Technology

Science and Nature

Signal Processing for Electrical Engineers

Linear and Digital Signals and Systems for Electrical EngineersBy Helen Marchetti

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Length12h 5m

About this audiobook

Transforms feel arbitrary until you see what they measure. This book keeps the instrument in view. Signal processing is often taught as a sequence of mathematical procedures, with the meaning of each transform left implicit. This book reverses that order. You begin by classifying signals and systems, then build convolution by hand in both continuous and discrete time, and decompose responses into zero-input and zero-state parts. Fourier series and transforms arrive as answers to real questions about frequency content, with line spectra, energy relations and the Gibbs phenomenon explained rather than asserted. You then take sampling seriously: Nyquist limits, aliasing you can see, reconstruction and anti-alias filtering. Discrete chapters cover the discrete Fourier transform with windowing and leakage, fast algorithms, the z-transform, and FIR and IIR filter design with finite word length effects. Closing chapters handle noise, power spectral density and modulation. Throughout, the emphasis stays on what each tool measures and when it applies, so that the mathematics remains connected to the signals on the wire. What you will learn • Classify signals and systems, and test linearity, time invariance, causality and stability • Build convolution by hand in continuous and discrete time, and interpret impulse responses • Solve differential and difference equations, separating zero-input and zero-state responses • Read Fourier series and transforms as frequency-content measurements, including line spectra, energy relations and the Gibbs phenomenon • Apply the Laplace transform to system analysis and stability • Handle sampling correctly: Nyquist limits, visible aliasing, reconstruction and anti-alias filtering • Use the discrete Fourier transform with windowing and leakage control, and apply fast algorithms • Work with the z-transform, and design FIR and IIR filters with finite word length effects in view • Analyze random signals, noise, power spectral density and modulation in practical settings For electrical engineering students and practising engineers working with sampled data, this book provides a coherent path from signal classification to filter design and noise analysis. If you need to understand not only how to compute a transform but what it tells you about a real signal, this treatment keeps the instrument in view from the first chapter to the last.

Audiobook details

GenreTechnology, Science and Nature

Length12 hrs 5 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateSep 28, 2026

LanguageEnglish

1Foreword

2Preface

3About This Book

4Chapter 1: Signals and Their Operations

51.1 Continuous-Time and Discrete-Time Signals

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61.2 Signal Classification: Periodic, Aperiodic, Even and Odd

71.3 Energy, Power and Average Value

81.4 Time Shifting, Scaling and Reflection

91.5 Elementary Signal Models

101.6 Signal Operations on Sampled Records

11Chapter 2: Systems and Their Properties

122.1 Systems as Transformations

132.2 Linearity and Superposition

142.3 Time Invariance, Causality and Memory

152.4 Invertibility and Bounded-Input Bounded-Output Stability

162.5 A Classification Procedure and Worked Case

17Chapter 3: LTI Systems and Convolution

183.1 The Impulse Response and the Convolution Integral

193.2 Graphical and Analytic Evaluation

203.3 Discrete Convolution and Its Evaluation

213.4 Step Response, Interconnections and Stability Revisited

22Chapter 4: Differential and Difference Equations

234.1 Continuous-Time Differential Equation Models

244.2 Natural Response and Characteristic Modes

254.3 Zero-Input and Zero-State Decomposition

264.4 Difference Equations and Recursive Solution

274.5 Impulse Response from the Equation

28Chapter 5: Fourier Series

295.1 Periodic Signals and the Trigonometric Series

305.2 Exponential Form and Line Spectra

315.3 Symmetry Properties and Simplifications

325.4 Convergence and the Gibbs Phenomenon

335.5 Power Relations for Periodic Signals

34Chapter 6: The Continuous-Time Fourier Transform

356.1 From Series to Transform

366.2 Transform Properties and Pairs

376.3 Convolution and Multiplication in the Frequency Domain

386.4 Energy Spectral Density and Parseval’s Relation

39Chapter 7: The Laplace Transform

407.1 Definition, Region of Convergence and Uniqueness

417.2 The Unilateral Transform and Initial Conditions

427.3 Poles, Zeros and Transfer Functions

437.4 Inverse Transforms by Partial Fractions

447.5 System Response and Stability from the s-Plane

45Chapter 8: Sampling and Reconstruction

468.1 The Sampling Theorem and the Nyquist Condition

478.2 Aliasing in Time and Frequency

488.3 Ideal Reconstruction and the Zero-Order Hold

498.4 Anti-Alias Filtering and Practical Sampling Chains

50Chapter 9: Discrete-Time Fourier Analysis

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