Decision and Estimation Theory

Science & Math

Melsa, James L.

Mathematics

McGraw-Hill Companies

Computer Programs for Computational Assistance in the Study of Linear Control Theory

Computers & Technology

Computer Science

This undergraduate text presents a modern approach to the techniques of control theory. The book presents the best of modern topics such as robustness, ramifications of model inaccuracies on the design of control systems, computer examples using MATLAB, and design problems, and provides applications examples for electrical, mechanical, aerospace and chemical engineering students at undergraduate level.

Linear Control Systems

Engineering & Transportation

Schultz, Donald G.

Engineering

McGraw-Hill Education (ISE Editions)

This text offers students valuable tools for the description and analysis of random effects in complex systems. The treatment emphasizes concepts with practical value and illustrates their application to realistic situations. Geared toward college seniors and first-year graduate students, this volume is designed for a one-semester course in probability and stochastic processes. The sole prerequisite is a familiarity with system analysis, including state-variable and Laplace-transform concepts, and two appendixes provide a review of these concepts.Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. The coverage extends to major concepts essential to modern systems analysis and presents several applications of theory to realistic practical problems in science and technology. Numerous examples and problems appear throughout the text, many with worked-out solutions.

An introduction to probability and stochastic processes (Prentice-Hall information and system sciences series)

PRENTICE/HALL

This text offers students valuable tools for the description and analysis of random effects in complex systems. The treatment emphasizes concepts with practical value and illustrates their application to realistic situations. Geared toward college seniors and first-year graduate students, this volume is designed for a one-semester course in probability and stochastic processes. The sole prerequisite is a familiarity with system analysis, including state-variable and Laplace-transform concepts, and two appendixes provide a review of these concepts. Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. The coverage extends to major concepts essential to modern systems analysis and presents several applications of theory to realistic practical problems in science and technology. Numerous examples and problems appear throughout the text, many with worked-out solutions.

An Introduction to Probability and Stochastic Processes (Dover Books on Mathematics)

Sage, Andrew P.

Dover `

Even with the advances in signal processing and digital communications, robustness to uncertain channel statistics continues to be a fundamental issue in the design and performance analysis of today's communications, radar, and sonar systems. The variability of digital communications systems consistently challenges the communications system designer, while new applications have channels that almost defy accurate modeling. As a result, parametric detectors, which are excellent when model assumptions are satisfied, do not maintain the satisfactory performance necessary for detection.This core IEEE Press reissue is the only book devoted solely to nonparametric detection - the key to maintaining good performance over a wide range of conditions. Throughout, the authors employ the classical Neyman-Pearson approach, which is widely applicable to detection problems in communications, radar, sonar, acoustics, and geophysics. Topics covered include: nonparametric detection theory, basic detection theory, one-input and two-input detectors and performance, tied observations, dependent sample performance, and engineering applications.

Introduction to Nonparametric Detection With Applications

Gibson, Jerry D.

IEEE

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Books by Melsa, James L.