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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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embedded real-time System Trusted Protection Technology for Supply Chain Scenarios 12

Embedded Real-time System Trusted Protection Technology for ...

引用

12th international conference on Communication and Network Security, ICCNS 2022

作者： Zhou, Jin Ning, Zhenhu Wang, Qi Wang, Yuchen Faculty of Information Technology Beijing University of Technology China Trusted Computing Technology Co. Ltd Shenzhen China

ISBN: (纸本)9781450397520

embedded real-time operating systems are widely used in communication, industrial control and weapon intelligent control and other fields. However, due to the complex personnel and environment involved in the development of embedded systems, the development software is difficult to control independently, so it is extremely vulnerable to malicious code implantation attacks. Based on this, this paper focuses on the UCOS real-time operating system, analyzes the security threats faced by the UCOS development environment GCC in the process of precompiling, compiling, assembling and linking, and designs a plug-in-based GCC attack mode, which can implement malicious code implantation attacks on UCOS. . At the same time, a trusted protection framework for embedded real-time systems is proposed. By implementing active control and integrity measurement of firmware, operating systems and applications in embedded real-time systems, the trusted startup of embedded real-time systems is guaranteed and the supply chain stage is independently resisted. malicious code implantation attack. © 2022 ACM.

关键词： Supply chains

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Robot Tasks with Fuzzy time Requirements from Natural Language Instructions 8

Robot Tasks with Fuzzy Time Requirements from Natural Langua...

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8th international conference on Robotic computing

作者： Sucker, Sascha Neubauer, Michael Henrich, Dominik Univ Bayreuth Chair Appl Comp Sci 3 Robot & Embedded Syst Bayreuth Germany

ISBN: (纸本)9798331521561;9798331521554

Natural language allows robot programming to be accessible to everyone. However, the inherent fuzziness in natural language poses challenges for inflexible, traditional robot systems. We focus on instructions with fuzzy time requirements (e.g., "start in a few minutes"). Building on previous robotics research, we introduce fuzzy skills. These define an execution by the robot with so-called satisfaction functions representing vague execution time requirements. Such functions express a user's satisfaction over potential starting times for skill execution. When the robot handles multiple fuzzy skills, the satisfaction function provides a temporal tolerance window for execution, thus, enabling optimal scheduling based on satisfaction. We generalized such functions based on individual user expectations with a user study. The participants rated their satisfaction with an instruction's execution at various times. Our investigations reveal that trapezoidal functions best approximate the users' satisfaction. Additionally, the results suggest that users are more lenient if the execution is specified further into the future.

关键词： intelligent and flexible manufacturing scheduling user satisfaction temporal expectations

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Hierarchical Fault-Tolerant NoC Architecture for Reliable Communication 17

Hierarchical Fault-Tolerant NoC Architecture for Reliable Co...

引用

17th ieee international Symposium on embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Khalil, Kasem Mohaidat, Tamador Sherif, Ahmed Bayoumi, Magdy University of Mississippi Department of Electrical and Computer Engineering MS United States University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA United States

ISBN: (纸本)9798331530471

As multi-core systems continue to grow in complexity, Network-on-Chip (NoC) architectures have emerged as a scalable and efficient solution for managing on-chip communication. However, ensuring reliable communication in the presence of router faults remains a critical challenge, particularly for applications in safety-critical systems where failure can lead to catastrophic consequences. This paper proposes a novel fault-tolerant NoC architecture designed to improve reliability in multi-core systems. The proposed method divides the NoC into multiple regions, each managed by a dedicated Region Router (RR) and interconnected via a Centralized Router (CR). This hierarchical structure ensures that even in the presence of multiple faulty routers, data packets can be rerouted effectively, maintaining system functionality. We implement the proposed method on a Xilinx Virtex-7 FPGA and evaluate its performance in terms of latency, throughput, power consumption, and resource utilization. The experimental results demonstrate that the proposed method achieves a 15% improvement in system reliability with only a 3.2% increase in resource utilization, compared to the baseline NoC. Additionally, Mean time to Failure (MTTF) is extended by 25%, while the latency remains within an acceptable range of 3.2 ns. These findings highlight the potential of our approach for use in safety-critical and high-performance computing systems, offering enhanced fault tolerance without significantly compromising performance. © 2024 ieee.

关键词： Hierarchical systems

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A Traffic Road Sign Recognition System using Raspberry pi with Open CV Libraries Based on embedded Technologies

A Traffic Road Sign Recognition System using Raspberry pi wi...

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2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Ajay Sriram, D. Radhakrishnan, P. Raja, E. Praveen, M. Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798350348217

The use of computer systems for the purpose of image processing, which is also known as digital image processing, has applications in a variety of sectors, some of which include geology, oceanography, medicine, and space exploration to name a few. The major objective of this piece is to provide examples of different ways in which image processing techniques may be used even in environments with limited computational resources. In particular, we developed a traffic sign recognition system that is capable of reading and interpreting speed signs. This system is based on embedded technologies, and it was constructed by us. This research covers a wide range of topics, some of which are the characteristics of speed limit signs, the requirements and challenges of constructing an embedded real-time base system, and the methods for manipulating numbers that make use of image processing techniques that are based on form and dimension analysis. The study also provides a presentation of, and a demonstration for, several classification and recognition strategies. This research brings to light a number of difficulties with the consistency of color identification as a consequence of lighting circumstances;as a result, avoiding the usage of color models may result in a more effective solution than using color models. In addition to this, color analysis plays a particularly significant role in a broad range of other applications for the identification of road signs. These applications may be found all over the world. For the most part, lightweight solutions were selected for this project since the real-time applications that could be used as well as the capabilities of the Raspberry Pi were severely limited. The Raspberry Pi is the primary focus of concentration for the implementation as a whole. The Raspberry Pi is able to connect sensors, databases, and the results of image processing, in addition to carrying out actions on devices that are external to itself. © 2023 ieee.

关键词： real time systems

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A New Systematic Network Intrusion Detection System Using Deep Belief Network

A New Systematic Network Intrusion Detection System Using De...

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2023 ieee international conference on Quantum Technologies, Communications, computing, Hardware and embedded systems Security, iQ-CCHESS 2023

作者： Akshai, A. Anushri, M. Sonu, P. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350314946

This study discusses a Network Intrusion Detection System (NIDS) leveraging Deep Belief Networks to classify network traffic into multiple categories. By stacking Multiple Restricted Boltzmann Machines, the NIDS model fosters a Deep Belief Network-a generative graphical model. This implied approach is particularly good at identifying and identifying high-dimensional representations. Using Gibbs Sampling and the Contrastive Divergence method, the Deep Belief Network is first pre-trained unsupervised before being fine-tuned supervised. Our research study utilized the CICIDS2018 dataset to train and assess the effectiveness of our proposed DBN technique. Our empirical findings show that our two-phase training technique not only maintains impressive performance against other types of assaults, but also dramatically improves detection accuracy when comprehending samples of peculiar attacks. © 2023 ieee.

关键词： Intrusion detection

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Optimizing Deep Learning on Sustainable embedded systems: A Study of Handwritten Digit Recognition with CNN and OpenCV

Optimizing Deep Learning on Sustainable Embedded Systems: A ...

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ieee Region 10 international conference TENCON

作者： Chiang Liang Kok Chee Kit Ho R Vicknesh Charles Lee Yit Yan Koh College of Engineering The University of Newcastle Australia Engineering Cluster Singapore Institute of Technology Singapore

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This senior thesis develops a real-time handwritten digit identification system using a Raspberry Pi 3B+ with a camera module, leveraging a lightweight CNN optimized with MNIST. The project highlights the effective implementation of deep learning on edge computing devices through seamless integration of CNN, TensorFlow Lite, and OpenCV's real-time image processing. The system is both cost-effective and precise, enabling real-time digit recognition tasks. This proposed work illustrates the potential of AI applications in education, industry, and commerce, setting the stage for future advancements in embedded AI systems.

关键词： Deep learning Industries Handwriting recognition embedded systems Image processing real-time systems Convolutional neural networks Artificial intelligence ieee Regions Edge computing

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Design and Implementation of Oral English Intelligent Platform System Based on 5G Network 2

Design and Implementation of Oral English Intelligent Platfo...

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2nd ieee international conference on Image Processing and Computer applications, ICIPCA 2024

作者： Jin, Xiang Zhao, Zhijian Jiangsu Union Technical Institute Nanjing Engineering Branch Nanjing China

ISBN: (纸本)9798350360240

With the development of 5G network technology, real-time and efficient distance education and intelligent systems become feasible. This paper introduces anoral English Intelligent system based on 5G platform. The system takes advantage of the high speed and low latency of 5G network to achieve real-time capture, transmission, processing and intelligent of users' spoken English. The system uses advanced speech recognition technology combined with deep learning algorithm to accurately evaluate the user's pronunciation, intonation, fluency and other dimensions. In addition, the system integrates natural language processing technology to understand and respond to user queries and provide personalized learning recommendations. The experimental results show that the system can not only ensure the accuracy, but also significantly improve the user's learning experience and efficiency. © 2024 ieee.

关键词： 5G mobile communication systems

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Hierarchical, Distributed and Brain-Inspired Learning for Internet of Things systems 43

Hierarchical, Distributed and Brain-Inspired Learning for In...

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43rd ieee international conference on Distributed computing systems (ICDCS)

作者： Imani, Mohsen Kim, Yeseong Khaleghi, Behnam Morris, Justin Alimohamadi, Haleh Imani, Farhad Latapie, Hugo UC Irvine Irvine CA 92697 USA DGIST Daegu South Korea Qualcomm San Diego CA USA Calif State Univ San Marcos San Marcos TX USA UC Los Angeles Los Angeles CA USA Univ Connecticut Storrs CT USA CISCO San Jose CA USA

ISBN: (纸本)9798350339864

In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online training and inference in a highly distributed, cost-effective way. We use brain-inspired hyperdimensional (HD) computing as the key enabler. HD computing performs the computation tasks on a high-dimensional space to emulate functionalities of the human memory, such as inter-data relationship reasoning and information aggregation. EdgeHD exploits HD computing to effectively learn the classification models on individual devices and combine the models through the hierarchical IoT nodes without high communication costs. We also propose a hardware design that accelerates EdgeHD on low-power FPGA platforms. We evaluated EdgeHD for a wide range of real-world classification applications. The evaluation shows that EdgeHD provides highly efficient computation with reduced communication. For example, EdgeHD achieves on average 3.4x and 11.7x (1.9x and 7.8x) speedup and energy efficiency improvement during the training (inference) as compared to the centralized learning approach. It reduces the communication costs by 85% for the training and 78% for the inference.

关键词： Internet of things

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An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for real-time systems 26

An integrated approach of Genetic Algorithm and Machine Lear...

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26th ieee/ACM international Symposium on Distributed Simulation and real time applications (DS-RT)

作者： Kumar, Vikash Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (数字)9781665497992

ISBN: (纸本)9781665497992

Determining Worst-Case Execution time (WCET) is essential for temporal verification of real-time and embedded systems. These systems are designed to meet the stringent timing constraints imposed by the regulations. If a system gets delayed due to non-compliance with the deadline, it will lead to disastrous events. Worst-Case Data which gives maximum execution time, plays a vital role in the estimation of WCET. An evolutionary algorithm such as the Genetic Algorithm has been employed to generate the Worst-Case Data. The complexity of an evolutionary algorithm requires the use of several computational resources. This paper presents a novel method to replace the hardware and simulator used in the evolution process with machine learning models. This method reduces the overall time required to generate Worst-Case Data. Different machine learning models are trained to integrate with genetic algorithms. Our machine learning models are created using the Pygad Framework. The feasibility of the proposed approach is validated using benchmarks from different domains. The results show the speedup in the generation of Worst-Case Data.

关键词： embedded System Genetic Algorithm Machine Learning real-time System

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Effective Estimation of Histogram Differenced Value using Multiple Contiguous Virtual Layer (MCVL) for ITS applications 12

Effective Estimation of Histogram Differenced Value using Mu...

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12th international conference on Traffic and Logistic Engineering (ICTLE)

作者： Sankaranarayanan, Manipriya Indian Inst Informat Technol Dept Comp Sci & Engn Chittoor Andhra Pradesh India

ISBN: (纸本)9798350362800;9798350362794

For successful operation, any video-based Intelligent Transportation systems (ITS) application requires real-time road traffic information or characteristics such as speed, density, average delay, categorization, and so on. This paper proposes the Multiple Contiguous Virtual Layer (MCVL), a robust and unique vehicle identification framework that estimates any macroscopic traffic characteristics using computer vision algorithms on traffic video. This work mainly focuses on estimating a new parameter known as Histogram Differenced Value (HDV) for MCVL, which uses spatial color information to reveal substantial differences in traffic condition. Several benchmark traffic video datasets are used to test the performance and accuracy of estimations utilizing the proposed framework, with the results being discussed. The results indicate that using the proposed HDV parameter, the accuracy of the vehicle recognition process is improved with the combination of lowered computing cost of MCVL.

关键词： Intelligent Transportation systems Vehicle detection Macroscopic traffic parameters Computer Vision MCVL Histogram Differenced Value Spatial color information

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