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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Zifeng Kong, Dejun Gao, Yucen Dai, Haipeng Gao, Xiaofeng He, Tian Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Nanjing University Department of Computer Science and Technology Nanjing China University of Minnesota Department of Computer Science and Technology MN United States

ISBN: (纸本)9798350304855

Wireless Charging Vehicles (WCVs) have been widely explored as a means of enabling continuous operation of sensors that are powered by batteries. However, the energy consumption of WCVs can be inefficient, leading to insufficient energy supply for sensors that are located in challenging-to-access areas. Consequently, there is a need to design an effective charging and energy sharing scheme for sensors to improve the quality of service in this setup. This paper focuses on the Joint optimization of Mobile charging and Energy sharing of sensors (JOIN-ME) problem, which is known to be NP-hard. To address this challenge, we first transform JOIN-ME into a submodular maximization problem with general constraints. Subsequently, we propose the Routing planning, Mobile charging, and Energy sharing for Sensing devices (RMES) algorithm, which has an approximation ratio of 1/8(1-1/e). Finally, we conduct experiments to showcase the superior performance of RMES compared to existing baselines, under varying scales and constraints. Our work on the design of an efficient charging and energy sharing scheme for sensors can significantly improve the reliability and longevity of wireless sensor networks, enabling the deployment of these networks in critical applications such as environmental monitoring, crowd sensing, and security surveillance. © 2023 IEEE.

关键词： Approximation algorithms

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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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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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An Efficient and Reliable Multicasting for Smart Cities

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computers, Materials & Continua 2022年第7期72卷 663-678页

作者： Faheem Khan Muhammad Zahid Hüseyin Gürüler Ilhan Tarimer Taegkeun Whangbo Department of Computer Engineering Artificial intelligence LabGachon UniversitySeongnam13120Korea Department of Computer Science Bacha Khan University CharsaddaCharsadda24450Pakistan Department of Information Systems Engineering Mugla Sitki Kocman UniversityMugla48000Turkey

The Internet of thing(IoT)is a growing concept for smart cities,and it is compulsory to communicate data between different networks and *** the IoT,communication should be rapid with less delay and *** this purpose,flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead,energy consumption and packet drop *** paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT *** this paper,multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster *** cities networks are divided into cluster head,and each cluster head or core node will be responsible for transferring data to the desired *** protocol is a novel approach according to the best of our knowledge,and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of *** results are implemented in Network simulator 2(NS-2).The result shows that the proposed protocol shows performance in overhead,throughput,packet drop,delay and energy consumption as compared to benchmark schemes.

关键词： Multicasting unicasting flooding network simulator-2 internet of thing smart cities

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Towards a Green Digital Currency for Smart Communities: An AI-Powered Ecosystem for Citizen Green Stewardship 14

Towards a Green Digital Currency for Smart Communities: An A...

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14th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2025

作者： Kerkad, Amira Gouri, Rabah LSI-Computer Science Faculty USTHB Algiers Algeria IECPS Lab Department of Industrial Engineering & Maintenance ENSTA Algiers Algeria

ISBN: (纸本)9789897587511

This paper proposes a new ecosystem vision designed to measure and incentivize citizen and corporate engagement in environmental stewardship through circular economy (CE). The ecosystem uses a gamified platform where participants earn digital points for performing two types of operations: (1) recycling matters and (2) reusing items, within smart communities. We propose to consider these points as a new global digital currency redeemable for rewards and services offered by participating businesses worldwide. The businesses are mainly the polluters that aim to have green advertisement and engagement in the "polluter pays" principle. Consequently, these businesses will benefit simultaneously from green advertising and tax reductions. A suite of digital services and AI-powered tools are used to optimize operations and generate valuable data for improving both urban and rural sustainability initiatives. By making this new currency, we can assess environmental engagement, motivate citizens to adhere into CE, and impose hierarchical participation in sustainable practices. This paper outlines the ecosystem's components, discusses the feasibility of our proposal, and explores its potential benefits and challenges for various stakeholders. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Competitive intelligence

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

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npj Computational Materials 2023年第1期9卷 1072-1083页

作者： Han Liu Yuhan Liu Kevin Li Zhangji Zhao Samuel S.Schoenholz Ekin D.Cubuk Puneet Gupta Mathieu Bauchy SOlids inFormaTics AI-Laboratory(SOFT-AI-Lab) College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China AIMSOLID Research Wuhan 430223China Physics of AmoRphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA Google Research Brain TeamMountain ViewCAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Numerical simulations have revolutionized material ***,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of ***,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end *** to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm ***,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific *** approach holds promise to accelerate inverse materials design.

关键词： enough inverse isotherm

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Theoretical analysis and design of a dual-wavelength and selectable all-optical broadband QDs semiconductor optical amplifier (QDs-SOA) with inhomogeneous broadening

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Optical and Quantum Electronics 2025年第2期57卷 1-26页

作者： Eyvazi, Mahdiyeh Yadipour, Reza Rostami, Ali Rostami, Parisa Mirtagioglu, Hamid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran SP-EPT Lab ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran Faculty of Medicine Tabriz University of Medical Science Tabriz Iran Department of Statistics Faculty of Science and Literature University of Bitlis Eren Bitlis Turkey

This study delves into the significant role played by Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) in meeting the ever-growing bandwidth demands. QD-SOAs offer a unique blend of cost-effectiveness, integration capabilities, wide bandwidth, rapid responsiveness, robust power output, stability, and spectral adaptability, driving notable advancements in optical communication systems. In this work, we introduce a novel two-wavelength amplifier structure based on Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) that utilizes quantum dots of different sizes to achieve efficient amplification at specific mid-infrared wavelengths. This innovative approach, which incorporates quantum dots with varying sizes, enables enhanced performance by optimizing the amplification process for each specific wavelength. Furthermore, this work demonstrates the use of tailored optical pumping mechanisms that enhance the carrier recovery process and reduce carrier relaxation times in the active region. This novel optical pumping technique leads to a significant increase in the efficiency and speed of the amplifier, distinguishing this study from others in the field. Simulation results provide detailed insights into the complex interplay between carrier interactions and gain spectra, offering a comprehensive understanding of QD-SOA operational dynamics. The proposed Quantum Dot Semiconductor Optical Amplifier (QD-SOA) achieves significant advancements in optical communication, offering maximum amplification rates of 26.9 and 19.9 times for QD1 and QD2, respectively, and bandwidths of 12 THz and 15 THz. The study highlights the role of quantum dot size, homogeneous and inhomogeneous broadening, and optical pumping in enhancing gain and performance, demonstrating the potential of QD-SOAs for high-gain, wide-bandwidth applications in photonic systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semiconductor quantum dots

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COVID-19 Waves and Their Impacts to Society

COVID-19 Waves and Their Impacts to Society

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Guwahati Subsection Conference (GCON), IEEE

作者： Sunil Kumar Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Patanjali CPS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

The COVID-19 pandemic has led to a global medical crisis and significant disruptions to daily life since its emergence in December 2019. Rapidly, it spread to 218 countries affecting more than 754 million people. The virus appears in different variants bringing significant implications at all societal levels. Recently, different variants of the virus have emerged, which have caused significant consequences in society. This paper presents the state-of-the-art on other waves caused by COVID-19 variants, their impacts on society, and challenges. The paper also details recent advancements to combat this disease.

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