Design and MATLAB concepts have been integrated in text. _ Integrates applications as it relates signals to a remote sensing system, a controls system, radio astronomy, a biomedical system and seismology.
Design and MATLAB concepts have been integrated in text. _ Integrates applications as it relates signals to a remote sensing system, a controls system, radio astronomy, a biomedical system and seismology.
Simon Haykin, PhD, is Distinguished University Professor and Director of the Cognitive Systems Laboratory in the Faculty of Engineering at McMaster University. A world-renowned authority on adaptive and learning systems, Dr. Haykin has pioneered signal-processing techniques and systems for radar and communication applications, culminating in the study of cognitive dynamic systems, which has become his research passion.
Inhaltsangabe
Chapter 1. Introduction. Chapter 2. Time-Domain Representations of Linear Time-Invariant Systems. Chapter 3. Fourier Representations of Signals and Linear Time Invariant Systems. Chapter 4. Applications of Fourier Representations to Mixed Signal Classes. Chapter 5. Application to Communication Systems. Chapter 6. Representing Signals by Using Continuous-Time Complex Exponentials: The Laplace Transform. Chapter 7. Representing Signals by Using Discrete-Time Complex Exponentials: The z-Transform. Chapter 8. Application to Filters and Equalizers. Chapter 9. Application to Linear Feedback Systems. Chapter 10. Epilogue. Appendix A: Selected Mathematical Identities. Appendix B: Partial-Fraction Expansions. Appendix C: Tables of Fourier Representations and Properties. Appendix D: Tables of Laplace Transforms and Properties. Appendix E: Tables of z-Transforms and Properties. Appendix F: Introduction to MATLAB. Index.
Chapter 1. Introduction. Chapter 2. Time-Domain Representations of Linear Time-Invariant Systems. Chapter 3. Fourier Representations of Signals and Linear Time Invariant Systems. Chapter 4. Applications of Fourier Representations to Mixed Signal Classes. Chapter 5. Application to Communication Systems. Chapter 6. Representing Signals by Using Continuous-Time Complex Exponentials: The Laplace Transform. Chapter 7. Representing Signals by Using Discrete-Time Complex Exponentials: The z-Transform. Chapter 8. Application to Filters and Equalizers. Chapter 9. Application to Linear Feedback Systems. Chapter 10. Epilogue. Appendix A: Selected Mathematical Identities. Appendix B: Partial-Fraction Expansions. Appendix C: Tables of Fourier Representations and Properties. Appendix D: Tables of Laplace Transforms and Properties. Appendix E: Tables of z-Transforms and Properties. Appendix F: Introduction to MATLAB. Index.
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