| Description |
1 online resource (349 p.) |
| Contents |
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 |
| Notes |
Description based upon print version of record |
| Subject |
Machine learning.
|
|
Machine learning
|
| Form |
Electronic book
|
| Author |
Gupta, Arjun
|
|
Rojo-Álvarez, José Luis
|
| ISBN |
9781630817763 |
|
1630817767 |
|
