Machine Learning in Signal Processing
Applications, Challenges, and the Road Ahead
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 4 October 2024
- ISBN 9780367618926
- Binding Paperback
- No. of pages388 pages
- Size 254x178 mm
- Weight 453 g
- Language English
- Illustrations 211 Illustrations, black & white; 33 Halftones, black & white; 178 Line drawings, black & white; 53 Tables, black & white 601
Categories
Short description:
Machine Learning in Signal Processing: Applications, Challenges and Road Ahead offers a comprehensive approach towards research orientation for familiarising ‘signal processing (SP)’ concepts to machine learning (ML).
MoreLong description:
Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML).
ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML.
The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML.
FEATURES
- Focuses on addressing the missing connection between signal processing and ML
- Provides a one-stop guide reference for readers
- Oriented toward material and flow with regards to general introduction and technical aspects
- Comprehensively elaborates on the material with examples and diagrams
This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.
MoreTable of Contents:
1. Introduction to Signal Processing and Machine Learning
Kavitha Somaraj
2. Learning Theory (Supervised/Unsupervised) for Signal Processing
Ruby Jain, Bhuvan Jain, and Manimala Puri
3. Supervised and Unsupervised Learning Theory for Signal Processing
Sowmya K. B.
4. Applications of Signal Processing
Anuj Kumar Singh and Ankit Garg
5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing
V. Ajantha Devi and Mohd Naved
6. Brain–Computer Interfacing
Paras Nath Singh
7. Adaptive Filters and Neural Net
Sowmya K. B., Chandana G., and Anjana Mahaveer Daigond
8. Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network
Saikat Majumder
9. Intelligent Video Surveillance Systems Using Deep Learning Methods
Anjanadevi Bondalapati and Manjaiah D. H.
10. Stationary Signal, Autocorrelation, and Linear and Discriminant Analysis
Bandana Mahapatra and Kumar Sanjay Bhorekar
11. Intelligent System for Fault Detection in Rotating Electromechanical Machines.
Pascal Dore, Saad Chakkor, and Ahmed El Oualkadi
12. Wavelet Transformation and Machine Learning Techniques for Digital Signal Analysis in IoT Systems
Rajalakshmi Krishnamurthi and Dhanalekshmi Gopinathan
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