top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Digital spectral analysis [[electronic resource] ] : parametric, non-parametric and advanced methods / / edited by Francis Castanié
Digital spectral analysis [[electronic resource] ] : parametric, non-parametric and advanced methods / / edited by Francis Castanié
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (401 p.)
Disciplina 621.382/2
Altri autori (Persone) CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Spectral theory (Mathematics)
Signal processing - Digital techniques - Mathematics
Spectrum analysis
ISBN 1-118-60187-4
1-118-60183-1
1-118-60176-9
1-299-18770-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. Tools and spectral analysis -- pt. 2. Non-parametric methods -- pt. 3. Parametric methods -- pt. 4. Advanced concepts.
Record Nr. UNINA-9910138864503321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Digital spectral analysis : parametric, non-parametric and advanced methods / / edited by Francis Castanié
Digital spectral analysis : parametric, non-parametric and advanced methods / / edited by Francis Castanié
Edizione [1st ed.]
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (401 p.)
Disciplina 621.382/2
Altri autori (Persone) CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Spectral theory (Mathematics)
Signal processing - Digital techniques - Mathematics
Spectrum analysis
ISBN 1-118-60187-4
1-118-60183-1
1-118-60176-9
1-299-18770-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. 1. Tools and spectral analysis -- pt. 2. Non-parametric methods -- pt. 3. Parametric methods -- pt. 4. Advanced concepts.
Record Nr. UNINA-9910809234003321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Set-theoretic fault-tolerant control in multisensor systems [[electronic resource] /] / Florin Stoican, Sorin Olaru ; series editor, Francis Castanié
Set-theoretic fault-tolerant control in multisensor systems [[electronic resource] /] / Florin Stoican, Sorin Olaru ; series editor, Francis Castanié
Autore Stoican Florin
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (168 p.)
Altri autori (Persone) OlaruSorin
CastaniéFrancis
Collana Automation-control and industrial engineering series
Soggetto topico Sensor networks
Multisensor data fusion
Fault tolerance (Engineering)
Fault-tolerant computing
ISBN 1-118-64942-7
1-118-64944-3
1-118-64943-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Contents; Preface; Introduction; Chapter 1. State of the Art in Fault-tolerantControl; 1.1. Fault detection and isolation; 1.2. Control reconfiguration; 1.3. Sets in control; 1.3.1. Set generalities; 1.3.2. Set operations; 1.3.3. Dynamic systems and sets; 1.3.4. Other set-theoretic issues; 1.4. Existing set-theoretic methods in FTC; Chapter 2. Fault Detection and Isolation inMultisensor Systems; 2.1. Problem statement; 2.1.1. Multisensor scheme; 2.1.2. Fault scenarios; 2.2. Fault detection and isolation; 2.2.1. Partition of the sensor indices; 2.2.2. Residual sets for FDI
2.3. Recovery mechanism2.3.1. Necessary and sufficient conditions; 2.3.2. Construction of set SR; 2.3.3. Inclusion time computation; Chapter 3. Residual Generation and ReferenceGovernor Design; 3.1. Residual signals; 3.1.1. Measurement equations residual; 3.1.2. Observer-based residual; 3.1.3. Receding observation window-based residual; 3.2. Reference governor synthesis; Chapter 4. Reconfiguration of the ControlMechanism for Fault-tolerant Control; 4.1. Active FTC with fix gain feedback; 4.1.1. Fix gain feedback synthesis; 4.1.2. Reference governor synthesis; 4.2. Active FTC with MPC control
4.2.1. A classic MPC design4.2.2. Toward a cooperative view of FTC-MPC; 4.3. Passive FTC control; 4.3.1. Quadratic cost function; 4.3.2. Penalty function using the gauge function of the healthy invariant set; Chapter 5. Related Problems and Applications; 5.1. Set theoretic issues; 5.1.1. Over-approximation methods; 5.1.2. Convergence time issues; 5.1.3. Cyclic invariance for dwell-time systems; 5.2. Illustrative examples; 5.2.1. Fault detection and isolation; 5.2.2. Recovery mechanism; 5.2.3. Feasible reference generation; 5.2.4. Fault-tolerant control results; Conclusions; Bibliography
Index
Record Nr. UNINA-9910139245803321
Stoican Florin  
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Set-theoretic fault-tolerant control in multisensor systems / / Florin Stoican, Sorin Olaru ; series editor, Francis Castanié
Set-theoretic fault-tolerant control in multisensor systems / / Florin Stoican, Sorin Olaru ; series editor, Francis Castanié
Autore Stoican Florin
Edizione [1st ed.]
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (168 p.)
Altri autori (Persone) OlaruSorin
CastaniéFrancis
Collana Automation-control and industrial engineering series
Soggetto topico Sensor networks
Multisensor data fusion
Fault tolerance (Engineering)
Fault-tolerant computing
ISBN 1-118-64942-7
1-118-64944-3
1-118-64943-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Contents; Preface; Introduction; Chapter 1. State of the Art in Fault-tolerantControl; 1.1. Fault detection and isolation; 1.2. Control reconfiguration; 1.3. Sets in control; 1.3.1. Set generalities; 1.3.2. Set operations; 1.3.3. Dynamic systems and sets; 1.3.4. Other set-theoretic issues; 1.4. Existing set-theoretic methods in FTC; Chapter 2. Fault Detection and Isolation inMultisensor Systems; 2.1. Problem statement; 2.1.1. Multisensor scheme; 2.1.2. Fault scenarios; 2.2. Fault detection and isolation; 2.2.1. Partition of the sensor indices; 2.2.2. Residual sets for FDI
2.3. Recovery mechanism2.3.1. Necessary and sufficient conditions; 2.3.2. Construction of set SR; 2.3.3. Inclusion time computation; Chapter 3. Residual Generation and ReferenceGovernor Design; 3.1. Residual signals; 3.1.1. Measurement equations residual; 3.1.2. Observer-based residual; 3.1.3. Receding observation window-based residual; 3.2. Reference governor synthesis; Chapter 4. Reconfiguration of the ControlMechanism for Fault-tolerant Control; 4.1. Active FTC with fix gain feedback; 4.1.1. Fix gain feedback synthesis; 4.1.2. Reference governor synthesis; 4.2. Active FTC with MPC control
4.2.1. A classic MPC design4.2.2. Toward a cooperative view of FTC-MPC; 4.3. Passive FTC control; 4.3.1. Quadratic cost function; 4.3.2. Penalty function using the gauge function of the healthy invariant set; Chapter 5. Related Problems and Applications; 5.1. Set theoretic issues; 5.1.1. Over-approximation methods; 5.1.2. Convergence time issues; 5.1.3. Cyclic invariance for dwell-time systems; 5.2. Illustrative examples; 5.2.1. Fault detection and isolation; 5.2.2. Recovery mechanism; 5.2.3. Feasible reference generation; 5.2.4. Fault-tolerant control results; Conclusions; Bibliography
Index
Record Nr. UNINA-9910819005803321
Stoican Florin  
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Signal and image multiresolution analysis [[electronic resource] /] / edited by Abdeljalil Ouahabi ; series editor, Francis Castanié
Signal and image multiresolution analysis [[electronic resource] /] / edited by Abdeljalil Ouahabi ; series editor, Francis Castanié
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (308 p.)
Disciplina 621.3822
Altri autori (Persone) OuahabiAbdeldjalil
CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Signal processing
Image processing
ISBN 1-118-56876-1
1-118-56859-1
1-118-56866-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright Page; Table of Contents; Introduction; Chapter 1. Introduction to Multiresolution Analysis; 1.1. Introduction; 1.2. Wavelet transforms: an introductory review; 1.2.1. Brief history; 1.2.2. Continuous wavelet transforms; 1.2.2.1. Wavelet transform modulus maxima; 1.2.2.2. Reconstruction; 1.2.3. Discrete wavelet transforms; 1.3. Multiresolution; 1.3.1. Multiresolution analysis and wavelet bases; 1.3.1.1. Approximation spaces; 1.3.1.2. Detail spaces; 1.3.2. Multiresolution analysis: points to remember; 1.3.3. Decomposition and reconstruction
1.3.3.1. Calculation of coefficients1.3.3.2. Implementation of MRA: Mallat algorithm; 1.3.3.3. Extension to images; 1.3.4. Wavelet packets; 1.3.5. Multiresolution analysis summarized; 1.4. Which wavelets to choose?; 1.4.1. Number of vanishing moments, regularity, support (compactness), symmetry, etc.; 1.4.2. Well-known wavelets, scaling functions and associated filters; 1.4.2.1. Haar wavelet; 1.4.2.2. Daubechies wavelets; 1.4.2.3. Symlets; 1.4.2.4. Coiflets; 1.4.2.5. Meyer wavelets; 1.4.2.6. Polynomial spline wavelets; 1.5. Multiresolution analysis and biorthogonal wavelet bases
1.5.1. Why biorthogonal bases?1.5.2. Multiresolution context; 1.5.3. Example of biorthogonal wavelets, scaling functions and associated filters; 1.5.4. The concept of wavelet lifting; 1.5.4.1. The notion of lifting; 1.5.4.2. Significance of structure lifting; 1.6. Wavelet choice at a glance; 1.6.1. Regularity; 1.6.2. Vanishing moments; 1.6.3. Other criteria; 1.6.4. Conclusion; 1.7. Worked examples; 1.7.1. Examples of multiresolution analysis; 1.7.2. Compression; 1.7.3. Denoising (reduction of noise); 1.8. Some applications; 1.8.1. Discovery and contributions of wavelets
1.8.2. Biomedical engineering1.8.2.1. ECG, EEG and BCI; 1.8.2.2. Medical imaging; 1.8.3. Telecommunications; 1.8.3.1. Adaptive compression for sensor networks; 1.8.3.2. Masking image encoding and transmission errors; 1.8.3.3. Suppression of correlated noise; 1.8.4. "Compressive sensing", ICA, PCA and MRA; 1.8.4.1. Principal component analysis; 1.8.4.2. Independent component analysis; 1.8.4.3. Compressive sensing; 1.8.5. Conclusion; 1.9. Bibliography; Chapter 2. Discrete Wavelet Transform-Based Multifractal Analysis; 2.1. Introduction; 2.1.1. Fractals and wavelets: a happy marriage?
2.1.2. Background2.1.3. Mono/multifractal processes; 2.1.4. Chapter outline; 2.2. Fractality, variability and complexity; 2.2.1. System complexity; 2.2.2. Complex phenomena properties; 2.2.2.1. Tendency of autonomous agents to self-organize; 2.2.2.2. Variability and adaptability; 2.2.2.3. Bifurcation concept and chaotic model; 2.2.2.4. Hierarchy and scale invariance; 2.2.2.5. Self-organized critical phenomena; 2.2.2.6. Highly optimized tolerance; 2.2.3. Fractality; 2.3. Multifractal analysis; 2.3.1. Point-wise regularity; 2.3.2. Hölder exponent
2.3.3. Signal classification according to the regularity properties
Record Nr. UNINA-9910141495903321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Signal and image multiresolution analysis [[electronic resource] /] / edited by Abdeljalil Ouahabi ; series editor, Francis Castanié
Signal and image multiresolution analysis [[electronic resource] /] / edited by Abdeljalil Ouahabi ; series editor, Francis Castanié
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (308 p.)
Disciplina 621.3822
Altri autori (Persone) OuahabiAbdeldjalil
CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Signal processing
Image processing
ISBN 1-118-56876-1
1-118-56859-1
1-118-56866-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright Page; Table of Contents; Introduction; Chapter 1. Introduction to Multiresolution Analysis; 1.1. Introduction; 1.2. Wavelet transforms: an introductory review; 1.2.1. Brief history; 1.2.2. Continuous wavelet transforms; 1.2.2.1. Wavelet transform modulus maxima; 1.2.2.2. Reconstruction; 1.2.3. Discrete wavelet transforms; 1.3. Multiresolution; 1.3.1. Multiresolution analysis and wavelet bases; 1.3.1.1. Approximation spaces; 1.3.1.2. Detail spaces; 1.3.2. Multiresolution analysis: points to remember; 1.3.3. Decomposition and reconstruction
1.3.3.1. Calculation of coefficients1.3.3.2. Implementation of MRA: Mallat algorithm; 1.3.3.3. Extension to images; 1.3.4. Wavelet packets; 1.3.5. Multiresolution analysis summarized; 1.4. Which wavelets to choose?; 1.4.1. Number of vanishing moments, regularity, support (compactness), symmetry, etc.; 1.4.2. Well-known wavelets, scaling functions and associated filters; 1.4.2.1. Haar wavelet; 1.4.2.2. Daubechies wavelets; 1.4.2.3. Symlets; 1.4.2.4. Coiflets; 1.4.2.5. Meyer wavelets; 1.4.2.6. Polynomial spline wavelets; 1.5. Multiresolution analysis and biorthogonal wavelet bases
1.5.1. Why biorthogonal bases?1.5.2. Multiresolution context; 1.5.3. Example of biorthogonal wavelets, scaling functions and associated filters; 1.5.4. The concept of wavelet lifting; 1.5.4.1. The notion of lifting; 1.5.4.2. Significance of structure lifting; 1.6. Wavelet choice at a glance; 1.6.1. Regularity; 1.6.2. Vanishing moments; 1.6.3. Other criteria; 1.6.4. Conclusion; 1.7. Worked examples; 1.7.1. Examples of multiresolution analysis; 1.7.2. Compression; 1.7.3. Denoising (reduction of noise); 1.8. Some applications; 1.8.1. Discovery and contributions of wavelets
1.8.2. Biomedical engineering1.8.2.1. ECG, EEG and BCI; 1.8.2.2. Medical imaging; 1.8.3. Telecommunications; 1.8.3.1. Adaptive compression for sensor networks; 1.8.3.2. Masking image encoding and transmission errors; 1.8.3.3. Suppression of correlated noise; 1.8.4. "Compressive sensing", ICA, PCA and MRA; 1.8.4.1. Principal component analysis; 1.8.4.2. Independent component analysis; 1.8.4.3. Compressive sensing; 1.8.5. Conclusion; 1.9. Bibliography; Chapter 2. Discrete Wavelet Transform-Based Multifractal Analysis; 2.1. Introduction; 2.1.1. Fractals and wavelets: a happy marriage?
2.1.2. Background2.1.3. Mono/multifractal processes; 2.1.4. Chapter outline; 2.2. Fractality, variability and complexity; 2.2.1. System complexity; 2.2.2. Complex phenomena properties; 2.2.2.1. Tendency of autonomous agents to self-organize; 2.2.2.2. Variability and adaptability; 2.2.2.3. Bifurcation concept and chaotic model; 2.2.2.4. Hierarchy and scale invariance; 2.2.2.5. Self-organized critical phenomena; 2.2.2.6. Highly optimized tolerance; 2.2.3. Fractality; 2.3. Multifractal analysis; 2.3.1. Point-wise regularity; 2.3.2. Hölder exponent
2.3.3. Signal classification according to the regularity properties
Record Nr. UNINA-9910830486403321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Spectral analysis [[electronic resource] ] : parametric and non-parametric digital methods / / edited by Francis Castanié
Spectral analysis [[electronic resource] ] : parametric and non-parametric digital methods / / edited by Francis Castanié
Pubbl/distr/stampa London ; ; Newport Beach, CA, : ISTE Ltd., 2006
Descrizione fisica 1 online resource (264 p.)
Disciplina 621.382/2
Altri autori (Persone) CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Signal processing - Digital techniques
Spectrum analysis - Statistical methods
ISBN 1-280-60344-5
9786610603442
1-84704-455-7
0-470-61219-3
0-470-39444-7
1-84704-555-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Spectral Analysis; Table of Contents; Preface; Specific Notations; PART I. Tools and Spectral Analysis; Chapter 1. Fundamentals; 1.1. Classes of signals; 1.1.1. Deterministic signals; 1.1.2. Random signals; 1.2. Representations of signals; 1.2.1. Representations of deterministic signals; 1.2.1.1. Complete representations; 1.2.1.2. Partial representations; 1.2.2. Representations of random signals; 1.2.2.1. General approach; 1.2.2.2. 2nd order representations; 1.2.2.3. Higher order representations; 1.3. Spectral analysis: position of the problem; 1.4. Bibliography
Chapter 2. Digital Signal Processing2.1. Introduction; 2.2. Transform properties; 2.2.1. Some useful functions and series; 2.2.2. Fourier transform; 2.2.3. Fundamental properties; 2.2.4. Convolution sum; 2.2.5. Energy conservation (Parseval's theorem); 2.2.6. Other properties; 2.2.7. Examples; 2.2.8. Sampling; 2.2.9. Practical calculation, FFT; 2.3. Windows; 2.4. Examples of application; 2.4.1. LTI systems identification; 2.4.2. Monitoring spectral lines; 2.4.3. Spectral analysis of the coefficient of tide fluctuation; 2.5. Bibliography; Chapter 3. Estimation in Spectral Analysis
3.1. Introduction to estimation3.1.1. Formalization of the problem; 3.1.2. Cramér-Rao bounds; 3.1.3. Sequence of estimators; 3.1.4. Maximum likelihood estimation; 3.2. Estimation of 1st and 2nd order moments; 3.3. Periodogram analysis; 3.4. Analysis of estimators based on cxx (m); 3.4.1. Estimation of parameters of an AR model; 3.4.2. Estimation of a noisy cisoid by MUSIC; 3.5. Conclusion; 3.6. Bibliography; Chapter 4. Time-Series Models; 4.1. Introduction; 4.2. Linear models; 4.2.1. Stationary linear models; 4.2.2. Properties; 4.2.2.1. Stationarity; 4.2.2.2. Moments and spectra
4.2.2.3. Relation with Wold's decomposition4.2.3. Non-stationary linear models; 4.3. Exponential models; 4.3.1. Deterministic model; 4.3.2. Noisy deterministic model; 4.3.3. Models of random stationary signals; 4.4. Non-linear models; 4.5. Bibliography; PART II. Non-Parametric Methods; Chapter 5. Non-Parametric Methods; 5.1. Introduction; 5.2. Estimation of the power spectral density; 5.2.1. Filter bank method; 5.2.2. Periodogram method; 5.2.3. Periodogram variants; 5.3. Generalization to higher order spectra; 5.4. Bibliography; PART III. Parametric Methods
Chapter 6. Spectral Analysis by Stationary Time Series Modeling6.1. Parametric models; 6.2. Estimation of model parameters; 6.2.1. Estimation of AR parameters; 6.2.2. Estimation of ARMA parameters; 6.2.3. Estimation of Prony parameters; 6.2.4. Order selection criteria; 6.3. Properties of spectral estimators produced; 6.4. Bibliography; Chapter 7. Minimum Variance; 7.1. Principle of the MV method; 7.2. Properties of the MV estimator; 7.2.1. Expressions of the MV filter; 7.2.2. Probability density of the MV estimator; 7.2.3. Frequency resolution of the MV estimator
7.3. Link with the Fourier estimators
Record Nr. UNISA-996217137903316
London ; ; Newport Beach, CA, : ISTE Ltd., 2006
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Spectral analysis [[electronic resource] ] : parametric and non-parametric digital methods / / edited by Francis Castanié
Spectral analysis [[electronic resource] ] : parametric and non-parametric digital methods / / edited by Francis Castanié
Pubbl/distr/stampa London ; ; Newport Beach, CA, : ISTE Ltd., 2006
Descrizione fisica 1 online resource (264 p.)
Disciplina 621.382/2
Altri autori (Persone) CastaniéFrancis
Collana Digital signal and image processing series
Soggetto topico Signal processing - Digital techniques
Spectrum analysis - Statistical methods
ISBN 1-280-60344-5
9786610603442
1-84704-455-7
0-470-61219-3
0-470-39444-7
1-84704-555-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Spectral Analysis; Table of Contents; Preface; Specific Notations; PART I. Tools and Spectral Analysis; Chapter 1. Fundamentals; 1.1. Classes of signals; 1.1.1. Deterministic signals; 1.1.2. Random signals; 1.2. Representations of signals; 1.2.1. Representations of deterministic signals; 1.2.1.1. Complete representations; 1.2.1.2. Partial representations; 1.2.2. Representations of random signals; 1.2.2.1. General approach; 1.2.2.2. 2nd order representations; 1.2.2.3. Higher order representations; 1.3. Spectral analysis: position of the problem; 1.4. Bibliography
Chapter 2. Digital Signal Processing2.1. Introduction; 2.2. Transform properties; 2.2.1. Some useful functions and series; 2.2.2. Fourier transform; 2.2.3. Fundamental properties; 2.2.4. Convolution sum; 2.2.5. Energy conservation (Parseval's theorem); 2.2.6. Other properties; 2.2.7. Examples; 2.2.8. Sampling; 2.2.9. Practical calculation, FFT; 2.3. Windows; 2.4. Examples of application; 2.4.1. LTI systems identification; 2.4.2. Monitoring spectral lines; 2.4.3. Spectral analysis of the coefficient of tide fluctuation; 2.5. Bibliography; Chapter 3. Estimation in Spectral Analysis
3.1. Introduction to estimation3.1.1. Formalization of the problem; 3.1.2. Cramér-Rao bounds; 3.1.3. Sequence of estimators; 3.1.4. Maximum likelihood estimation; 3.2. Estimation of 1st and 2nd order moments; 3.3. Periodogram analysis; 3.4. Analysis of estimators based on cxx (m); 3.4.1. Estimation of parameters of an AR model; 3.4.2. Estimation of a noisy cisoid by MUSIC; 3.5. Conclusion; 3.6. Bibliography; Chapter 4. Time-Series Models; 4.1. Introduction; 4.2. Linear models; 4.2.1. Stationary linear models; 4.2.2. Properties; 4.2.2.1. Stationarity; 4.2.2.2. Moments and spectra
4.2.2.3. Relation with Wold's decomposition4.2.3. Non-stationary linear models; 4.3. Exponential models; 4.3.1. Deterministic model; 4.3.2. Noisy deterministic model; 4.3.3. Models of random stationary signals; 4.4. Non-linear models; 4.5. Bibliography; PART II. Non-Parametric Methods; Chapter 5. Non-Parametric Methods; 5.1. Introduction; 5.2. Estimation of the power spectral density; 5.2.1. Filter bank method; 5.2.2. Periodogram method; 5.2.3. Periodogram variants; 5.3. Generalization to higher order spectra; 5.4. Bibliography; PART III. Parametric Methods
Chapter 6. Spectral Analysis by Stationary Time Series Modeling6.1. Parametric models; 6.2. Estimation of model parameters; 6.2.1. Estimation of AR parameters; 6.2.2. Estimation of ARMA parameters; 6.2.3. Estimation of Prony parameters; 6.2.4. Order selection criteria; 6.3. Properties of spectral estimators produced; 6.4. Bibliography; Chapter 7. Minimum Variance; 7.1. Principle of the MV method; 7.2. Properties of the MV estimator; 7.2.1. Expressions of the MV filter; 7.2.2. Probability density of the MV estimator; 7.2.3. Frequency resolution of the MV estimator
7.3. Link with the Fourier estimators
Record Nr. UNINA-9910143311403321
London ; ; Newport Beach, CA, : ISTE Ltd., 2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Zonotopes : from guaranteed state-estimation to control / / Vu Tuan Hieu Le [and four others] ; series editor, Francis Castanie
Zonotopes : from guaranteed state-estimation to control / / Vu Tuan Hieu Le [and four others] ; series editor, Francis Castanie
Pubbl/distr/stampa London, [England] ; Hoboken, New Jersey : , : John Wiley and Sons, Incorporation, , 2013
Descrizione fisica 1 online resource (168 p.)
Disciplina 629.8
Altri autori (Persone) LeVu Tuan Hieu
CastaniéFrancis
Collana Automation-control and industrial engineering series
Soggetto topico Automatic control
Estimation theory
ISBN 1-118-76159-6
1-118-76158-8
1-118-76154-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title page; Contents; Notations; Acronyms; Introduction; Chapter 1. Uncertainty Representation Based on Set Theory; 1.1. Basic set definitions: advantages and weaknesses; 1.1.1. Interval set; 1.1.2. Ellipsoidal set; 1.1.3. Polyhedral set; 1.1.4. Zonotopic set; 1.2. Main properties of zonotopes; Chapter 2. Several Approaches on Zonotopic Guaranteed Set-Membership Estimation; 2.1. Context; 2.2. Problem formulation; 2.2.1. Singular Value Decomposition-based method 35; 2.2.2. Optimization-based methods; Chapter 3. Zonotopic Guaranteed State Estimation Based on P-Radius Minimization
3.1. Single-Output systems approach3.2. Multi-Output systems approaches; 3.2.1. General formulation; 3.2.2. Extensions of the Single-Output systems methodology; 3.2.3. Dedicated approach for Multi-Output systems; Chapter 4. Tube Model Predictive Control Based on Zonotopic Set-Membership Estimation; 4.1. Context; 4.2. Problem formulation; 4.3. Tube-based output feedback Model Predictive Control design; 4.4. Application on the magnetic levitation system; 4.4.1. System description; 4.4.2. Control problem; Conclusion and Perspectives; Appendix. Basic Matrix Operation Definitions; Bibliography
Index
Record Nr. UNINA-9910140184003321
London, [England] ; Hoboken, New Jersey : , : John Wiley and Sons, Incorporation, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Zonotopes : from guaranteed state-estimation to control / / Vu Tuan Hieu Le [and four others] ; series editor, Francis Castanie
Zonotopes : from guaranteed state-estimation to control / / Vu Tuan Hieu Le [and four others] ; series editor, Francis Castanie
Pubbl/distr/stampa London, [England] ; Hoboken, New Jersey : , : John Wiley and Sons, Incorporation, , 2013
Descrizione fisica 1 online resource (168 p.)
Disciplina 629.8
Altri autori (Persone) LeVu Tuan Hieu
CastaniéFrancis
Collana Automation-control and industrial engineering series
Soggetto topico Automatic control
Estimation theory
ISBN 1-118-76159-6
1-118-76158-8
1-118-76154-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title page; Contents; Notations; Acronyms; Introduction; Chapter 1. Uncertainty Representation Based on Set Theory; 1.1. Basic set definitions: advantages and weaknesses; 1.1.1. Interval set; 1.1.2. Ellipsoidal set; 1.1.3. Polyhedral set; 1.1.4. Zonotopic set; 1.2. Main properties of zonotopes; Chapter 2. Several Approaches on Zonotopic Guaranteed Set-Membership Estimation; 2.1. Context; 2.2. Problem formulation; 2.2.1. Singular Value Decomposition-based method 35; 2.2.2. Optimization-based methods; Chapter 3. Zonotopic Guaranteed State Estimation Based on P-Radius Minimization
3.1. Single-Output systems approach3.2. Multi-Output systems approaches; 3.2.1. General formulation; 3.2.2. Extensions of the Single-Output systems methodology; 3.2.3. Dedicated approach for Multi-Output systems; Chapter 4. Tube Model Predictive Control Based on Zonotopic Set-Membership Estimation; 4.1. Context; 4.2. Problem formulation; 4.3. Tube-based output feedback Model Predictive Control design; 4.4. Application on the magnetic levitation system; 4.4.1. System description; 4.4.2. Control problem; Conclusion and Perspectives; Appendix. Basic Matrix Operation Definitions; Bibliography
Index
Record Nr. UNINA-9910807704103321
London, [England] ; Hoboken, New Jersey : , : John Wiley and Sons, Incorporation, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui