Methods and Techniques in Deep Learning - by Avik Santra & Souvik Hazra & Lorenzo Servadei & Thomas Stadelmayer & Michael Stephan & Anand Dubey

About the Book

"The advent of deep learning has transformed many fields and resulted in state-of-art solutions in computer vision, natural language processing and speech processing, etc. However, the application of deep learning algorithms to radars is still by and large at its nascent stage. A radar system consists of two parts: first, the radar hardware, including the RF transceiver, waveform generator, receiver unit, antenna and system packaging. State-of-art SiGe and CMOS are candidate technologies for mm-wave short-range radars and offer flexibility for integration and smaller form-factor. Second part is the sensing aspect, which relies on signal processing or deep learning algorithms that parses the radar return echo into meaningful target information facilitating a desired application"--

Book Synopsis

Methods and Techniques in Deep Learning

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 provides 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 skills that are relevant 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 are 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 is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

From the Back Cover

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 provides 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 skills that are relevant 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 are 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 is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

About the Author

Avik Santra is Head of Advanced Artificial Intelligence at Infineon Technologies, Munich, Germany.

Souvik Hazra is a Senior Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany.

Lorenzo Servadei is a Senior Staff Machine Learning Engineer at Infineon Technologies and a Lecturer at The Technical University of Munich (TU München), Germany.

Thomas Stadelmayer is a Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany.

Michael Stephan is a PhD candidate at Infineon Technologies, Munich, Germany and Friedrich-Alexander-University of Erlangen-Nürnberg, Germany.

Anand Dubey is a Staff Machine Learning Engineer at Infineon Technologies.