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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Cao, Jun Feng, An Kang Wang, Bo Research Institute Department of Communication Engineering Nanjing China Research Institute Department of Electrical and Computer Engineering Suzhou China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Falls have become the second leading cause of unintentional injury death in the elderly. Timely detection of falls in the elderly can avoid greater injuries. Therefore, it is increasingly important to study fall detection systems. Based on this background, a fall detection system is designed in this paper. A fall detection system is usually divided into two parts: data acquisition and feature recognition. Due to its excellent confidentiality and applicability, millimeter-wave radar is used for data acquisition in this paper. Usually, the data collected by millimeter-wave radar is processed in a computer, and feedback is given to fall or not. But the data processing software in the computer brings great convenience to the system at the same time, it also limits the stability and portability of the system and hinders the practical application of the system. For this, the system introduces FPGA (Field Programmable Gate Array) to replace the computer for all the data processing including short-time Fourier transform and CNN (Convolutional Neural Network) feature recognition. Experimental results show that the system can detect falls accurately in simple cases, which preliminarily proved the feasibility of FPGA application in the fall detection system. The FPGA-based fall detection system can realize miniaturization and low price through tape-out after further improvement, and promote the practical application of the fall detection system. © 2022 IEEE.

关键词： Fall detection

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

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Digital Communications and Networks 2024年第2期10卷 237-247页

作者： Jiajie Guo Muhammad Bilal Yuying Qiu Cheng Qian Xiaolong Xu Kim-Kwang Raymond Choo School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Department of Computer Engineering Hankuk University of Foreign StudiesGyeonggi-do17035South Korea Jiangsu Hydraulic Research Institute Nanjing210044China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly *** have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive ***,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied *** addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

关键词： Internet of vehicles Digital twin Simulation Traffic systems

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Inverse Design of Embedded Inductor with Hierarchical Invertible Neural Transport Net 31

Inverse Design of Embedded Inductor with Hierarchical Invert...

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IEEE 31st Conference on electrical Performance of Electronic Packaging and systems (EPEPS)

作者： Akinwande, Oluwaseyi Bhatti, Osama Waqar Swaminathan, Madhavan School of Electrical and Computer Engineering 3D Systems Packaging Research Center Georgia Institute of Technology Atlanta USA School of Material Science and Engineering 3D Systems Packaging Research Center Georgia Institute of Technology Atlanta USA

ISBN: (数字)9781665450751

ISBN: (纸本)9781665450751

Heterogeneous integration of voltage regulators in power delivery networks is a growing trend that employs embedded inductor as a key component in significantly improving the power distribution. In this work, we propose a neural network framework called the hierarchical invertible neural transport for the inverse design of an embedded inductor. With this invertible method, we obtain the probability distributions of the parameters of the embedded inductor design space that most likely satisfy the desired specifications. We also learn the impedance response for free in the forward design. In the forward design, our results show a 2.14% normalized mean square error when compared with the output response of a fullwave EM simulator.

关键词： power delivery inverse design emdedded inductor neural networks transport maps

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TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing 24

TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wu, Jinting Zheng, Haodong Wang, Yu Yue, Tai Zhang, Fengwei Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9798400706011

Hardware-based tracing, being efficient, can be a good alternative to the computationally-expensive software-based instrumentation in binary-only greybox fuzzing. However, it only records all branches within a specified address range, lacking the flexibility to re-filter them. To overcome these limitations, this paper introduces Tatoo, a hardware platform that employs tagged architectures and hardware tracing to enable users to perform instruction-level tagging, which can significantly reduce the volume of traced data and improve fuzzing efficiency. Tatoo also supports recording the dataflow information for smart mutations. Implemented on a real hardware FPGA platform, Tatoo demonstrates a mere 8.7% performance overhead. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 1

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3rd International Conference on Advances in Computing research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9783031876479

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Tunable Hybrid Plasmonic-Semiconductor Laser Incorporating ENZ Material

Tunable Hybrid Plasmonic-Semiconductor Laser Incorporating E...

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2023 Photonics North, PN 2023

作者： Saeidi, Shayan Cheben, Pavel Schmid, Jens H. Berini, Pierre University of Ottawa School of Electrical Engineering and Computer Science Nexqt Institute Ottawa Canada National Research Council of Canada Ottawa Canada Nexqt Institute University of Ottawa School of Electrical Engineering and Computer Science Department of Physics Ottawa Canada

ISBN: (纸本)9798350326734

In this paper, we suggest a new approach to achieve tunability in semiconductor lasers, based on mode loss perturbation. We propose a hybrid plasmonic-semiconductor laser, in which we add a metal oxide semiconductor (MOS) on top of a conventional Fabry-Perot (FP) InP-based diode laser. This hybrid laser experiences significant change in the imaginary part of the mode effective index when the MOS operates in the accumulation region, which constitutes the basis of our tuning mechanism. © 2023 IEEE.

关键词： Semiconductor lasers

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications 24

Resource Efficient Federated Learning for Deep Anomaly Detec...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Lalos, Aris Industrial Systems Institute Athena Research Center Greece University of Patras Dept. of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350339598

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%. © 2023 IEEE.

关键词： Anomaly detection

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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1708-1722页

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencode...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Liu, Yingxiang Razeghi-Jahromi, Mohammad Stoupis, James University of Southern California Department of Electrical and Computer Engineering Los Angeles United States Abb Us Research Center Principal Scientist at Electrical Systems Department Raleigh United States Abb Us Research Center Electrical Systems Department Manager Raleigh United States

ISBN: (纸本)9798350320169

Detection of high impedance faults (HIFs) in power distribution networks remains a challenging task. Due to the low current magnitude and diverse characteristics of HIFs, conventional over-current relays are ineffective in detecting such faults. Machine learning-based methods have emerged as a promising solution, but most rely on supervised learning techniques to detect HIF by performing classifications that require a significant amount of HIF data that are difficult to acquire in the real world. As a result, the reliability and generalization of these models are limited when the load profiles and faults are not present in the training data. In this paper, we propose an unsupervised HIF detection framework that employs autoencoder and principal component analysis-based monitoring techniques to identify changes in the correlation structure within the current waveforms that deviate from normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate the superiority of our proposed method over the commercially available HIF detection technique while ensuring high security by avoiding false detections during normal load conditions. © 2023 IEEE.

关键词： Principal component analysis

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