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METHODS AND TECHNIQUES IN DEEP LEARNING - ADVANCEMENTS IN MMWAVE RADAR SOLUTIONS Hardback - 2022
by SANTRA
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- Hardback
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Details
- Title METHODS AND TECHNIQUES IN DEEP LEARNING - ADVANCEMENTS IN MMWAVE RADAR SOLUTIONS
- Author SANTRA
- Binding Hardback
- Condition New
- Pages 336
- Volumes 1
- Language ENG
- Publisher JOHN WILEY
- Publication date 2022
- Features Bibliography, Index
- Bookseller's Inventory # Adhya-9781119910657
- ISBN 9781119910657 / 111991065X
- Weight 1.37 lbs (0.62 kg)
- Dimensions 9 x 6 x 0.81 in (22.86 x 15.24 x 2.06 cm)
- Category Technology & Industrial Arts
- Library of Congress subjects Deep learning (Machine learning), Millimeter wave radar - Data processing
- Library of Congress Catalogue Number 2022036520
- Dewey Decimal Code 621.384
- Quantity available 500
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From the publisher
From the rear 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.
