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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 36882 条记录，以下是401-410 订阅

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EffiE: Efficient Convolutional Neural Network for real-time EMG Pattern Recognition System on Edge Devices 11

EffiE: Efficient Convolutional Neural Network for Real-Time ...

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11th international ieee EMBS conference on Neural Engineering (ieee/EMBS NER)

作者： Lu, Jimmy Liang, Philip Rhim, Jin Chul Zhang, Xiaorong Qin, Zhuwei Lowell High Sch 1101 Eucalyptus Dr San Francisco CA 94132 USA San Francisco State Univ Sch Engn 1600 Holloway Ave San Francisco CA 94132 USA

ISBN: (纸本)9781665462921

With the advancement of deep learning (DL) technologies, applying DL methods to processing surface electromyographic (sEMG) signals for movement intent recognition has gained increasing interest in the research community. Compared to conventional non-DL methods commonly used for EMG pattern recognition (PR), DL algorithms have the advantage of automatically extracting sEMG features without the cumbersome manual feature engineering step and are especially effective in processing sEMG signals collected from 1-dimentional (1D) or 2D sensor arrays. However, a key challenge to the deployment of DL methods in sEMG-controlled neural-machine interface (NMI) applications (e.g., myoelectric controlled prostheses) is the high computational cost associated with DL algorithms (e.g., convolutional neural network (CNN)) since most NMI applications need to be implemented on resource-constrained embedded computer systems and have real-time requirements. In this paper, we designed and implemented EffiE - an efficient CNN for real-time EMG PR system on edge devices. The development of the EffiE system integrated several strategies including a deep transfer learning strategy to adaptively and quickly update the pre-trained CNN model based on the user's newly collected data on the edge device, and a deep learning quantization method that can dramatically reduce the memory consumption and computational load of the CNN model without sacrificing the model accuracy. The proposed EffiE system has been implemented on a Sony Spresense 6-core microcontroller board as a working prototype for real-time NMIs. The embedded NMI prototype has integrated input/output interfaces as well as efficient memory management and precise timing control schemes to achieve real-time DL-based myoelectric control of a bionic arm using hand gestures. We released all the source code at: https://***/MIC-Laboratory/EffiE

关键词： Convolutional neural networks

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Microprocessor load measurement in an embedded system 24

Microprocessor load measurement in an embedded system

引用

24th international Carpathian Control conference, ICCC 2023

作者： Drotos, Daniel Vasarhelyi, Jozsef University of Miskolc Institute of Automation and Infocommunication Miskolc Hungary

ISBN: (纸本)9798350310221

embedded systems often use FPGA (Field Programmable Gate Array) chips for their implementation. Soft processors are optimized for their implementation in such circuits. embedded system processor load is crucial when real-time applications are implemented. Processor load can be analyzed in several ways. The application described in this paper demonstrates a possible method to analyze the processor behavior and a model was developed, which is able to classify processor load. The model was implemented in FPGA without any modification of the original embedded system. The results of the analysis mark the time periods when the processor is overloaded. © 2023 ieee.

关键词： embedded systems

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Efficiency Meets Resilience: Accelerating Object Detection in embedded Environments Through Compressive Sensing 4th

Efficiency Meets Resilience: Accelerating Object Detection i...

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4th international conference on Intelligent systems and Pattern Recognition-ISPR

作者： Bouderbal, Imene Amamra, Abdenour Ecole Natl Preparatoire Etud Ingn Badji Mokhtar Rouiba BP 05 Algiers Algeria Ecole Mil Polytech Bordj El Bahri BP 17 Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

Optimizing convolutional neural networks (CNNs) for real-time performance in resource-constrained settings remains a challenge. This study introduces a novel approach to CNN design, integrating compressive sensing (CS) techniques to enhance both efficiency and robustness. Our proposed CS-based CNN architecture facilitates real-time object detection in embedded environments by significantly reducing computational complexity and improving transmission efficiency. Specifically, the CS paradigm is employed as a sampling-reconstruction network within the detection CNN, resulting in streamlined processing and reduced model complexity. Previous research demonstrated a remarkable 57% reduction in floating-point operations per second (FLOPs) and a 30% increase in speed for the redesigned SSD model compared to its original counterpart [4]. In this study, we extend our investigation to assess the resiliency of the CS-based CNN architecture against adversarial perturbations. Utilizing different augmented test sets, we evaluate the performance of the CS-based model alongside its traditional baseline. Our findings reveal that the CS-based CNN exhibits superior resilience, with a smaller decline in mean average precision (mAP) under adversarial conditions: a drop of 29.1% for the CS-based model compared to a 50.8% drop for the original architecture, for the worst-case scenario. These results underscore the potential of CS-based CNNs to enable efficient and robust real-time computer vision applications in embedded environments.

关键词： Convolutional Neural Networks Compressive Sensing real-time Performance Robustness Adversarial attacks embedded systems Object Detection

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Enhanced HW-NAS for affordance segmentation on wearable robotics 31

Enhanced HW-NAS for affordance segmentation on wearable robo...

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31st conference on Electronics Circuits and systems

作者： Ragusa, Edoardo Zunino, Rodolfo Loconsole, Claudio Frisoli, Antonio Gastaldo, Paolo Univ Genoa DITEN Genoa Italy Univ Mercatorum Fac Technol & Innovat Sci I-00186 Rome Italy Scuola Super Sant Anna Inst Mech Intelligence I-56010 Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI I-56127 Pisa Italy

ISBN: (纸本)9798350377217;9798350377200

The automatic design of deep neural network architectures executed in real-time on mobile processors would pave the way for new developments in wearable robotics. Processing information from cameras in real-time is essential to implement semiautonomous control pipelines. This work presents a hardware-aware neural architecture search suitable to generate architectures for affordance segmentation supported by mobile processors. The procedure uses a weight-sharing mechanism to speed up the search procedure and improves the convergence capability of the network selection procedure. In addition, the proposed search space has been design to induce multi-resolution features. These factors allow the network generation procedure to select architectures with a better trade-off between generalization performance and hardware requirements when compared to existing solutions.

关键词： embedded systems CNNs Wearable robots Affordance segmentation Grasping

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One System Call Hook to Rule All TEE OSes in the Cloud 17

One System Call Hook to Rule All TEE OSes in the Cloud

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ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Qin, Kailun Gu, Dawu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350368543;9798350368536

Confidential computing has revolutionized the way of in-use data protection in the Cloud, using the concept of Trusted Execution Environments (TEEs). Emerging from this paradigm are TEE OSes. They are extensively deployed in production settings, providing isolation protection and allowing legacy code to execute with minimal changes. However, they encounter challenges in cloud environments, particularly in creating compatibility layers, ensuring runtime protection, and efficiently managing TEE boundary transitions. In response, our work proposes to extend TEE OSes through a unified approach centered on system call (syscall) rewriting and interposition. We present xpoline++ - a stepwise (++) binary rewriting strategy executed on-the-fly with its trampoline set up at a manageable address (x). This allows for efficient construction of a compatibility layer at the binary syscall level and seamless transition to custom hook functions. Further, we introduce two syscall interposition extensions, namely xfilter and xswitchless, which respectively reduce the attack surface and improve the efficiency of TEE boundary switching to better serve the needs of cloud applications. Evaluations on a set of real-world workloads confirmed their effectiveness.

关键词： confidential containers trusted execution environment operating systems binary rewriting syscall interposition attack surface reduction switchless calls

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Data Volume-aware Computation Task Scheduling for Smart Grid Data Analytic applications

Data Volume-aware Computation Task Scheduling for Smart Grid...

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ieee international conference on Communications (ieee ICC)

作者： Guo, Binquan Li, Hongyan Yan, Ye Zhang, Zhou Wang, Peng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Tianjin Artificial Intelligence Innovat Ctr TAIIC Tianjin Peoples R China

ISBN: (纸本)9781538674628

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

关键词： Smart grid applications data analytics task scheduling job completion time branch and cut disjunctive formulation

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Intelligent Load Control with embedded Automation and Mobile Application 2

Intelligent Load Control with Embedded Automation and Mobile...

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2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Dusarlapudi, Kalyan Suresh, Appana Naga Naveen, Basa Likhitha, Komarina Lakshmi Koneru Lakshmaiah Education Foundation Eee Department Guntur India Koneru Lakshmaiah Education Foundation Cse Department Guntur India

ISBN: (纸本)9798350382952

The escalating rate of energy consumption and its adverse environmental effects, coupled with the reliance on non-renewable sources like fossil fuels, has heightened the importance of energy management. The framework for 'Intelligent Load Control with embedded Automation and Mobile Application' is presented, emphasizing the optimization of energy consumption. Utilizing Long short-term memory (LSTM) model from deep learning, to implement energy generation forecasting. These precise forecasts interface with an advanced load scheduling algorithm implemented by using Autoregressive Integrated Moving Average (ARIMA) Model, prioritizing loads based on priority matrix technique and efficiently scheduling loads based on predefined criteria and temporal proximity. Further, a controller enables dynamic load shifting between renewable sources and the grid further this will control the loads as per scheduled timetable, enhancing overall energy utilization. The integration of these methodologies, along with a mobile application for real-time monitoring, establishes an intelligent system poised to significantly improve energy efficiency and promote sustainable practices in the context of embedded automation and mobile applications. © 2024 ieee.

关键词： Energy utilization

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Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields

Loc-NeRF: Monte Carlo Localization using Neural Radiance Fie...

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ieee international conference on Robotics and Automation (ICRA)

作者： Maggio, Dominic Abate, Marcus Shi, Jingnan Mario, Courtney Carlone, Luca MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Draper Percept & Embedded ML Grp Cambridge MA 02139 USA Draper Draper Percept & Embedded ML Grp Cambridge MA USA

ISBN: (纸本)9798350323658

We present Loc-NeRF, a real-time vision-based robot localization approach that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our system uses a pre-trained NeRF model as the map of an environment and can localize itself in real-time using an RGB camera as the only exteroceptive sensor onboard the robot. While neural radiance fields have seen significant applications for visual rendering in computer vision and graphics, they have found limited use in robotics. Existing approaches for NeRF-based localization require both a good initial pose guess and significant computation, making them impractical for real-time robotics applications. By using Monte Carlo localization as a workhorse to estimate poses using a NeRF map model, LocNeRF is able to perform localization faster than the state of the art and without relying on an initial pose estimate. In addition to testing on synthetic data, we also run our system using real data collected by a Clearpath Jackal UGV and demonstrate for the first time the ability to perform real-time and global localization (albeit over a small workspace) with neural radiance fields. We make our code publicly available at https://***/MIT-SPARK/Loc-NeRF.

关键词： Computer vision

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A Power Consumption Perception and Dynamic Adjustment Model for embedded real-time systems 23

A Power Consumption Perception and Dynamic Adjustment Model ...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yingjie Bai, Fengbo Wang, Zumin Wang, Zixu Wang, Zizhong College of Information Engineering Dalian University Dalian116622 China Institute of Evidence Law and Forensic Science China University of Political Science and Law Beijing100088 China Technology Co. Ltd Beijing100088 China First Research Institute of the Ministry of Public Security of Prc Beijing100048 China School of Software Technology Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781665494571

embedded real-time systems are usually adopted on platforms with constrained resources in energy, but these systems have been required more and more powerful, of which, the powerful functions would inevitably increase the power consumption. Therefore, reduction of the power consumption for an embedded system becomes as important as the accomplishment of the task execution deadline. That means, the strategy of task scheduling technology and selection of voltage/frequency mechanism should be considered together, so that scheduling could provide an approach to achieve system energy saving. In the practice of this topic for energy consumption optimization, experimental facts have shown that the adoption with combining the Dynamic Voltage and Frequency Scaling (DVFS) technology and scheduling strategies could be a promising method to reduce system energy consumption. In this paper, an energy-aware scheduling model of embedded real-time system adopting the combination with dynamic power management is proposed, to ensure the tasks executed before the deadline and minimize processor energy consumption. Based on both considering aspects of timing control and energy consumption control, a dynamic power management scheduling algorithm is designed to sufficiently explore the adoption of the DVFS technology, to reduce the energy consumption, with fulfilling the execution deadline requirement and energy consumption adjustment, under the condition of multiple preemptive tasks. © 2021 ieee.

关键词： Energy consumption Power demand Scheduling algorithms Power system management Heuristic algorithms Process control Dynamic scheduling

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time-Aware Probabilistic Roadmaps for Multi-Query Path Planning in Dynamic Environments 8

Time-Aware Probabilistic Roadmaps for Multi-Query Path Plann...

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8th international conference on Robotic computing

作者： Schlapbach, Julius Schopferer, Simon German Aerosp Ctr DLR Braunschweig Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798331521561;9798331521554

In the evolving landscape of robotic applications, there is an increasing demand for advanced path planning algorithms capable of navigating dynamic environments. Established sampling-based algorithms like Probabilistic Roadmaps (PRM/PRM*) or Rapidly-exploring Random Trees (RRT/RRT*) and their derivatives have marked significant progress in this domain. However, there remains room for improvements when it comes to effectively planning cost-optimal paths through a priori known time-varying environments without resorting to online replanning. Addressing limitations of related algorithms and extending the range of considered dynamic factors, we propose time-Aware Probabilistic Roadmaps (TA-PRM*), combining PRM with the concept of time-dependent graphs. Our approach distinctively incorporates dynamic weighting into PRM and introduces a temporal dimension to the A* algorithm, while preserving both asymptotic optimality and probabilistic completeness properties. We also propose techniques for pruning in the temporal dimension and heuristic tuning to improve runtime efficiency, while slightly compromising optimality. Through comprehensive evaluations, we benchmark TA-PRM*'s efficacy against that of its best-known contemporaries and demonstrate its applicability in a real-world scenario for unmanned aerial vehicle trajectory planning.

关键词： Motion and Path Planning Autonomous Navigation Aerial systems: applications

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