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Advanced digital signal processing and noise reduction [[electronic resource] /] / Saeed V. Vaseghi
| Advanced digital signal processing and noise reduction [[electronic resource] /] / Saeed V. Vaseghi |
| Autore | Vaseghi Saeed V |
| Edizione | [4th ed.] |
| Pubbl/distr/stampa | Chichester, U.K., : J. Wiley & Sons, 2008 |
| Descrizione fisica | 1 online resource (546 p.) |
| Disciplina |
621.382/2
621.3822 |
| Soggetto topico |
Signal processing
Electronic noise Digital filters (Mathematics) |
| ISBN |
1-282-00380-1
9786612003806 0-470-74015-9 0-470-74016-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Digital Signal Processing and Noise Reduction; Contents; Preface; Acknowledgements; Symbols; Abbreviations; 1 Introduction; 1.1 Signals, Noise and Information; 1.2 Signal Processing Methods; 1.2.1 Transform-Based Signal Processing; 1.2.2 Source-Filter Model-Based Signal Processing; 1.2.3 Bayesian Statistical Model-Based Signal Processing; 1.2.4 Neural Networks; 1.3 Applications of Digital Signal Processing; 1.3.1 Digital Watermarking; 1.3.2 Bio-medical, MIMO, Signal Processing; 1.3.3 Echo Cancellation; 1.3.4 Adaptive Noise Cancellation; 1.3.5 Adaptive Noise Reduction
1.3.6 Blind Channel Equalisation1.3.7 Signal Classification and Pattern Recognition; 1.3.8 Linear Prediction Modelling of Speech; 1.3.9 Digital Coding of Audio Signals; 1.3.10 Detection of Signals in Noise; 1.3.11 Directional Reception of Waves: Beam-forming; 1.3.12 Space-Time Signal Processing; 1.3.13 Dolby Noise Reduction; 1.3.14 Radar Signal Processing: Doppler Frequency Shift; 1.4 A Review of Sampling and Quantisation; 1.4.1 Advantages of Digital Format; 1.4.2 Digital Signals Stored and Transmitted in Analogue Format; 1.4.3 The Effect of Digitisation on Signal Bandwidth 1.4.4 Sampling a Continuous-Time Signal1.4.5 Aliasing Distortion; 1.4.6 Nyquist Sampling Theorem; 1.4.7 Quantisation; 1.4.8 Non-Linear Quantisation, Companding; 1.5 Summary; Bibliography; 2 Noise and Distortion; 2.1 Introduction; 2.1.1 Different Classes of Noise Sources and Distortions; 2.1.2 Different Classes and Spectral/Temporal Shapes of Noise; 2.2 White Noise; 2.2.1 Band-Limited White Noise; 2.3 Coloured Noise; Pink Noise and Brown Noise; 2.4 Impulsive and Click Noise; 2.5 Transient Noise Pulses; 2.6 Thermal Noise; 2.7 Shot Noise; 2.8 Flicker (I/f ) Noise; 2.9 Burst Noise 2.10 Electromagnetic (Radio) Noise2.10.1 Natural Sources of Radiation of Electromagnetic Noise; 2.10.2 Man-made Sources of Radiation of Electromagnetic Noise; 2.11 Channel Distortions; 2.12 Echo and Multi-path Reflections; 2.13 Modelling Noise; 2.13.1 Frequency Analysis and Characterisation of Noise; 2.13.2 Additive White Gaussian Noise Model (AWGN); 2.13.3 Hidden Markov Model and Gaussian Mixture Models for Noise; Bibliography; 3 Information Theory and Probability Models; 3.1 Introduction: Probability and Information Models; 3.2 Random Processes 3.2.1 Information-bearing Random Signals vs Deterministic Signals3.2.2 Pseudo-Random Number Generators (PRNG); 3.2.3 Stochastic and Random Processes; 3.2.4 The Space of Variations of a Random Process; 3.3 Probability Models of Random Signals; 3.3.1 Probability as a Numerical Mapping of Belief; 3.3.2 The Choice of One and Zero as the Limits of Probability; 3.3.3 Discrete, Continuous and Finite-State Probability Models; 3.3.4 Random Variables and Random Processes; 3.3.5 Probability and Random Variables - The Space and Subspaces of a Variable 3.3.6 Probability Mass Function - Discrete Random Variables |
| Record Nr. | UNINA-9910145251303321 |
Vaseghi Saeed V
|
||
| Chichester, U.K., : J. Wiley & Sons, 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced digital signal processing and noise reduction [[electronic resource] /] / Saeed V. Vaseghi
| Advanced digital signal processing and noise reduction [[electronic resource] /] / Saeed V. Vaseghi |
| Autore | Vaseghi Saeed V |
| Edizione | [4th ed.] |
| Pubbl/distr/stampa | Chichester, U.K., : J. Wiley & Sons, 2008 |
| Descrizione fisica | 1 online resource (546 p.) |
| Disciplina |
621.382/2
621.3822 |
| Soggetto topico |
Signal processing
Electronic noise Digital filters (Mathematics) |
| ISBN |
1-282-00380-1
9786612003806 0-470-74015-9 0-470-74016-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Digital Signal Processing and Noise Reduction; Contents; Preface; Acknowledgements; Symbols; Abbreviations; 1 Introduction; 1.1 Signals, Noise and Information; 1.2 Signal Processing Methods; 1.2.1 Transform-Based Signal Processing; 1.2.2 Source-Filter Model-Based Signal Processing; 1.2.3 Bayesian Statistical Model-Based Signal Processing; 1.2.4 Neural Networks; 1.3 Applications of Digital Signal Processing; 1.3.1 Digital Watermarking; 1.3.2 Bio-medical, MIMO, Signal Processing; 1.3.3 Echo Cancellation; 1.3.4 Adaptive Noise Cancellation; 1.3.5 Adaptive Noise Reduction
1.3.6 Blind Channel Equalisation1.3.7 Signal Classification and Pattern Recognition; 1.3.8 Linear Prediction Modelling of Speech; 1.3.9 Digital Coding of Audio Signals; 1.3.10 Detection of Signals in Noise; 1.3.11 Directional Reception of Waves: Beam-forming; 1.3.12 Space-Time Signal Processing; 1.3.13 Dolby Noise Reduction; 1.3.14 Radar Signal Processing: Doppler Frequency Shift; 1.4 A Review of Sampling and Quantisation; 1.4.1 Advantages of Digital Format; 1.4.2 Digital Signals Stored and Transmitted in Analogue Format; 1.4.3 The Effect of Digitisation on Signal Bandwidth 1.4.4 Sampling a Continuous-Time Signal1.4.5 Aliasing Distortion; 1.4.6 Nyquist Sampling Theorem; 1.4.7 Quantisation; 1.4.8 Non-Linear Quantisation, Companding; 1.5 Summary; Bibliography; 2 Noise and Distortion; 2.1 Introduction; 2.1.1 Different Classes of Noise Sources and Distortions; 2.1.2 Different Classes and Spectral/Temporal Shapes of Noise; 2.2 White Noise; 2.2.1 Band-Limited White Noise; 2.3 Coloured Noise; Pink Noise and Brown Noise; 2.4 Impulsive and Click Noise; 2.5 Transient Noise Pulses; 2.6 Thermal Noise; 2.7 Shot Noise; 2.8 Flicker (I/f ) Noise; 2.9 Burst Noise 2.10 Electromagnetic (Radio) Noise2.10.1 Natural Sources of Radiation of Electromagnetic Noise; 2.10.2 Man-made Sources of Radiation of Electromagnetic Noise; 2.11 Channel Distortions; 2.12 Echo and Multi-path Reflections; 2.13 Modelling Noise; 2.13.1 Frequency Analysis and Characterisation of Noise; 2.13.2 Additive White Gaussian Noise Model (AWGN); 2.13.3 Hidden Markov Model and Gaussian Mixture Models for Noise; Bibliography; 3 Information Theory and Probability Models; 3.1 Introduction: Probability and Information Models; 3.2 Random Processes 3.2.1 Information-bearing Random Signals vs Deterministic Signals3.2.2 Pseudo-Random Number Generators (PRNG); 3.2.3 Stochastic and Random Processes; 3.2.4 The Space of Variations of a Random Process; 3.3 Probability Models of Random Signals; 3.3.1 Probability as a Numerical Mapping of Belief; 3.3.2 The Choice of One and Zero as the Limits of Probability; 3.3.3 Discrete, Continuous and Finite-State Probability Models; 3.3.4 Random Variables and Random Processes; 3.3.5 Probability and Random Variables - The Space and Subspaces of a Variable 3.3.6 Probability Mass Function - Discrete Random Variables |
| Record Nr. | UNINA-9910830221303321 |
|
Vaseghi Saeed V
|
||
| Chichester, U.K., : J. Wiley & Sons, 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced digital signal processing and noise reduction / / Saeed V. Vaseghi
| Advanced digital signal processing and noise reduction / / Saeed V. Vaseghi |
| Autore | Vaseghi Saeed V |
| Edizione | [4th ed.] |
| Pubbl/distr/stampa | Chichester, U.K., : J. Wiley & Sons, 2008 |
| Descrizione fisica | 1 online resource (546 p.) |
| Disciplina |
621.382/2
621.3822 |
| Soggetto topico |
Signal processing
Electronic noise Digital filters (Mathematics) |
| ISBN |
9786612003806
9781282003804 1282003801 9780470740156 0470740159 9780470740163 0470740167 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Digital Signal Processing and Noise Reduction; Contents; Preface; Acknowledgements; Symbols; Abbreviations; 1 Introduction; 1.1 Signals, Noise and Information; 1.2 Signal Processing Methods; 1.2.1 Transform-Based Signal Processing; 1.2.2 Source-Filter Model-Based Signal Processing; 1.2.3 Bayesian Statistical Model-Based Signal Processing; 1.2.4 Neural Networks; 1.3 Applications of Digital Signal Processing; 1.3.1 Digital Watermarking; 1.3.2 Bio-medical, MIMO, Signal Processing; 1.3.3 Echo Cancellation; 1.3.4 Adaptive Noise Cancellation; 1.3.5 Adaptive Noise Reduction
1.3.6 Blind Channel Equalisation1.3.7 Signal Classification and Pattern Recognition; 1.3.8 Linear Prediction Modelling of Speech; 1.3.9 Digital Coding of Audio Signals; 1.3.10 Detection of Signals in Noise; 1.3.11 Directional Reception of Waves: Beam-forming; 1.3.12 Space-Time Signal Processing; 1.3.13 Dolby Noise Reduction; 1.3.14 Radar Signal Processing: Doppler Frequency Shift; 1.4 A Review of Sampling and Quantisation; 1.4.1 Advantages of Digital Format; 1.4.2 Digital Signals Stored and Transmitted in Analogue Format; 1.4.3 The Effect of Digitisation on Signal Bandwidth 1.4.4 Sampling a Continuous-Time Signal1.4.5 Aliasing Distortion; 1.4.6 Nyquist Sampling Theorem; 1.4.7 Quantisation; 1.4.8 Non-Linear Quantisation, Companding; 1.5 Summary; Bibliography; 2 Noise and Distortion; 2.1 Introduction; 2.1.1 Different Classes of Noise Sources and Distortions; 2.1.2 Different Classes and Spectral/Temporal Shapes of Noise; 2.2 White Noise; 2.2.1 Band-Limited White Noise; 2.3 Coloured Noise; Pink Noise and Brown Noise; 2.4 Impulsive and Click Noise; 2.5 Transient Noise Pulses; 2.6 Thermal Noise; 2.7 Shot Noise; 2.8 Flicker (I/f ) Noise; 2.9 Burst Noise 2.10 Electromagnetic (Radio) Noise2.10.1 Natural Sources of Radiation of Electromagnetic Noise; 2.10.2 Man-made Sources of Radiation of Electromagnetic Noise; 2.11 Channel Distortions; 2.12 Echo and Multi-path Reflections; 2.13 Modelling Noise; 2.13.1 Frequency Analysis and Characterisation of Noise; 2.13.2 Additive White Gaussian Noise Model (AWGN); 2.13.3 Hidden Markov Model and Gaussian Mixture Models for Noise; Bibliography; 3 Information Theory and Probability Models; 3.1 Introduction: Probability and Information Models; 3.2 Random Processes 3.2.1 Information-bearing Random Signals vs Deterministic Signals3.2.2 Pseudo-Random Number Generators (PRNG); 3.2.3 Stochastic and Random Processes; 3.2.4 The Space of Variations of a Random Process; 3.3 Probability Models of Random Signals; 3.3.1 Probability as a Numerical Mapping of Belief; 3.3.2 The Choice of One and Zero as the Limits of Probability; 3.3.3 Discrete, Continuous and Finite-State Probability Models; 3.3.4 Random Variables and Random Processes; 3.3.5 Probability and Random Variables - The Space and Subspaces of a Variable 3.3.6 Probability Mass Function - Discrete Random Variables |
| Record Nr. | UNINA-9911019323403321 |
|
Vaseghi Saeed V
|
||
| Chichester, U.K., : J. Wiley & Sons, 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced digital signal processing and noise reduction / / Saeed V. Vaseghi
| Advanced digital signal processing and noise reduction / / Saeed V. Vaseghi |
| Autore | Vaseghi Saeed V. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England : , : John Wiley & Sons, Ltd, , 2000 |
| Descrizione fisica | 1 online resource (499 p.) |
| Disciplina |
621.382/2
621.3822 |
| Altri autori (Persone) |
MusgraveP. W (Peter William)
SelleckR. J. W <1934-> (Richard Joseph Wheeler) |
| Soggetto topico |
Signal processing
Electronic noise Digital filters (Mathematics) |
| ISBN |
1-280-55505-X
9786610555055 0-470-84162-1 0-470-85342-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Digital Signal Processing and Noise Reduction Second Edition; CONTENTS; PREFACE; FREQUENTLY USED SYMBOLS AND ABBREVIATIONS; CHAPTER 1 INTRODUCTION; CHAPTER 2 NOISE AND DISTORTION; CHAPTER 3 PROBABILITY MODELS; CHAPTER 4 BAYESIAN ESTIMATION; CHAPTER 5 HIDDEN MARKOV MODELS; CHAPTER 6 WIENER FILTERS; CHAPTER 7 ADAPTIVE FILTERS; CHAPTER 8 LINEAR PREDICTION MODELS; CHAPTER 9 POWER SPECTRUM AND CORRELATION; CHAPTER 10 INTERPOLATION; CHAPTER 11 SPECTRAL SUBTRACTION; CHAPTER 12 IMPULSIVE NOISE; CHAPTER 13 TRANSIENT NOISE PULSES; CHAPTER 14 ECHO CANCELLATION
CHAPTER 15 CHANNEL EQUALIZATION AND BLIND DECONVOLUTIONINDEX |
| Record Nr. | UNINA-9910142490603321 |
|
Vaseghi Saeed V.
|
||
| Chichester, West Sussex, England : , : John Wiley & Sons, Ltd, , 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced digital signal processing and noise reduction / Saeed V. Vaseghi
| Advanced digital signal processing and noise reduction / Saeed V. Vaseghi |
| Autore | Vaseghi, Saeed V. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester ; New York : John Wiley, c2000 |
| Descrizione fisica | xxiii, 473 p. : ill. ; 25 cm. |
| Disciplina | 621.382 |
| Soggetto topico |
Signal processing
Electronic noise Digital filters (Mathematics) |
| ISBN | 0471626929 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991001916119707536 |
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Vaseghi, Saeed V.
|
||
| Chichester ; New York : John Wiley, c2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Digital filters / / edited by Fausto Pedro García Márquez
| Digital filters / / edited by Fausto Pedro García Márquez |
| Pubbl/distr/stampa | [Place of publication not identified] : , : IntechOpen, , [2015] |
| Descrizione fisica | 1 online resource (302 pages) |
| Disciplina | 621.38043 |
| Soggetto topico | Digital filters (Mathematics) |
| ISBN | 953-51-5526-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910138447003321 |
| [Place of publication not identified] : , : IntechOpen, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital Signal Processing : Theory and Practice
| Digital Signal Processing : Theory and Practice |
| Autore | Bellanger Maurice |
| Edizione | [10th ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (397 pages) |
| Disciplina | 621.3822 |
| Altri autori (Persone) | EngelBenjamin A |
| Soggetto topico |
Signal processing
Digital filters (Mathematics) |
| ISBN |
9781394182695
1394182694 9781394182671 1394182678 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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. 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 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. 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. 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. |
| Record Nr. | UNINA-9910876973703321 |
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Bellanger Maurice
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi
| Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi |
| Autore | Bertein Jean-Claude |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | London, United Kingdom : , : ISTE |
| Descrizione fisica | 1 online resource (301 p.) |
| Disciplina |
621.382/2
621.3822 |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Signal processing - Mathematics
Digital filters (Mathematics) Stochastic processes |
| ISBN |
1-118-60035-5
1-299-18742-0 1-118-60048-7 1-118-60053-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Discrete Stochastic Processes and Optimal Filtering; Title Page; Copyright Page; Table of Contents; Preface; Introduction; Chapter 1. Random Vectors; 1.1. Definitions and general properties; 1.2. Spaces L1 (dP) and L2 (dP); 1.2.1. Definitions; 1.2.2. Properties; 1.3. Mathematical expectation and applications; 1.3.1. Definitions; 1.3.2. Characteristic functions of a random vector; 1.4. Second order random variables and vectors; 1.5. Linear independence of vectors of L2 (dP); 1.6. Conditional expectation (concerning random vectors with density function); 1.7. Exercises for Chapter 1
Chapter 2. Gaussian Vectors2.1. Some reminders regarding random Gaussian vectors; 2.2. Definition and characterization of Gaussian vectors; 2.3. Results relative to independence; 2.4. Affine transformation of a Gaussian vector; 2.5. The existence of Gaussian vectors; 2.6. Exercises for Chapter 2; Chapter 3. Introduction to Discrete Time Processes; 3.1. Definition; 3.2. WSS processes and spectral measure; 3.2.1. Spectral density; 3.3. Spectral representation of a WSS process; 3.3.1. Problem; 3.3.2. Results; 3.4. Introduction to digital filtering; 3.5. Important example: autoregressive process 3.6. Exercises for Chapter 3Chapter 4. Estimation; 4.1. Position of the problem; 4.2. Linear estimation; 4.3. Best estimate - conditional expectation; 4.4. Example: prediction of an autoregressive process AR (1); 4.5. Multivariate processes; 4.6. Exercises for Chapter 4; Chapter 5. The Wiener Filter; 5.1. Introduction; 5.1.1. Problem position; 5.2. Resolution and calculation of the FIR filter; 5.3. Evaluation of the least error; 5.4. Resolution and calculation of the IIR filter; 5.5. Evaluation of least mean square error; 5.6. Exercises for Chapter 5 Chapter 6. Adaptive Filtering: Algorithm of the Gradient and the LMS6.1. Introduction; 6.2. Position of problem; 6.3. Data representation; 6.4. Minimization of the cost function; 6.4.1. Calculation of the cost function; 6.5. Gradient algorithm; 6.6. Geometric interpretation; 6.7. Stability and convergence; 6.8. Estimation of gradient and LMS algorithm; 6.8.1. Convergence of the algorithm of the LMS; 6.9. Example of the application of the LMS algorithm; 6.10. Exercises for Chapter 6; Chapter 7. The Kalman Filter; 7.1. Position of problem; 7.2. Approach to estimation; 7.2.1. Scalar case 7.2.2. Multivariate case7.3. Kalman filtering; 7.3.1. State equation; 7.3.2. Observation equation; 7.3.3. Innovation process; 7.3.4. Covariance matrix of the innovation process; 7.3.5. Estimation; 7.3.6. Riccati's equation; 7.3.7. Algorithm and summary; 7.4. Exercises for Chapter 7; 7.5. Appendices; 7.6. Examples treated using Matlab software; Table of Symbols and Notations; Bibliography; Index |
| Record Nr. | UNINA-9910141486303321 |
|
Bertein Jean-Claude
|
||
| London, United Kingdom : , : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi
| Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi |
| Autore | Bertein Jean-Claude |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | London, United Kingdom : , : ISTE |
| Descrizione fisica | 1 online resource (301 p.) |
| Disciplina |
621.382/2
621.3822 |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Signal processing - Mathematics
Digital filters (Mathematics) Stochastic processes |
| ISBN |
1-118-60035-5
1-299-18742-0 1-118-60048-7 1-118-60053-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Discrete Stochastic Processes and Optimal Filtering; Title Page; Copyright Page; Table of Contents; Preface; Introduction; Chapter 1. Random Vectors; 1.1. Definitions and general properties; 1.2. Spaces L1 (dP) and L2 (dP); 1.2.1. Definitions; 1.2.2. Properties; 1.3. Mathematical expectation and applications; 1.3.1. Definitions; 1.3.2. Characteristic functions of a random vector; 1.4. Second order random variables and vectors; 1.5. Linear independence of vectors of L2 (dP); 1.6. Conditional expectation (concerning random vectors with density function); 1.7. Exercises for Chapter 1
Chapter 2. Gaussian Vectors2.1. Some reminders regarding random Gaussian vectors; 2.2. Definition and characterization of Gaussian vectors; 2.3. Results relative to independence; 2.4. Affine transformation of a Gaussian vector; 2.5. The existence of Gaussian vectors; 2.6. Exercises for Chapter 2; Chapter 3. Introduction to Discrete Time Processes; 3.1. Definition; 3.2. WSS processes and spectral measure; 3.2.1. Spectral density; 3.3. Spectral representation of a WSS process; 3.3.1. Problem; 3.3.2. Results; 3.4. Introduction to digital filtering; 3.5. Important example: autoregressive process 3.6. Exercises for Chapter 3Chapter 4. Estimation; 4.1. Position of the problem; 4.2. Linear estimation; 4.3. Best estimate - conditional expectation; 4.4. Example: prediction of an autoregressive process AR (1); 4.5. Multivariate processes; 4.6. Exercises for Chapter 4; Chapter 5. The Wiener Filter; 5.1. Introduction; 5.1.1. Problem position; 5.2. Resolution and calculation of the FIR filter; 5.3. Evaluation of the least error; 5.4. Resolution and calculation of the IIR filter; 5.5. Evaluation of least mean square error; 5.6. Exercises for Chapter 5 Chapter 6. Adaptive Filtering: Algorithm of the Gradient and the LMS6.1. Introduction; 6.2. Position of problem; 6.3. Data representation; 6.4. Minimization of the cost function; 6.4.1. Calculation of the cost function; 6.5. Gradient algorithm; 6.6. Geometric interpretation; 6.7. Stability and convergence; 6.8. Estimation of gradient and LMS algorithm; 6.8.1. Convergence of the algorithm of the LMS; 6.9. Example of the application of the LMS algorithm; 6.10. Exercises for Chapter 6; Chapter 7. The Kalman Filter; 7.1. Position of problem; 7.2. Approach to estimation; 7.2.1. Scalar case 7.2.2. Multivariate case7.3. Kalman filtering; 7.3.1. State equation; 7.3.2. Observation equation; 7.3.3. Innovation process; 7.3.4. Covariance matrix of the innovation process; 7.3.5. Estimation; 7.3.6. Riccati's equation; 7.3.7. Algorithm and summary; 7.4. Exercises for Chapter 7; 7.5. Appendices; 7.6. Examples treated using Matlab software; Table of Symbols and Notations; Bibliography; Index |
| Record Nr. | UNINA-9910830177303321 |
|
Bertein Jean-Claude
|
||
| London, United Kingdom : , : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi
| Discrete stochastic processes and optimal filtering / / Jean-Claude Bertein, Roger Ceschi |
| Autore | Bertein Jean-Claude |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Newport Beach, California : , : ISTE, , 2007 |
| Descrizione fisica | 1 online resource (303 p.) |
| Disciplina | 621.382/2 |
| Collana | ISTE |
| Soggetto topico |
Signal processing - Mathematics
Digital filters (Mathematics) Stochastic processes |
| ISBN |
1-118-61549-2
1-280-84785-9 9786610847853 0-470-39493-5 0-470-61229-0 1-84704-624-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Discrete Stochastic Processes and Optimal Filtering; Table of Contents; Preface; Introduction; Chapter 1. Random Vectors; 1.1. Definitions and general properties; 1.2. Spaces L1(dP) and L2(dP); 1.2.1. Definitions; 1.2.2. Properties; 1.3. Mathematical expectation and applications; 1.3.1. Definitions; 1.3.2. Characteristic functions of a random vector; 1.4. Second order random variables and vectors; 1.5. Linear independence of vectors of L2(dP); 1.6. Conditional expectation (concerning random vectors with density function); 1.7. Exercises for Chapter 1; Chapter 2. Gaussian Vectors
2.1. Some reminders regarding random Gaussian vectors2.2. Definition and characterization of Gaussian vectors; 2.3. Results relative to independence; 2.4. Affine transformation of a Gaussian vector; 2.5. The existence of Gaussian vectors; 2.6. Exercises for Chapter 2; Chapter 3. Introduction to Discrete Time Processes; 3.1. Definition; 3.2. WSS processes and spectral measure; 3.2.1. Spectral density; 3.3. Spectral representation of a WSS process; 3.3.1. Problem; 3.3.2. Results; 3.3.2.1. Process with orthogonal increments and associated measurements; 3.3.2.2. Wiener stochastic integral 3.3.2.3. Spectral representation3.4. Introduction to digital filtering; 3.5. Important example: autoregressive process; 3.6. Exercises for Chapter 3; Chapter 4. Estimation; 4.1. Position of the problem; 4.2. Linear estimation; 4.3. Best estimate - conditional expectation; 4.4. Example: prediction of an autoregressive process AR (1); 4.5. Multivariate processes; 4.6. Exercises for Chapter 4; Chapter 5. The Wiener Filter; 5.1. Introduction; 5.1.1. Problem position; 5.2. Resolution and calculation of the FIR filter; 5.3. Evaluation of the least error 5.4. Resolution and calculation of the IIR filter5.5. Evaluation of least mean square error; 5.6. Exercises for Chapter 5; Chapter 6. Adaptive Filtering: Algorithm of the Gradient and the LMS; 6.1. Introduction; 6.2. Position of problem; 6.3. Data representation; 6.4. Minimization of the cost function; 6.4.1. Calculation of the cost function; 6.5. Gradient algorithm; 6.6. Geometric interpretation; 6.7. Stability and convergence; 6.8. Estimation of gradient and LMS algorithm; 6.8.1. Convergence of the algorithm of the LMS; 6.9. Example of the application of the LMS algorithm 6.10. Exercises for Chapter 6Chapter 7. The Kalman Filter; 7.1. Position of problem; 7.2. Approach to estimation; 7.2.1. Scalar case; 7.2.2. Multivariate case; 7.3. Kalman filtering; 7.3.1. State equation; 7.3.2. Observation equation; 7.3.3. Innovation process; 7.3.4. Covariance matrix of the innovation process; 7.3.5. Estimation; 7.3.6. Riccati's equation; 7.3.7. Algorithm and summary; 7.4. Exercises for Chapter 7; Table of Symbols and Notations; Bibliography; Index |
| Record Nr. | UNISA-996213244803316 |
|
Bertein Jean-Claude
|
||
| Newport Beach, California : , : ISTE, , 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
