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Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim
| Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Newport Beach, CA, : ISTE Ltd., c2006 |
| Descrizione fisica | 1 online resource (387 p.) |
| Disciplina |
600
621.3822 |
| Altri autori (Persone) | NajimMohamed |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques Image processing - Digital techniques |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-84773-5
9786610847730 0-470-61206-1 0-470-39469-2 1-84704-595-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Digital Filters Design for Signal and Image Processing; Table of Contents; Introduction; Chapter 1. Introduction to Signals and Systems; 1.1. Introduction; 1.2. Signals: categories, representations and characterizations; 1.2.1. Definition of continuous-time and discrete-time signals; 1.2.2. Deterministic and random signals; 1.2.3. Periodic signals; 1.2.4. Mean, energy and power; 1.2.5. Autocorrelation function; 1.3. Systems; 1.4. Properties of discrete-time systems; 1.4.1. Invariant linear systems; 1.4.2. Impulse responses and convolution products; 1.4.3. Causality
1.4.4. Interconnections of discrete-time systems1.5. Bibliography; Chapter 2. Discrete System Analysis; 2.1. Introduction; 2.2. The z-transform; 2.2.1. Representations and summaries; 2.2.2. Properties of the z-transform; 2.2.2.1. Linearity; 2.2.2.2. Advanced and delayed operators; 2.2.2.3. Convolution; 2.2.2.4. Changing the z-scale; 2.2.2.5. Contrasted signal development; 2.2.2.6. Derivation of the z-transform; 2.2.2.7. The sum theorem; 2.2.2.8. The final-value theorem; 2.2.2.9. Complex conjugation; 2.2.2.10. Parseval's theorem; 2.2.3. Table of standard transform; 2.3. The inverse z-transform 2.3.1. Introduction2.3.2. Methods of determining inverse z-transforms; 2.3.2.1. Cauchy's theorem: a case of complex variables; 2.3.2.2. Development in rational fractions; 2.3.2.3. Development by algebraic division of polynomials; 2.4. Transfer functions and difference equations; 2.4.1. The transfer function of a continuous system; 2.4.2. Transfer functions of discrete systems; 2.5. Z-transforms of the autocorrelation and intercorrelation functions; 2.6. Stability; 2.6.1. Bounded input, bounded output (BIBO) stability; 2.6.2. Regions of convergence; 2.6.2.1. Routh's criterion 2.6.2.2. Jury's criterionChapter 3. Frequential Characterization of Signals and Filters; 3.1. Introduction; 3.2. The Fourier transform of continuous signals; 3.2.1. Summary of the Fourier series decomposition of continuous signals; 3.2.1.1. Decomposition of finite energy signals using an orthonormal base; 3.2.1.2. Fourier series development of periodic signals; 3.2.2. Fourier transforms and continuous signals; 3.2.2.1. Representations; 3.2.2.2. Properties; 3.2.2.3. The duality theorem; 3.2.2.4. The quick method of calculating the Fourier transform; 3.2.2.5. The Wiener-Khintchine theorem 3.2.2.6. The Fourier transform of a Dirac comb3.2.2.7. Another method of calculating the Fourier series development of a periodic signal; 3.2.2.8. The Fourier series development and the Fourier transform; 3.2.2.9. Applying the Fourier transform: Shannon's sampling theorem; 3.3. The discrete Fourier transform (DFT); 3.3.1. Expressing the Fourier transform of a discrete sequence; 3.3.2. Relations between the Laplace and Fourier z-transforms; 3.3.3. The inverse Fourier transform; 3.3.4. The discrete Fourier transform; 3.4. The fast Fourier transform (FFT) 3.5. The fast Fourier transform for a time/frequency/energy representation of a non-stationary signal |
| Record Nr. | UNINA-9910143317203321 |
Najim Mohamed
|
||
| Newport Beach, CA, : ISTE Ltd., c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim
| Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Newport Beach, CA, : ISTE Ltd., c2006 |
| Descrizione fisica | 1 online resource (387 p.) |
| Disciplina |
600
621.3822 |
| Altri autori (Persone) | NajimMohamed |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques Image processing - Digital techniques |
| ISBN |
1-280-84773-5
9786610847730 0-470-61206-1 0-470-39469-2 1-84704-595-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Digital Filters Design for Signal and Image Processing; Table of Contents; Introduction; Chapter 1. Introduction to Signals and Systems; 1.1. Introduction; 1.2. Signals: categories, representations and characterizations; 1.2.1. Definition of continuous-time and discrete-time signals; 1.2.2. Deterministic and random signals; 1.2.3. Periodic signals; 1.2.4. Mean, energy and power; 1.2.5. Autocorrelation function; 1.3. Systems; 1.4. Properties of discrete-time systems; 1.4.1. Invariant linear systems; 1.4.2. Impulse responses and convolution products; 1.4.3. Causality
1.4.4. Interconnections of discrete-time systems1.5. Bibliography; Chapter 2. Discrete System Analysis; 2.1. Introduction; 2.2. The z-transform; 2.2.1. Representations and summaries; 2.2.2. Properties of the z-transform; 2.2.2.1. Linearity; 2.2.2.2. Advanced and delayed operators; 2.2.2.3. Convolution; 2.2.2.4. Changing the z-scale; 2.2.2.5. Contrasted signal development; 2.2.2.6. Derivation of the z-transform; 2.2.2.7. The sum theorem; 2.2.2.8. The final-value theorem; 2.2.2.9. Complex conjugation; 2.2.2.10. Parseval's theorem; 2.2.3. Table of standard transform; 2.3. The inverse z-transform 2.3.1. Introduction2.3.2. Methods of determining inverse z-transforms; 2.3.2.1. Cauchy's theorem: a case of complex variables; 2.3.2.2. Development in rational fractions; 2.3.2.3. Development by algebraic division of polynomials; 2.4. Transfer functions and difference equations; 2.4.1. The transfer function of a continuous system; 2.4.2. Transfer functions of discrete systems; 2.5. Z-transforms of the autocorrelation and intercorrelation functions; 2.6. Stability; 2.6.1. Bounded input, bounded output (BIBO) stability; 2.6.2. Regions of convergence; 2.6.2.1. Routh's criterion 2.6.2.2. Jury's criterionChapter 3. Frequential Characterization of Signals and Filters; 3.1. Introduction; 3.2. The Fourier transform of continuous signals; 3.2.1. Summary of the Fourier series decomposition of continuous signals; 3.2.1.1. Decomposition of finite energy signals using an orthonormal base; 3.2.1.2. Fourier series development of periodic signals; 3.2.2. Fourier transforms and continuous signals; 3.2.2.1. Representations; 3.2.2.2. Properties; 3.2.2.3. The duality theorem; 3.2.2.4. The quick method of calculating the Fourier transform; 3.2.2.5. The Wiener-Khintchine theorem 3.2.2.6. The Fourier transform of a Dirac comb3.2.2.7. Another method of calculating the Fourier series development of a periodic signal; 3.2.2.8. The Fourier series development and the Fourier transform; 3.2.2.9. Applying the Fourier transform: Shannon's sampling theorem; 3.3. The discrete Fourier transform (DFT); 3.3.1. Expressing the Fourier transform of a discrete sequence; 3.3.2. Relations between the Laplace and Fourier z-transforms; 3.3.3. The inverse Fourier transform; 3.3.4. The discrete Fourier transform; 3.4. The fast Fourier transform (FFT) 3.5. The fast Fourier transform for a time/frequency/energy representation of a non-stationary signal |
| Record Nr. | UNISA-996216944903316 |
|
Najim Mohamed
|
||
| Newport Beach, CA, : ISTE Ltd., c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim
| Digital filters design for signal and image processing [[electronic resource] /] / edited by Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Newport Beach, CA, : ISTE Ltd., c2006 |
| Descrizione fisica | 1 online resource (387 p.) |
| Disciplina |
600
621.3822 |
| Altri autori (Persone) | NajimMohamed |
| Collana | Digital signal and image processing series |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques Image processing - Digital techniques |
| ISBN |
1-280-84773-5
9786610847730 0-470-61206-1 0-470-39469-2 1-84704-595-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Digital Filters Design for Signal and Image Processing; Table of Contents; Introduction; Chapter 1. Introduction to Signals and Systems; 1.1. Introduction; 1.2. Signals: categories, representations and characterizations; 1.2.1. Definition of continuous-time and discrete-time signals; 1.2.2. Deterministic and random signals; 1.2.3. Periodic signals; 1.2.4. Mean, energy and power; 1.2.5. Autocorrelation function; 1.3. Systems; 1.4. Properties of discrete-time systems; 1.4.1. Invariant linear systems; 1.4.2. Impulse responses and convolution products; 1.4.3. Causality
1.4.4. Interconnections of discrete-time systems1.5. Bibliography; Chapter 2. Discrete System Analysis; 2.1. Introduction; 2.2. The z-transform; 2.2.1. Representations and summaries; 2.2.2. Properties of the z-transform; 2.2.2.1. Linearity; 2.2.2.2. Advanced and delayed operators; 2.2.2.3. Convolution; 2.2.2.4. Changing the z-scale; 2.2.2.5. Contrasted signal development; 2.2.2.6. Derivation of the z-transform; 2.2.2.7. The sum theorem; 2.2.2.8. The final-value theorem; 2.2.2.9. Complex conjugation; 2.2.2.10. Parseval's theorem; 2.2.3. Table of standard transform; 2.3. The inverse z-transform 2.3.1. Introduction2.3.2. Methods of determining inverse z-transforms; 2.3.2.1. Cauchy's theorem: a case of complex variables; 2.3.2.2. Development in rational fractions; 2.3.2.3. Development by algebraic division of polynomials; 2.4. Transfer functions and difference equations; 2.4.1. The transfer function of a continuous system; 2.4.2. Transfer functions of discrete systems; 2.5. Z-transforms of the autocorrelation and intercorrelation functions; 2.6. Stability; 2.6.1. Bounded input, bounded output (BIBO) stability; 2.6.2. Regions of convergence; 2.6.2.1. Routh's criterion 2.6.2.2. Jury's criterionChapter 3. Frequential Characterization of Signals and Filters; 3.1. Introduction; 3.2. The Fourier transform of continuous signals; 3.2.1. Summary of the Fourier series decomposition of continuous signals; 3.2.1.1. Decomposition of finite energy signals using an orthonormal base; 3.2.1.2. Fourier series development of periodic signals; 3.2.2. Fourier transforms and continuous signals; 3.2.2.1. Representations; 3.2.2.2. Properties; 3.2.2.3. The duality theorem; 3.2.2.4. The quick method of calculating the Fourier transform; 3.2.2.5. The Wiener-Khintchine theorem 3.2.2.6. The Fourier transform of a Dirac comb3.2.2.7. Another method of calculating the Fourier series development of a periodic signal; 3.2.2.8. The Fourier series development and the Fourier transform; 3.2.2.9. Applying the Fourier transform: Shannon's sampling theorem; 3.3. The discrete Fourier transform (DFT); 3.3.1. Expressing the Fourier transform of a discrete sequence; 3.3.2. Relations between the Laplace and Fourier z-transforms; 3.3.3. The inverse Fourier transform; 3.3.4. The discrete Fourier transform; 3.4. The fast Fourier transform (FFT) 3.5. The fast Fourier transform for a time/frequency/energy representation of a non-stationary signal |
| Record Nr. | UNINA-9910831191503321 |
|
Najim Mohamed
|
||
| Newport Beach, CA, : ISTE Ltd., c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, estimation and optimal filtration in signal processing [[electronic resource] /] / Mohamed Najim
| Modeling, estimation and optimal filtration in signal processing [[electronic resource] /] / Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina |
621.382/2
621.3822 |
| Collana | ISTE |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-16500-3
9786612165009 0-470-61110-3 0-470-39368-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Modeling, Estimation and Optimal Filtering in Signal Processing; Table of Contents; Preface; Chapter 1. Parametric Models; 1.1. Introduction; 1.2. Discrete linear models; 1.2.1. The moving average (MA) model; 1.2.2. The autoregressive (AR) model; 1.3. Observations on stability, stationarity and invertibility; 1.3.1. AR model case; 1.3.2. ARMA model case; 1.4. The AR model or the ARMA model?; 1.5. Sinusoidal models; 1.5.1. The relevance of the sinusoidal model; 1.5.2. Sinusoidal models; 1.6. State space representations; 1.6.1. Definitions
1.6.2. State space representations based on differential equation representation1.6.3. Resolution of the state equations; 1.6.4. State equations for a discrete-time system; 1.6.5. Some properties of systems described in the state space; 1.6.5.1. Introduction; 1.6.5.2. Observability; 1.6.5.3. Controllability; 1.6.5.4. Plurality of the state space representation of the system; 1.6.6. Case 1: state space representation of AR processes; 1.6.7. Case 2: state space representation of MA processes; 1.6.8. Case 3: state space representation of ARMA processes 1.6.9. Case 4: state space representation of a noisy process1.6.9.1. An AR process disturbed by a white noise; 1.6.9.2. AR process disturbed by colored noise itself modeled by another AR process; 1.6.9.3. AR process disturbed by colored noise itself modeled by a MA process; 1.7. Conclusion; 1.8. References; Chapter 2. Least Squares Estimation of Parameters of Linear Models; 2.1. Introduction; 2.2. Least squares estimation of AR parameters; 2.2.1. Determination or estimation of parameters?; 2.2.2. Recursive estimation of parameters; 2.2.3. Implementation of the least squares algorithm 2.2.4. The least squares method with weighting factor2.2.5. A recursive weighted least squares estimator; 2.2.6. Observations on some variants of the least squares method; 2.2.6.1. The autocorrelation method; 2.2.6.2. Levinson's algorithm; 2.2.6.3. The Durbin-Levinson algorithm; 2.2.6.4. Lattice filters; 2.2.6.5. The covariance method; 2.2.6.6. Relation between the covariance method and the least squares method; 2.2.6.7. Effect of a white additive noise on the estimation of AR parameters; 2.2.6.8. A method for alleviating the bias on the estimation of the AR parameters 2.2.7. Generalized least squares method2.2.8. The extended least squares method; 2.3. Selecting the order of the models; 2.4. References; Chapter 3. Matched and Wiener Filters; 3.1. Introduction; 3.2. Matched filter; 3.2.1. Introduction; 3.2.2. Matched filter for the case of white noise; 3.2.3. Matched filter for the case of colored noise; 3.2.3.1. Formulation of problem; 3.2.3.2. Physically unrealizable matched filter; 3.2.3.3. A matched filter solution using whitening techniques; 3.3. The Wiener filter; 3.3.1. Introduction; 3.3.2. Formulation of problem; 3.3.3. The Wiener-Hopf equation 3.3.4. Error calculation in a continuous physically non-realizable Wiener filter |
| Record Nr. | UNINA-9910139493903321 |
|
Najim Mohamed
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, estimation and optimal filtration in signal processing [[electronic resource] /] / Mohamed Najim
| Modeling, estimation and optimal filtration in signal processing [[electronic resource] /] / Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina |
621.382/2
621.3822 |
| Collana | ISTE |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques |
| ISBN |
1-282-16500-3
9786612165009 0-470-61110-3 0-470-39368-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Modeling, Estimation and Optimal Filtering in Signal Processing; Table of Contents; Preface; Chapter 1. Parametric Models; 1.1. Introduction; 1.2. Discrete linear models; 1.2.1. The moving average (MA) model; 1.2.2. The autoregressive (AR) model; 1.3. Observations on stability, stationarity and invertibility; 1.3.1. AR model case; 1.3.2. ARMA model case; 1.4. The AR model or the ARMA model?; 1.5. Sinusoidal models; 1.5.1. The relevance of the sinusoidal model; 1.5.2. Sinusoidal models; 1.6. State space representations; 1.6.1. Definitions
1.6.2. State space representations based on differential equation representation1.6.3. Resolution of the state equations; 1.6.4. State equations for a discrete-time system; 1.6.5. Some properties of systems described in the state space; 1.6.5.1. Introduction; 1.6.5.2. Observability; 1.6.5.3. Controllability; 1.6.5.4. Plurality of the state space representation of the system; 1.6.6. Case 1: state space representation of AR processes; 1.6.7. Case 2: state space representation of MA processes; 1.6.8. Case 3: state space representation of ARMA processes 1.6.9. Case 4: state space representation of a noisy process1.6.9.1. An AR process disturbed by a white noise; 1.6.9.2. AR process disturbed by colored noise itself modeled by another AR process; 1.6.9.3. AR process disturbed by colored noise itself modeled by a MA process; 1.7. Conclusion; 1.8. References; Chapter 2. Least Squares Estimation of Parameters of Linear Models; 2.1. Introduction; 2.2. Least squares estimation of AR parameters; 2.2.1. Determination or estimation of parameters?; 2.2.2. Recursive estimation of parameters; 2.2.3. Implementation of the least squares algorithm 2.2.4. The least squares method with weighting factor2.2.5. A recursive weighted least squares estimator; 2.2.6. Observations on some variants of the least squares method; 2.2.6.1. The autocorrelation method; 2.2.6.2. Levinson's algorithm; 2.2.6.3. The Durbin-Levinson algorithm; 2.2.6.4. Lattice filters; 2.2.6.5. The covariance method; 2.2.6.6. Relation between the covariance method and the least squares method; 2.2.6.7. Effect of a white additive noise on the estimation of AR parameters; 2.2.6.8. A method for alleviating the bias on the estimation of the AR parameters 2.2.7. Generalized least squares method2.2.8. The extended least squares method; 2.3. Selecting the order of the models; 2.4. References; Chapter 3. Matched and Wiener Filters; 3.1. Introduction; 3.2. Matched filter; 3.2.1. Introduction; 3.2.2. Matched filter for the case of white noise; 3.2.3. Matched filter for the case of colored noise; 3.2.3.1. Formulation of problem; 3.2.3.2. Physically unrealizable matched filter; 3.2.3.3. A matched filter solution using whitening techniques; 3.3. The Wiener filter; 3.3.1. Introduction; 3.3.2. Formulation of problem; 3.3.3. The Wiener-Hopf equation 3.3.4. Error calculation in a continuous physically non-realizable Wiener filter |
| Record Nr. | UNINA-9910830372403321 |
|
Najim Mohamed
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, estimation and optimal filtration in signal processing / / Mohamed Najim
| Modeling, estimation and optimal filtration in signal processing / / Mohamed Najim |
| Autore | Najim Mohamed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina | 621.382/2 |
| Collana | ISTE |
| Soggetto topico |
Electric filters, Digital
Signal processing - Digital techniques |
| ISBN |
1-282-16500-3
9786612165009 0-470-61110-3 0-470-39368-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Modeling, Estimation and Optimal Filtering in Signal Processing; Table of Contents; Preface; Chapter 1. Parametric Models; 1.1. Introduction; 1.2. Discrete linear models; 1.2.1. The moving average (MA) model; 1.2.2. The autoregressive (AR) model; 1.3. Observations on stability, stationarity and invertibility; 1.3.1. AR model case; 1.3.2. ARMA model case; 1.4. The AR model or the ARMA model?; 1.5. Sinusoidal models; 1.5.1. The relevance of the sinusoidal model; 1.5.2. Sinusoidal models; 1.6. State space representations; 1.6.1. Definitions
1.6.2. State space representations based on differential equation representation1.6.3. Resolution of the state equations; 1.6.4. State equations for a discrete-time system; 1.6.5. Some properties of systems described in the state space; 1.6.5.1. Introduction; 1.6.5.2. Observability; 1.6.5.3. Controllability; 1.6.5.4. Plurality of the state space representation of the system; 1.6.6. Case 1: state space representation of AR processes; 1.6.7. Case 2: state space representation of MA processes; 1.6.8. Case 3: state space representation of ARMA processes 1.6.9. Case 4: state space representation of a noisy process1.6.9.1. An AR process disturbed by a white noise; 1.6.9.2. AR process disturbed by colored noise itself modeled by another AR process; 1.6.9.3. AR process disturbed by colored noise itself modeled by a MA process; 1.7. Conclusion; 1.8. References; Chapter 2. Least Squares Estimation of Parameters of Linear Models; 2.1. Introduction; 2.2. Least squares estimation of AR parameters; 2.2.1. Determination or estimation of parameters?; 2.2.2. Recursive estimation of parameters; 2.2.3. Implementation of the least squares algorithm 2.2.4. The least squares method with weighting factor2.2.5. A recursive weighted least squares estimator; 2.2.6. Observations on some variants of the least squares method; 2.2.6.1. The autocorrelation method; 2.2.6.2. Levinson's algorithm; 2.2.6.3. The Durbin-Levinson algorithm; 2.2.6.4. Lattice filters; 2.2.6.5. The covariance method; 2.2.6.6. Relation between the covariance method and the least squares method; 2.2.6.7. Effect of a white additive noise on the estimation of AR parameters; 2.2.6.8. A method for alleviating the bias on the estimation of the AR parameters 2.2.7. Generalized least squares method2.2.8. The extended least squares method; 2.3. Selecting the order of the models; 2.4. References; Chapter 3. Matched and Wiener Filters; 3.1. Introduction; 3.2. Matched filter; 3.2.1. Introduction; 3.2.2. Matched filter for the case of white noise; 3.2.3. Matched filter for the case of colored noise; 3.2.3.1. Formulation of problem; 3.2.3.2. Physically unrealizable matched filter; 3.2.3.3. A matched filter solution using whitening techniques; 3.3. The Wiener filter; 3.3.1. Introduction; 3.3.2. Formulation of problem; 3.3.3. The Wiener-Hopf equation 3.3.4. Error calculation in a continuous physically non-realizable Wiener filter |
| Record Nr. | UNINA-9910876934703321 |
|
Najim Mohamed
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||