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Biblioteca

MARC Lista (tabellare)
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
Autore Setoodeh Peyman <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Descrizione fisica 1 online resource (242 pages) : illustrations
Disciplina 621.384
Collana Adaptive and cognitive dynamic systems: signal processing, learning, communications and control
Soggetto topico Cognitive radio networks
Wireless communication systems
ISBN 1-119-40584-X
1-119-40583-1
1-119-40581-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Game theory -- Cognitive radio transceiver -- Cognitive radio networks -- Sustainability of the spectrum supply chain network -- Cognitive heterogeneous networks.
Record Nr. UNINA-9910271011803321
Setoodeh Peyman <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
Fundamentals of cognitive radio / / Peyman Setoodeh, Simon Haykin
Autore Setoodeh Peyman <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Descrizione fisica 1 online resource (242 pages) : illustrations
Disciplina 621.384
Collana Adaptive and cognitive dynamic systems: signal processing, learning, communications and control
Soggetto topico Cognitive radio networks
Wireless communication systems
ISBN 1-119-40584-X
1-119-40583-1
1-119-40581-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Game theory -- Cognitive radio transceiver -- Cognitive radio networks -- Sustainability of the spectrum supply chain network -- Cognitive heterogeneous networks.
Record Nr. UNINA-9910828331703321
Setoodeh Peyman <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Nonlinear filters : theory and applications / / Peyman Setoodeh, Saeid Habibi, Simon Haykin
Nonlinear filters : theory and applications / / Peyman Setoodeh, Saeid Habibi, Simon Haykin
Autore Setoodeh Peyman <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (307 pages)
Disciplina 629.8/36
Soggetto topico Nonlinear control theory
Digital filters (Mathematics)
Signal processing - Digital techniques
ISBN 1-119-07818-0
1-119-07815-6
1-119-07816-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality.
5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter.
9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA.
Record Nr. UNINA-9910555153203321
Setoodeh Peyman <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Nonlinear filters : theory and applications / / Peyman Setoodeh, Saeid Habibi, Simon Haykin
Nonlinear filters : theory and applications / / Peyman Setoodeh, Saeid Habibi, Simon Haykin
Autore Setoodeh Peyman <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica 1 online resource (307 pages)
Disciplina 629.8/36
Soggetto topico Nonlinear control theory
Digital filters (Mathematics)
Signal processing - Digital techniques
ISBN 1-119-07818-0
1-119-07815-6
1-119-07816-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- List of Figures -- List of Table -- Preface -- Acknowledgments -- Acronyms -- Chapter 1 Introduction -- 1.1 State of a Dynamic System -- 1.2 State Estimation -- 1.3 Construals of Computing -- 1.4 Statistical Modeling -- 1.5 Vision for the Book -- Chapter 2 Observability -- 2.1 Introduction -- 2.2 State‐Space Model -- 2.3 The Concept of Observability -- 2.4 Observability of Linear Time‐Invariant Systems -- 2.4.1 Continuous‐Time LTI Systems -- 2.4.2 Discrete‐Time LTI Systems -- 2.4.3 Discretization of LTI Systems -- 2.5 Observability of Linear Time‐Varying Systems -- 2.5.1 Continuous‐Time LTV Systems -- 2.5.2 Discrete‐Time LTV Systems -- 2.5.3 Discretization of LTV Systems -- 2.6 Observability of Nonlinear Systems -- 2.6.1 Continuous‐Time Nonlinear Systems -- 2.6.2 Discrete‐Time Nonlinear Systems -- 2.6.3 Discretization of Nonlinear Systems -- 2.7 Observability of Stochastic Systems -- 2.8 Degree of Observability -- 2.9 Invertibility -- 2.10 Concluding Remarks -- Chapter 3 Observers -- 3.1 Introduction -- 3.2 Luenberger Observer -- 3.3 Extended Luenberger‐Type Observer -- 3.4 Sliding‐Mode Observer -- 3.5 Unknown‐Input Observer -- 3.6 Concluding Remarks -- Chapter 4 Bayesian Paradigm and Optimal Nonlinear Filtering -- 4.1 Introduction -- 4.2 Bayes' Rule -- 4.3 Optimal Nonlinear Filtering -- 4.4 Fisher Information -- 4.5 Posterior Cramér-Rao Lower Bound -- 4.6 Concluding Remarks -- Chapter 5 Kalman Filter -- 5.1 Introduction -- 5.2 Kalman Filter -- 5.3 Kalman Smoother -- 5.4 Information Filter -- 5.5 Extended Kalman Filter -- 5.6 Extended Information Filter -- 5.7 Divided‐Difference Filter -- 5.8 Unscented Kalman Filter -- 5.9 Cubature Kalman Filter -- 5.10 Generalized PID Filter -- 5.11 Gaussian‐Sum Filter -- 5.12 Applications -- 5.12.1 Information Fusion -- 5.12.2 Augmented Reality.
5.12.3 Urban Traffic Network -- 5.12.4 Cybersecurity of Power Systems -- 5.12.5 Incidence of Influenza -- 5.12.6 COVID‐19 Pandemic -- 5.13 Concluding Remarks -- Chapter 6 Particle Filter -- 6.1 Introduction -- 6.2 Monte Carlo Method -- 6.3 Importance Sampling -- 6.4 Sequential Importance Sampling -- 6.5 Resampling -- 6.6 Sample Impoverishment -- 6.7 Choosing the Proposal Distribution -- 6.8 Generic Particle Filter -- 6.9 Applications -- 6.9.1 Simultaneous Localization and Mapping -- 6.10 Concluding Remarks -- Chapter 7 Smooth Variable‐Structure Filter -- 7.1 Introduction -- 7.2 The Switching Gain -- 7.3 Stability Analysis -- 7.4 Smoothing Subspace -- 7.5 Filter Corrective Term for Linear Systems -- 7.6 Filter Corrective Term for Nonlinear Systems -- 7.7 Bias Compensation -- 7.8 The Secondary Performance Indicator -- 7.9 Second‐Order Smooth Variable Structure Filter -- 7.10 Optimal Smoothing Boundary Design -- 7.11 Combination of SVSF with Other Filters -- 7.12 Applications -- 7.12.1 Multiple Target Tracking -- 7.12.2 Battery State‐of‐Charge Estimation -- 7.12.3 Robotics -- 7.13 Concluding Remarks -- Chapter 8 Deep Learning -- 8.1 Introduction -- 8.2 Gradient Descent -- 8.3 Stochastic Gradient Descent -- 8.4 Natural Gradient Descent -- 8.5 Neural Networks -- 8.6 Backpropagation -- 8.7 Backpropagation Through Time -- 8.8 Regularization -- 8.9 Initialization -- 8.10 Convolutional Neural Network -- 8.11 Long Short‐Term Memory -- 8.12 Hebbian Learning -- 8.13 Gibbs Sampling -- 8.14 Boltzmann Machine -- 8.15 Autoencoder -- 8.16 Generative Adversarial Network -- 8.17 Transformer -- 8.18 Concluding Remarks -- Chapter 9 Deep Learning‐Based Filters -- 9.1 Introduction -- 9.2 Variational Inference -- 9.3 Amortized Variational Inference -- 9.4 Deep Kalman Filter -- 9.5 Backpropagation Kalman Filter -- 9.6 Differentiable Particle Filter.
9.7 Deep Rao-Blackwellized Particle Filter -- 9.8 Deep Variational Bayes Filter -- 9.9 Kalman Variational Autoencoder -- 9.10 Deep Variational Information Bottleneck -- 9.11 Wasserstein Distributionally Robust Kalman Filter -- 9.12 Hierarchical Invertible Neural Transport -- 9.13 Applications -- 9.13.1 Prediction of Drug Effect -- 9.13.2 Autonomous Driving -- 9.14 Concluding Remarks -- Chapter 10 Expectation Maximization -- 10.1 Introduction -- 10.2 Expectation Maximization Algorithm -- 10.3 Particle Expectation Maximization -- 10.4 Expectation Maximization for Gaussian Mixture Models -- 10.5 Neural Expectation Maximization -- 10.6 Relational Neural Expectation Maximization -- 10.7 Variational Filtering Expectation Maximization -- 10.8 Amortized Variational Filtering Expectation Maximization -- 10.9 Applications -- 10.9.1 Stochastic Volatility -- 10.9.2 Physical Reasoning -- 10.9.3 Speech, Music, and Video Modeling -- 10.10 Concluding Remarks -- Chapter 11 Reinforcement Learning‐Based Filter -- 11.1 Introduction -- 11.2 Reinforcement Learning -- 11.3 Variational Inference as Reinforcement Learning -- 11.4 Application -- 11.4.1 Battery State‐of‐Charge Estimation -- 11.5 Concluding Remarks -- Chapter 12 Nonparametric Bayesian Models -- 12.1 Introduction -- 12.2 Parametric vs Nonparametric Models -- 12.3 Measure‐Theoretic Probability -- 12.4 Exchangeability -- 12.5 Kolmogorov Extension Theorem -- 12.6 Extension of Bayesian Models -- 12.7 Conjugacy -- 12.8 Construction of Nonparametric Bayesian Models -- 12.9 Posterior Computability -- 12.10 Algorithmic Sufficiency -- 12.11 Applications -- 12.11.1 Multiple Object Tracking -- 12.11.2 Data‐Driven Probabilistic Optimal Power Flow -- 12.11.3 Analyzing Single‐Molecule Tracks -- 12.12 Concluding Remarks -- References -- Index -- EULA.
Record Nr. UNINA-9910831039103321
Setoodeh Peyman <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui