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 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
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 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
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-9910840541303321 |
Vaseghi Saeed V
![]() |
||
Chichester, U.K., : J. Wiley & Sons, 2008 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
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 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
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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 |
Vaseghi, Saeed V.
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||
Chichester ; New York : John Wiley, c2000 | ||
![]() | ||
Lo trovi qui: Univ. del Salento | ||
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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] | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
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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 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
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 | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
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
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Newport Beach, California : , : ISTE, , 2007 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Geophysical signal processing / E. Robinson, L. Peardon, T. Durrani |
Autore | Durrani, T. |
Pubbl/distr/stampa | Englewood Cliffs, NJ : Prentice-Hall, 1986 |
Descrizione fisica | xi, 481 p. : ill. ; 24 cm. |
Altri autori (Persone) |
Peardon, L.
Robinson, Enders A. |
Soggetto topico |
Digital filters (Mathematics)
Seismic prospecting Seismology |
Classificazione |
52.9.3
510.65 551'.028 TN269 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNISALENTO-991000977499707536 |
Durrani, T.
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Englewood Cliffs, NJ : Prentice-Hall, 1986 | ||
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Lo trovi qui: Univ. del Salento | ||
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