{"product_id":"methods-and-techniques-in-deep-learning-advancements-in-mmwave-radar-solutions-9781119910657","title":"Methods and Techniques in Deep Learning: Advancements in Mmwave Radar Solutions","description":"\u003cb\u003eMethods and Techniques in Deep Learning\u003c\/b\u003e \u003cp\u003e\u003cb\u003eIntroduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMethods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eA 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: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms\u003c\/li\u003e \u003cli\u003e Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors\u003c\/li\u003e \u003cli\u003e Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow\u003c\/li\u003e \u003cli\u003e Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMethods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions\u003c\/i\u003e is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Avik Santra, Souvik Hazra, Lorenzo Servadei\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley-IEEE Press\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/08\/2022\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 336\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.37lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.81d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781119910657\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAvik Santra\u003c\/b\u003e is Head of Advanced Artificial Intelligence at Infineon Technologies, Munich, Germany. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSouvik Hazra\u003c\/b\u003e is a Senior Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLorenzo Servadei\u003c\/b\u003e is a Senior Staff Machine Learning Engineer at Infineon Technologies and a Lecturer at The Technical University of Munich (TU München), Germany. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThomas Stadelmayer\u003c\/b\u003e is a Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMichael Stephan\u003c\/b\u003e is a PhD candidate at Infineon Technologies, Munich, Germany and Friedrich-Alexander-University of Erlangen-Nürnberg, Germany. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAnand Dubey\u003c\/b\u003e is a Staff Machine Learning Engineer at Infineon Technologies.\u003cbr\u003e\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Hardcover","offer_id":40803307716723,"sku":"9.78112E+12","price":226.42,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/products\/img_aa3cafc1-1262-4c06-8b76-cf987ef9277a.jpg?v=1682513169","url":"https:\/\/bookstorenmore.com\/products\/methods-and-techniques-in-deep-learning-advancements-in-mmwave-radar-solutions-9781119910657","provider":"Bookstore N More","version":"1.0","type":"link"}