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Course Criteria
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3.00 Credits
Methods of analysis for continuous and discrete-time linear systems. Convolution, classical solution of dynamic equations, transforms and matrices are reviewed. Emphasis is on the concept of state space. Linear spaces, concept of state, modes, controllability, observability, state transition matrix. State variable feedback, compensation, decoupling. Prerequisites/Corequisites: Prerequisite: ECSE 2410 or equivalent. When Offered: Fall term annually. Credit Hours: 3
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3.00 Credits
Phenomena peculiar to nonlinear systems. Linearization, iteration, and perturbation procedures. Describing function stability analysis. Phase plane methods. Relaxation oscillations and limit cycles. Stability analysis by Lyapunov's method. Popov's theorem. Adaptive control systems. Sensitivity analysis.Prerequisites/Corequisites: Prerequisite: ECSE 6400 or permission of instructor. When Offered: Spring term odd-numbered years. Credit Hours: 3
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3.00 Credits
Linear programming, nonlinear programming, iterative methods, and dynamic programming are presented, especially as they relate to optimal control problems. Discrete and continuous optimal regulators are derived from dynamic programming approach, which also leads to the Hamilton-Jacobi-Bellman Equation and the Minimum Principle. Linear quadratic regulators, linear tracking problems, and output regulators are treated. Linear observer and the separation theorem are developed for feedback controller implementation. Prerequisites/Corequisites: Prerequisite: ECSE 2410. Corequisite: ECSE 6400. When Offered: Fall term annually. Credit Hours: 3
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3.00 Credits
The concepts, techniques, and tools related to optimal control for dynamical systems. Major topics include calculus of variation, minimum principle, dynamic programming, optimal estimation, and differential games. Both discrete time systems and continuous times are addressed. Particular consideration is given to linear time invariant systems in terms of linear quadratic regulator and Kalman filter. Prerequisites/Corequisites: Prerequisite: ECSE 6400. When Offered: Spring term even-numbered years. Credit Hours: 3
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3.00 Credits
Tools and methods for the analysis and design of linear multivariable feedback systems. Topics include the connection between frequency domain and state space models and methods, model identification, model reduction, model uncertainty and closed loop performance, convex analysis and design methods, optimal controller synthesis using H2, H-infinity, and structured singular value criteria. Prerequisites/Corequisites: Prerequisite: ECSE 6400. When Offered: Fall term even-numbered years. Credit Hours: 3
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3.00 Credits
This course contains the fundamental theory required to design adaptive systems. Topics include parameter identification, ARMA modeling, model reference systems, model algorithmic control, self-tuning systems, and adaptive filtering. Applications to physical and physiological systems are introduced. Prerequisites/Corequisites: Prerequisite: ECSE 6400 or equivalent. When Offered: Spring term odd-numbered years. Cross Listed: Cross-listed as BMED 6480. Students cannot receive credit for both this course and BMED 6480. Credit Hours: 3
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3.00 Credits
This course introduces methods that leverage the basic analysis techniques learned in Robotics I to develop numerical and algorithmic techniques needed to endow robots with the "intelligence" to devise strategies to solve problems they will encounter. Once these abilities are sufficiently well developed, robots will become safe and autonomous, thus paving the way for pervasive personal robots. Topics include: configuration space representation, cell decomposition, roadmap methods, rapidly-exploring random trees, simultaneous localization and mapping, contact modeling, grasping, and dexterous manipulation.Prerequisites/Corequisites: Prerequisite: ECSE 4480 or CSCI 4480. When Offered: Spring term. Cross Listed: Cross-listed as ECSE 4490, CSCI 4490 and CSCI 6490. Students cannot receive credit for both this course and ECSE 4490, CSCI 4490 or CSCI 6490. Credit Hours: 3
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3.00 Credits
Deterministic signal representations and analysis, introduction to random processes and spectral analysis, correlation function and power spectral density of stationary processes, noise mechanisms, the Gaussian and Poisson processes. Markov processes, the analysis of linear and nonlinear systems with random inputs, stochastic signal representations, orthogonal expansions, the Karhunen-Loeve series, channel characterization, introduction to signal detection, linear mean-square filtering, the orthogonality principle, optimum Wiener and Kalman filtering, modulation theory, and systems analysis. Prerequisites/Corequisites: Prerequisites: ECSE 2410 and ECSE 4500 or equivalent. When Offered: Fall term annually. Credit Hours: 3
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3.00 Credits
Classical statistical decision theory, decision criteria, binary and composite hypothesis tests. Statistical models of signals and noise. Detection of known signals in Gaussian noise. Receiver operating characteristics and error probability. Applications to radar and communications. Detection of signals with unknown or random parameters, detection of stochastic signals, nonparametric detection techniques. Statistical estimation theory, performance measures. Cramer-Rao bounds, estimation of unknown signal parameters, optimum demodulation, signal design. Prerequisites/Corequisites: Prerequisites: probability theory and ECSE 6510. When Offered: Spring term annually. . Credit Hours: 3
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3.00 Credits
Information measures, characterization of information sources, coding for discrete sources, the noiseless coding theorems, construction of Huffman codes. Discrete channel characterization, channel capacity, noisy-channel coding theorems, reliability exponents. Various error-control coding and decoding techniques, including block and convolutional codes. Introduction to waveform channels and rate distortion theory. Prerequisites/Corequisites: Prerequisite: probability theory. Corequisite: ECSE 6510. When Offered: Fall term annually. Credit Hours: 3
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