LEADER 00835nam0-22003011i-450 001 990001789040403321 005 20190529131422.0 035 $a000178904 035 $aFED01000178904 035 $a(Aleph)000178904FED01 035 $a000178904 100 $a20030910f19..----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aProgrammazione della lotta anticrittogamica$fBruno Casarini 210 $aBologna$cOsservatorio fitopatologico$d[19..] 215 $a3 p.$d24 cm 610 0 $aAnticrittogamici 676 $a632.952 700 1$aCasarini,$bBruno$070594 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aLG 912 $a990001789040403321 952 $a60 OP. 135/31$fFAGBC 959 $aFAGBC 996 $aProgrammazione della lotta anticrittogamica$9408500 997 $aUNINA LEADER 11303nam 2200601 450 001 9910150209303321 005 20230803221438.0 010 $a0-273-77572-3 035 $a(CKB)2550000001307925 035 $a(MiAaPQ)EBC5173892 035 $a(MiAaPQ)EBC5175880 035 $a(MiAaPQ)EBC5832864 035 $a(MiAaPQ)EBC5137979 035 $a(MiAaPQ)EBC6398913 035 $a(Au-PeEL)EBL5137979 035 $a(CaONFJC)MIL613611 035 $a(OCoLC)1024286337 035 $a(EXLCZ)992550000001307925 100 $a20210317d2014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aAdaptive filter theory /$fSimon Haykin ; international edition contributions by Telagarapu Prabhakar 205 $aFifth edition, International edition. 210 1$aUpper Saddle River :$cPearson,$d[2014] 210 4$d©2014 215 $a1 online resource (912 pages) $cillustrations (some color) 225 1 $aAlways learning 311 $a0-273-76408-X 311 $a1-306-82360-9 320 $aIncludes bibliographical references and index. 327 $aCover -- Title -- Contents -- Preface -- Acknowledgments -- Background and Preview -- 1. The Filtering Problem -- 2. Linear Optimum Filters -- 3. Adaptive Filters -- 4. Linear Filter Structures -- 5. Approaches to the Development of Linear Adaptive Filters -- 6. Adaptive Beamforming -- 7. Four Classes of Applications -- 8. Historical Notes -- Chapter 1 Stochastic Processes and Models -- 1.1 Partial Characterization of a Discrete-Time Stochastic Process -- 1.2 Mean Ergodic Theorem -- 1.3 Correlation Matrix -- 1.4 Correlation Matrix of Sine Wave Plus Noise -- 1.5 Stochastic Models -- 1.6 Wold Decomposition -- 1.7 Asymptotic Stationarity of an Autoregressive Process -- 1.8 Yule-Walker Equations -- 1.9 Computer Experiment: Autoregressive Process of Order Two -- 1.10 Selecting the Model Order -- 1.11 Complex Gaussian Proceses -- 1.12 Power Spectral Density -- 1.13 Propert ies of Power Spectral Density -- 1.14 Transmission of a Stationary Process Through a Linear Filter -- 1.15 Crame?r Spectral Representation for a Stationary Process -- 1.16 Power Spectrum Estimation -- 1.17 Other Statistical Characteristics of a Stochastic Process -- 1.18 Polyspectra -- 1.19 Spectral-Correlation Density -- 1.20 Summary and Discussion -- Problems -- Chapter 2 Wiener Filters -- 2.1 Linear Optimum Filtering: Statement of the Problem -- 2.2 Principle of Orthogonality -- 2.3 Minimum Mean-Square Error -- 2.4 Wiener-Hopf Equations -- 2.5 Error-Performance Surface -- 2.6 Multiple Linear Regression Model -- 2.7 Example -- 2.8 Linearly Constrained Minimum-Variance Filter -- 2.9 Generalized Sidelobe Cancellers -- 2.10 Summary and Discussion -- Problems -- Chapter 3 Linear Prediction -- 3.1 Forward Linear Prediction -- 3.2 Backward Linear Prediction -- 3.3 Levinson-Durbin Algorithm -- 3.4 Properties of Prediction-Error Filters -- 3.5 Schur-Cohn Test. 327 $a3.6 Autoregressive Modeling of a Stationary Stochastic Process -- 3.7 Cholesky Factorization -- 3.8 Lattice Predictors -- 3.9 All-Pole, All-Pass Lattice Filter -- 3.10 Joint-Process Estimation -- 3.11 Predictive Modeling of Speech -- 3.12 Summary and Discussion -- Problems -- Chapter 4 Method of Steepest Descent -- 4.1 Basic Idea of the Steepest-Descent Algorithm -- 4.2 The Steepest-Descent Algorithm Applied to the Wiener Filter -- 4.3 Stability of the Steepest-Descent Algorithm -- 4.4 Example -- 4.5 The Steepest-Descent Algorithm Viewed as a Deterministic Search Method -- 4.6 Virtue and Limitation of the Steepest-Descent Algorithm -- 4.7 Summary and Discussion -- Problems -- Chapter 5 Method of Stochastic Gradient Descent -- 5.1 Principles of Stochastic Gradient Descent -- 5.2 Application 1: Least-Mean-Square (LMS) Algorithm -- 5.3 Application 2: Gradient-Adaptive Lattice Filtering Algorithm -- 5.4 Other Applications of Stochastic Gradient Descent -- 5.5 Summary and Discussion -- Problems -- Chapter 6 The Least-Mean-Square (LMS) Algorithm -- 6.1 Signal-Flow Graph -- 6.2 Optimality Considerations -- 6.3 Applications -- 6.4 Statistical Learning Theory -- 6.5 Transient Behavior and Convergence Considerations -- 6.6 Efficiency -- 6.7 Computer Experiment on Adaptive Prediction -- 6.8 Computer Experiment on Adaptive Equalization -- 6.9 Computer Experiment on a Minimum-Variance Distortionless-Response Beamformer -- 6.10 Summary and Discussion -- Problems -- Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization -- 7.1 Normalized LMS Algorithm: The Solution to a Constrained Optimization Problem -- 7.2 Stability of the Normalized LMS Algorithm -- 7.3 Step-Size Control for Acoustic Echo Cancellation -- 7.4 Geometric Considerations Pertaining to the Convergence Process for Real-Valued Data -- 7.5 Affine Projection Adaptive Filters. 327 $a7.6 Summary and Discussion -- Problems -- Chapter 8 Block-Adaptive Filters -- 8.1 Block-Adaptive Filters: Basic Ideas -- 8.2 Fast Block LMS Algorithm -- 8.3 Unconstrained Frequency-Domain Adaptive Filters -- 8.4 Self-Orthogonalizing Adaptive Filters -- 8.5 Computer Experiment on Adaptive Equalization -- 8.6 Subband Adaptive Filters -- 8.7 Summary and Discussion -- Problems -- Chapter 9 Method of Least-Squares -- 9.1 Statement of the Linear Least-Squares Estimation Problem -- 9.2 Data Windowing -- 9.3 Principle of Orthogonality Revisited -- 9.4 Minimum Sum of Error Squares -- 9.5 Normal Equations and Linear Least-Squares Filters -- 9.6 Time-Average Correlation Matrix ? -- 9.7 Reformulation of the Normal Equations in Terms of Data Matrices -- 9.8 Properties of Least-Squares Estimates -- 9.9 Minimum-Variance Distortionless Response (MVDR) Spectrum Estimation -- 9.10 Regularized MVDR Beamforming -- 9.11 Singular-Value Decomposition -- 9.12 Pseudoinverse -- 9.13 Interpretation of Singular Values and Singular Vectors -- 9.14 Minimum-Norm Solution to the Linear Least-Squares Problem -- 9.15 Normalized LMS Algorithm Viewed as the Minimum-Norm Solution to an Underdetermined Least-Squares Estimation Problem -- 9.16 Summary and Discussion -- Problems -- Chapter 10 The Recursive Least-Squares (RLS) Algorithm -- 10.1 Some Preliminaries -- 10.2 The Matrix Inversion Lemma -- 10.3 The Exponentially Weighted RLS Algorithm -- 10.4 Selection of the Regularization Parameter -- 10.5 Updated Recursion for the Sum of Weighted Error Squares -- 10.6 Example: Single-Weight Adaptive Noise Canceller -- 10.7 Statistical Learning Theory -- 10.8 Efficiency -- 10.9 Computer Experiment on Adaptive Equalization -- 10.10 Summary and Discussion -- Problems -- Chapter 11 Robustness -- 11.1 Robustness, Adaptation, and Disturbances. 327 $a11.2 Robustness: Preliminary Considerations Rooted in H? Optimization -- 11.3 Robustness of the LMS Algorithm -- 11.4 Robustness of the RLS Algorithm -- 11.5 Comparative Evaluations of the LMS and RLS Algorithms from the Perspective of Robustness -- 11.6 Risk-Sensitive Optimality -- 11.7 Trade-Offs Between Robustness and Efficiency -- 11.8 Summary and Discussion -- Problems -- Chapter 12 Finite-Precision Effects -- 12.1 Quantization Errors -- 12.2 Least-Mean-Square (LMS) Algorithm -- 12.3 Recursive Least-Squares (RLS) Algorithm -- 12.4 Summary and Discussion -- Problems -- Chapter 13 Adaptation in Nonstationary Environments -- 13.1 Causes and Consequences of Nonstationarity -- 13.2 The System Identification Problem -- 13.3 Degree of Nonstationarity -- 13.4 Criteria for Tracking Assessment -- 13.5 Tracking Performance of the LMS Algorithm -- 13.6 Tracking Performance of the RLS Algorithm -- 13.7 Comparison of the Tracking Performance of LMS and RLS Algorithms -- 13.8 Tuning of Adaptation Parameters -- 13.9 Incremental Delta-Bar-Delta (IDBD) Algorithm -- 13.10 Autostep Method -- 13.11 Computer Experiment: Mixture of Stationary and Nonstationary Environmental Data -- 13.12 Summary and Discussion -- Problems -- Chapter 14 Kalman Filters -- 14.1 Recursive Minimum Mean-Square Estimation for Scalar Random Variables -- 14.2 Statement of the Kalman Filtering Problem -- 14.3 The Innovations Process -- 14.4 Estimation of the State Using the Innovations Process -- 14.5 Filtering -- 14.6 Initial Conditions -- 14.7 Summary of the Kalman Filter -- 14.8 Optimality Criteria for Kalman Filtering -- 14.9 Kalman Filter as the Unifying Basis for RLS Algorithms -- 14.10 Covariance Filtering Algorithm -- 14.11 Information Filtering Algorithm -- 14.12 Summary and Discussion -- Problems -- Chapter 15 Square-Root Adaptive Filtering Algorithms. 327 $a15.1 Square-Root Kalman Filters -- 15.2 Building Square-Root Adaptive Filters on the Two Kalman Filter Variants -- 15.3 QRD-RLS Algorithm -- 15.4 Adaptive Beamforming -- 15.5 Inverse QRD-RLS Algorithm -- 15.6 Finite-Precision Effects -- 15.7 Summary and Discussion -- Problems -- Chapter 16 Order-Recursive Adaptive Filtering Algorithm -- 16.1 Order-Recursive Adaptive Filters Using Least-Squares Estimation: An Overview -- 16.2 Adaptive Forward Linear Prediction -- 16.3 Adaptive Backward Linear Prediction -- 16.4 Conversion Factor -- 16.5 Least-Squares Lattice (LSL) Predictor -- 16.6 Angle-Normalized Estimation Errors -- 16.7 First-Order State-Space Models for Lattice Filtering -- 16.8 QR-Decomposition-Based Least-Squares Lattice (QRD-LSL) Filters -- 16.9 Fundamental Properties of the QRD-LSL Filter -- 16.10 Computer Experiment on Adaptive Equalization -- 16.11 Recursive (LSL) Filters Using A Posteriori Estimation Errors -- 16.12 Recursive LSL Filters Using A Priori Estimation Errors with Error Feedback -- 16.13 Relation Between Recursive LSL and RLS Algorithms -- 16.14 Finite-Precision Effects -- 16.15 Summary and Discussion -- Problems -- Chapter 17 Blind Deconvolution -- 17.1 Overview of Blind Deconvolution -- 17.2 Channel Identifiability Using Cyclostationary Statistics -- 17.3 Subspace Decomposition for Fractionally Spaced Blind Identification -- 17.4 Bussgang Algorithm for Blind Equalization -- 17.5 Extension of the Bussgang Algorithm to Complex Baseband Channels -- 17.6 Special Cases of the Bussgang Algorithm -- 17.7 Fractionally Spaced Bussgang Equalizers -- 17.8 Estimation of Unknown Probability Distribution Function of Signal Source -- 17.9 Summary and Discussion -- Problems -- Epilogue -- 1. Robustness, Efficiency, and Complexity -- 2. Kernel-Based Nonlinear Adaptive Filtering -- Appendix A Theory of Complex Variables. 327 $aA.1 Cauchy-Riemann Equations. 330 $aFor courses in Adaptive Filters. Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible. 410 0$aAlways learning. 606 $aAdaptive filters 615 0$aAdaptive filters. 676 $a621.3815324 700 $aHaykin$b Simon S.$f1931-$08857 702 $aPrabhakar$b T.$f1977- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910150209303321 996 $aAdaptive filter theory$9112739 997 $aUNINA LEADER 04969nam 22008415 450 001 9910483830903321 005 20250609111513.0 010 $a3-319-15449-4 024 7 $a10.1007/978-3-319-15449-7 035 $a(CKB)3710000000379599 035 $a(EBL)2095352 035 $a(SSID)ssj0001465463 035 $a(PQKBManifestationID)11919362 035 $a(PQKBTitleCode)TC0001465463 035 $a(PQKBWorkID)11470775 035 $a(PQKB)10589929 035 $a(DE-He213)978-3-319-15449-7 035 $a(MiAaPQ)EBC2095352 035 $a(PPN)184890314 035 $a(MiAaPQ)EBC3109240 035 $a(EXLCZ)993710000000379599 100 $a20150330d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aeCommerce and the Effects of Technology on Taxation $eCould VAT be the eTax Solution? /$fby Anne Michèle Bardopoulos 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (367 p.) 225 1 $aLaw, Governance and Technology Series,$x2352-1902 ;$v22 300 $aDescription based upon print version of record. 311 08$a3-319-15448-6 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aDedication -- Acknowledgements -- Preface -- Classified Guide To The Paper -- Introductory Chapter -- Part I - History Of Taxation And Development Of Globalisation -- Part II: Internet, Ecommerce, Servers And ISPS -- Part III: Defining ?Residence? For Taxation Purposes -- Part IV: Defining ?Source? For Taxation Purposes -- Part V: Value Added Taxation -- Part VI: Practical Examples -- Part VII: Other Taxing Issues Emanating From Ecommerce -- Part VIII: Conclusion And Opinion Advocated With Regard To The Etax Solution -- Appendix -- Appendix I ? Jurisdictional Analysis -- Appendix II ? Table 1 As Set Out In HMRC Electronically Supplied Services: A Guide To Interpretation Which Is Taken From Annex L Of The EU Vat On E-Commerce Directive (2002/38/EC) -- Appendix III - Table 2 As Set Out In HMRC Electronically Supplied Services: A Guide To Interpretation Which Is Taken From Annex L Of The EU Vat On E-Commerce Directive (2002/38/EC) -- Index. 330 $aThis book focuses on the impact of technology on taxation and deals with the broad effect of technology on diverse taxation systems.  It addresses the highly relevant eTax issue and argues that while VAT may not be the ultimate solution with regard to taxing electronic commerce, it can be demonstrated to be the most effective solution to date. The book analyzes the application and the effectiveness of traditional income tax principles in contradistinction to VAT principles. Taking into account rapidly ameliorating technology, the book next assesses the compatibility between electronic commerce and diverse systems of taxation.  Using case studies of Amazon.com and Second Life, as well as additional practical examples, the book demonstrates the effectiveness of VAT in respect of electronic commerce and ameliorating technology in the incalculable and borderless realm of cyberspace. 410 0$aLaw, Governance and Technology Series,$x2352-1902 ;$v22 606 $aLaw?Philosophy 606 $aLaw 606 $aTax accounting 606 $aTaxation$xLaw and legislation 606 $aComputers 606 $aLaw and legislation 606 $aCommercial law 606 $aFinance, Public 606 $aTheories of Law, Philosophy of Law, Legal History$3https://scigraph.springernature.com/ontologies/product-market-codes/R11011 606 $aBusiness Taxation/Tax Law$3https://scigraph.springernature.com/ontologies/product-market-codes/511010 606 $aLegal Aspects of Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I24059 606 $aCommercial Law$3https://scigraph.springernature.com/ontologies/product-market-codes/R12026 606 $aFinancial Law/Fiscal Law$3https://scigraph.springernature.com/ontologies/product-market-codes/R17044 615 0$aLaw?Philosophy. 615 0$aLaw. 615 0$aTax accounting. 615 0$aTaxation$xLaw and legislation. 615 0$aComputers. 615 0$aLaw and legislation. 615 0$aCommercial law. 615 0$aFinance, Public. 615 14$aTheories of Law, Philosophy of Law, Legal History. 615 24$aBusiness Taxation/Tax Law. 615 24$aLegal Aspects of Computing. 615 24$aCommercial Law. 615 24$aFinancial Law/Fiscal Law. 676 $a336.278 700 $aBardopoulos$b Anne Michèle$4aut$4http://id.loc.gov/vocabulary/relators/aut$01225543 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483830903321 996 $aECommerce and the Effects of Technology on Taxation$92845417 997 $aUNINA