Santra Avik et al. Methods & Techniques in Deep Learning: Advancements in mmWave Radar Solutions

Santra Avik, Hazra Souvik, Servadei Lorenzo, Thomas Stadelmayer, Michael Stephan, Anand Dubey. — Wiley-IEEE Press, 2023. — 332 p. — ISBN: 978-1119910657.Introduces multiple state-of-the-art Deep Learning architectures for mmwave radar in a variety of advanced applications.Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provide a timely and authoritative overview of the use of Artificial Intelligence (AI)-based processing for various mmwave radar applications. Focusing on practical Deep Learning techniques, this comprehensive volume explains the fundamentals of Deep Learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of Machine Learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready Deep Learning solutions while learning relevant skills for building any industrial-grade, sensor-based Deep Learning solution.A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmwave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of Machine Learning algorithms, and geometric deep learning used for processing point clouds. In addition, the book:Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms.
Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmwave radar sensors.
Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow.
Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization, and tracking in-cabin automotive occupancy sensing.Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions are an invaluable resource for industry professionals, researchers, and graduate students in systems engineering, signal processing, sensors, data science, and AI.