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COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

computers, Materials & Continua 2023年第5期75卷 3883-3901页

作者： Ali Akbar Siddique S.M.Umar Talha M.Aamir Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Telecommunication Engineering Sir Syed University of Engineering&TechnologyKarachi75300Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its *** to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big *** who test positive for Covid-19 are diagnosed via a nasal PCR *** comparison,polymerase chain reaction(PCR)findings take a few hours to a few *** PCR test is expensive,although the government may bear expenses in certain ***,subsets of the population resist invasive testing like ***,chest X-rays or computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response *** advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest *** article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning *** order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the *** two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the *** proposed model is trained using an image dataset that can be found on *** are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are *** edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has *** is no association between the datasets of the various nodes that are included in the *** doing it in this manner,each of the nodes will have access to a separate image collection that has no correl

关键词： Artificial intelligence deep learning federated learning COVID-19 decentralized image dataset

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems Science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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Impact of Harmonics of Distributed Generators on the Harmonic Profiling of Distribution Networks

Impact of Harmonics of Distributed Generators on the Harmoni...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Rahman, Syed Khan, Irfan Ahmad Iqbal, Atif Texas A&M University Electrical & Computer Engineering Department College Station United States Lab Galveston United States Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9781665490719

Distributed generators (DGs) have emerged as a go-to solution to accommodate increasing loads, with the existing grid infrastructure. The DG-injected power can be consumed locally, thereby reducing the grid current and improving the voltage profile. The power-electronics interface-based DGs, controlled as current sources, employ a grid-side filter for injecting sinusoidal current into the grid. As the filter is designed to meet IEEE compliance for DG grid integration, the current THD is restricted to less than 5%. Although the compliance is met, these harmonics injected into the grid significantly deteriorate the harmonic profile of the entire grid. This study quantifies the impact of two types of DGs - constant power DGs (Type-1) and varying power DGs (Type-II) on the utility grid. An actual 24-hour data-based IEEE 33-bus distribution network, consisting of both linear and non-linear loads, is used to quantify the DGs' harmonic impact. Post-DGs addition, it is observed that Type-I DGs increase the voltage THD by at least 1% at the farthest nodes, although the pattern remains consistent with that of prior DG addition. However, with Type-II DGs, the profile obtained is further deteriorated and inconsistent and exhibits a strong dependence on the power generation of the DG. © 2023 IEEE.

关键词： Electric load flow

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Fast optical-writing recognition based on two-dimensional photothermoelectric effect assisted with deep learning

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InfoMat 2023年第3期5卷 33-44页

作者： Yujia Zhong Haoyu Fang Yutong Ran Hongwei Zhu State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China Department of Electrical and Computer Engineering New York UniversityNew YorkNew YorkUSA

The photothermoelectric(PTE)effect enables a simple structure to construct a noncontact and self-powered writing *** detectors based on active materials and one-dimensional(1D)position-sensitive PTE effect have attracted considerable ***,there is no research on applying the 1D position-sensitive PTE effect to a two-dimensional(2D)***,flexible writing devices based on the PTE effect have not been ***,we demonstrate a self-powered flexible optical writing device based on the 2D position-sensitive PTE effect of SnSe thin *** positioning,tracking,and distinguishing of optically written characters have been realized by only measuring the bi-directional output voltages during the writing process.A deep convolutional neural network has been introduced to recognize optically written numbers and characters with high recognition accuracy(92%)and fast processing speed(8.6 ms).The flexible optical-writing device with a simple structure could rapidly and accurately recognize characters in a noncontact fashion using a small amount of training data.

关键词： deep neural network optical writing photothermoelectric effect tin selenide

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Auxiliary-Variable Adaptive Control Lyapunov Barrier Functions for Spatio-Temporally Constrained Safety-Critical Applications 63

Auxiliary-Variable Adaptive Control Lyapunov Barrier Functio...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Shuo Xiao, Wei Belta, Calin A. Boston University Department of Mechanical Engineering BrooklineMA United States Massachusetts Institute of Technology Computer Science and Artificial Intelligence Lab CambridgeMA United States University of Maryland College Park Department of Electrical and Computer Engineering and the Department of Computer Science MD United States

ISBN: (纸本)9798350316339

Recent work has shown that stabilizing an affine control system while optimizing a quadratic cost subject to state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this method is that the QPs could easily become infeasible under safety and spatio-temporal constraints with tight control bounds. In our own recent work, we defined Auxiliary-Variable Adaptive CBFs (AVCBFs) to improve the feasibility of the CBF-based QP, while avoiding extensive parameter tuning. In this paper, we consider spatio-temporal constraints as finite-time reachability requirements. In order to satisfy these requirements, we generalize AVCBFs to AuxiliaryVariable Adaptive Control Lyapunov Barrier Functions (AVCLBFs) that work for systems and constraints with arbitrary relative degrees. We show that our method has fewer conflicts with safety and input constraints, and outperforms the state of the art in term of adaptivity and feasibility in solving the QP. We illustrate our approach on an optimal control problem for a unicycle. © 2024 IEEE.

关键词： Lyapunov functions

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CATPlan: Loss-based Collision Prediction in End-to-End Autonomous Driving

arXiv

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arXiv 2025年

作者： Xiong, Ziliang Liu, Shipeng Helgesen, Nathaniel Johnander, Joakim Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden Zenseact Sweden

In recent years, there has been increased interest in the design, training, and evaluation of end-to-end autonomous driving (AD) systems. One often overlooked aspect is the uncertainty of planned trajectories predicted by these systems, despite awareness of their own uncertainty being key to achieve safety and robustness. We propose to estimate this uncertainty by adapting loss prediction from the uncertainty quantification literature. To this end, we introduce a novel light-weight module, dubbed CATPlan, that is trained to decode motion and planning embeddings into estimates of the collision loss used to partially supervise end-to-end AD systems. During inference, these estimates are interpreted as collision risk. We evaluate CATPlan on the safety-critical, nerf-based, closed-loop benchmark NeuroNCAP and find that it manages to detect collisions with a 54.8% relative improvement to average precision over a GMM-based baseline in which the predicted trajectory is compared to the forecasted trajectories of other road users. Our findings indicate that the addition of CATPlan can lead to safer end-to-end AD systems and hope that our work will spark increased interest in uncertainty quantification for such systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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Parameter-Efficient Federated Cooperative Learning for 3D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C.M. University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple Connected Autonomous Vehicles (CAVs) to enhance the performance of Deep Learning (DL) based object detection methods. However, traditional FL faces hurdles such as extensive data synchronization requirements, limited data variance, and high communication costs. This paper introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and Roadside Units (RSUs). The framework combines local cooperative perception with global federated learning through a parameter-efficient federated learning adapter (PEFLA) and a lazy communication (LazyComm) strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multi-agent-multi-town dataset V2X-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model federated learning, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional federated learning strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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Broadband quantum-dot frequency-modulated comb laser

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Light(Science & Applications) 2023年第8期12卷 1696-1707页

作者： Bozhang Dong Mario Dumont Osama Terra Heming Wang Andrew Netherton John E Bowers Institute for Energy Efficiency University of CaliforniaSanta BarbaraCAUSA Department of Electrical and Computer Engineering University of CaliforniaSanta BarbaraCAUSA Primary Length and Laser Technology Lab National Institute of StandardsGizaEgypt

Frequency-modulated(FM)laser combs,which offer a quasi-continuous-wave output and a flat-topped optical spectrum,are emerging as a promising solution for wavelength-division multiplexing applications,precision metrology,and ultrafast optical *** generation of FM combs relies on spatial hole burning,group velocity dispersion,Kerr nonlinearity,and four-wave mixing(FWM).While FM combs have been widely observed in quantum cascade Fabry-Perot(FP)lasers,the requirement for a low-dispersion FP cavity can be a challenge in platforms where the waveguide dispersion is mainly determined by the *** we report a 60 GHz quantum-dot(QD)mode-locked laser in which both the amplitude-modulated(AM)and the FM comb can be generated *** high FWM efficiency of-5 dB allows the QD laser to generate FM comb *** also demonstrate that the Kerr nonlinearity can be practically engineered to improve the FM comb bandwidth without the need for GVD *** maximum 3-dB bandwidth that our QD platform can deliver is as large as 2.2 *** study gives novel insights into the improvement of FM combs and paves the way for small-footprint,electrically pumped,and energy-efficient frequency combs for silicon photonic integrated circuits(PICs).

关键词： laser waveguide pumped

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Neural Implicit representations for 3D scene modeling and visualization using multipass SAR 15

Neural Implicit representations for 3D scene modeling and vi...

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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Sugavanam, Nithin Ertin, Emre Jamora, Jan Rainer Department of Electrical and computer engineering The Ohio State university ColumbusOH43201 United States Air-force Research Lab United States

ISBN: (纸本)9783800762873

Synthetic aperture radar (SAR) collects samples of the 3D Spatial Fourier transform on a two-dimensional surfaces corresponding to the backscatter data of wideband pulses launched from different look angles along the synthetic aperture. In this paper we present a coordinate-based multi-layer perceptron (MLP) that enforces the smooth surface-prior and models the complex-valued scattering coefficients on the surface of the imaged object. The resulting implicit neural representation is capable of predicting phase history data for arbitrary apertures in the span of the training data. The 3D surface is represented using a signed distance function, while the scattering coefficients are represented using real and imaginary channels. Since estimating a smooth surface from a sparse and noisy point cloud is an ill-posed problem, in this work, we build on our previous work to regularize the surface estimation by sampling points from the implicit surface representation during the training step. The loss function also incorporates the consistency between the predicted complex-valued scattering coefficients and the true scattering coefficients. We validate the model's ability to represent target scattering using simulated data of Civilian vehicle data domes. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Synthetic aperture radar

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