discussed including their high frequency response. Oscillators, wave shaping circuits, filters, feedback, stability, and power amplifiers will also be covered. A weekly three-hour laboratory will illustrate concepts and circuits discussed in the class.
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  • ESE 325: Fourier Analysis and Applications in Engineering,Mathematics,and the Sciences

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Math 240, Junior or Senior Standing. This course focuses on the mathematics behind Fourier theory and a wide variety of its applications in divers problems in mathematics, engineering, and the sciences. The course is very mathematical in content and students signing up for it should have junior or senior standing. The topics covered are chosen from: functions and signals; systems of differential equations; superpositions, memory, and non-linearity; resonance, eigenfunctions; the Fourier series and transform, spectra; convergence theorems; inner product spaces; mean-square approximation; interpolation and prediction, sampling; random processes, stationarity; wavelets, Brownian motion; stability and control, Laplace transforms. The applications of the mathematical theory that will be presented vary from year to year but a representative sample includes: polynomial approximation, Weierstrass's theorem; efficient computation via Monte Carlo; linear and non-linear oscillators; the isoperimetric problem; the heat equation, underwater communication; the wave equation, tides; testing for randomness, fraud; nowhere differentiable continuous functions; does Brownian motion exist ; error-correction; phase conjugate optics and four-wave mixing; cryptography and secure communications; how fast can we compute ; X-ray crystallography; cosmology; and what the diffusion equation has to say about mathematical finance and risk free investment.
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  • ESE 350: Embedded Systems/Microcontroller Laboratory

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Knowledge of C programming or permission of the instructor. An introduction to interfacing real-world sensors and actuators to embedded microprocessor systems. Concepts needed for building electronic systems for real-time operation and user interaction, such as digital input/outputs, interrupt service routines, serial communications, and analog-to-digital conversion will be covered. The course will conclude with a final project where student-designed projects are featured in presentations and demonstrations.
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  • ESE 351: Logistics,Manufacturing and Transportation

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 304, Freshmen and Sophmores require instructor permission. Introduction to supply chains -- the production, distribution, and transportation of goods -- and the role of engineers and managers in the design and operation of that system. Supply chain as a physical process. Transportation service options and design. Impact of Information Technology (IT) and Intelligent Transportation Systems (ITS). Basic routing and distribution strategies. Future trends in transportation and supply chains in light of sustainability concerns.
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  • ESE 360: Introduction to Environmental Systems

    3.00 Credits
    University of Pennsylvania

    The principles of green design, life cycle analysis, industrial ecology, pollution prevention and waste minimization, and sustainable development are introduced to engineers of all disciplines as a means to identify and solve a variety of emerging environmental problems. Case studies are used to assess the problems and devise rational solutions to minimize environmental consequences.
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