Digital Signal Processing: DSP and Applications
This book is a uniquely practical DSP text which places the emphasis on understanding the principles and applications of DSP with a minimum of mathematics. In one volume, it covers a broad area of digital signal processing systems such as A/D and D/A converters, adaptive filters, spectral estimation, neural networks, Kalman filters, fuzzy logic, data compression, error correction and DSP programming. Many courses will find that this book will replace several texts currently in use.
The level is ideal for introductory university modules, and similar courses such as HNC/D. As DSP has come to be studied at a lower academic level over recent years this text meets a genuine need. It is also suitable for use on industrial training courses and ideal as a reference text for professionals.
A readable introduction to the practical application of DSP
Broad coverage of the subject means this will cover a typical undergraduate module in just one book
Practical focus with maths treated as a practical tool - not an advanced maths text
Why Read This Book
You should read this book if you want a wide-angle, application-oriented introduction to DSP that emphasizes concepts and practical implementation over heavy mathematics. It gets you quickly comfortable with real-world blocks — A/D and D/A, FFT/spectral tools, adaptive and Kalman filters, and basic DSP programming — so you can start applying DSP techniques in projects or industrial training.
Who Will Benefit
Undergraduate students, technicians, and practicing engineers who need a pragmatic, low-math introduction to a broad range of DSP topics and want a single-volume reference for applied techniques.
Level: Intermediate — Prerequisites: Basic calculus and complex numbers, elementary signals & systems concepts (sampling, convolution) or willingness to pick these up alongside the book.
Key Takeaways
- Explain the principles and practical issues of sampling and A/D-D/A conversion and anti-aliasing.
- Design and implement common FIR and IIR digital filters for real applications.
- Apply FFT-based spectral analysis and basic spectral estimation techniques.
- Implement and tune adaptive filters and understand the basic Kalman filtering concept for state estimation.
- Integrate neural-network and fuzzy-logic ideas into DSP tasks and appreciate their practical uses.
- Plan and implement DSP algorithms in code and understand real-time/implementation constraints.
Topics Covered
- Introduction to DSP and applications
- Signals, systems and a short review of the math
- Sampling, A/D and D/A conversion practicalities
- Discrete-time Fourier transforms and the FFT
- Digital filter types: FIR and IIR design and implementation
- Windowing and spectral analysis / spectral estimation
- Multirate ideas and practical considerations
- Adaptive filtering: algorithms and applications
- Kalman filters: concepts and engineering use
- Neural networks and fuzzy logic in signal processing
- Data compression and error-correction basics
- DSP implementation and programming considerations (software/hardware)
- Applications: audio, communications and assorted case studies
Languages, Platforms & Tools
How It Compares
More application-focused and less mathematical than Oppenheim & Schafer's Discrete-Time Signal Processing; similar in accessibility to Richard Lyons' Understanding Digital Signal Processing but broader in topics (e.g., Kalman, fuzzy, error correction) and shallower in depth.
