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Enhancing WRSN Sustainability through On-demand Directional Wireless Charging with Multiple Green-powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun The School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China The Advanced Networking Laboratory Helen & John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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computer Systems Science & engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： Image pre-processing detection classification x-ray images filter

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On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

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engineering 2023年第11期30卷 55-62页

作者： Xin Wang Long Li Tie Jun Cui Mingyu Lu School of Electrical Engineering Chongqing UniversityChongqing 400044China School of Electronic Engineering Xidian UniversityXi’an 710071China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Department of Electrical and Computer Engineering West Virginia University Institute of TechnologyBeckleyWV 25801USA

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power ***,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power ***,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in *** conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

关键词： Wireless power transmission Microwave antenna array Retro-reflective beamforming Pilot signal Multiple access

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence Technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

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China Communications 2023年第7期20卷 137-174页

作者： Musbahu Mohammed Adam Liqiang Zhao Kezhi Wang Zhu Han State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Department of Computer and Information Sciences Northumbria UniversityNewcastle upon TyneUK Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004 USAand also with the Department of Computer Science and EngineeringKyung Hee UniversitySeoul 02447 South Korea

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular *** challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)*** this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use ***,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration *** review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration *** also highlight the need for intelligence in resources ***,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various ***,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

关键词： 4C 6G integration of communication,computing,caching,and control i4C multi-access edge computing(MEC)

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haoran Xu, Zhezhuang Zhu, Yuhang Lin, Ye College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface. © 2024 IEEE.

关键词： Discoloration

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RIS Assisted Dual-Function Radar and Secure Communications Based on Frequency-Shifted Chirp Spread Spectrum Index Modulation

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China Communications 2023年第10期20卷 85-99页

作者： Xiaoping Jin Peng Zhang Chuan Wan Dingyou Ma Yudong Yao Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information EngineeringChina Jiliang UniversityHangzhou 310018China Key Laboratory of Universal Wireless Communications Ministry of EducationSchool of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA

Reconfigurable intelligent surface(RIS)assisted dual-function radar communications(DFRC)system is a promising integrated sensing and communication(ISAC)technology for future *** this paper,we propose a scheme of RIS-assisted DFRC system based on frequency shifted chirp spread spectrum index modulation(RDFI)for secure *** proposed RDFI achieves the sensing and transmission of target location information in its radar and communication modes,*** both modes,the frequency-shifted chirp spread spectrum index modulation(FSCSS-IM)signal is used as the baseband signal for radar and communications,so that the signal sent by the radar also carries *** scheme implements the RIS-assisted beamforming in the communication mode through the azimuth information of the target acquired in the radar mode,so that the signal received from the eavesdropper is distorted in amplitude and *** addition,this paper analyzes the radar measurement accuracy and communication security of the FSCSS-IM signal using ambiguity function and secrecy rate(SR)analysis,*** results show that RDFI achieves both excellent bit error rate(BER)performance and physical layer security of communications.

关键词： index modulation secure communications ISAC DFRC reconfigurable intelligent surface

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Ying Chen, Kang Xu, Zhezhuang Lin, Ye Zeng, Qingbo College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement. © 2024 IEEE.

关键词： Contrastive Learning

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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