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27th International symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Khara, Anirban Javid, Iqra Khara, Sibaram Prasad, Ramjee George Washington University WashingtonDC United States Sharda University Dept. of Electrical Electronics and Communication Engineering Greater Noida India Aarhus University Department of Business Development and Technology Herning Denmark

ISBN: (纸本)9798350392319

G will have extensive features of AI not only in providing AI-based services to users, but also in managing 6G networks making it more intelligent through its embedded AI tools. It will facilitate to implement AI-driven services using massive sensors and IoT devices. Native AI embedded in 6G network architecture will help optimize the spectrum, resources and performance in real-time. AI will make 6 G an autonomous network being self-organizing, self-healing and self-managed. The AI-driven applications are useful in sensing, industry automation, smart city and healthcare. We present the 6G network architecture for different sliced networks like sensing, IoT, distributed computing and satellite integrated 6G networks to identify different network operations that are to be managed by native AI. This also outlines the main technological challenges in 6 G and associated roles of AI for solutions. We present AI deployment scenarios based on various technological issues in 6G. Finally, we emphasize the importance of using appropriate AI tools specific 6 G issues giving examples of mapping between 6 G applications and recommended AI tools. We present the burning issues of AI implementation in 6G and challenges ahead. This work shall be useful for researchers to take up the issues and challenges for development of AI models and testify in the network for implementation. © 2024 ieee.

关键词： Queueing networks

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Learning Target-Aware Vision Transformers for real-time UAV Tracking

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ieee TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Li, Shuiwang Yang, Xiangyang Wang, Xucheng Zeng, Dan Ye, Hengzhou Zhao, Qijun Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sys Guilin 541004 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610017 Peoples R China

In recent years, the field of unmanned aerial vehicle (UAV) tracking has grown rapidly, finding numerous applications across various industries. While the discriminative correlation filters (DCF)-based trackers remain the most efficient and widely used in the UAV tracking, recently lightweight convolutional neural network (CNN)-based trackers using filter pruning have also demonstrated impressive efficiency and precision. However, the performance of these lightweight CNN-based trackers is still far from satisfactory. In the generic visual tracking, emerging vision transformer (ViT)-based trackers have shown great success by using cross-attention instead of correlation operation, enabling more effective capturing of relationships between the target and the search image. But to best of the authors' knowledge, the UAV tracking community has not yet well explored the potential of ViTs for more effective and efficient template-search coupling for UAV tracking. In this article, we propose an efficient ViT-based tracking framework for real-time UAV tracking. Our framework integrates feature learning and template-search coupling into an efficient one-stream ViT to avoid an extra heavy relation modeling module. However, we observe that it tends to weaken the target information through transformer blocks due to the significantly more background tokens. To address this problem, we propose to maximize the mutual information (MI) between the template image and its feature representation produced by the ViT. The proposed method is dubbed TATrack. In addition, to further enhance efficiency, we introduce a novel MI maximization-based knowledge distillation, which strikes a better trade-off between accuracy and efficiency. Exhaustive experiments on five benchmarks show that the proposed tracker achieves state-of-the-art performance in UAV tracking. Code is released at: https://***/xyyang317/TATrack.

关键词： real time target aware unmanned aerial vehicle (UAV) tracking vision transform

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A Task Admission Scheduling Strategy Based on Adaptive time Slicing in Mobile Edge Computing 22

A Task Admission Scheduling Strategy Based on Adaptive Time ...

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22nd ieee International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Tang, Yanpu Liao, Zhuofan Tang, Xiaoyong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

ISBN: (纸本)9798331509712

The rise of Multi-access Edge Computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this paper, we propose an Adaptive time Slice Admission Scheduling (ATSAS) strategy to solve these problems. Specifically, ATSAS proposes an Adaptive time Slice Algorithm (ATSA) and a task Admission Scheduling Algorithm (ASA). ATSA dynamically adjusts the time slice based on real-time network conditions and task flow characteristics. ASA, assisted by ATSA, dynamically adjusts task priorities based on the remaining data amount, deadline requirements, real-time network conditions, and the location of the task buffer. After that, ASA makes different scheduling strategies for tasks. Through the coordination of ATSA and ASA, the proposed strategy ensures the high efficiency and fairness of processing tasks in different realistic scenarios. The simulation results show that, compared with the existing O2A and OTDS in different scenarios, the proposed ATSAS reduces the average number of waiting services in the edge server buffer by 38.63% and 35.34%, and accelerates the average response speed of tasks by about 39.76% and 32.41%. © 2024 ieee.

关键词： Mobile edge computing

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Work-in-Progress: real-time On-board Processing for Cloud Detection in FACSAT-2 Multispectral Satellite Imagery 43

Work-in-Progress: Real-Time On-board Processing for Cloud De...

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43rd ieee real-time Systems symposium (RTSS)

作者： Mendez Gomez, Javier E. Cheng, Albert M. K. Res Ctr Aerosp Technol Colombian Air Force Cali 760003 Colombia Univ Houston Dept Comp Sci Real Time Syst Lab Houston TX 77204 USA

ISBN: (数字)9781665453462

ISBN: (纸本)9781665453462

There are several optical sensors available that can be integrated into a small satellite for the remote recording of different places on Earth. Colombia in its FACSAT-2 satellite mission has selected a multispectral optical sensor of medium resolution for this task. However, the associated data processing model does not allow the generation of useful products for end users, given the effects that phenomena such as the presence of clouds could cause in 8-band multispectral images captured by FACSAT-2. Therefore, this work proposes the possibility of establishing a real-time system for on-board processing of the data recorded by the satellite optical sensor, taking advantage of the processing resources offered by the embedded computer system in charge of integrating the payloads to the satellite bus and initiating the development of software applications based on artificial intelligence techniques, so that together they can meet the need for autonomously estimating the percentage of cloud coverage in multispectral images prior to the storage and download process, while complying with the restrictions imposed on this type of systems with respect to the time variable.

关键词： real-time remote sensing Earth observation multispectral sensor cloud detection embedded system artificial intelligence

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An On-Chip Current-Sink-Free Adaptive-Timing Power Impedance Measurement (PIM) Unit for 3D-IC in 5nm FinFET technology

An On-Chip Current-Sink-Free Adaptive-Timing Power Impedance...

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2024 ieee symposium on VLSI technology and Circuits, VLSI technology and Circuits 2024

作者： Lu, Tsung-Che Fu, Chin-Ming Wang, Wei-Hsiang Kuo, Fred Chang, Chih-Hsien Hsieh, Kenny Tam, King-Ho Huang, Tze-Chiang Chen, Tom Wong, Mei Changchien, Wei-Pin Lee, Frank Hsinchu Taiwan

ISBN: (纸本)9798350361469

This work presents a power impedance measurement (PIM) architecture for the 3D-IC platform with high-performance computing (HPC) applications. Proposed current-sink-free architecture improves stimulus current capacity without area overhead. Another adaptive-timing scheme saves 50% testing time. The PIM IP has been fabricated in 5nm FinFET process with TSMC-SoIC® technology for characterization of an embedded ARM® core. Compared to state-of-the-art works, the 3.6-A stimulus current yields 116x improvement while the 0.006 mm2 core area achieves 79% reduction. © 2024 ieee.

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Integrating embedded Systems with Natural Language Processing: Innovations and applications in Intelligent Systems 4

Integrating Embedded Systems with Natural Language Processin...

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4th ieee International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Rawat, Ruchira Al-Fatlawy, Ramy Riad Bansal, Saloni Sehgal, Archana Gupta, Priyanka Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

The amalgamation of Natural Language Processing (NLP) with embedded Systems signifies a revolutionary progression in the creation of intelligent systems, permitting them to participate in interactions that are akin to those of humans. This study looks into the creative ways that NLP and embedded technology can be used in a variety of industries, such as healthcare, automotive, and smart homes. The paper illustrates how NLP enables embedded devices to comprehend, interpret, and react to human commands, improving user experience and operational efficiency, by looking at the underlying principles. The main obstacles to this integration are thoroughly examined, including resource shortages, the need for real-time processing, and environmental restrictions. The study emphasises how important it is to use low-power, compact NLP models that enable real-time language processing without sacrificing system efficiency. Additionally, the research discusses the fundamental approaches for improving these models' using methods like transfer learning, quantisation, and model reduction. In the end, this work seeks to shed light on the enormous potential of voice-enabled intelligence in embedded systems, opening the door to more interactive, intuitive, and adaptable technologies that can easily adjust to changing human needs. This study advances the continuous development of smart gadgets by bridging the linguistic and technological divide, increasing their usability and accessibility in daily life. © 2024 ieee.

关键词： Hidden Markov models

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time Sensitive Networking for Future Enabling Technologies: overview and measurement issues for metrological characterization 7

Time Sensitive Networking for Future Enabling Technologies: ...

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ieee International symposium on Measurements and Networking (M and N)

作者： Amodei, Andrea Capriglione, Domenico Cheminod, Manuel Ferrari, Paolo Miele, Gianfranco Morato, Alberto Tramarin, Federico Vitturi, Stefano Sisinni, Emiliano Zunino, Claudio Univ Cassino & Southern Lazio Cassino Italy Natl Res Council Italy Rome Italy Univ Brescia Brescia Italy Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350370546;9798350370539

The consolidation of Industry 4.0 and the perspective of the evolution toward the new Industry 5.0 paradigm are drastically accelerating the integration and the performance improvement of critical enabling technologies, as well as the uptake of new ones inside all industrial processes and future emerging applications. For example, increasingly sophisticated sensors and ultra-high-definition cameras, machine learning algorithms, and Artificial Intelligence are now starting to work together effectively. They can deliver real-world advantages for many applications, from manufacturing processes to advanced applications in modern vehicles, aircraft, and surveillance. Communication technologies play a fundamental role in these aims, and they have to ensure proper bandwidths, security, latencies, synchronisation, packet loss, reliability, and interoperability. time Sensitive Networking (TSN) represents the technology that fulfils the mandatory requirements for these parameters. To satisfy the related (hard) constraints and to speed up the diffusion of TSN, it becomes of paramount importance to accurately measure and characterise the relevant features of all the devices involved in the channel access and data transfer operations, starting from the network nodes and bridges, up to the communication links. In this framework, the paper aims to briefly analyse the TSN domain and the general measurement issues (methods, procedures, test sites, test setup) to address the metrological performance assessment of the network devices involved in such applications.

关键词： TSN ieee 802.1 Industry 5.0 IIoT

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A Near-Eye DVS -Based End -to -End Eye Tracking Processor for AR/VR applications

A Near-Eye DVS -Based End -to -End Eye Tracking Processor fo...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Tan, Shihang Huang, Jiayu Yan, Quanshu Zheng, Lirong Zou, Zhuo Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350330991;9798350331004

This paper presents a near-eye DVS -based end -to end eye tracking processor for augmented reality/virtual reality (AR/VR) devices, addressing the need for low -power, high frame rate and accurate eye tracking deployment on wearable devices with extended battery life. The processor features a dedicated hardware implementation for energy -efficient event driven pre-processing and acceleration, and delivers accurate gaze estimation utilizing a customized neural network (NN) with constant -event-count sample partitioning method. It is capable of performing end -to-end eye tracking exclusively with the Dynamic Vision Sensor (DVS), achieving an average accuracy of 0.91 in a 96 x 64 FoV. The processor is implemented and simulated using UMC 40-nm LP CMOS technology, with a core area of 1.88 mm(2). Operating at a clock frequency of 200 MHz, it achieves a dynamic frame rate of up to 1250 Hz with a power dissipation of 8.09 mW, corresponding to a 20.82 ti,J energy per gaze estimation. With the integration of DVS, this processor enables real-time, low power and accurate eye tracking for an immersive interaction experience on AR/VR devices.

关键词： Eye tracking Dynamic Vision Sensor (DVS) Neural Network (NN) Event -driven AR/VR device

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Work in Progress: Increasing Schedulability via on-GPU Scheduling

Work in Progress: Increasing Schedulability via on-GPU Sched...

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ieee symposium on real-time and embedded technology and applications

作者： Joshua Bakita James H. Anderson Department of Computer Science University of North Carolina at Chapel Hill USA

ISBN: (数字)9798331543402

ISBN: (纸本)9798331543419

GPUs are increasingly needed to run a variety of tasks in embedded systems, from object recognition to conver-sational chat. Some of these tasks are safety-critical, real-time tasks, where completing each by its deadline is essential for system safety. To meet the practical constraints of real-world systems, these tasks much also be run efficiently. Unfortunately, current techniques to schedule GPU-using tasks onto a single GPU while respecting deadlines impart high overheads, leading to inefficiency and substantial capacity loss during formal analysis. We address this problem by moving GPU scheduling from the CPU to the GPU. Our approach limits overheads, increasing the proportion of CPU tasks which can meet their deadlines by as much as 12.1% while increasing available GPU capacity.

关键词： Schedules embedded systems Costs Graphics processing units Parallel processing real-time systems Scheduling Safety Object recognition Contracts

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Pruning from Scratch via Shared Pruning Module and Nuclear norm-based Regularization

Pruning from Scratch via Shared Pruning Module and Nuclear n...

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ieee/CVF Winter Conference on applications of Computer Vision (WACV)

作者： Lee, Donghyeon Leek, Eunho Hwang, Youngbae Chungbuk Natl Univ Dept Intelligent Syst & Robot Cheongju South Korea

ISBN: (纸本)9798350318920;9798350318937

Most pruning methods focus on determining redundant channels from the pre-trained model. However, they overlook the cost of training large networks and the significance of selecting channels for effective reconfiguration. In this paper, we present a "pruning from scratch" framework that considers reconfiguration and expression capacity. Our Shared Pruning Module (SPM) handles a channel alignment problem in residual blocks for lossless reconfiguration after pruning. Moreover, we introduce nuclear norm-based regularization to preserve the representability of large networks during the pruning process. By combining it with MACs-based regularization, we achieve an efficient and powerful pruned network while compressing towards target MACs. The experimental results demonstrate that our method prunes redundant channels effectively to enhance representation capacity of the network. Our approach compresses ResNet50 on ImageNet without requiring additional resources, achieving a top-1 accuracy of 75.25% with only 41% of the original model's MACs. Code is available at https://***/jsleeg98/NuSPM.

关键词： Algorithms applications embedded sensing / real-time techniques Image recognition and understanding

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