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arXiv 2022年

作者： Li, Xinyu Cui, Yuanhao Andrew Zhang, J. Liu, Fan Zhang, Daqing Hanzo, Lajos The Faculty of Engineering and Information Technology University of Technology Ultimo SydneyNSW2007 Australia Beijing100876 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Telecom SudParis France Peking University China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Advances in wireless communication and signal processing facilitate integrated sensing and communication (ISAC) - a compelling technology that intrinsically combines sensing and communication functionalities for the dual-purpose exploitation of wireless/hardware resources and pursues mutual benefits. Consequently, the next-generation communications network will be perceptive. In this article, we provide a review of human-related sensing in the context of ISAC. We first present a general ISAC receiver signal processing framework, with a focus on human activity recognition (HAR). Based on its specific spatial deployments, we then categorize ISAC HAR into monostatic, bistatic, and distributed deployments, and discuss their properties, critical research problems and solutions. To facilitate the system's realization and improve its recognition performance, we then explore the inherent connections between the physical-layer system parameters and HAR performance metrics. Experimental results are presented for characterizing the sensing potentials of different ISAC systems. Finally, we review the technical challenges and identify the open research problems. © 2022, CC BY.

关键词： Signal receivers

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Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

arXiv

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arXiv 2023年

作者： Zhang, Fan Mao, Tianqi Liu, Ruiqi Han, Zhu Chen, Sheng Wang, Zhaocheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China The MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communications simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communications and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a 'locally' perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Disentanglement of intrinsic and extrinsic side-jump scattering induced spin Hall effect in N-implanted Pt

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Physical Review B 2023年第6期107卷 064402-064402页

作者： Utkarsh Shashank Yoji Nakamura Yu Kusaba Takafumi Tomoda Razia Nongjai Asokan Kandasami Rohit Medwal Rajdeep Singh Rawat Hironori Asada Surbhi Gupta Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Materials Science Division Inter University Accelerator Centre Aruna Asaf Ali Marg New Delhi 110067 India Department of Physics and Centre for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand 248007 India Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

The rapidly evolving utilization of spin Hall effect (SHE) arising from spin-orbit coupling in 5d transition metals and alloys has made giant strides in the development of designing low-power, robust, and nonvolatile magnetic memory. Recent studies, on incorporating nonmetallic lighter elements such as oxygen, nitrogen, and sulfur into 5d transition metals, have shown an enhancement in dampinglike torque efficiency θDL due to the modified SHE, but the mechanism behind this enhancement is not clear. In this paper, we study θDL at different temperatures (100–293 K) to disentangle the intrinsic and extrinsic side-jump scattering induced SHE in N-implanted Pt. We observe a crossover of intrinsic to extrinsic side-jump mechanism as the implantation dose increases from 2×1016 to 1×1017ions/cm2. A sudden decrease in the intrinsic spin Hall conductivity is counterbalanced by the increase in the extrinsic side-jump induced SHE efficiency. These results conclude that studying θDL as a function of implantation dose, and as a function of temperature, is important to understand the physical mechanism contributing to SHE.

关键词： Spin Hall effect Spin-orbit coupling Spin-orbit torque Spintronics Thin films Ferromagnetic resonance Ion implantation

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Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing

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Remote Sensing 2023年第3期15卷

作者： Chen, Ying-Nong Fan, Kuo-Chin Chang, Yang-Lang Moriyama, Toshifumi Center for Space and Remote Sensing Research National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City32001 Taiwan Department of Computer Science and Information Engineering National Central University No. 300 Jhongda Rd. Jhongli Dist. Taoyuan City32001 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Graduate School of Engineering Nagasaki University 1-14 Bunkyo-Machi Nagasaki852-8521 Japan

Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. Therefore, we organized a Special Issue on remote sensing titled "Artificial Intelligence and Machine Learning Applications in Remote Sensing." In this paper, we review nine articles included in this Special Issue, most of which report studies based on satellite data and DL, reflecting the most prevalent trends in remote sensing research, as well as how DL architecture and the functioning of DL models can be analyzed and explained is a hot topic in AI research. DL methods can outperform conventional machine learning methods in remote sensing;however, DL remains a black box and understanding the details of the mechanisms through which DL models make decisions is difficult. Therefore, researchers must continue to investigate how explainable DL methods for use in the field of remote sensing can be developed. © 2023 by the authors.

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Handcrafted local feature descriptor-based point cloud registration and its applications: A review

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IEEE Transactions on Visualization and computer Graphics 2025年

作者： Tao, Wuyong Wang, Ruisheng Hua, Xianghong Liu, Jingbin Chen, Xijiang Zang, Yufu Chen, Dong Xu, Dong Zhao, Bufan Nanchang University School of Mathematics and Computer Sciences Nanchang330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen518060 China University of Calgary Department of Geomatics Engineering CalgaryT2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing210000 China Nanjing Forestry University College of Civil Engineering Nanjing210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou51100 China

Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain. © 1995-2012 IEEE.

关键词： Three dimensional computer graphics

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Student procrastination analysis in virtual learning environments

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AIP Conference Proceedings 2022年第1期2433卷

作者： Manzoor Ahmed Hashmani Mehak Maqbool Memon Mansoor Ebrahim Kamran Raza Adnan Ashraf Arain 1Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Malaysia 2Faculty of Engineering Science and Technology Iqra University Karachi Pakistan 3Department of Computer Systems Engineering Mehran University of Engineering Technology Jamshoro Pakistan

With the increase in Massive Open Online Courses (MOOC) and Virtual Learning Environments (VLE), a general intuition about the student performance degradation is always attributed to the incorporation of technology in academic system. Procrastination is one of the perennial challenges faced by students that affects the overall academic performance. computer-based learning environments have enhanced the overall academic system, but it sometimes a challenge for students to stick to assigned tasks and avoid procrastination. This is a comprehensive study conducted to uncover students’ behavior based on their interactions with the VLE. information of students from versatile educational arenas including science, technology, engineering, mathematics, and social sciences have been included to gain understanding of student behavior to classify student behavior as probable to procrastinate or not. The study uses different unsupervised machine learning techniques including k-means, mini-batch k-means, agglomerative and hierarchical clustering to identify the correlations. Astonishing facts revealed by the study, representing little to absolutely no correlation between the final scores and the activities performed while interacting with the computer-based learning environments. The study also emphasizes on the need of detailed logs of students’ activity and properly annotated data to get valued insights for procrastination and academic performance of an individual, which would be helpful to identify students at risk of adverse academic performances and would lead to assist such students to improve their performance before off-track.

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Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

arXiv

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arXiv 2024年

作者： Liang, Feng Zhang, Zhen Lu, Haifeng Li, Chengming Leung, Victor C.M. Guo, Yanyi Hu, Xiping Artificial Intelligence Research Institute Shenzhen MSU-BIT University 518107 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University 518107 China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University 730000 China Department of Electrical and Computer Engineering The University of British Columbia V6T 1Z4 Canada Frontier Cross Disciplinary Research Institute Shenzhen MSU-BIT University China School of Mechanical and Electrical Engineering Beijing Institute of Technology China School of Medical Technology Beijing Institute of Technology 10081 China

With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and workload scheduling have become the key to high-performance deep learning. The large-scale environment with large volumes of datasets, models, and computational and communication resources raises various unique challenges for resource allocation and workload scheduling in distributed deep learning, such as scheduling complexity, resource and workload heterogeneity, and fault tolerance. To uncover these challenges and corresponding solutions, this survey reviews the literature, mainly from 2019 to 2024, on efficient resource allocation and workload scheduling strategies for large-scale distributed DL. We explore these strategies by focusing on various resource types, scheduling granularity levels, and performance goals during distributed training and inference processes. We highlight critical challenges for each topic and discuss key insights of existing technologies. To illustrate practical large-scale resource allocation and workload scheduling in real distributed deep learning scenarios, we use a case study of training large language models. This survey aims to encourage computer science, artificial intelligence, and communications researchers to understand recent advances and explore future research directions for efficient framework strategies for large-scale distributed deep learning. © 2024, CC BY.

关键词： Fault tolerance

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Highly Efficient THz Waves Using Laser Chaos

Highly Efficient THz Waves Using Laser Chaos

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International Conference on Infrared and Millimeter Waves

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Osamu Morikawa Takuya Shirao Kazuyuki Iwao Kazuyoshi Kurihara Hideaki Kitahara Takeshi Furuya Kenji Wada Yuki Kawakami Takeshi Moriyasu Makoto Nakajima Masahiko Tani Fukui University of Technology 3-6-1 Gakuen Fukui Japan Electrical and Computer Engineering Department University of California Los Angeles 66-147E Engineering IV 420 Westwood Plaza Los Angeles CA USA Chair of Liberal Arts Japan Coast Guard Academy Wakabacho 5-1 Kure Hiroshima Japan School of Education. University. of Fukui 3-9-1 Bunkyo Fukui Japan Research Center for Development of Far-Infrared Region University of Fukui 3-9-1 Bunkyo Fukui Japan Department of Physics and Electronics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai Osaka Japan Department of Electronics and Information Engineering National Institute of Technology (KOSEN) Fukui College Geshi-Cho Sabae Fukui Japan Faculty of Engineering University of Fukui 3-9-1 Bunkyo University of Fukui Fukui-city Fukui Japan Institute of Laser engineering Osaka Univ. 2-6 Yamadaoka Suita Osaka Japan

Efficiency of optical beats in a chaotically oscillating laser diode is confirmed comparing that of free running CW laser using a highly efficient plasmonic photomixer. The great potential of chaotically oscillating l...

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Quantum repeaters: From quantum networks to the quantum internet

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Reviews of Modern Physics 2023年第4期95卷 045006-045006页

作者： Koji Azuma Sophia E. Economou David Elkouss Paul Hilaire Liang Jiang Hoi-Kwong Lo Ilan Tzitrin NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan and NTT Research Center for Theoretical Quantum Information NTT Corporation 3-1 Morinosato-Wakamiya Atsugi Kanagawa 243-0198 Japan Department of Physics Virginia Tech Blacksburg Virginia 24061 USA QuTech Delft University of Technology Lorentzweg 1 2628 CJ Delft The Netherlands and Networked Quantum Devices Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan Department of Physics Virginia Tech Blacksburg Virginia 24061 USA and Quandela SAS 10 Boulevard Thomas Gobert 91120 Palaiseau France Pritzker School of Molecular Engineering The University of Chicago Chicago Illinois 60637 USA Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong and Center for Quantum Information and Quantum Control Department of Physics and Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Toronto Toronto Ontario Canada

A quantum internet is the holy grail of quantum information processing, enabling the deployment of a broad range of quantum technologies and protocols on a global scale. However, numerous challenges must be addressed before the quantum internet can become a reality. Perhaps the most crucial of these is the realization of a quantum repeater, an essential component in the long-distance transmission of quantum information. As the analog of a classical repeater, extender, or booster, the quantum repeater works to overcome loss and noise in the quantum channels constituting a quantum network. Here the conceptual frameworks and architectures for quantum repeaters, as well as the experimental progress toward their realization, are reviewed. Various near-term proposals to overcome the limits to the communication rates set by point-to-point quantum communication are also discussed. Finally, the manner in which quantum repeaters fit within the broader challenge of designing and implementing a quantum internet is overviewed.

关键词： Quantum communication Quantum memories Quantum networks Quantum repeaters Internet

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Time-space Viewpoint Planning for Guard Robot with Chance Constraint

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International Journal of Automation and computing 2019年第4期16卷 475-490页

作者： Igi Ardiyanto Jun Miura Department of Electrical Engineering and Information Technology Universitas Gadjah Mada No. 2 Jl. Grafika Yogyakarta 55281 Indonesia Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tenpaku-cho Toyohashi Aichi 441-8580 Japan

This paper presents a novel movement planning algorithm for a guard robot in an indoor environment, imitating the job of human security. A movement planner is employed by the guard robot to continuously observe a certain person. This problem can be distinguished from the person following problem which continuously follows the object. Instead, the movement planner aims to reduce the movement and the energy while keeping the target person under its visibility. The proposed algorithm exploits the topological features of the environment to obtain a set of viewpoint candidates, and it is then optimized by a cost-based set covering problem. Both the robot and the target person are modeled using geodesic motion model which considers the environment shape. Subsequently, a particle model-based planner is employed, considering the chance constraints over the robot visibility, to choose an optimal action for the robot. Simulation results using 3D simulator and experiments on a real environment are provided to show the feasibility and effectiveness of our algorithm.

关键词： Guard robot viewpoint planning state-time space uncertainty topology chance-constraint

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