Titlebook: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing; Use Cases and Emergi Sudeep Pasricha,Muhammad Shafique Book 2024 The

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An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare App, by acting as clinical assistants, to analyzing electronic health records, deep learning models have proved to be beneficial in identifying health abnormalities and aiding diagnostics. This chapter discusses a framework that can be used to explore the design space of embedded neural network models

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Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applicationsnd data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute-intensive machine learning (ML) algorithms. Run-time variations with continuous stream of noisy input data, unreliable network conn

階層

A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applicationsly combines these concepts, leading to new application areas. Specifically, machine learning algorithms offer reliable decision-making, classification, and regression performance, while embedded devices allow these algorithms to run at the edge with limited computational power. New embedded devices

階層

Reinforcement Learning for Energy-Efficient Cloud Offloading of Mobile Embedded Applicationson response time. Offloading refers to the act of transferring computations from a mobile device to servers in the cloud. We believe that the effect of different wireless network technologies such as 3G, 4G, and Wi-Fi on the performance of offloading is a major concern that needs to be addressed. Ne

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Context-Aware Adaptive Anomaly Detection in IoT Systemsability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, most works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is overlooked. I

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Machine Learning Components for Autonomous Navigation Systemshuman supervision. Such systems have several intermediate tasks such as sensor data fusion, perception, planning, etc., that are critical for safe operation in the real world. Traditional CPS systems used model-based algorithms for such tasks that required domain knowledge, but in recent years machi

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Machine Learning for Efficient Perception in Automotive Cyber-Physical Systemsand performance goals. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We pr