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Biblioteca

MARC Lista (tabellare)
Advanced engineering mathematics with MATLAB / / Dean G. Duffy
Advanced engineering mathematics with MATLAB / / Dean G. Duffy
Autore Duffy Dean G.
Edizione [Fourth edition.]
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2017]
Descrizione fisica 1 online resource (1,005 pages) : illustrations
Disciplina 620.0028553
Collana Advances in applied mathematics
Soggetto topico Engineering mathematics - Data processing
ISBN 1-4987-3966-0
1-315-36928-1
1-4987-3967-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Classic engineering mathematics -- Transform methods -- Stochastic processes.
Record Nr. UNINA-9910155108203321
Duffy Dean G.  
Boca Raton : , : CRC Press, , [2017]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced engineering mathematics with MATLAB / Dean G. Duffy
Advanced engineering mathematics with MATLAB / Dean G. Duffy
Autore Duffy, Dean G.
Edizione [2nd ed.]
Pubbl/distr/stampa Boca Raton, Fla. : Chapman & Hall/CRC, c2003
Descrizione fisica 818 p. : ill. ; 24 cm
Disciplina 510
Soggetto topico Engineering mathematics - Data processing
ISBN 1584883499
Classificazione AMS 00A69
LC TA345.D84
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001807379707536
Duffy, Dean G.  
Boca Raton, Fla. : Chapman & Hall/CRC, c2003
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Advanced mathematics and mechanics applications using MATLAB / Howard B. Wilson, Louis H. Turcotte, David Halpern
Advanced mathematics and mechanics applications using MATLAB / Howard B. Wilson, Louis H. Turcotte, David Halpern
Autore Wilson, H. B.
Edizione [3rd ed]
Pubbl/distr/stampa Boca Raton [etc] : Chapman & Hall/CRC, c2003
Descrizione fisica xiii, 678 p. : ill. ; 25 cm
Disciplina 620.00151
Altri autori (Persone) Turcotte, Louis H.
Halpern, David
Soggetto topico Engineering mathematics - Data processing
Mechanics, Applied - Data processing
ISBN 158488262X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991000949299707536
Wilson, H. B.  
Boca Raton [etc] : Chapman & Hall/CRC, c2003
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Autore Duin Robert
Edizione [1st edition]
Pubbl/distr/stampa Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (441 p.)
Disciplina 620.0015118
681/.2
Altri autori (Persone) HeijdenFerdinand van der
Soggetto topico Engineering mathematics - Data processing
Measurement - Data processing
Estimation theory - Data processing
ISBN 1-280-26895-6
9786610268955
0-470-09015-4
1-60119-496-X
0-470-09014-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography
3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography
4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References
5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis
7.1.2 Multi-dimensional scaling
Record Nr. UNINA-9910143693003321
Duin Robert  
Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Classification, parameter estimation, and state estimation [[electronic resource] ] : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Autore Duin Robert
Edizione [1st edition]
Pubbl/distr/stampa Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (441 p.)
Disciplina 620.0015118
681/.2
Altri autori (Persone) HeijdenFerdinand van der
Soggetto topico Engineering mathematics - Data processing
Measurement - Data processing
Estimation theory - Data processing
ISBN 1-280-26895-6
9786610268955
0-470-09015-4
1-60119-496-X
0-470-09014-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography
3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography
4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References
5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis
7.1.2 Multi-dimensional scaling
Record Nr. UNINA-9910830828303321
Duin Robert  
Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
Autore Heijden Ferdinand van der
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2017
Descrizione fisica 1 online resource (431 pages) : illustrations
Disciplina 681/.2
Soggetto topico Engineering mathematics - Data processing
Measurement - Data processing
Estimation theory - Data processing
ISBN 1-119-15245-3
1-119-15244-5
1-119-15248-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910270899103321
Heijden Ferdinand van der  
Hoboken, New Jersey : , : Wiley, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / Bangjun Lei [and six others]
Autore Heijden Ferdinand van der
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2017
Descrizione fisica 1 online resource (431 pages) : illustrations
Disciplina 681/.2
Soggetto topico Engineering mathematics - Data processing
Measurement - Data processing
Estimation theory - Data processing
ISBN 1-119-15245-3
1-119-15244-5
1-119-15248-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910811584503321
Heijden Ferdinand van der  
Hoboken, New Jersey : , : Wiley, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Classification, parameter estimation, and state estimation : an engineering approach using MATLAB / / F. van der Heijden ... [et al.]
Edizione [1st edition]
Pubbl/distr/stampa Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Descrizione fisica 1 online resource (441 p.)
Disciplina 681/.2
Altri autori (Persone) HeijdenFerdinand van der
Soggetto topico Engineering mathematics - Data processing
Measurement - Data processing
Estimation theory - Data processing
ISBN 1-280-26895-6
9786610268955
0-470-09015-4
1-60119-496-X
0-470-09014-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Classification, Parameter Estimation and State Estimation; Contents; Preface; Foreword; 1 Introduction; 1.1 The scope of the book; 1.1.1 Classification; 1.1.2 Parameter estimation; 1.1.3 State estimation; 1.1.4 Relations between the subjects; 1.2 Engineering; 1.3 The organization of the book; 1.4 References; 2 Detection and Classification; 2.1 Bayesian classification; 2.1.1 Uniform cost function and minimum error rate; 2.1.2 Normal distributed measurements; linear and quadratic classifiers; 2.2 Rejection; 2.2.1 Minimum error rate classification with reject option
2.3 Detection: the two-class case2.4 Selected bibliography; 2.5 Exercises; 3 Parameter Estimation; 3.1 Bayesian estimation; 3.1.1 MMSE estimation; 3.1.2 MAP estimation; 3.1.3 The Gaussian case with linear sensors; 3.1.4 Maximum likelihood estimation; 3.1.5 Unbiased linear MMSE estimation; 3.2 Performance of estimators; 3.2.1 Bias and covariance; 3.2.2 The error covariance of the unbiased linear MMSE estimator; 3.3 Data fitting; 3.3.1 Least squares fitting; 3.3.2 Fitting using a robust error norm; 3.3.3 Regression; 3.4 Overview of the family of estimators; 3.5 Selected bibliography
3.6 Exercises4 State Estimation; 4.1 A general framework for online estimation; 4.1.1 Models; 4.1.2 Optimal online estimation; 4.2 Continuous state variables; 4.2.1 Optimal online estimation in linear-Gaussian systems; 4.2.2 Suboptimal solutions for nonlinear systems; 4.2.3 Other filters for nonlinear systems; 4.3 Discrete state variables; 4.3.1 Hidden Markov models; 4.3.2 Online state estimation; 4.3.3 Offline state estimation; 4.4 Mixed states and the particle filter; 4.4.1 Importance sampling; 4.4.2 Resampling by selection; 4.4.3 The condensation algorithm; 4.5 Selected bibliography
4.6 Exercises5 Supervised Learning; 5.1 Training sets; 5.2 Parametric learning; 5.2.1 Gaussian distribution, mean unknown; 5.2.2 Gaussian distribution, covariance matrix unknown; 5.2.3 Gaussian distribution, mean and covariance matrix both unknown; 5.2.4 Estimation of the prior probabilities; 5.2.5 Binary measurements; 5.3 Nonparametric learning; 5.3.1 Parzen estimation and histogramming; 5.3.2 Nearest neighbour classification; 5.3.3 Linear discriminant functions; 5.3.4 The support vector classifier; 5.3.5 The feed-forward neural network; 5.4 Empirical evaluation; 5.5 References
5.6 Exercises6 Feature Extraction and Selection; 6.1 Criteria for selection and extraction; 6.1.1 Inter/intra class distance; 6.1.2 Chernoff-Bhattacharyya distance; 6.1.3 Other criteria; 6.2 Feature selection; 6.2.1 Branch-and-bound; 6.2.2 Suboptimal search; 6.2.3 Implementation issues; 6.3 Linear feature extraction; 6.3.1 Feature extraction based on the Bhattacharyya distance with Gaussian distributions; 6.3.2 Feature extraction based on inter/intra class distance; 6.4 References; 6.5 Exercises; 7 Unsupervised Learning; 7.1 Feature reduction; 7.1.1 Principal component analysis
7.1.2 Multi-dimensional scaling
Record Nr. UNINA-9910877862403321
Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electronically scanned arrays [[electronic resource] ] : MATLAB modeling and simulation / / edited by Arik D. Brown
Electronically scanned arrays [[electronic resource] ] : MATLAB modeling and simulation / / edited by Arik D. Brown
Autore Brown Arik D (Arik Darnell)
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton, Fla., : CRC Press, 2012
Descrizione fisica 1 online resource (229 p.)
Disciplina 681.2
Altri autori (Persone) BrownArik D
Soggetto topico Antenna arrays - Mathematical models
Beamforming - Mathematical models
Antenna radiation patterns - Mathematical models
Adaptive filters
Engineering mathematics - Data processing
ISBN 1-315-21713-9
1-4398-6164-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; Preface; Acknowledgments; Contributors; Chapter 3: Subarrray Beamforming; Chapter 4: Pattern Optimization; Chapter 5: Spaceborne Application of Electronically Scanned Arrays; Appendix A: Array Factor (AF) Derivation; Appendix B: Instantaneous Bandwidth (IBW) Derivation; Appendix C: Triangular Grating Lobes Derivation; Back Cover
Record Nr. UNINA-9910779173903321
Brown Arik D (Arik Darnell)  
Boca Raton, Fla., : CRC Press, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electronically scanned arrays : MATLAB modeling and simulation / / edited by Arik D. Brown
Electronically scanned arrays : MATLAB modeling and simulation / / edited by Arik D. Brown
Autore Brown Arik D (Arik Darnell)
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton, Fla., : CRC Press, 2012
Descrizione fisica 1 online resource (229 p.)
Disciplina 681.2
Altri autori (Persone) BrownArik D
Soggetto topico Antenna arrays - Mathematical models
Beamforming - Mathematical models
Antenna radiation patterns - Mathematical models
Adaptive filters
Engineering mathematics - Data processing
ISBN 1-315-21713-9
1-4398-6164-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; Preface; Acknowledgments; Contributors; Chapter 3: Subarrray Beamforming; Chapter 4: Pattern Optimization; Chapter 5: Spaceborne Application of Electronically Scanned Arrays; Appendix A: Array Factor (AF) Derivation; Appendix B: Instantaneous Bandwidth (IBW) Derivation; Appendix C: Triangular Grating Lobes Derivation; Back Cover
Record Nr. UNINA-9910813645903321
Brown Arik D (Arik Darnell)  
Boca Raton, Fla., : CRC Press, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui