the automotive industry relies on wide range of networking protocols to facilitate effective communication and data exchange within vehicles. Local Interconnect Network (LIN) is a cost-effective protocol commonly util...
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the paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. the objective was to identify the optimal algorithm ...
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Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-pr...
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ISBN:
(数字)9781665453448
ISBN:
(纸本)9781665453448
Edge appliances built with machine learning applications have been gradually adopted in a wide variety of application fields, such as intelligent transportation, the banking industry, and medical diagnosis. Privacy-preserving computation approaches can be used on smart appliances in order to secure the privacy of sensitive data, including application data and the parameters of machine learning models. Nevertheless, the data privacy is achieved at the cost of execution time. that is, the execution speed of a secure machine learning application is several orders of magnitude slower than that of the application in plaintext. Especially, the performance gap is enlarged for edge appliances. In this work, in order to improve the execution efficiency of secure applications, an open-source software framework CrypTen is targeted, which is widely used for building secure machine learning applications using the Secure Multi-Party Computation (SMPC) based privacy-preserving computation approach. We analyze the performance characteristics of the secure machine learning applications built with CrypTen, and the analysis reveals that the communication overhead hinders the execution of the secure applications. To tackle the issue, a communication library, OpenMPI, is added to the CrypTen framework as a new communication backend to boost the application performance by up to 50%. We further develop a hybrid communication scheme by combining the OpenMPI backend withthe original communication backend withthe CrypTen framework. the experimental results show that the enhanced CrypTen framework is able to provide better performance for the small-size data (LeNet5 on MNIST dataset by up to 50% of speedup) and maintain similar performance for large-size data (AlexNet on CIFAR-10), compared to the original CrypTen framework.
In recent years, the large-scale integration of new energy sources, such as wind and photovoltaic power into power grid, has necessitated higher standards for the safe, stable, and flexible control of power systems. T...
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this study presents a lightweight deep learning model developed for DPU-accelerated systems. It aims to provide real-time autonomous driving on resource-constrained systems such as the Ultra96v2. A customized kids ele...
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Execution of resource-intensive tasks, such as artificial intelligence (AI), big-data algorithms, video processing, etc. is a common requirement in distributed embeddedsystems today. the typical solution is to execut...
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the SSE-YOLO model, optimized for object detection, has exhibited exceptional effectiveness in fire detection tasks, particularly within environments where computational resources are limited, such as embedded devices...
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In the world of real-timesystems (RTS), security has often been overlooked in the design process. However, withthe emergence of the Internet of things and Cyber-Physical systems, RTS are now frequently used in inter...
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In today's fast, advanced and tech-oriented world, human life has been simplified and upgraded at a much higher level which withtime will increase with a great spectrum. According to the report, the smart card ma...
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ISBN:
(纸本)9789380544519
In today's fast, advanced and tech-oriented world, human life has been simplified and upgraded at a much higher level which withtime will increase with a great spectrum. According to the report, the smart card market is projected to experience a growth rate (CAGR) of 7.33% by 2033. Payment systems have evolved a lot withtime and from which we are about to discuss security payment transactions using smart cards. A smart card is a card with a chip comprising CPU, RAM and ROM for storage purposes. this concept of using a plastic card with an embedded chip was first patented in 1968 by two German inventors, Jurgen Dethloff and Helmut Grottrup. Motorola and Bull developed the first smart card microchips in 1977. Due to its ability to provide more secured and authenticated transaction smart cards are fit for use in contactless payments as compared to barcodes or machine-readable stripes. Smart cards find utility in scenarios demanding heightened authentication and security in contrast to alternative machine-readable data stores such as barcodes and magnetic stripes. the first is passwords, confidential and public cryptographic keys for encryption and decryption etc. It performs personal identification by collecting such personal information. this involves biometric information like fingerprint scans, iris scans and images. the second is digital currency. Electronic cash is debited at the time of purchase and the cardholder must pay 'real' money to complete it. Personal information (excluding personal information), such as purchase information at certain stores, medical history and travel history. One of the most crucial factors in the success of smart cards is that they are secure, transferable and can maintain data integrity and then data is stored on the card. In this article we will examine the security of contactless payment systems, considering personal threats and different attacks which the system must protect against. Our review focused on the importance of trus
the emerging field of ubiquitous computing leads to new application and processing speed requirements in various every day aspects. When it comes to image processing tasks, which require many computations, the limited...
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