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检索条件"任意字段=IEEE Symposium on Real-Time and Embedded Technology and Applications"

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ANN-based Performance Estimation of embedded Software for RISC-V Processors 33

ANN-based Performance Estimation of Embedded Software for RI...

引用

33rd ieee International Workshop on Rapid System Prototyping (RSP) - Shortening the Path from Specification to Prototype

作者： Zhang, Weiyan Goli, Mehran Mahzoon, Alireza Drechsler, Rolf DFKI GmbH Cyber Phys Syst D-28359 Bremen Germany Univ Bremen Inst Comp Sci D-28359 Bremen Germany

ISBN: (纸本)9798350398519

The demand for optimized and efficient embedded software is increasing in many applications such as the Internet of Things (IoT) or other Cyber-Physical Systems (CPS). Hence, early performance analysis of embedded software is essential to perform Design Space Exploration (DSE), ensure efficiency, and meet time-to-market constraints. Designers usually use real hardware, simulators, or static analyzers to obtain the performance. However, these methods suffer from serious drawbacks as real hardware is not available in the early stage of the design process, simulators either do not support any timing accuracy or require large execution time, and static analyzers need details of the hardware microarchitecture. In this paper, we present a novel Artificial Neural Network (ANN)-based approach that allows a fast and accurate performance estimation of embedded software for RISC-V processors in the early design phases. This can significantly reduce the burden on designers to perform DSE. The proposed approach takes advantage of the dynamic analysis technique and analytical models and does not require any microarchitecturerelated parameters such as cache misses, cache hits, and memorylevel parallelism. We compare our proposed microarchitectureindependent approach with state-of-the-art in terms of speed and accuracy. Our experiments on various benchmarks demonstrate that the proposed approach achieves a speed-up of 4.41x compared to a RISC-V Virtual Prototype (VP) at the Electronic System Level (ESL), while the estimation results have only a Mean Absolute Percentage Error (MAPE) of 2%.

关键词： RISC-V performance estimation embedded software artificial neural network

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Advancing Fire Safety in Hong Kong Buildings: An IoT-Based Fire Accident Indication System

Advancing Fire Safety in Hong Kong Buildings: An IoT-Based F...

引用

2023 ieee International symposium on Product Compliance Engineering - Asia 2023, ISPCE-AS 2023

作者： Lee, C.C. Lam, S.K. Kwong, W.K. Lau, W.K. Tung, C.N. Cho, Y.M. Li, C.Y. Hong Kong Metropolitan University School of Science and Technology Hong Kong China

ISBN: (纸本)9798350361209

This study focuses on the development of an innovative fire accident indication system for buildings in Hong Kong, leveraging the Internet of Things (IoT) technology. The proposed system integrates IoT devices and sensors to enable real-time monitoring, early fire detection, and prompt notification of fire incidents. By utilizing the power of interconnected devices and data analytics, the system enhances fire safety measures, improves emergency response time, and minimizes potential damage. The research considers the unique characteristics and challenges of Hong Kong's urban environment, including high population density and complex building structures. The outcomes of this study contribute to advancing fire safety standards and emergency management practices in Hong Kong, with potential applications in other regions facing similar circumstances. © 2023 ieee.

关键词： Internet of things

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2023 ieee 17th International symposium on Medical Information and Communication technology, ISMICT 2023

2023 IEEE 17th International Symposium on Medical Informatio...

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17th ieee International symposium on Medical Information and Communication technology, ISMICT 2023

ISBN: (纸本)9798350304176

The proceedings contain 21 papers. The topics discussed include: a preliminary study of on/off-body propagation channels for brain telemetry using a flexible wearable antenna;estimation of source direction with spherical array receiver in molecular communication;medical imaging system design using microwave antennas and portable platform;channel modeling activities of international standardization on ieee 802.15 TG6MA for human and vehicle body area networks;standardization activities of ieee P802.15.6ma wireless human and vehicle area networks;hybrid smartwatch multi-factor authentication;simulation and implementation of an IoT-based secure smart biology laboratory for smart cities;reinforcement learning based cloud and edge resource allocation for real-time telemedicine;and visible light communications for healthcare applications: opportunities and challenges.

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NetReach: Guaranteed Network Availability and Reachability to enable Resilient Networks for embedded Systems

NetReach: Guaranteed Network Availability and Reachability t...

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ieee European symposium on Security and Privacy Workshops

作者： Tom Van Eyck Sam Michiels Xiaojiang Du Danny Hughes DistriNet KU Leuven Leuven Belgium Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798350367294

ISBN: (纸本)9798350367324

Networked embedded devices are increasingly deployed in safety critical environments such as robotics, smart manufacturing and autonomous vehicles. Availability is an essential prerequisite of safety critical systems, which depend upon timely access to sensed data to inform the real-time control of actuators. Recent work has demonstrated that trusted computing features can be used to guarantee the availability of local resources to the safety-critical applications. However, prior work fails to guarantee the availability of a network connection, which is essential for correct system operation. To address this issue, we contribute NetReach, which uses Arm TrustZone to guarantee network availability to, and the reachability of, critical applications via a secure backup channel. Evaluation of NetReach shows that it can preserve the network connectivity of critical applications while under attack, with a worst case overhead of 18.66 % for networked software running in the Normal World. Furthermore, NetReach introduces minimal additional code in the Secure World (only 418 lines of code). The presented features of NetReach enable future work toward resilient networks.

关键词： Trusted computing Codes embedded systems Systems operation Robot sensing systems Software real-time systems

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real-time Fall Detection for embedded System Using Deep Learning

Real-Time Fall Detection for Embedded System Using Deep Lear...

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2022 International symposium on Imaging, Sensing, and Optical Memory, ODS 2022

作者： Yang, Chih-Chieh Lee, Chieh-En Huang, Tzu-Yuan Tien, Chung-Hao Department of Photonics National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

In this study, we developed an embedded visual system which can real-time detect the falls by depth video in multiple views. Based on MoViNets, we successfully achieved 99.21% accuracy under 380 testing events. ©...

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Comparative Analysis of Hardware Resource Utilization for MPPT Techniques: P&O, Incremental Conductance, and ANN Using Processor-in-the-Loop Testing on STM32F407 12

Comparative Analysis of Hardware Resource Utilization for MP...

引用

2024 12th International Conference on Systems and Control, ICSC 2024

作者： Rimouche, W. Talha, A. Boumaaraf, H. University of Sciences and Technology Houari Boumediene Laboratory of Instrumentation Faculty of Electrical Engenniring BP 32 El-Alia Algiers16111 Algeria University of Sciences and Technology Houari Boumediene Faculty of Electrical Engenniring BP 32 El-Alia Algiers16111 Algeria

ISBN: (纸本)9798331531812

This paper presents a comparative analysis of hardware resource utilization for three Maximum Power Point Tracking (MPPT) techniques: Perturb and Observe (P&O), Incremental Conductance (IC), and Artificial Neural Network (ANN). The evaluation uses Processor-in-the-Loop (PIL) testing on the STM32F407 microcontroller. The study aims to provide insights into the efficiency and practicality of each MPPT method when implemented on an embedded system. Key performance metrics, including volatile memory consumption and task execution time, are measured and compared. The results which indicate significant differences in resource utilization between the classical P&O and IC algorithms and the ANN algorithm, highlight the trade-offs between computational complexity and tracking accuracy. These findings have practical implications for engineers and researchers in the field, as they quantify the impact of using intelligent techniques on hardware requirements in real world implementations, thereby engaging the reader with the potential applications of the research. © 2024 ieee.

关键词： Hardware-in-the-loop simulation

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Execution of Resource Intensive Tasks on a Heterogeneous SoC for Low-Latency embedded Compute 24

Execution of Resource Intensive Tasks on a Heterogeneous SoC...

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24th International Carpathian Control Conference, ICCC 2023

作者： Fekete, Gabor Kovacshazy, Tamas Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary

ISBN: (纸本)9798350310221

Execution of resource-intensive tasks, such as artificial intelligence (AI), big-data algorithms, video processing, etc. is a common requirement in distributed embedded systems today. The typical solution is to execute such algorithms on some cloud computing service, as embedded systems do not have the computing resources for such tasks. However, this approach is not viable in real-time Cyber-Physical Systems (RT-CPS) due to the high-latency, non-real-time nature of cloud computing. The latency requirements can be only satisfied with an edge compute solution in the form of an edge data center or by a heterogeneous System on Chip (SoC) based implementation, which has the required high-performance computing resources on the chip. In the paper, we investigate the viability of the heterogeneous SoC compute approach on the Texas Instruments AM5729 SoC, which was specifically designed for such applications. The paper introduces the hardware capabilities of the SoC, the software development, and the run-time (Linux-based) environment, and presents the results of a detailed performance evaluation on relevant computing tasks. © 2023 ieee.

关键词： System-on-chip

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Research on real-time Video Stream Authentication Based on SM2 in H.264 4

Research on Real-Time Video Stream Authentication Based on S...

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2023 4th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Li, Xiaohui Yu, Zhe Han, Lin Yao, Qiushi Zhongyuan University of Technology The Frontier Information Technology Research Institute Zhengzhou China

ISBN: (纸本)9798350308877

Due to the vulnerability of H.264/AVC video streams to tampering or substitution attacks during network transmission, the integrity and authenticity of transmitted video stream data cannot be guaranteed. To address this issue, this paper proposes a real-time video stream authentication method based on the SM2 algorithm. Utilizing SM2's digital signature technology on an embedded MCU, the method employs GOP (Group of Pictures) as the unit for digital signatures. Through the H.264 protocol's custom type of NALU, the digital signature results are cleverly overlaid onto the original transmission stream. This approach ensures that the overlaid signature results maintain compliance with the H.264 standard, causing no disruption to the original video stream data while preserving compatibility and non-intrusiveness. The method provides an efficient, secure, and viable solution for video stream authentication, with potential widespread applications in the fields of security monitoring and video transmission. © 2023 ieee.

关键词： Network security

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P-YOLOv8: Efficient and Accurate real-time Detection of Distracted Driving

P-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

引用

2024 ieee High Performance Extreme Computing Conference, HPEC 2024

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Pyramids Higher Inst. for Engineering & Technology Dept. of Electronics and Communications Engineering Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350387131

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024 ieee.

关键词： Convolutional neural networks

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AttYOLO: A model for object detection

AttYOLO: A model for object detection

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Computer applications and Information technology (ISCAIT), International symposium on

作者： Quan Long College of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

As a fundamental task in computer vision, object detection serves as critical infrastructure for advanced image understanding by providing structured localization and classification data. Addressing the limitation of existing lightweight models in feature representation within complex scenarios, this study proposes an enhanced architecture based on YOLOv7-tiny. The integration of Efficient Channel Attention (ECA) modules establishes cross-channel interaction mechanisms, which significantly improves discriminative feature capture while preserving real-time inference capabilities. Experimental results demonstrate a $1.1 \%$ absolute improvement in mAP metrics on coco dataset, offering a novel technical solution for high-precision real-time detection on embedded systems.

关键词： Measurement Location awareness Computer vision embedded systems Computational modeling Object detection Computer architecture real-time systems Critical infrastructure Information technology

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