國立虎尾科技大學 |

Real-Time Machine Learning in Embedded Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Real-Time Machine Learning in Embedded Systems./
Author:	Chai, Fangming.
Description:	1 online resource (102 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:	Dissertations Abstracts International85-07A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381430790

Real-Time Machine Learning in Embedded Systems.
Chai, Fangming.

Real-Time Machine Learning in Embedded Systems. - 1 online resource (102 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: A.

Thesis (Ph.D.)--State University of New York at Binghamton, 2023.

Includes bibliographical references

Over the past decade, the field of machine learning has garnered significant attention within the realm of embedded systems, aiming to address new challenges and enhance solutions for existing issues. Machine learning techniques, such as data analysis and prediction, have played a transformative role in altering algorithms and applications deployed in embedded systems. Nevertheless, the utilization of machine learning in such systems is confronted with formidable constraints, most notably limited computational resources including CPU and memory. These constraints impose significant challenges in meeting critical requirements encompassing latency, power consumption, memory utilization, and computational capabilities.This scholarly work endeavors to present a diverse set of techniques applicable to networking, security, and real-time deep learning in the context of embedded systems. First, we introduce a novel approach to enhance transmission efficiency through the utilization of Bayes models in networking. This improvement is achieved through a meticulous analysis of link correlation patterns in Internet of Things (IoT) networks. Second, our research delves into the design of a new attack strategy, which centers on the analysis of Dynamic Voltage and Frequency Scaling (DVFS) enabled systems for the purpose of power analysis attacks. Additionally, we offer countermeasures to mitigate the vulnerabilities associated with such attacks. Finally, to meet the stringent real-time deadline requirements imposed on Convolutional Neural Networks (CNNs) in embedded systems, we propose a tailored design approach that involves the selection of appropriate models.To assess the efficacy of the solutions in this dissertation, we conduct a series of comprehensive simulations, providing empirical evidence of their effectiveness and performance characteristics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381430790Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Embedded systemsIndex Terms--Genre/Form:

554714
Electronic books.

Real-Time Machine Learning in Embedded Systems.
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