Handbook of Neural Network Signal Processing 1st ediiton by Yu Hen Hu, Jenq Neng Hwang ISBN 0849323592 978-0849323591

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ISBN 10: 0849323592
ISBN 13: 978-0849323591
Author:  Yu Hen Hu, Jenq Neng Hwang 

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Handbook of Neural Network Signal Processing 1st Table of contents:

Chapter 1: Introduction to Neural Networks and Signal Processing
1.1. Overview of Neural Networks
1.2. Principles of Signal Processing
1.3. Relationship Between Neural Networks and Signal Processing
1.4. Applications of Neural Networks in Signal Processing
1.5. Structure of the Handbook

Chapter 2: Fundamentals of Neural Networks
2.1. Artificial Neurons and Networks
2.2. Types of Neural Networks
2.3. Supervised vs. Unsupervised Learning
2.4. Training Algorithms for Neural Networks
2.5. Activation Functions and Their Properties

Chapter 3: Signal Processing Basics
3.1. Introduction to Signal Representation
3.2. Fourier Transform and Time-Frequency Analysis
3.3. Digital Signal Processing Fundamentals
3.4. Signal Filtering Techniques
3.5. Convolution and Correlation

Chapter 4: Neural Network Architectures for Signal Processing
4.1. Feedforward Neural Networks (FNN)
4.2. Recurrent Neural Networks (RNN)
4.3. Convolutional Neural Networks (CNN) for Signal Processing
4.4. Radial Basis Function Networks (RBFN)
4.5. Deep Learning Architectures for Signal Processing

Chapter 5: Signal Classification and Feature Extraction
5.1. Signal Classification Techniques
5.2. Feature Extraction Methods
5.3. Dimensionality Reduction and Principal Component Analysis
5.4. Support Vector Machines and Neural Networks for Classification
5.5. Applications of Signal Classification in Speech, Audio, and Biomedical Signals

Chapter 6: Time-Series Analysis and Forecasting
6.1. Overview of Time-Series Data
6.2. Neural Network Models for Time-Series Prediction
6.3. Recurrent Neural Networks (RNN) for Sequence Prediction
6.4. Long Short-Term Memory Networks (LSTM) for Forecasting
6.5. Case Studies in Time-Series Forecasting

Chapter 7: Signal Denoising and Noise Reduction
7.1. Sources of Noise in Signals
7.2. Signal Denoising Techniques
7.3. Neural Networks for Signal Denoising
7.4. Wavelet Transforms and Their Applications
7.5. Hybrid Approaches Combining Neural Networks and Traditional Methods

Chapter 8: Adaptive Filtering and Neural Networks
8.1. Fundamentals of Adaptive Filtering
8.2. Least Mean Squares (LMS) Algorithm
8.3. Recursive Least Squares (RLS) Algorithm
8.4. Neural Network-Based Adaptive Filtering
8.5. Applications in Communication Systems and Audio Enhancement

Chapter 9: Image and Video Signal Processing with Neural Networks
9.1. Image and Video Processing Overview
9.2. Image Denoising and Restoration with Neural Networks
9.3. Convolutional Neural Networks for Image Classification
9.4. Object Detection and Tracking
9.5. Video Signal Processing: Neural Networks for Motion Estimation

Chapter 10: Applications of Neural Networks in Speech and Audio Processing
10.1. Speech Recognition and Processing
10.2. Speech Enhancement Using Neural Networks
10.3. Music Genre Classification and Audio Classification
10.4. Audio Signal Compression with Neural Networks
10.5. Case Studies in Speech and Audio Processing

Chapter 11: Neural Networks for Biomedical Signal Processing
11.1. Overview of Biomedical Signals
11.2. EEG and ECG Signal Processing with Neural Networks
11.3. Biomedical Image Analysis Using Neural Networks
11.4. Diagnostic Systems and Healthcare Applications
11.5. Neural Networks in Medical Monitoring Systems

Chapter 12: Neural Network Hardware and Implementation
12.1. Hardware Requirements for Neural Networks
12.2. FPGA and ASIC Implementations for Neural Networks
12.3. Software Tools for Neural Network Design and Simulation
12.4. Parallel and Distributed Computing for Neural Network Processing
12.5. Real-Time Signal Processing with Neural Networks

Chapter 13: Advanced Topics and Future Trends
13.1. Deep Learning for Signal Processing
13.2. Reinforcement Learning and Its Applications in Signal Processing
13.3. Transfer Learning and Domain Adaptation
13.4. Quantum Computing and Neural Networks in Signal Processing
13.5. Future Research Directions in Neural Network Signal Processing

Appendices
A.1. Glossary of Terms
A.2. Key Mathematical Formulas and Equations
A.3. List of Popular Neural Network Software Tools
A.4. Bibliography and Further Reading
A.5. Index

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