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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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An Optimization Approach of Container Startup times for time-Sensitive embedded systems 24

An Optimization Approach of Container Startup Times for Time...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Stahlbock, Lukas Weber, Jan Koster, Frank Iav GmbH Germany Germany

ISBN: (纸本)9798350319934

Containers are a lightweight virtualization method that is widely adopted in Cloud Environments, Internet of Things (IoT) and embedded devices. Apart from virtualization, containers provide an efficient way of building, storing and distributing layered filesystems for applications. This is one reason why containers became state of the art for service development, deployment and administration in IT systems. Although containers are lightweight, the image layering and container creation add overhead to the application startup time causing a cold start problem in serverless computing. Therefore, the overhead must be kept as small as possible, especially for resource-constrained devices in time-sensitive systems. We present a concept to decrease container startup times and perform analysis on a resource constrained device, a Renesas RCar H3. The study shows that using our concept, the average container startup time can be reduced by up to 64% on target hardware. Furthermore, we evaluate how the solution scales on different hardware setups and determine the impact of container configuration parameters on startup times. © 2022 ieee.

关键词： Containers

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ViEdge: An Edge-based Platform for Video Analytics applications in Smart Estates 33

ViEdge: An Edge-based Platform for Video Analytics Applicati...

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33rd ieee international conference on Computer Communications and Networks (ICCCN)

作者： Choudhary, Vishal Aggarwal, Rahul Lim, Hock Beng Chen, Binbin Singapore Univ Technol & Design Ctr Smart Syst CS2 Singapore Singapore Singapore Univ Technol & Design Informat Syst Technol & Design Pillar ISTD Singapore Singapore

ISBN: (纸本)9798350348439;9798350384611

Video analytics plays a crucial role in the development of smart estates and cities. applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate applications. In this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge's customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

关键词： Edge computing Video Analytics Distributed Video Stream Processing Kubernetes Apache Storm

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Next-Generation embedded systems with Synergies VLSI with Deep Learning: Design Methodologies, Optimization Techniques, and real-World applications

Next-Generation Embedded Systems with Synergies VLSI with De...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kalarani, G. Mohanapriya, M. Pattunnarajam, P. Sasikala, G. Sivakumar, J. Suresh, M. Jaya Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Ece Tamil Nadu Sriperumbudur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India

ISBN: (纸本)9798331529833

New generation of embedded systems with superior intelligence, energy efficiency, and performance have emerged as a result of the merging of deep learning with Very-Large-Scale Integration (VLSI) technology. Methodologies for design, optimisation strategies, and practical uses of next-generation embedded systems are the foci of this study, which investigates the ways in which VLSI and deep learning might work together. These systems have the potential to transform several industries, such as transportation, medicine, robotics, and the IoT, by harnessing the processing power of deep neural networks with the improvements in semiconductor fabrication. Prior to delving into the advantages of bespoke hardware design for deep learning inference and training, we trace the history of very large scale integration (VLSI) technology and its incorporation with deep learning algorithms. Investigated here are the design techniques that, when applied to very large scale integration (VLSI) architectures like FPGAs and ASICs, allow for the efficient mapping of deep learning models onto these devices. We show case studies that show how these methods work and talk about the trade-offs between performance, power consumption, and adaptability. The development of next-generation embedded systems relies heavily on optimisation approaches. Model compression, quantisation, and pruning are some of the optimisation strategies that we examine;they lessen the memory and computational demands of deep learning models without drastically altering their accuracy. For embedded devices with limited resources, these methods are crucial for implementing deep learning models. Additionally, we explore the practical uses of embedded systems augmented with VLSI and deep learning. By capitalising on the complementary strengths of VLSI and deep learning, applications like autonomous driving, medical imaging, and smart home automation are revolutionising entire industries. In this paper, we examine the design

关键词： Integrated circuit design

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A Comprehensive Approach to real-time and Batch Processing for Energy-Efficient IoT Homes: Leveraging Lambda Architecture and Data Lakes 15

A Comprehensive Approach to Real-Time and Batch Processing f...

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15th international conference on Information, Intelligence, systems and applications, IISA 2024

作者： Serepas, Filippos Papias, Ioannis Bellos, Nikolaos Marinakis, Vangelis Holistic Ike Athens Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Athens Greece

ISBN: (纸本)9798350368833

The incorporation of IoT technology into energy-efficient home systems has resulted in a surge in data volume, prompting the need for sophisticated storage and processing solutions. This paper proposes a system that integrates the Lambda Architecture with data lakes to address real-time and batch processing needs in the context of energy-efficient homes. By leveraging technologies such as timescaleDB for short-term storage and Apache Hudi for long-term storage, coupled with Kafka for data streaming, the system ensures efficient data management and analysis. real-time insights are provided through GraphQL-powered visualizations, while batch processing facilitates advanced analytics and machine learning model training. The proposed system addresses the dual demands of end-users seeking real-time insights and data scientists requiring extensive datasets for analysis. © 2024 ieee.

关键词： Smart homes

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Improving the Reaction Latency Analysis of Message Synchronization in ROS

Improving the Reaction Latency Analysis of Message Synchroni...

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

作者： Chenhao Wu Ruoxiang Li Naijun Zhan Nan Guan SKLCS Institution of Software Chinese Academy of Sciences Beijing China City University of Hong Kong Hong Kong SAR School of Computer Science Peking University Beijing China

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Multi-sensor data fusion plays a crucial role in modern autonomous systems, enabling them to perceive the surroundings from multiple dimensions. However, the accuracy of fusion output can be compromised by the temporal inconsistency of input messages from different sources. ROS provides algorithms of message synchronization to mitigate this misalignment before the data fusion process. Nevertheless, this introduces additional latency in processing each message, which influences the real-time performance of ROS systems. Previous research [1] is the first to analyze and bound the reaction latency of the Approximatetime synchronization policy in ROS, which is essential for analyzing the system-level end-to-end reaction time. However, their bound is overly pessimistic. In this paper, we propose a safe and tight reaction latency upper bound for the Approximatetime policy. We conduct experiments to validate its accuracy and assess its improvements compared to [1].

关键词： Accuracy Upper bound Autonomous systems Data integration Approximation algorithms real-time systems Synchronization

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real-time Detection of Printed Dot-Matrix Characters on the Roll-to-Roll Manufactured Packages with embedded Computer

Real-Time Detection of Printed Dot-Matrix Characters on the ...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Ngo, Quoc Vinh Khuu, Long Duy Pham, Phuong-Tung Tran, Van-Xuan Nguyen, Quoc-Chi Phung, Thanh Huy Ho Chi Minh City University of Technology Dept. of Mechatronics Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam Edf R&d Uk Centre Manchester United Kingdom

ISBN: (纸本)9798350353983

Optical character recognition (OCR) is an important application in industrial manufacturing for identifying products and related information. In high-speed production lines, characters printed on products are often represented as dot-matrix patterns, which are typically printed using continuous inkjet methods. In such production lines, the image acquisition and detection processes must be synchronized with the moving products on the line and be sufficiently fast for real-time detection to meet productivity requirements. This study presents a simple high-speed detection method to ensure that the aforementioned objectives can be achieved. The method involves a synchronized imaging system to capture product images. In addition, a straightforward process is proposed for detecting and recognizing the characters. Accordingly, the images are preprocessed to locate the positions of characters. These images are then passed through a Convolutional Neural Network (CNN) for recognition. All processing is performed directly at the manufacturing site, and the results are then sent to the cloud for further tasks, forming an edge-computing system for manufacturing. In this study, the proposed concept method is implemented to detect winning codes printed on packages. These packages are manufactured on a roll-to-roll (R2R) system at high speeds. For real-time monitoring, the algorithm utilizes CUDA-based embedded systems, and the proposed method is employed to accelerate the detection process, reducing the overall processing time to approximately 30 ms per product. The CNN achieved an accuracy of about 98% for both training and validation, effectively meeting the system requirements. © 2024 ieee.

关键词： Optical character recognition

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Big Data Analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 ieee.

关键词： Data quality

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Revolutionizing Industries: AI-Driven Case Studies and Success Stories in real-World applications and Innovations

Revolutionizing Industries: AI-Driven Case Studies and Succe...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Tyagi, Lalit Gupta, Anish Sisodia, Vibhash Singh CEC Chandigarh Engineering College Jhanjeri Mohali India Shri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (纸本)9798350355338

Looking at the various case studies and success stories in this paper, one could realize how artificial intelligence (AI) has transcended industries. It showcases how AI-based innovations are changing the world through case examples of different industries including healthcare, finance, manufacturing, and customer service. This paper describes real-life application of AI which demonstrates practical advantages, difficulties, and possible solutions of AI application. Examples include improvement in the methods of diagnosing diseases in the medical field, analysis of credit risk in the financial secto, smart production systems in manufacturing industries, and customised services to the customer in a business setting. Thus, the information provided in the paper aspires to present an overall picture of the impact of the AI and how can it help in enabling effectiveness, creativity, and competitiveness in the present-day fast-paced digital environment. © 2024 ieee.

关键词： Smart manufacturing

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Implementation of Neural Networks in real-time Swarm Robotics applications

Implementation of Neural Networks in Real-Time Swarm Robotic...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Yazici, Emre Temeltas, Hakan Istanbul Technical University Department of Control and Automation Engineering Istanbul Turkey Software Development Department Niso Software Technologies Izmir Turkey

ISBN: (纸本)9798350337020

Over the last few decades, significant attention has been devoted to the coordinated motion of swarm systems, which involve multiple autonomous robots. These systems have a range of potential applications, such as creating formations with physical or non-physical bonds, navigating to desired locations while maintaining formation, and preventing collisions. To achieve coordinated motion, recent researches have focused on the potential function method. We previously validated novel potential functions and developed a sliding-mode speed controllers to promote collective behavior. In this study, we implement an accepted neural network with one hidden and one output layer to update potential function weights with decentralized manner in real-time, thereby enhancing swarm behavior for formation creation and maintenance. The effectiveness of this approach is demonstrated through simulation and processor-in-the-loop tests using two different micro-controllers as well as measuring execution time of neural networks' models. By successfully validating neural networks implementation in this context, our study extends the current understanding of swarm behavior and opens up new possibilities for real-time swarm applications. © 2023 ieee.

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Energy-Efficient Stochastic computing Architectures for Neural Network Performance in FPGA systems 6

Energy-Efficient Stochastic Computing Architectures for Neur...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Dinesh Babu, A. Gomathy, C. Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9798331522667

This paper presents an energy-efficient stochastic computing (SC) architecture tailored for neural network applications on FPGA platforms. The proposed architecture integrates a Bayesian neural network (BNN) computation unit with a novel hybrid stochastic number generator (SNG) that combines Linear Feedback Shift Registers (LFSRs) and Halton sequences. This hybrid approach enhances the quality of generated bit-streams, significantly reducing correlation and improving the accuracy of SC-based computations. Stochastic computing allows complex arithmetic operations to be performed using simple logical gates, reducing hardware complexity and power consumption. Bayesian neural networks, due to their probabilistic framework, align well with the random nature of SC, allowing for resilient and efficient computations in resource-constrained environments. Experimental evaluations were conducted on an FPGA, measuring power consumption, computational accuracy, and resource utilization across varying bit-stream lengths. Results indicate that the proposed architecture achieves approximately 25% lower power consumption compared to conventional SC-based neural network implementations, with an improvement in mean squared error (MSE) accuracy. This energy efficiency and accuracy make the architecture particularly suitable for embedded systems, IoT devices, and other applications requiring low-power, high-performance neural network solutions. The proposed design offers a scalable solution for future neural network implementations in energy-constrained environments, contributing to advancements in sustainable hardware design. © 2025 ieee.

关键词： Mean square error

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