Probability and Random Processes for Electrical Engineering. Dieser Artikel ist nicht verfügbar.Probability and Random Processes for Electrical Engineering

This textbook offers an interesting, straightforward introduction to probability and random processes. While helping students to develop their problem-solving skills, the book enables them to understand how to make the transition from real problems to probability models for those problems. To keep students motivated, the author uses a number of practical applications from various areas of electrical and computer engineering that demonstrate the relevance of probability theory to engineering practice. Discrete-time random processes are used to bridge the transition between random variables and continuous-time random processes. Additional material has been added to the second edition to provide a more substantial introduction to random processes.

This book offers an interesting, straightforward introduction to probability and random processes. While helping readers to develop their problem-solving skills, the book enables them to understand how to make the transition from real problems to probability models for those problems. To keep users motivated, the author uses a number of practical applications from various areas of electrical and computer engineering that demonstrate the relevance of probability theory to engineering practice. Discrete-time random processes are used to bridge the transition between random variables and continuous-time random processes. Additional material has been added to the second edition to provide a more substantial introduction to random processes.

The book's first five chapters form the basis of a traditional, introduction to probability and random variables. In addition to the standard topics, it offers optional sections on modeling, computer methods, combinatories, reliability, and entropy. Chapters 4 through 9 can accommodate a one-semester senior/first-year graduate course on random processes and linear systems, as well as Markov chains and queuing theory. Additional coverage includes cyclostationary random processes, Fourier series and Karhunen-Loeve expansion, continuity, derivatives and integrals, amplitude modulation. Wiener and Kalman filters, and time reversed Markov chains.

Features

• Chapter overviews: brief introduction outlining chapter coverage and learning objectives.
• Chapter summaries: concise, easy-reference sections providing quick overviews of each chapter's major topics.
• Checklist of important terms.
• Annotated references: suggestions of timely resources for additional coverage of critical material.
• Numerous examples: a wide selection of fully worked-out real-world examples.
• Problems: over 700 in all.