Machine Learning Applications in Electromagnetics and Antenna Array Processing

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Martínez-Ramón, Manel
Otros Autores: Gupta, Arjun, Rojo-Álvarez, José Luis
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Norwood : Artech House, 2021.
Temas:
Machine learning.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 3.4 Kernel Framework for Estimating Signal Models
  • 3.4.1 Primal Signal Models
  • 3.4.2 RKHS Signal Models
  • 3.4.3 Dual Signal Models
  • References
  • 4 The Basic Concepts of Deep Learning
  • 4.1 Introduction
  • 4.2 Feedforward Neural Networks
  • 4.2.1 Structure of a Feedforward Neural Network
  • 4.2.2 Training Criteria and Activation Functions
  • 4.2.3 ReLU for Hidden Units
  • 4.2.4 Training with the BP Algorithm
  • 4.3 Manifold Learning and Embedding Spaces
  • 4.3.1 Manifolds, Embeddings, and Algorithms
  • 4.3.2 Autoencoders
  • 4.3.3 Deep Belief Networks
  • References
  • 5 Deep Learning Structures
  • 5.1 Introduction
  • 5.2 Stacked Autoencoders
  • 5.3 Convolutional Neural Networks
  • 5.4 Recurrent Neural Networks
  • 5.4.1 Basic Recurrent Neural Network
  • 5.4.2 Training a Recurrent Neural Network
  • 5.4.3 Long Short-Term Memory Network
  • 5.5 Variational Autoencoders
  • References
  • 6 Direction of Arrival Estimation
  • 6.1 Introduction
  • 6.2 Fundamentals of DOA Estimation
  • 6.3 Conventional DOA Estimation
  • 6.3.1 Subspace Methods
  • 6.3.2 Rotational Invariance Technique
  • 6.4 Statistical Learning Methods
  • 6.4.1 Steering Field Sampling
  • 6.4.2 Support Vector Machine MuSiC
  • 6.5 Neural Networks for Direction of Arrival
  • 6.5.1 Feature Extraction
  • 6.5.2 Backpropagation Neural Network
  • 6.5.3 Forward-Propagation Neural Network
  • 6.5.4 Autoencoder Framework for DOA Estimation with Array Imperfections
  • 6.5.5 Deep Learning for DOA Estimation with Random Arrays
  • References
  • 7 Beamforming
  • 7.1 Introduction
  • 7.2 Fundamentals of Beamforming
  • 7.2.1 Analog Beamforming
  • 7.2.2 Digital Beamforming/Precoding
  • 7.2.3 Hybrid Beamforming
  • 7.3 Conventional Beamforming
  • 7.3.1 Beamforming with Spatial Reference
  • 7.3.2 Beamforming with Temporal Reference
  • 7.4 Support Vector Machine Beamformer
  • 7.5 Beamforming with Kernels.
  • 7.5.1 Kernel Array Processors with Temporal Reference
  • 7.5.2 Kernel Array Processor with Spatial Reference
  • 7.6 RBF NN Beamformer
  • 7.7 Hybrid Beamforming with Q-Learning
  • References
  • 8 Computational Electromagnetics
  • 8.1 Introduction
  • 8.2 Finite-Difference Time Domain
  • 8.2.1 Deep Learning Approach
  • 8.3 Finite-Difference Frequency Domain
  • 8.3.1 Deep Learning Approach
  • 8.4 Finite Element Method
  • 8.4.1 Deep Learning Approach
  • 8.5 Inverse Scattering
  • 8.5.1 Nonlinear Electromagnetic Inverse Scattering Using DeepNIS
  • References
  • 9 Reconfigurable Antennas and Cognitive Radio
  • 9.1 Introduction
  • 9.2 Basic Cognitive Radio Architecture
  • 9.3 Reconfiguration Mechanisms in Reconfigurable Antennas
  • 9.4 Examples
  • 9.4.1 Reconfigurable Fractal Antennas
  • 9.4.2 Pattern Reconfigurable Microstrip Antenna
  • 9.4.3 Star Reconfigurable Antenna
  • 9.4.4 Reconfigurable Wideband Antenna
  • 9.4.5 Frequency Reconfigurable Antenna
  • 9.5 Machine Learning Implementation on Hardware
  • 9.6 Conclusion
  • References
  • About the Authors
  • Index.

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