Overview

Full Product Details

Author:   Scott Kuzdeba
Publisher:   Artech House Publishers
Imprint:   Artech House Publishers
Edition:   Unabridged edition
Dimensions:   Width: 17.80cm , Height: 2.00cm , Length: 25.40cm
Weight:   0.676kg
ISBN:  

9781685690335

ISBN 10:   1685690335
Pages:   310
Publication Date:   31 January 2025
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Foreword Preface   1. Introduction 1.1 Radio Frequency Machine Learning 1.2 Where to Apply Deep Learning in the RF Domain 1.3 Shifting from Traditional to ML Processing 1.4 Book Organization 1.5 RF ML Ecosystem 1.6 Future Directions 1.7 References   2. RF ML Classification 2.1 RF ML: Applications & When to Use 2.2 Data for Classification 2.3. Algorithms & Architecture 2.4. Performance Assessment 2.5. Architecture Studies 2.6. Shifting from Classification to Regression 2.7. References   3 RF ML Clustering 3.1 RF ML: Applications & When to Use 3.2 Data for Clustering 3.3 Algorithms & Architecture 3.4 Offline vs. Online Operation 3.5 Performance Assessment 3.6 Architecture Studies 3.7 Using Supervision in Unsupervised Training 3.8 Segmentation 3.9 References   4 Waveform Synthesis (A Generative Approach) 4.1 RF ML: Applications & When to Use 4.2 Algorithms & Architecture 4.3 Performance Assessment 4.4 Architecture Studies 4.5 Reinforcement Learning-Driven Design 4.6 Adversarial RF ML 4.7 References   5 Designing for RF Systems 5.1 RF ML: Applications & When to Use 5.2 Streaming Operations 5.3 Detection 5.4 Hybrid Solutions 5.5 Considering the Environment and Scenario 5.6 Control 5.7 Multi-Modal Considerations 5.8 References   6 Developing Robust RF ML Solutions 6.1 Trustworthy AI 6.2 Transfer Learning 6.3 Operational Challenges 6.4 Explainability 6.5 Confidence 6.6 References   7 RF Data and Augmentation 7.1 Challenges 7.2 RF ML Datasets 7.3 Collecting Data 7.4 Modeling, Simulation, and Synthetic Data Generation 7.5 Data Augmentation 7.6 Data Imbalance & Sampling Methods 7.7 References   8 Edge, Federated, and Continual Learning 8.1 RF ML: Applications & When to Use 8.2 Efficient Algorithms (Tiny ML) 8.3 A Note on Training 8.4 Federated Learning 8.5 Continual and Active Learning 8.6 Adaptive Control 8.7 Hardware 8.8 References   Appendix – Background A.1 Artificial Neural Networks A.2 Understanding What it Means to Learning A.3 How Theory Impacts Design Decisions A.4 References   List of Acronyms and Abbreviations Author Biography Index

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Author Information

Scott Kuzdeba has been leading the development of RF machine learning (ML) algorithms and systems for over 15 years. He has served in numerous roles including chief scientist, principal investigator, subject matter expert, and capture leader. Currently Dr. Kuzdeba is the director of the Edge and Spectral Artificial Intelligence group within the BAE Systems FAST Labs(TM) research and development organization.

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