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

共 37219 条记录，以下是1831-1840 订阅

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Optimization and Design of Coding Algorithm for Monitoring System Based on embedded Structure 7

Optimization and Design of Coding Algorithm for Monitoring S...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Huang, Rubing Zhao, Lianyi Anhui Vocational and Technical College School of Information Engineering Hefei China Anhui Water Resources and Hydropower Vocational and Technical College School of Mechanical & Electrical Engineering Hefei China

ISBN: (纸本)9781665478571

In view of the problems of large transmission delay, low video image compression rate, and high network transmission overhead in traditional monitoring systems. According to the characteristics of ARM platform and H.264 encoding algorithm, an algorithm optimization method based on x264 encoder embedded platform is proposed. The New Three-step Search method is used to realize motion compensation prediction encoding. Based on streaming media technology, The RTP and RTSP protocol realizes the packaging and real-time transmission of video stream data. The optimized encoder is applied to the embedded video monitoring system, which effectively improves the video encoding efficiency of the system. © 2022 ieee.

关键词： Signal encoding

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EarlyBird: Energy belongs to those who wake up early

EarlyBird: Energy belongs to those who wake up early

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Hugo Reymond Jean-Luc Béchennec Mikaël Briday Sébastien Faucou Isabelle Puaut Erven Rohou Univ Rennes Inria CNRS IRISA France Nantes Université École Centrale Nantes CNRS LS2N Nantes France

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

By relying on ambient energy, battery-less devices significantly increase the autonomy of IoT devices, enabling maintenance-free operation in remote locations. However, due to the scarcity of ambient energy, these devices rely on capacitors to buffer energy, and alternate between power-off phases where the device is harvesting energy and computation bursts. In most existing techniques, the device resumes execution only when the capacitor is full. However, we argue that doing so is sub-optimal. Instead, we advocate that waking-up the device sooner may yield better performance since the microcontroller consumes less power when operating at lower voltage. To this extent, we introduce EarlyBird, a technique that automatically computes a fine-tuned wake-up voltage for each resume point. EarlyBird leverages static analysis to determine how much energy is needed before resuming from a given program location, and provides a runtime library to enforce the early wake-up strategy. We evaluated how EarlyBird improves existing checkpointing techniques and results show an increase in the number of benchmarks executed per minute of up to 5.65×.

关键词： Performance evaluation Checkpointing Runtime library Microcontrollers Capacitors Voltage Static analysis Benchmark testing real-time systems Internet of Things

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Design and Implementation of 1KB SRAM array in 45 nm Technology for Low-Power applications 3

Design and Implementation of 1KB SRAM array in 45 nm Technol...

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3rd international conference on Advances in computing, Communication, embedded and Secure systems, ACCESS 2023

作者： Kumar, Aruru Sai Rao, K. Nishanth Sujith, A. Dhanuja, T. Vinay, M. Venkata Sai Vnr Vjiet Department of Ece Hyderabad500090 India Mlrit Department of Ece Hyderabad500090 India

ISBN: (纸本)9798350346169

Static Random Access Memory (SRAM) is a critical component of digital circuits as it is used for high-speed data storage and retrieval. The 6T SRAM cell is a popular type of SRAM cell, which is widely used in various electronic devices such as microprocessors, DSP, and FPGA applications. In this paper, we present a detailed analysis of the 6T SRAM cell. We discuss the working principle of the 6T SRAM cell, its design considerations, and performance *** primary objective of the study is to develop a memory array that consumes minimal power, has low leakage, and is compact in size. The array has a 1024-bit capacity, and read and write operations' power requirements have been extensively investigated. The power consumption during read and write operations of proposed 1KB SRAM array structure is 50.46 μW and 410 μW, respectively. The paper highlights the importance of power dissipation in CMOS-based SRAM arrays and compares the performance attributes of the proposed array with those of previous works. The operation of a 45 nm 6T SRAM memory cell was validated using the Cadence Virtuoso tool. © 2023 ieee.

关键词： Electric power utilization

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2nd international conference on Intelligent systems Design and Engineering applications, ISDEA 2023

2nd International Conference on Intelligent Systems Design a...

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2nd international conference on Intelligent systems Design and Engineering applications, ISDEA 2023

ISBN: (纸本)9789819963027

The proceedings contain 14 papers. The special focus in this conference is on Intelligent systems Design and Engineering applications. The topics include: Cloud Distribution Forecasting Model Using Ground Altitude Information and CNN;UX and Industry 5.0: A Study in Repairing Equipment Using Augmented reality;augmented reality Towards Industry 5.0: Improving Voice and Tap Interaction Based on User Experience Feedback;food Allergen Database for Japanese Restaurants and Its Application to Menu Recommendation System to Foreign Travelers;analysis of Grasping Mechanism for Random Regular Object of Improved Prosthetic Robotic Arm;a Human-Like Intelligent Swing System Using the Machine Vision Approach;multi-robot Positioning and Anti-interference Based on Ultra Wide Band;designing Smart Disinfection Hangers in the Covid-19 Epidemic;an Automatic Kiss Camera System Using Deep Neural Network Technique;based on embedded Technology to real-time Control for Analogous Active Suspension System;Intelligent Manufacturing Transformation Development Strategy of Jilin City Automobile Industry: A Research Based on SWOT-AHP Model;Persistent UAV Formation Flight by Dynamic Agent Replacement and Leader Selection.

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Memory-Efficient Gaussian Fitting for Depth Images in real time 39

Memory-Efficient Gaussian Fitting for Depth Images in Real T...

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ieee international conference on Robotics and Automation (ICRA)

作者： Li, Peter Zhi Xuan Karaman, Sertac Sze, Vivienne MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

computing consumes a significant portion of energy in many robotics applications, especially the ones involving energy-constrained robots. In addition, memory access accounts for a significant portion of the computing energy. For mapping a 3D environment, prior approaches reduce the map size while incurring a large memory overhead used for storing sensor measurements and temporary variables during computation. In this work, we present a memory-efficient algorithm, named Single-Pass Gaussian Fitting (SPGF), that accurately constructs a compact Gaussian Mixture Model (GMM) which approximates measurements from a depthmap generated from a depth camera. By incrementally constructing the GMM one pixel at a time in a single pass through the depthmap, SPGF achieves higher throughput and orders-of-magnitude lower memory overhead than prior multi-pass approaches. By processing the depthmap row-by-row, SPGF exploits intrinsic properties of the camera to efficiently and accurately infer surface geometries, which leads to higher precision than prior approaches while maintaining the same compactness of the GMM. Using a low-power ARM Cortex-A57 CPU on the NVIDIA Jetson TX2 platform, SPGF operates at 32fps, requires 43KB of memory overhead, and consumes only 0.11J per frame (depthmap). Thus, SPGF enables real-time mapping of large 3D environments on energy-constrained robots.

关键词： Three-dimensional displays Program processors Fitting Memory management Robot sensing systems Cameras Throughput

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Missing Modality Completion for Multi Frequency Radar HRRP Recognition Using GAN 2

Missing Modality Completion for Multi Frequency Radar HRRP R...

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2nd ieee international conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhou, Qiang Yu, Bingqian Meng, Yuanhao Zhang, Xin Li, Yang Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss. © 2024 ieee.

关键词： Frequency modulation

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Weedspedia: Deep Learning-Based Approach for Weed Detection using R-CNN, YoloV3 and Centernet

Weedspedia: Deep Learning-Based Approach for Weed Detection ...

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

作者： Sneha, N. Sundaram, Meenakshi Ranjan, Rajeev Abhishek Reva University School of Computer Science and Engineering Bengaluru India Reva University School of Computer Science and Applications India Reva University School of Csa Bengaluru India

ISBN: (纸本)9798350314946

Weed identification is a crucial undertaking in agriculture, where management and identification of weed growth in fields need to be addressed through deep learning algorithms. This study uses the Region-Based Convolutional Neural Network (R-CNN), Yolo, and Centernet object recognition algorithms to identify objects in images. This study also proposes a novel method for weed identification based on weed size. Numerous image-processing applications, including those for healthcare, traffic analysis, and agriculture, frequently use R-CNN. The suggested technique provides a practical and affordable answer for weed identification in precision agriculture. Herbicide use by farmers can be reduced as a result of being able to more precisely identify and control weed growth. Identification of weeds aids in weed growth control, which can harm natural ecosystems, compete with crops, diminish yields, and increase maintenance costs. The methodology of measuring the length and breadth of a weed detected by R-CNN requires extra processing stages, which aid in understanding the growth and subsequent steps required to eliminate weeds dependent on the growth of the weeds. Researchers often begin by gathering a sizable collection of photos of various weed species to construct a deep learning model for weed identification. The paper focus on identification of weed using 3 pre trained models followed by growth prediction to take necessary steps in agricultural land. © 2023 ieee.

关键词： Deep learning

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Enhancing Energy Consumption in Business through Data Analysis

Enhancing Energy Consumption in Business through Data Analys...

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2024 international conference on Decision Aid Sciences and applications, DASA 2024

作者： Shatat, Abdallah Shatat, Ahmad Mobin, Mohammad Theeb, Yazeed Management Information Systems Applied Science University Al Eker Bahrain Akhtar Basic Sciences Riyadh Elm University Riyadh Saudi Arabia Mohammad Jordan University Hospital Jordan University Amman Jordan

ISBN: (纸本)9798350369106

This paper investigates how data analytics and machine learning can optimize energy consumption in business operations. It explores the integration of predictive analytics, big data, and smart technologies in energy systems to reduce waste, enhance sustain ability, and lower operational costs. By analyzing real-time energy data, businesses can implement efficient strategies, from predictive maintenance to smart energy grids. Case studies from various industries demonstrate significant energy savings and operational improvements. However, challenges remain regarding data privacy, the high cost of infrastructure, and the need for skilled personnel to manage these technologies. © 2024 ieee.

关键词： data analytics Energy efficiency machine learning predictive maintenance smart grids sustain ability

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Mapping and Integration of Event- and time-triggered real-time Tasks on Partitioned Multi-core systems 28

Mapping and Integration of Event- and Time-triggered Real-ti...

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28th ieee international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Meroni, Carlo Craciunas, Silviu S. Finzi, Anaïs Pop, Paul Technical University of Denmark Kongens Lyngby Denmark TTTech Computertechnik AG Vienna Austria

ISBN: (纸本)9798350339918

In order to meet the requirements of critical applications, modern multi-core multi-SoC real-time systems must handle both periodic and sporadic events within specified deadlines. A two-level scheduling hierarchy that combines time-triggered and fixed-priority scheduling is effective for managing periodic time-triggered (TT) and sporadic event-triggered (ET) tasks, respectively. We introduce two polling-based approaches, called simple and advanced polling, for guaranteeing both TT and ET task deadlines within single core systems. We then propose an optimization heuristic for the task-to-core allocation problem based on genetic algorithms for fully partitioned multi-core systems that can be applied to both polling methods. We evaluate the schedulability and runtime performance of the polling approaches and investigate the effectiveness of the allocation heuristic using synthetic test cases based on real-world application characteristics. The results show that both polling approaches achieve high schedulability with low runtime, and the allocation heuristic can generate good solutions for fully partitioned systems. Furthermore, we show that the server design problem of the advanced polling approach can be integrated into the allocation heuristic to achieve better solutions in terms of schedulability and that our choice of the genetic algorithm has a good speedup when being parallelized. © 2023 ieee.

关键词： Genetic algorithms

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Distributed Data Sharing and Access Control in Industrial IoT Using Blockchain Technology 5

Distributed Data Sharing and Access Control in Industrial Io...

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5th international conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Li, Rui Sun, Yan Liu, Caiyun Wen, Ying Liu, Yitong Fujian Provincial Industrial and Information Industry Development Research Center Fujian Fuzhou China China Industrial Control Systems Cyber Emergency Reponse Team Beijing China

ISBN: (纸本)9798331507992

With the rapid development of the Industrial Internet of Things (IIoT), secure and efficient data sharing has become crucial for enabling industrial automation and smart transformation. However, existing centralized solutions face limitations in scalability, latency, and data privacy. To address these challenges, this paper proposes a blockchain-based distributed data-sharing and access control system tailored for resource-constrained industrial edge networks. The system adopts a three-layer architecture that integrates edge computing and blockchain technology to provide decentralized, low-latency data management. A novel smart contract framework is introduced to enforce multi-level access control and support fine-grained permissions for intra-domain and cross-domain data sharing. Experimental results show the system's improved performance in data access efficiency and throughput, offering a robust solution for real-time, secure IIoT applications. © 2024 ieee.

关键词： Smart contract

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