pdf Digital Signal Processing: Foundations, Algorithms, Architectures and Applications (English Edition)

Digital signal processing has quietly become the invisible infrastructure of modern life. When a physician reads a foetal heart trace, when a pilot trusts a synthetic-aperture radar image, when a musician records a track in a bedroom studio, or when a billion people place a video call across continents, the same small family of mathematical ideas is at work: sampling, convolution, transformation and filtering. What makes the subject so remarkable is that these ideas are simultaneously deep enough to occupy research mathematicians and concrete enough to be implemented, in a few dozen lines of code, on a processor costing less than a cup of coffee.
This book grew out of two decades of teaching digital signal processing to senior undergraduate and postgraduate students of electronics, communication, instrumentation and computer engineering. Over those years I have watched a curious pattern repeat itself. Students who can compute a discrete Fourier transform flawlessly on paper are often unable to say why their spectral estimate of a real measurement looks nothing like the textbook picture. Students who can derive the bilinear transformation cannot decide whether an FIR or an IIR filter is right for their project. The gap is not one of intelligence or effort; it is a gap between formalism and judgement. This book is my attempt to close that gap.
Philosophy of the Book
Three convictions have shaped the writing.
First, the mathematics must be honest. Digital signal processing rests on a small number of theorems — the sampling theorem, the convolution theorem, Parseval's relation, the orthogonality of the DFT basis — and these are stated and derived here with care. Hand-waving in a first course produces engineers who cannot debug their own systems. Wherever a result is quoted without proof, the omission is flagged and a reference is given.
Second, every idea must be visible. The book contains more than ninety figures, every one of which was generated from the actual computation it illustrates rather than sketched by hand. When the text says that the Blackman window buys sixty decibels of sidelobe suppression at the cost of a wider main lobe, the reader can see exactly how much wider. Numerical tables give the reader quantities to hold on to; block diagrams give structure to hold on to.
Third, theory must terminate in implementation. A filter design is not finished when the coefficients are computed; it is finished when the filter runs, in finite precision, at the required sample rate, within the available memory and power budget. Chapters on finite word-length effects, on multirate structures and on processor architectures are therefore not appendices to the theory but part of it.
Organisation
The seventeen chapters fall naturally into five parts. Chapters 1 to 3 lay the foundations: what signals and systems are, how discrete-time systems behave in the time domain, and how the discrete world is connected to the continuous world through sampling and quantization. Chapters 4 to 7 develop the transform machinery — the z-transform, the discrete-time Fourier transform, the discrete Fourier transform and the fast algorithms that make it practical. Chapters 8 to 11 treat the design and realisation of filters: structures, FIR design, IIR design and the arithmetic realities of finite word length. Chapters 12 to 15 cover the modern topics that dominate contemporary practice: multirate processing and filter banks, wavelets and time–frequency analysis, power spectrum estimation and adaptive filtering. Chapters 16 and 17 turn to hardware architectures and to a survey of applications across speech, audio, imaging, biomedicine, radar and communications.