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IEEE International Conference on Communications (ICC)

作者： Huan Huang Hongliang Zhang Yi Cai Yunjing Zhang A. Lee Swindlehurst Zhu Han Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston USA Center for Pervasive Communications and Computing University of California Irvine USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

关键词： Array signal processing Precoding Computational modeling Interference MISO communication Physical layer security Reflection coefficient

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Channel Estimation for Movable Antenna Communication systems Based on Compressed Sensing

Channel Estimation for Movable Antenna Communication Systems...

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IEEE Conference on Wireless Communications and Networking

作者： Songqi Cao Lipeng Zhu Xiangyu Pi Zhenyu Xiao Boyu Ning School of Electronic and- Inforrmation Engineering Beihang University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper proposes a general channel estimation framework for movable antenna (MA) communication systems. In this framework, the channel state information between the entire transmitter (Tx) and receive (Rx) regions can be re-constructed, so as to find the optimal positions of the MAs for reaping performance gains. Specifically, the field-response channel structure is utilized to represent the channel response in terms of the angles of departure (AoDs), angles of arrival (AoAs), and complex coefficients of the multi-path components (MPCs). Then, the compressed sensing method is employed to jointly estimate the MPC information, i.e., the AoDs, AoAs, and complex coefficients of the paths, with a limited number of channel measurements. Notably, the measurement matrix under the proposed framework is fundamentally determined by the Tx-MA and Rx-MA measurement positions, which further affects the channel estimation performance. In this regard, four MA measurement position setups are proposed, and the channel estimation performance of each setup is further compared. Finally, simulation results show that the complete CSI between the entire Tx and Rx regions can be reconstructed by our proposed channel estimation framework with a high accuracy.

关键词： Antenna measurements Communication systems Atmospheric measurements Simulation Channel estimation Position measurement Particle measurements

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A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

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Applied Soft Computing 2024年 156卷

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse's urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process. © 2024 Elsevier B.V.

关键词： Multiobjective optimization

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Applying Explanatory Artificial Intelligence Using LIME to Analyze Arabic Online Handwriting Recognition Models

Applying Explanatory Artificial Intelligence Using LIME to A...

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International Conference on Information Networking

作者： Hasanien Ali Talib Alothman Wafa Lejmi Safa Ameur Mohamed Ali Mahjoub ISITCom - Higher Institute of Computer Science and Communication Sousse Tunisia LATIS - Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse (ENISo) University of Sousse Sousse Tunisia Mechatronics Engineering Department University of Mosul Mosul Iraq

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This study applies interpretable artificial intelligence (XAI) techniques in an unprecedented way to improve transparency in online Arabic handwriting recognition models. By using the LIME (Local Model-Independent Explanations) technique to analyze and interpret a convolutional neural network (CNN) model trained on the ADAB dataset. LIME highlights the key features that the model relies on in handwriting recognition. This research demonstrates the effectiveness of the LIME technique in uncovering the factors that the model relies on in an unconventional way and provides detailed explanations of how the decisions are made, which enhances transparency and increases users’ confidence in these models. The results provide useful information for improving the performance of handwriting recognition systems and a beginning for applying XAI techniques on a larger scale, including using more complex datasets and additional interpretation techniques to gain a deeper understanding of how models work with different handwriting styles. This study paves the way for the use of interpretable techniques in the field of Arabic handwriting recognition.

关键词： Deep learning Handwriting recognition Analytical models Explainable AI Shape Decision making Predictive models Prediction algorithms Data models Convolutional neural networks

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Interrupted sampling forwarding jamming parameter estimation based on YOLOv7-tiny

Interrupted sampling forwarding jamming parameter estimation...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhaopeng Shi Hao Wu Xuan Zhu Wantian Wang Xun Ni Jiahao Zhang School of Electronic Information and Communications Huazhong University of Science and Technology National Key Laboratory of Electromagnetic Energy Naval University of Engineering Wuhan China School of Electrical Engineering and Automation Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Interrupted sampling repeater jamming (ISRJ) is a new type of active coherent jamming technology based on the principle of under-sampling. It mainly uses the Doppler tolerance characteristics of wideband signals to achieve multi-false target jamming. The feasibility of the existing anti-ISRJ methods largely depends on the estimation accuracy of the key parameters of the interference. In order to ensure the accuracy and real-time performance of ISRJ parameter estimation under low jamming-to-noise ratio, this paper proposes a ISRJ feature parameter estimation algorithm based on YOLOv7-tiny object detection model with high precision and lightweight. Through simulation and comparative analysis of different methods, the method proposed in this paper can achieve parameter estimation for ISRJ under low jamming-to-noise ratio, and has high precision and real-time performance.

关键词： Training Time-frequency analysis Parameter estimation Accuracy Estimation Interference Object detection

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Single-and Multiuser Virtual Reality Training for Forklift Driving 2

Single-and Multiuser Virtual Reality Training for Forklift D...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Froland, Tord Hettervik Ravyse, Werner Luimula, Mika Heldal, Ilona Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Bergen Norway School of ICT: Futuristic Interactive Technologies Research Group Turku University of Applied Sciences Turku Finland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350338638

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single-or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single-and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single-and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed. © 2023 IEEE.

关键词： Virtual reality

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Enhancing Security and Efficiency in Vehicle-to-Sensor Authentication: A Multi-Factor Approach with Cloud Assistance

Enhancing Security and Efficiency in Vehicle-to-Sensor Authe...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xinrui Zhang Pincan Zhao Jason Jaskolka Department of Systems and Computer Engineering Carleton University Ottawa Canada State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Information Technology Carleton University Ottawa Canada

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Connected and Autonomous Vehicles (CAVs) can improve their perception by integrating data from roadside sensors. However, ensuring secure authentication between CAVs and sensors is challenging due to the limited capabilities of sensors and the growing number of vehicles. This paper introduces a secure authentication protocol that enables direct communication between CAVs and roadside sensors, addressing a critical gap in existing research focused on vehicle-to-cloud authentication. The proposed multi-factor authentication scheme combines password, biometric, and device-specific factors with Elliptic Curve Cryptography (ECC) and efficient key agreement protocols. A comprehensive adversary model tailored for vehicular networks is presented, along with an in-depth security analysis demonstrating the scheme’s resilience against various threats. The cloud-assisted authentication framework offloads computationally intensive tasks to the cloud server, reducing the burden on resource-constrained Roadside Units (RSUs) and ensuring scalability. Extensive performance evaluations showcase the scheme’s computational efficiency, low communication overhead, and storage costs compared to state-of-the-art solutions, highlighting its practical feasibility and potential for real-world deployment in intelligent transportation systems.

关键词： Performance evaluation Costs Protocols Multi-factor authentication Scalability Passwords Elliptic curve cryptography Sensors Servers Resilience

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Metalens-based stereoscopic microscope

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Photonics Research 2022年第6期10卷 1501-1508页

作者： YONG LONG JIANCHAO ZHANG ZHIHAO LIU WEIBIN FENG SONGMING GUO QIAN SUN QINFEI WU XIANGYANG YU JIANYING ZHOU EMILIANO RMARTINS HAOWEN LIANG JUNTAO LI State Key Laboratory of Optoelectronic Materials and Technologies School of PhysicsSun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519080China São Carlos School of Engineering Department of Electrical and Computer EngineeringUniversity of São PauloSão CarlosSP 13566-590Brazil

Stereoscopic microscopy is a promising technology to obtain three-dimensional microscopic *** microscopes are based on the parallax effect,and as such require two lenses to focus at two different *** constraints,however,restrict their numerical apertures to about 0.2,thus limiting the system’s *** numerical apertures(.35)can be achieved with designs using only one bulk lens,but such systems are0 times more costly than the conventional ***,there is a pressing need for alternative solutions to improve the resolution of stereoscopic ***,we show that high-resolution and low-cost stereoscopic systems can be obtained using birefringent single-layer *** design and fabricate a birefringent metalens operating at 532 nm with a numerical aperture as high as *** metalens is then used to demonstrate high-resolution stereoscopic imaging of biological *** microscopic images are further displayed and perceived vividly in an autostereoscopic *** demonstration paves the way to a new strategy to achieve high-resolution and low-cost stereoscopic microscopes.

关键词： resolution ens stereoscopic

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VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive Human Fear Responses in VR Stand-up Interactive Games

arXiv

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arXiv 2024年

作者： Zhang, He Li, Xinyang Sun, Yuanxi Fu, Xinyi Qiu, Christine Carroll, John M. The Future Laboratory Tsinghua University China College of Information Sciences and Technology Penn State University United States Academy of Arts & Design Tsinghua University China School of Computer and Cyber Sciences Communication University of China China School of Electrical Engineering and Computer Science The KTH Royal Institute of Technology Sweden

Understanding and recognizing emotions are important and challenging issues in the metaverse era. Understanding, identifying, and predicting fear, which is one of the fundamental human emotions, in virtual reality (VR) environments plays an essential role in immersive game development, scene development, and next-generation virtual human-computer interaction applications. In this article, we used VR horror games as a medium to analyze fear emotions by collecting multi-modal data (posture, audio, and physiological signals) from 23 players. We used an LSTM-based model to predict fear with accuracies of 65.31% and 90.47% under 6-level classification (no fear and five different levels of fear) and 2-level classification (no fear and fear), respectively. We constructed a multi-modal natural behavior dataset of immersive human fear responses (VRMN-bD) and compared it with existing relevant advanced datasets. The results show that our dataset has fewer limitations in terms of collection method, data scale and audience scope. We are unique and advanced in targeting multi-modal datasets of fear and behavior in VR stand-up interactive environments. Moreover, we discussed the implications of this work for communities and applications. The dataset and pre-trained model are available at https://***/KindOPSTAR/VRMN-bD. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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Application of digital twin technology in mining machinery education

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International Journal of Mechanical engineering Education 2025年

作者： Su, Chang Huang, Hao Wei, Zhongliang Xu, Naizhong State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine Anhui University of Science and Technology Huainan China School of Mechanical and Electrical Engineering Anhui University of Science and Technology Huainan China School of Computer Science and Engineering Anhui University of Science and Technology Huainan China School of Miningand Engineering Anhui University of Science and Technology Huainan China

This paper investigates the application of digital twin technology in the teaching of mining machinery. Digital twin technology, an innovative approach that has significantly transformed engineering education and practice, creates dynamic digital replicas of physical entities in virtual environments. This allows for the simulation, prediction, and optimization of system performance without the need for physical intervention. In mechanical engineering education, digital twins provide students with real-time, interactive learning experiences, enabling a more intuitive understanding of complex mechanical structures. Students can conduct experiments and make modifications within a virtual setting, enhancing both learning efficiency and reducing experimental costs. Furthermore, this technology offers a scalable and replicable model for the future of engineering education. © The Author(s) 2025.

关键词： Virtual environments

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