Deep Learning and Signal-Processing Methods for Multisensor Data Fusion | 7.26 MB
Title: Deep Learning and Signal-Processing Methods for Multisensor Data Fusion
Author: Arlene John · Barry Cardiff · Deepu John
Category: Nonfiction, Computers, Advanced Computing, Engineering, Cybernetics, Science & Nature, Technology, Electronics, Circuits
Language: English | 267 Pages | ISBN: 3031967232
Description:
This book focuses on the development of multisensor fusion algorithms for wearable devices that are useful in ambulatory health monitoring using signal-processing and deep learning-based methods. The algorithms described account for the signal quality prior to fusion, in order to enable reliable inferences without contributing to additional computational overhead. The content discussed is beneficial in the broad application of multisensor fusion, as the algorithms developed or discussed in the final chapters are generalized cases of the methods developed in the initial chapters, offering relevance to the broader multisensor fusion community.
This book focuses on the development of multisensor fusion algorithms for wearable devices that are useful in ambulatory health monitoring using signal-processing and deep learning-based methods. The algorithms described account for the signal quality prior to fusion, in order to enable reliable inferences without contributing to additional computational overhead. The content discussed is beneficial in the broad application of multisensor fusion, as the algorithms developed or discussed in the final chapters are generalized cases of the methods developed in the initial chapters, offering relevance to the broader multisensor fusion community.
- Provides a single-source reference to the history, methods and analysis of fusion algorithms
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