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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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DedupChain: A secure Blockchain-enabled Storage System with Deduplication for Zero-Trust Network

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2070-2086页

作者： Qi, Saiyu Wang, Qiuhao Wei, Wei Yang, Xu Zhao, Hongguang Liu, Yuhao Yang, Xu Qi, Yong Xi'an Jiaotong University School of Computer Science and Technology China Xi'an University of Technology School of Computer Science and Engineering China Alibaba Group China Northwestern Polytechnical University School of Electronics and Information China

Permissioned blockchain is a promising methodology to build zero-trust storage foundation with trusted data storage and sharing for the zero-trust network. However, the inherent full-backup feature of the permissioned blockchain poses potential data privacy risks and substantial storage costs, hindering its usage as a storage medium. These issues necessitate the usage of secure data deduplication technology to mitigate them. Unfortunately, current secure data deduplication schemes are predominantly designed with centralized cloud servers in mind and are not suitable for distributed blockchain systems. The reason is that the full backup feature of the permissioned blockchain renders a wide attack surface to offline brute-force and frequency analysis attacks. In response, we propose DedupChain, a secure blockchain-enabled storage system with deduplication for zero-trust networks. DedupChain employs a trusted execution environment (i.e., Inter SGX enclave) in conjunction with Oblivious RAM (ORAM) to offer a novel security guarantee named oblivious data deduplication, which empowers DedupChain with the ability to defend offline brute-force and frequency analysis attacks. DedupChain also proposes several novel techniques to address the security and efficiency issues raised by the SGX enclave. We implemented a system prototype of DedupChain and evaluated its performance metrics. Our experimental results show that DedupChain exhibits satisfactory operational delays, throughput, and storage overhead. Security analysis shows that DedupChain is robust enough to withstand several types of attacks. To the best of our knowledge, we are the first to apply secure data deduplication techniques to address data privacy and storage cost issues raised by permissioned blockchain when used as a zero-trust storage medium. © 1983-2012 IEEE.

关键词： Blockchain

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Spray pyrolysis-derived W-doped MoSe_(2)/rGO paper-like microspheres:optimization of microstructure and mesostructure for enhanced lithium storage

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Rare Metals 2024年第7期43卷 3019-3031页

作者： Wei Wang Jun-Yu Chen Jie Ouyang Hong Yin Ao-Jie Li Liang Chen Jun-Lin Huang Yu-Can Zhu Gang-Yong Li Zhao-Hui Hou Key Laboratory of Hunan Province for Advanced Carbon-based Functional Materials Hunan Institute of Science and TechnologyYueyang 414006China School of Chemistry and Chemical Engineering Hunan Institute of Science and TechnologyYueyang414006China Hunan Province Key Laboratory of Materials Surface&Interface Science and Technology Material Science and Engineering SchoolCentral South University of Forestry and TechnologyChangsha 410004China International Iberian Nanotechnology Laboratory(INL) AvMestre Jose Veiga4715-330 BragaPortuga

Two-dimensional MoSe_(2) is a promising candidate for lithium-ion battery ***,its conductivity and lithium storage volumetric effect still need to be *** this work,W-doped MoSe_(2)/rGO paper-like microspheres are successfully prepared through ultrasonic spray pyrolysis,achieving optimization at both the microstructure and mesostructure to enhance the lithium storage performance of the ***,by utilizing the similar two-dimensional structure between MoSe_(2) and rGO,self-assembly is achieved through spray pyrolysis,resulting in a well-defined van der Waals heterostructure at the interface on the microscale,enhancing the electron and ion transfer capability of the ***,the mesoscale paper-like microsphere morphology provides additional volume expansion buffering ***,W-doping not only increases the interlayer spacing of MoSe_(2)(0.73 nm),thereby reducing the diffusion resistance of Li+,but also allow for the modulation of the energy band structure of the *** functional theory(DFT)calculations confirm that W-doped MoSe_(2)/rGO exhibits the narrowest bandgap(0.892 eV).Therefore,the composite demonstrates excellent lithium storage performance,maintaining a specific capacity of 732.9 mAh·g^(-1)after 300 cycles at a current density of 1 A·g^(-1).

关键词： Molybdenum selenide Heterostructure Microspheres Li-ion batteries Kinetics

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Research on lightweight pavement disease detection model based on YOLOv7

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10573-10589页

作者： Wang, Chishe Li, Jun Wang, Jie Zhao, Weikang School of Computer Science and Engineering Anhui University of Science and Technology Huainan China Jinling Institute of Technology Nanjing China

Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network's parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network's feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers' receptive field range. To optimize the model's boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing's urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. © 2024 - IOS Press. All rights reserved.

关键词： Roads and streets

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Object Detection Model for Remote Sensing Images Based on YOLOv9

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 840-847页

作者： Hou, Donghao Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenges faced by traditional object detection methods in remote sensing images, such as the large number of targets and complex backgrounds, by proposing a novel network based on YOLOv9. The network innovatively introduces the C3_CD_CGA module, an enhanced module based on Cascaded Group Attention, designed to reduce computational redundancy and increase attention diversity, and enhances the processing capability of multi-scale information through the CD module. The C3 module employs deep asymmetric convolution to mitigate information loss and increase the receptive field. Additionally, the network integrates DSConv with the RepNCSPELAN4 module to adaptively focus on and precisely capture the features of elongated and curved local structures, such as vehicles. The introduction of the CARAFE module further improves the spatial resolution of the feature maps, significantly enhancing performance across various visual tasks. Experimental results show that the improved YOLOv9 achieves a mean average precision (mAP) of 88% on the SIMD dataset, which is an improvement of 1.6% compared to the baseline YOLOv9 model and 1.5% higher than the state-of-the-art YOLO-SE model. This model not only achieves more effective multi-target recognition in complex backgrounds but also strikes a good balance between accuracy and efficiency. © (2025), (International Association of Engineers). All rights reserved.

关键词： Urban planning

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Prediction of Agricultural Gross Domestic Product Proportion in Shixing County Based on GM (1,1) Model 24

Prediction of Agricultural Gross Domestic Product Proportion...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Liu, Lidong He, Yantao Liu, Sijia Liu, Suping Luo, Yongbin Hu, Jiajia Liu, Yuan Zhou, Keer Department of Computer Science Guangdong University of Science and Technology Dongguan Guangdong China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan Hunan China

ISBN: (纸本)9798400717147

Agriculture plays a pivotal role in the national economy, with its development trajectory directly impacting food security, farmers' income and living standards, as well as rural economic growth. The Gross Domestic Product (GDP) of agriculture serves as a crucial metric for assessing agricultural progress. By monitoring fluctuations in agricultural GDP, we can discern the trends and characteristics of agricultural development, and gain insights into the position and significance of agriculture within the national economy. This study utilizes the agricultural gross production value data from Shixing County in Shaoguan City over a six-year period to construct a GM (1,1) prediction model. This model aims to forecast the proportion of agricultural GDP in Shixing County, thereby providing insights into the county's future agricultural development status. To validate the model's accuracy and practicality, a residual and relative error test was conducted on the predicted data. The results indicate that the average relative error is a mere 6.26%, underscoring the model's practicality. Consequently, the predicted data can serve as a valuable reference for local agricultural GDP development. © 2024 ACM.

关键词： Agriculture

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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Analysis of recent techniques in marine object detection: a review

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Multimedia Tools and Applications 2024年 1-99页

作者： Bhalla, Shikha Kumar, Ashish Kushwaha, Riti School of Computer Science Engineering and Technology Bennett University Greater Noida India

Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to marine surveillance. However, marine environment presents unique set of challenges that makes the detection process tedious. Marine environment often has low visibility due to color cast, haziness and intricate background. Moreover, marine target objects appear at varying scales and occluded by the surrounding objects. For comprehensive understanding of this domain, we have analyzed the significant studies from the year (2012-2024) in this work. Prisma model is utilized to identify the crucial 111 studies for this review. The review organizes marine object detection techniques into conventional and advance learning approach through the proposed hierarchical tree-like structure. The organized research has been tabulated, offering an in-depth summary of significant work and highlighting the strengths and weakness of the identified studies for the conventional and advance learning approach. Furthermore, a brief examination of 31 benchmark datasets categorized according to the different application domains (marine species, ship and debris) has been conducted and their key attributes are tabulated. In addition, statistical analysis has been performed to understand the growth and future directions in marine object detection domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Decoding Li^(+)/H^(+)ion exchange route toward low-temperature synthesis of layered oxide cathode materials for lithium-ion batteries

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Rare Metals 2024年第12期43卷 6329-6339页

作者： Pei-Yao Li Ying-De Huang Yu-Hong Luo Han-Xin Wei Rui Luo Lin-Bo Tang He-Zhang Chen Xia-Hui Zhang Jun-Chao Zheng School of Metallurgy and Environment Central South UniversityChangsha410083China School of Chemistry and Chemical Engineering Hunan University of Science and TechnologyXiangtan411201China

The synthesis of layered oxide cathode materials by the traditional high-temperature ceramic method usually requires calcination and annealing at temperatures in the range of 700-1000℃,with high energy consumption and serious cation mixing ***,we present a novel hydrothermalLi^(+)/H^(+)exchange method for the preparation of layered oxide cathodes at temperatures as low as 200℃.In contrast to the widely reported Li^(+)/Na^(+)exchange method using sodium-containing:precursors,layered oxide cathodes can be directly synthesized by hydrothermal reaction between commercial hydroxide precursors and LiOH·*** reaction pathway consists of two steps.(1)The hydroxyl oxide intermediate is obtained by oxidizing the hydroxide precursor.(2)The layered oxide product is obtained by theLi^(+)/H^(+)exchange reaction of the hydroxyl oxide with Li+in *** studying the time-resolved structural evolution,we reveal that the mechanism of material formation duringLi^(+)/H^(+)ion exchange is in situ crystallization,and the ion exchange process is accompanied by lattice distortion caused by internal diffusion of *** findings not only provide valuable insights into theLi^(+)/H^(+)exchange process,but also provide a new paradigm for the lowtemperature synthesis of advanced cathode materials.

关键词： Lithium-ion battery Cathode materials Ion exchange

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