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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： He, Bin Zheng, Nianying Lu, Yuxuan Chen, Hong Gao, Mingyi Shao, Weidong Peng, Limei Ho, Pin-Han Chen, Bowen Soochow University School of Electronic and Information Engineering Jiangsu Province Suzhou215006 China Guilin University of Electronic Technology School of Information and Communication Guangxi Guilin541004 China Kyungpook National University Department of Computer Science Daegu41566 Korea Republic of University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350351255

Quantum key distribution (QKD)-enabled optical networks utilize quantum mechanics to secure communications by establishing secure quantum channels. A critical criterion for QKD-enabled optical networks is ensuring network availability, which requires each quantum key service to adhere to a maximum unavailability constraint to maintain the network availability. This paper addresses the challenge of offering dedicated path protection for quantum key services in QKD-enabled optical networks. It ensures that the unavailability gap between the working and protection paths remains within the allowable limit for each quantum key service, adhering to specific unavailability constraints. Considering the constraints of the limited timeslot resources and network availability, we proposed quantum key service approach named a maximum availability (MA) algorithm. Simulation results indicate that the MA algorithm surpasses both the traditional dedicated-path protection (TDP) and fixed dedicated-path routing (FDR) algorithms in reducing total timeslot consumption and enhancing average availability. © 2024 IEEE.

关键词： Quantum channel

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Quantum Face Recognition With Multigate Quantum Convolutional Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6330-6341页

作者： Zhu, Yijie Bouridane, Ahmed Celebi, M. Emre Konar, Debanjan Angelov, Plamen Ni, Qiang Jiang, Richard LIRA Center Lancaster University LancasterLA1 4YW United Kingdom University of Sharjah Sharjah27272 United Arab Emirates The Department of Computer Science University of Central Arkansas ConwayAR72035 United States School of Industrial Engineering Purdue University West LafayetteIN47906 United States

In the last decade, quantum computing has showcased its unique mechanism across diverse fields, highlighting significant potential for data-driven applications requiring substantial computational resources. Within this landscape, quantum machine learning emerges as a promising frontier, poised to harness the unique advantages of quantum computing for machine learning tasks. Nonetheless, the current generation of quantum hardware, typified by noisy intermediate-scale quantum (NISQ) devices, grapples with severe resource constraints, particularly in terms of qubit availability. While quantum computing offers tantalizing capabilities such as superposition and entanglement, which can be strategically leveraged to optimize the performance of quantum neural networks, the challenge remains in mitigating the resource limitations while upholding high recognition accuracy. To address this imperative, we introduce a pioneering face recognition method christened the multigate quantum convolutional neural network (MG-QCNN). This innovation is engineered to surmount the resource bottleneck endemic to NISQ devices while preserving exceptional recognition accuracy. Our empirical investigations conducted on benchmark datasets, including the Yale face dataset and the ORL face database, illuminate the remarkable potential of this approach. Specifically, our proposed variational quantum circuit architecture consistently achieves an impressive average accuracy of 96%, which is better than the 95% of the classic CNN. Our model underscores the efficacy of quantum convolution operations in the extraction of feature maps, exhibiting a transformative stride toward unlocking the full potential of quantum-enhanced face recognition, and compared with other quantum models, our method has more advantages in accuracy and efficiency. harnessed the unique capabilities of quantum computing to overcome resource limitations and achieve an astounding 96% average accuracy on face recognition tasks. This

关键词： Face recognition

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An Intersection-Based Traffic Awareness Routing Protocol in VANETs Using Deep Reinforcement Learning

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Wireless Personal Communications 2024年第1期138卷 659-683页

作者： Song, Ya-Jing Yen, Chin-En Hsieh, Yu-Hsuan Kuo, Chunghui Chang, Ing-Chau Department of Computer Science and Information Engineering National Changhua University of Education No. 1 Jinde Rd. Changhua County Changhua City Taiwan Department of Early Childhood Development and Education Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung Taiwan Raycers Technology Fairport New York14450 United States

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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Emotion Classification Through Speech Data Analysis

Emotion Classification Through Speech Data Analysis

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2023 Winter Simulation Conference, WSC 2023

作者： Marcos, Luzalen Mai, Kristiina Valter Abhari, Abdolreza Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Resilience Engineering Lab 350 Victoria Street TorontoONM5B 2K3 Canada Lab Department of Computer Science 245 Church Street TorontoONM5B 1Z4 Canada

ISBN: (纸本)9798350369663

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER. © 2023 IEEE.

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Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4176-4191页

作者： Cheng, Zilong Ma, Jun Wang, Wenxin Zhu, Zicheng de Silva, Clarence W. Lee, Tong Heng The Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Guangzhou China The Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China The Department of Mechanical Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

This article investigates the collision-free control problem for multi-agent systems. For such multi-agent systems, it is the typical situation where conventional methods using either the usual centralized model predictive control (MPC), or even the distributed counterpart, would suffer from substantial difficulty in balancing optimality and computational efficiency. Additionally, the nonconvex characteristics that invariably arise in such collision-free control and optimization problems render it difficult to effectively derive a reliable solution (and also to thoroughly analyze the associated convergence properties). To overcome these challenging issues, this work establishes a suitably novel parallel computation framework through an innovative mathematical problem formulation;and then with this framework and formulation, a parallel algorithm based on alternating direction method of multipliers (ADMM) is presented to solve the subproblems arising from the resulting parallel structure. Furthermore, an efficient and intuitive initialization procedure is developed to accelerate the optimization process, and the optimum is thus determined with significantly improved computational efficiency. As supported by rigorous proofs, the convergence of the proposed ADMM iterations for this nonconvex optimization problem is analyzed and discussed in detail. Finally, a simulation with a group of unmanned aerial vehicles (UAVs) serves as an illustrative example here to demonstrate the effectiveness and efficiency of the proposed approach. Also, the simulation results verify significant improvements in accuracy and computational efficiency compared to other baselines, including primal quadratic mixed integer programming (PQ-MIP), nonconvex quadratic mixed integer programming (NC-MIP), and nonconvex quadratically constrained quadratic programming (NC-QCQP). © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Computational efficiency

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A distinguished-bit tracking knowledge-based query tree for RFID tag identification

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computer Communications 2024年 218卷 166-175页

作者： Lin, Chih-Chung Lai, Yuan-Cheng Hailemariam, Zelalem Legese Department of Computer Information and Network Engineering Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan District Taoyuan City 333326 Taiwan Department of Information Management National Taiwan University of Science and Technology No. 43 Sec. 4 Keelung Road Taipei 106 Taiwan

—Many RFID systems own the information of RFID tags in the database, so the reader knows how many tags will be identified and which tags’ IDs might appear. How the RFID anti-collision protocols take advantage of this knowledge is a challenge. Some previous anti-collision protocols, including Query Tree with Shortcutting and Couple Resolution (QTSC) and Bit-tracking Knowledge-based Query Tree (BKQT), solved this task with the knowledge. We propose one novel protocol, Distinguished-bit Tracking Knowledge-based Query Tree (DKQT), which integrates the knowledge with the bit-tracking technique. Utilizing the knowledge and knowing the collided-bit locations, DKQT adopts a distinguishable technique, which can identify the tags that have a distinguished-bit representing that only one tag has a unique “0” or “1” in a bit location of the tag ID, to speed up the tag identification. Innovatively, adopting a distinguishable technique, DKQT forms an n-ary tree where a node has some leaves that are the tags having distinguished-bits. Thus, DKQT using the distinguishable technique can recognize multiple appearing tags in a collision by traversing this n-ary tree. The simulation results show that DKQT can reduce the number of slots by 36.94 % and 15.58 %, and the identification time by 37.47 % and 17.86 %, compared to the previous knowledge-based protocols, QTSC and BKQT, respectively. © 2024 Elsevier B.V.

关键词： Distinguished-bit Knowledge-based tree RFID anti-collision Tag identification

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics PooleBH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This letter considers the communication system assisted by a fixed-trajector uncrewed aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition

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ACM Transactions on Internet of Things 2023年第4期4卷 1-29页

作者： Wu, Fang-Jing Lai, Sheng-Wun Sou, Sok-Ian TU Dortmund University Otto-Hahn-Str. 6 Dortmund44227 Germany Department of Computer Science and Information Engineering National Taiwan University No. 1 Section 4 Roosevelt Rd Da’an District Taipei10617 Taiwan Department of Electrical Engineering National Cheng Kung University No. 1 Daxue Rd. East Dist. Tainan701401 Taiwan

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%. © 2023 Copyright held by the owner/author(s)

关键词： Object detection

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Multi-degree-of-freedom control of nonlinear optical two-dimensional quantum materials 8

Multi-degree-of-freedom control of nonlinear optical two-dim...

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2D Photonic Materials and Devices VIII 2025

作者： Tang, Haoning Wang, Yiting Ni, Xueqi Watanabe, Kenji Taniguchi, Takashi Fan, Shanhui Mazur, Eric Yacoby, Amir Cao, Yuan School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States National Institute for Materials Science Namiki 1-1 Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States

ISBN: (纸本)9781510684843

We present the first on-chip microelectromechanical system (MEMS) capable of in situ tuning twisted 2D materials. This research allows for the dynamic control of interfacial properties, the creation of synthetic topological singularities, and the development of tunable-polarization light sources, enabling advanced quantum material manipulation in integrated 2D-3D devices. © 2025 SPIE.

关键词： Nonlinear optics

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A Study on the Effectiveness of Interactive Systems Using Electromyography

A Study on the Effectiveness of Interactive Systems Using El...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Hanawa, Shinnosuke Ho, Yihsin Kobayashi, Iciro Sato-Shimokawara, Eri Shibata, Hiroki Obo, Takenori Takushoku University Graduate School of Engineering 815-1 TatemachiHachioji Tokyo Japan Takushoku University Faculty of Engineering 815-1 TatemachiHachioji Tokyo Japan Tokyo Metropolitan University Department of Computer Science 6-6 AsahigaokaHino Tokyo Japan Tokyo Metropolitan University Department of Mechanical Systems Engineering 6-6 AsahigaokaHino Tokyo Japan

ISBN: (纸本)9798331531614

The paper presented an intuitive control system using electromyography (EMG) data that is obtained from the Myo gesture control armband. The aim of this study is to enable users to control multiple devices with a single EMG device in an intuitive way. The presented system shows the ability of EMG-based gestures to control Phillips Hue that allow user to control light bulb by simple hand movements. Moreover, the authors also developed new functionalities, which is according to users' preferences, to register gestures. These functions aim to improve the usability and enhance the naturalness of operations. Additionally, unique gestures, which less common in everyday life, is defined to reduce misrecognition when switching between different devices. Furthermore, to achieve reliable and accurate recognition of multiple gestures, the Support Vector Machine (SVM) models is considered to be a machine learning method for training processed EMG data. The experiment results demonstrate significant improvements in user experience and practical applicability in various interactive scenarios. © 2025 IEEE.

关键词： Support vector machines

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