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[null,"1.","Record Nr.",null,"UNINA9910876973703321"]
[null,null,"Autore",null,"Bellanger Maurice"]
[null,null,"Titolo",null,"Digital Signal Processing : Theory and Practice"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781394182695"]
[{"$nulls":4},"1394182694"]
[{"$nulls":4},"9781394182671"]
[{"$nulls":4},"1394182678"]
[null,null,"Edizione",null,"[10th ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (397 pages)"]
[null,null,"Disciplina",null,"621.3822"]
[null,null,"Soggetti",null,"Signal processing"]
[{"$nulls":4},"Digital filters (Mathematics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright -- Contents -- Foreword (Historical Perspective) -- Preface -- Introduction -- Chapter 1 Signal Digitizing - Sampling and Coding -- 1.1 Fourier Analysis -- 1.1.1 Fourier Series Expansion of a Periodic Function -- 1.1.2 Fourier Transform of a Function -- 1.2 Distributions -- 1.2.1 Definition -- 1.2.2 Differentiation of Distributions -- 1.2.2.1 The Fourier Transform of a Distribution -- 1.3 Some Commonly Studied Signals -- 1.3.1 Deterministic Signals -- 1.3.2 Random Signals -- 1.3.3 Gaussian Signals -- 1.3.3.1 Peak Factor of a Random Signal -- 1.4 The Norms of a Function -- 1.5 Sampling -- 1.6 Frequency Sampling -- 1.7 The Sampling Theorem -- 1.8 Sampling of Sinusoidal and Random Signals -- 1.8.1 Sinusoidal Signals -- 1.8.2 Discrete Random Signals -- 1.8.3 Discrete Noise Generation -- 1.9 Quantization -- 1.10 The Coding Dynamic Range -- 1.11 Nonlinear Coding with the 13‐segment A‐law -- 1.12 Optimal Coding -- 1.13 Quantity of Information and Channel Capacity -- 1.14 Binary Representations -- Exercises -- References -- Chapter 2 The Discrete Fourier Transform -- 2.1 Definition and Properties of the Discrete Fourier Transform -- 2.2 Fast Fourier Transform (FFT) -- 2.2.1 Decimation‐in‐time Fast Fourier Transform -- 2.2.2 Decimation‐in‐frequency Fast Fourier Transform -- 2.2.3 Radix‐4 FFT Algorithm -- 2.2.4 Split‐radix FFT Algorithm -- 2.3 Degradation Arising from Wordlength Limitation Effects -- 2.4 Calculation of a "]
[{"$nulls":4},"Spectrum Using the DFT -- 2.4.1 The Filtering Function of the DFT -- 2.4.2 Spectral Resolution -- 2.5 Fast Convolution -- 2.6 Calculations of a DFT Using Convolution -- 2.7 Implementation -- Exercises -- References -- Chapter 3 Other Fast Algorithms for the FFT -- 3.1 Kronecker Product of Matrices -- 3.2 Factorizing the Matrix of a Decimation‐in‐Frequency Algorithm -- 3.3 Partial Transforms."]
[{"$nulls":4},"3.3.1 Transform of Real Data and Odd DFT -- 3.3.2 The Odd‐time Odd‐frequency DFT -- 3.3.3 Sine and Cosine Transforms -- 3.3.4 The Two‐dimensional DCT -- 3.4 Lapped Transform -- 3.5 Other Fast Algorithms -- 3.6 Binary Fourier Transform - Hadamard -- 3.7 Number‐Theoretic Transforms -- Exercises -- References -- Chapter 4 Time‐Invariant Discrete Linear Systems -- 4.1 Definition and Properties -- 4.2 The Z‐Transform -- 4.3 Energy and Power of Discrete Signals -- 4.4 Filtering of Random Signals -- 4.5 Systems Defined by Difference Equations -- 4.6 State Variable Analysis -- Exercises -- References -- Chapter 5 Finite Impulse Response (FIR) Filters -- 5.1 FIR Filters -- 5.2 Practical Transfer Functions and Linear Phase Filters -- 5.3 Calculation of Coefficients by Fourier Series Expansion for Frequency Specifications -- 5.4 Calculation of Coefficients by the Least‐Squares Method -- 5.5 Calculation of Coefficient by Discrete Fourier Transform -- 5.6 Calculation of Coefficients by Chebyshev Approximation -- 5.7 Relationships Between the Number of Coefficients and the Filter Characteristic -- 5.8 Raised‐Cosine Transition Filter -- 5.9 Structures for Implementing FIR Filters -- 5.10 Limitation of the Number of Bits for Coefficients -- 5.11 Z-Transfer Function of an FIR Filter -- 5.12 Minimum‐Phase Filters -- 5.13 Design of Filters with a Large Number of Coefficients -- 5.14 Two‐Dimensional FIR Filters -- 5.15 Coefficients of Two‐Dimensional FIR Filters by the Least‐Squares Method -- Exercises -- References -- Chapter 6 Infinite Impulse Response (IIR) Filter Sections -- 6.1 First‐Order Section -- 6.2 Purely Recursive Second‐Order Section -- 6.3 General Second‐Order Section -- 6.4 Structures for Implementation -- 6.5 Coefficient Wordlength Limitation -- 6.6 Internal Data Wordlength Limitation -- 6.7 Stability and Limit Cycles -- Exercises -- References."]
[{"$nulls":4},"Chapter 7 Infinite Impulse Response Filters -- 7.1 General Expressions for the Properties of IIR Filters -- 7.2 Direct Calculations of the Coefficients Using Model Functions -- 7.2.1 Impulse Invariance -- 7.2.2 Bilinear Transform -- 7.2.2.1 Butterworth Filters -- 7.2.2.2 Elliptic Filters -- 7.2.2.3 Calculating any Filter by Transformation of a Low‐pass Filter -- 7.2.3 Iterative Techniques for Calculating IIR Filter with Frequency -- 7.2.3.1 Minimizing the Mean Square Error -- 7.2.3.2 Chebyshev Approximation -- 7.2.4 Filters Based on Spheroidal Sequences -- 7.2.5 Structures Representing the Transfer Function -- 7.2.6 Limiting the Coefficient Wordlength -- 7.2.7 Round‐Off Noise -- 7.2.8 Comparison of IIR and FIR Filters -- Exercises -- References -- Chapter 8 Digital Ladder Filters -- 8.1 Properties of Two‐Port Circuits -- 8.2 Simulated Ladder Filters -- 8.3 Switched‐Capacitor Filters -- 8.4 Lattice Filters -- 8.5 Comparison Elements -- Exercises -- References -- Chapter 9 Complex Signals - Quadrature Filters - Interpolators -- 9.1 The Fourier Transform of a Real and Causal Set -- 9.2 Analytic Signals -- 9.3 Calculating the Coefficients of an FIR Quadrature Filter -- 9.4 Recursive 90° Phase Shifters -- 9.5 Single Side‐Band Modulation -- 9.6 Minimum‐Phase Filters -- 9.7 Differentiator -- 9.8 Interpolation Using FIR Filters -- 9.9 Lagrange Interpolation -- 9.10 Interpolation by Blocks - Splines -- 9.11 Interpolations and Signal Restoration -- 9.12 Conclusion -- Exercises -- References -- Chapter 10 Multirate Filtering -- 10.1 Decimation and Z‐Transform -- 10.2 Decomposition of a Low‐Pass FIR Filter -- 10.3 Half‐Band FIR Filters -- 10.4 Decomposition with "]
[{"$nulls":4},"Half‐Band Filters -- 10.5 Digital Filtering by Polyphase Network -- 10.6 Multirate Filtering with IIR Elements -- 10.7 Filter Banks Using Polyphase Networks and DFT -- 10.8 Conclusion -- Exercises."]
[{"$nulls":4},"References -- Chapter 11 QMF Filters and Wavelets -- 11.1 Decomposition into Two Sub‐Bands and Reconstruction -- 11.2 QMF Filters -- 11.3 Perfect Decomposition and Reconstruction -- 11.4 Wavelets -- 11.5 Lattice Structures -- Exercises -- References -- Chapter 12 Filter Banks -- 12.1 Decomposition and Reconstruction -- 12.2 Analyzing the Elements of the Polyphase Network -- 12.3 Determining the Inverse Functions -- 12.4 Banks of Pseudo‐QMF Filters -- 12.5 Determining the Coefficients of the Prototype Filter -- 12.6 Realizing a Bank of Real Filters -- Exercises -- References -- Chapter 13 Signal Analysis and Modeling -- 13.1 Autocorrelation and Intercorrelation -- 13.2 Correlogram Spectral Analysis -- 13.3 Single‐Frequency Estimation -- 13.4 Correlation Matrix -- 13.5 Modeling -- 13.6 Linear Prediction -- 13.7 Predictor Structures -- 13.7.1 Sensor Networks - Antenna Processing -- 13.8 Multiple Sources - MIMO -- 13.9 Conclusion -- Appendix: Estimation Bounds -- Exercises -- References -- Chapter 14 Adaptive Filtering -- 14.1 Principle of Adaptive Filtering -- 14.2 Convergence Conditions -- 14.3 Time Constant -- 14.4 Residual Error -- 14.5 Complexity Parameters -- 14.6 Normalized Algorithms and Sign Algorithms -- 14.7 Adaptive FIR Filtering in Cascade Form -- 14.8 Adaptive IIR Filtering -- 14.9 Conclusion -- Exercises -- References -- Chapter 15 Neural Networks -- 15.1 Classification -- 15.2 Multilayer Perceptron -- 15.3 The Backpropagation Algorithm -- 15.4 Examples of Application -- 15.5 Convolution Neural Networks -- 15.6 Recurrent/Recursive Neural Networks -- 15.7 Neural Network and Signal Processing -- 15.8 On Activation Functions -- 15.9 Conclusion -- Exercises -- References -- Chapter 16 Error‐Correcting Codes -- 16.1 Reed-Solomon Codes -- 16.1.1 Predictable Signals -- 16.1.2 Reed-Solomon Codes in the Frequency Domain."]
[{"$nulls":4},"16.1.3 Reed-Solomon Codes in the Time Domain -- 16.1.4 Computing in a Finite Field -- 16.1.5 Performance of Reed-Solomon Codes -- 16.2 Convolutional Codes -- 16.2.1 Channel Capacity -- 16.2.2 Approaching the Capacity Limit -- 16.2.3 A Simple Convolutional Code -- 16.2.4 Coding Gain and Error Probability -- 16.2.5 Decoding and Output Signals -- 16.2.6 Recursive Systematic Coding (RSC) -- 16.2.7 Principle of Turbo Codes -- 16.2.8 Trellis‐Coded Modulations -- 16.3 Conclusion -- Exercises -- References -- Chapter 17 Applications -- 17.1 Frequency Detection -- 17.2 Phase‐locked Loop -- 17.3 Differential Coding of Speech -- 17.4 Coding of Sound -- 17.5 Echo Cancelation -- 17.5.1 Data Echo Canceller -- 17.5.1.1 Two‐wire Line -- 17.5.2 Acoustic Echo Canceler -- 17.6 Television Image Processing -- 17.7 Multicarrier Transmission - OFDM -- 17.8 Mobile Radiocommunications -- References -- Exercises: Solutions and Hints -- Index -- EULA."]
[null,null,null,"Sommario/riassunto","This book, 'Digital Signal Processing: Theory and Practice', authored by Maurice Bellanger, serves as a comprehensive guide to the principles and applications of digital signal processing (DSP). It is designed to make complex digital techniques accessible by connecting theoretical knowledge with practical applications. The book covers a wide range of topics including digitizing signals, the discrete Fourier transform, fast algorithms for FFT, finite and infinite impulse response filters, and adaptive filtering. It also addresses advanced subjects like neural networks and applications in sound coding. The content is structured to benefit engineering students and professionals looking for a thorough understanding of DSP technology and its real-world "]
[{"$nulls":4},"applications. The text includes exercises and references to facilitate deeper learning."]