Signal Processing and Machine Learning Theory

Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.

1. Introduction to Signal Processing and Machine Learning Theory
2. Continuous-Time Signals and Systems
3. Discrete-Time Signals and Systems
4. Random Signals and Stochastic Processes
5. Sampling and Quantization
6. Digital Filter Structures and Their Implementation
7. Multi-rate Signal Processing for Software Radio Architectures
8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
9. Discrete Multi-Scale Transforms in Signal Processing
10. Frames in Signal Processing
11. Parametric Estimation
12. Adaptive Filters
13. Signal Processing over Graphs
14. Tensors for Signal Processing and Machine Learning
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation

Paulo S. R. Diniz's teaching and research interests are in analog and digital signal processing, adaptive signal processing, digital communications, wireless communications, multirate systems, stochastic processes, and electronic circuits. He has published over 300 refereed papers in some of these areas and wrote two textbooks and a research book. He has received awards for best papers and technical achievements