Estimation Theory With Applications To Communications And Control By Andrew P Sage And James L Melsa

Estimation Theory with Applications to Communications and Control [by] Andrew P. Sage [and] James L. Melsa

Essentials of Automatic Control with MATLAB in 20 Lessons

This book is designed to explain the fundamental principles of automatic control through 20 lessons, each incorporating worked examples and MATLAB®-based exercises to help readers effectively understand and apply the proposed methods. It offers a concise and accessible resource for learning automatic control, blending simplicity with clarity. Drawing on decades of teaching experience, the authors aim to provide an approachable introduction to the core concepts of the discipline, encouraging further exploration through independent study. Integrates MATLAB® within a 20-lesson framework Enhances learning by supporting both theoretical understanding and practical problem-solving Promotes critical thinking and problem-solving skills, essential competencies in today's AI-driven world. Focuses on classical topics related to Single-Input Single-Output (SISO) linear continuous-time systems Provides introductory material on topics such as Multiple-Input Multiple-Output (MIMO) and discrete-time systems, serving as a bridge to more advanced studies. By establishing a strong foundation in these areas, the book prepares readers to tackle complex challenges in modern automatic control and excel in future academic and professional endeavors. Primarily intended for undergraduate engineering students, the book is also suitable for a wider audience. As automatic control is a foundational subject across numerous academic programs, this resource equips readers with essential analytical tools and introduces key problems, fostering deeper insights into the subject.

An Introduction to Probability and Stochastic Processes

Detailed coverage of probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.