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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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Enhancing Networked embedded systems Using Deep Learning Techniques 3

Enhancing Networked Embedded Systems Using Deep Learning Tec...

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3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Asha, K. Kumar, Ritesh Fiza, Samreen Department of Life Sciences Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

Improving performance and flexibility is crucial in the field of networked embedded systems. Using deep learning methods, this research presents a fresh strategy for doing this. Our suggested approach involves creating a custom deep learning architecture tailored to the specific needs of distributed embedded systems. This strategy makes use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) to evaluate real-time data streams, expedite decision-making, and intelligently adapt to ever-changing environments. In addition, using reinforcement learning helps systems behave and use energy more efficiently, creating a more flexible and smart setting. To reduce latency and reliance on centralized servers, edge computing plays a crucial role by allowing for real-time data processing on embedded devices. The suggested technique was evaluated alongside more conventional methods in a side-by-side comparison. Imaginary numbers were utilized for demonstration purposes. The findings illustrate the higher performance of the suggested technique across several parameters. When compared to baseline deep learning methods like Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Federated Learning, Transfer Learning, and time Series Analysis with Deep Learning, the proposed method shows marked improvements in accuracy, latency, energy efficiency, robustness, security, scalability, and resource utilization. Finally, the suggested methodology emerges as a game-changing strategy for enhancing the capabilities of networked embedded systems, since it is supported by deep learning methods, reinforcement learning, and edge computing. Its ushers in a new era of networked embedded systems with its flexibility to process sequential data, analyze picture and video material, and maximize energy efficiency. The results of the comparison study validate the superior

关键词： Energy efficiency

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Framework for the Analysis and Configuration of real-time OpenMP applications 21

Framework for the Analysis and Configuration of Real-Time Op...

引用

ieee 21st international conference on Industrial Informatics (INDIN)

作者： Carvalho, Tiago Pinho, Luis Miguel Samadi, Mohammad Royuela, Sara Munera, Adrian Quinones, Eduardo Polytech Inst Porto Porto Portugal Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781665493130

High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.

关键词： Cyber-Physical systems real-time Timing Analysis Task-to-thread Mapping

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Improved the Accuracy of Indoor Positioning at the Meter Level in Large and Complex Environments Through AI-Powered Sensor Fusion

Improved the Accuracy of Indoor Positioning at the Meter Lev...

引用

2024 ieee international conference on computing, ICOCO 2024

作者： Asaduzzaman, Mina Tan, Kim Geok Tan, Choo Peng Sayeed, Md. Shohel Hossain, Ferdous Hasan, Md. Abu Sayed Mahfuz Faculty of InformationScience and Technology Multimedia University Melaka Malaysia Faculty of Engineering and Technology Multimedia University Melaka Malaysia Faculty of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798331530303

Indoor positioning systems (IPS) continue to encounter significant challenges in achieving meter-level accuracy, particularly in large and intricate environments such as airports, hospitals, and industrial sites. Despite substantial advancements in technologies like Wi-Fi Fine time Measurement (FTM), Ultra-Wideband (UWB), and 5G, their scalability and precision often suffer in dynamic, obstacle-laden settings. To enhance accuracy and adaptability, this study presents an innovative AI-driven sensor fusion technique that integrates machine learning models with various positioning technologies. This study achieves significant improvements in positioning accuracy through dynamic sensor input adjustments and real-time environmental awareness. Additionally, this study also includes results from a thorough evaluation of the system's effectiveness across diverse practical applications, while exploring its potential uses within the Internet of Things (IoT) framework. ©2024 ieee.

关键词： time measurement

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Sensor Classifications and Their applications in IoT systems

Sensor Classifications and Their Applications in IoT Systems

引用

2023 ieee international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Sajja, Komalsai Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Vasistha IoT Lab Brics Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350340129

The Internet of Things (IoT), which connects various systems and devices to create more innovative environments, has completely changed how we interact with technology. Sensors are essential to the success of IoT systems because they are vital in gathering and analyzing real-time data from the physical environment. This paper comprehensively analyses sensor classifications and their applications in IoT systems. It explores the different types of sensors commonly used in IoT, their functionality, and their suitability for various IoT-enabled intelligent applications. This paper also provides valuable insights for IoT designers and researchers in selecting appropriate sensors and developing more efficient and reliable intelligent systems for the future. © 2023 ieee.

关键词： Internet of things

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Supporting Deep Learning Based Autonomous Driving applications on Resource-Constrained Devices 2

Supporting Deep Learning Based Autonomous Driving Applicatio...

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2nd ieee international conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Uprety, Ishparsh Agnello, Griffen Zhao, Xinghui Washington State University Vancouver School of Engineering and Computer Science 14204 NE Samon Creek Ave. VancouverWA United States

ISBN: (纸本)9798350307733

With recent advances in computing and sensing technologies, autonomous driving has gained increasing interest and become a promising platform to support the next generation intelligent transportation systems. A critical requirement for autonomous driving systems is to be able to utilize AI and machine learning techniques to make reliable decisions on edge devices in a timely manner. Deploying reliable machine learning models on edge devices in a real-time environment is a challenging task. real-time applications such as traffic surveillance or traffic sign detection require consistently low latency in order for the device to keep up with its environment. Edge devices are able to compute machine learning tasks without needing to offload computation to a cloud server, however they often have a tight limit on the amount of available resources. Quantization is a model optimization technique used to increase inference speed at the cost of accuracy. In this paper, we carried out a comprehensive study on utilizing quantization to enable efficient and effective decision making for autonomous driving applications. Various quantization methods are analyzed, with a focus on the trade-offs between the learning performance and computational performance. Furthermore, we identify pre-processing techniques and types of data input that may improve the model's accuracy. The proposed approach will enhance the decision making process in resource-constrained environment, and provide more flexibility for deploying the autonomous driving applications. © 2024 ieee.

关键词： Autonomous vehicles

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Development of an Internet of Things-Based Supervisory Control and Data Acquisition System for Wastewater Monitoring on the Communal Wastewater Treatment Plant

Development of an Internet of Things-Based Supervisory Contr...

引用

2024 international conference on Information Technology and computing, ICITCOM 2024

作者： Soetedjo, Aryuanto Hendriarianti, Evy Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350379839

The wastewater parameters should be monitored regularly to maintain the quality in the range of allowed standard. A real-time wastewater monitoring system is required for effective monitoring. This paper presents an implementation of Internet of Things (IoT)-based Supervisory Control and Data Acquisition (SCADA) for wastewater monitoring in the communal wastewater treatment plant (WWTP). The proposed system adopts the Haiwell Human Machine Interface (HMI) as the SCADA system and integrates it with the IoT platform. To evaluate the proposed SCADA, the WWTP Simulator is developed to model the wastewater parameters, implement them on the embedded modules, and communicate with the HMI using WiFi. The experimental results show that the real-time monitoring, historical data, and alarm features work appropriately and offer benefits for wastewater monitoring and analysis. Further, the performance of Modbus Transmission Control Protocol (TCP) and Message Queuing Telemetry Transport (MQTT) protocols used for communication between HMI and WWTP Simulator is compared, where the data delivery ratio of the Modbus TCP and MQTT protocols is 99.03% and 99.78%, respectively. © 2024 ieee.

关键词： SCADA systems

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Design Challenges in HPC for AI/ML applications 17

Design Challenges in HPC for AI/ML Applications

引用

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

作者： Rajan, Vikram Kumar, Niwaran Chandra Ranjan, Nihar Krishna, Amgothu Murali Bharat Electronics Limited Product Development and Innovation Centre Bangalore India

ISBN: (纸本)9798331530471

High-performance computing (HPC) and Artificial Intelligence (AI) are two areas of computing that are seeing fast-paced growth. The HPC systems have evolved to incorporate the capability of running mid-level AI algorithms for C4I (Command, Control, Communications, Computers and Intelligence) networks. HPC solutions can be used at the edge or in the cloud. They are used to solve large-scale computing problems, which makes it suitable for a wide range of problems. They are able to perform this in reasonable cost and time. AI enables HPC systems to more intelligently process workloads and make full use of the processing capability. With a variety of CPUs, GPUs, TPUs, and ASICs, design of the system architecture to keep pace with the advancing technology is an uphill task. In the current paper, we discuss about one such HPC system of CPUs and GPUs and the challenges involved in architecture and design. Specifically, on ensuring the bandwidth between the CPU and GPU and ensuring the thermal performance of the system. © 2024 ieee.

关键词： Graphics processing unit

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Repairing Neural Networks for Safety in Robotic systems using Predictive Models

Repairing Neural Networks for Safety in Robotic Systems usin...

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2024 international conference on Intelligent Robots and systems

作者： Majd, Keyvan Clark, Geoffrey Fainekos, Georgios Ben Amor, Heni Arizona State Univ SCAI Tempe AZ 85281 USA Toyota NA R&D Ann Arbor MI USA

ISBN: (纸本)9798350377712;9798350377705

This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety constraints. The predictive models can encompass various aspects relevant to robot learning applications, such as proprioceptive states and collision likelihood. Our experimental results demonstrate that the learned policy successfully adheres to a predefined set of safety constraints on two applications: mobile robot navigation, and real-world lower-leg prostheses. Additionally, we have shown that our method effectively reduces repeated interaction with the robot, leading to substantial time savings during the learning process.

关键词： Robot learning

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Optimizing Quality of Service in IoMT Through Fog computing-Enabled Service Composition 13

Optimizing Quality of Service in IoMT Through Fog Computing-...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Anand, Dinesh Kaur, Avinash Singh, Parminder School of Computer Applications Lovely Professional University Punjab Phagwara India Guru Nanak Dev Engineering College Department of Computer Applications Punjab Ludhiana India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350380583

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the vital nature of health- care data, and the necessity for real-time response, guaranteeing Quality of Service (QoS) in IoMT systems offers considerable issues. This research presents a technique for addressing these difficulties in IoMT by exploiting Fog computing-Enabled Service Composition. The issue statement centers on the need to improve QoS in IoMT systems, which includes response time, reliability, and energy consumption. Existing solutions struggle to match the severe criteria of IoMT, where service deployment, orchestration, and data processing must be performed in a fast, energy-efficient, and reliable way. To address these concerns, we propose a set of techniques that combine Fog computing with service composition to optimize QoS in IoMT. The framework is proposed to incorporate dynamics service orchestration, context ware decision-making and balance the load into the IoMT systems to allow service composition at network edge. The implementation of proposed technique is done and demonstrates how computing nodes are worked inside the IoMT system to enable dynamic service composition. Our results show improvements in the QoS parameters like response times, optimized energy consumption and enhanced reliability. © 2024 ieee.

关键词： Fog computing Internet of Medical Things Quality of Service Service Composition

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Object Detection Model for Tacking System Implemtation using Deep Learning Technique 5

Object Detection Model for Tacking System Implemtation using...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh Thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Thing discovery and tracking using deep learning has emerged as a cutting-edge technology with applications spanning autonomous vehicles, surveillance systems, robotics, and more. This paper provides an overview of the principles, methodologies, and recent advancements in the field of object detection and tracking using deep learning techniques. We delve into the fundamental concepts of convolutional neural networks (CNNs), region-based approaches, and tracking algorithms that have revolutionized object detection. Furthermore, we explore the challenges and potential applications of this technology, from real-time video analysis to enhancing safety and security. The research in this domain continues to push the boundaries of what is achievable, making thing discovery and following by means of bottomless erudition a key enabler of future smart systems. Object detection and tracking are important tasks in computer vision that have numerous applications in various domains, including surveillance, robotics, and autonomous driving. Deep learning algorithms have shown remarkable performance in these tasks, enabling accurate and efficient detection and tracking of objects in complex environments. This paper presents an overview of object detection and tracking using deep learning, highlighting the key techniques and algorithms used in these tasks. We discuss popular deep learning architectures, such as Faster R-CNN, YOLO, and SSD, and their applications in object detection. We also review deep learning-based tracking algorithms, such as Siamese networks and correlation filters, and their performance in object tracking. Finally, we discuss the challenges and future directions of object detection and tracking using deep learning. © 2024 ieee.

关键词： Computer vision Convolutional Neural Networks (CNNs) Deep learning Object detection Object tracking real-time video analysis and Surveillance systems Region-based approaches

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