Signal processinga traditional branch of electrical engineeringfocuses on the modeling and analysis of data representations of physical events, lying at the heart of today's digital world. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains. The faculty of the Signal Processing and Machine Learning emphasis area explore enabling technologies for the transformation and interpretation of information. This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). Signal processing algorithms, architectures, and systems are at the heart of modern technologies that generate. The signal processing algorithms that use machine learning techniques are anticipated to improve the channel capacity, link reliability, device compatibility.

These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Machine learning and signal processing for human pose recovery and behavior analysis. Experts in Signal Processing and Machine Learning are much needed as the related applications are infinite: from creating data-driven solutions for medical and biological problems to enabling self-driving cars and autonomous robots.

Signal processing machine learning