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Course Criteria
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4.00 Credits
Theoretical and practical aspects in design, analysis, and fabrication of MEMS devices. Fabrication processes, including bulk and surface micromachining. MEMS design and layout. MEMS CAD tools. Mechanical and e rical design. Applications such as micro sensors and actuators, or chemical and thermal transducers, recent advances. Offered: jointly with M E 504/ MSE 504.
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4.00 Credits
Foundations for the engineering analysis of random processes: set theoretic fundamentals, basic axioms of probability models, conditional probabilities and independence, discrete and continuous random variables, multiple random variables, sequences of random variables, limit theorems, models of stochastic processes, noise, stationarity and ergodicity, Gaussian processes, power spectral densities. .
Prerequisite:
graduate standing and understanding of probability at the level of E E 416
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3.00 Credits
Ritcey Review of stochastic processes. Communication system models. Channel noise and capacity. Optimum detection, modulation and coding, convolutional coders and decoders. Typical channels, random and fading channels. Waveform communication, optimum filters. .
Prerequisite:
E E 505 or equivalent
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3.00 Credits
Ritcey Review of stochastic processes. Communication system models. Channel noise and capacity. Optimum detection, modulation and coding, convolutional coders and decoders. Typical channels, random and fading channels. Waveform communication, optimum filters.
Prerequisite:
E E 506 or equivalent
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3.00 Credits
Modeling and analysis of random processes encountered in engineering applications. Stationarity and ergodicity. Harmonic analysis, power spectral densities. Karhunen-Loeve expansions. Poisson, Gaussian, and Markov processes. Stochastic integrals and differential equations.
Prerequisite:
E E 505 or permission of instructor
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4.00 Credits
Mathematical foundations for system theory presented from an engineering viewpoint. Includes set theory; functions, inverse functions; metric spaces; finite dimensional linear spaces; linear operators on finite dimensional spaces; projections on Hilbert spaces. Applications to engineering systems stressed. Offered: jointly with CHEM E 510/A A 510/M E 510; A.
Prerequisite:
graduate standing or permission of instructor
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4.00 Credits
Covers fication and estimation of vector observations, including both parametric and nonparametric approaches. Includes classification with likelihood functions and general discriminant functions, density estimation, supervised and unsupervised learning, feature reduction, model selection, and performance estimation. Offered: W.
Prerequisite:
either E E 505 or CSE 515
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4.00 Credits
Bayesian networks, Markov random fields, factor graphs, Markov properties, standard models as graphical models, graph theory (e.g., moralization and triangulation), probabilistic inference (including pearl’s belief propagation, Hugin, and Shafer-Shenoy), junction threes, dynamic Bayesian networks (including hidden Markov models), learning new models, models in practice. Offered: Sp.
Prerequisite:
E E 508; E E 511
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4.00 Credits
No course description available.
Prerequisite:
Separate File
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4.00 Credits
Includes advanced modern statistical coding techniques (statistical coding), advanced codes n graphs, source coding with errors (rate distortion), alternating minimization principles, channel coding with errors, network information theory, multiple description coding, and information theory in other areas including pattern recognition, bio-informatics, natural language processing, and computer science.
Prerequisite:
E E 514
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