检索结果-内蒙古大学图书馆

Demonstration of an on-chip TE-pass polarizer using a silicon hybrid plasmonic grating

学校读者我要写书评

暂无评论

Photonics Research 2019年第3期7卷 289-293页

作者： BOWEN BAI FENGHE YANG ZHIPING ZHOU State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Peking University Shenzhen Research Institute

An on-chip, high extinction ratio transverse electric(TE)-pass polarizer using a silicon hybrid plasmonic grating is proposed and experimentally demonstrated. Utilizing plasmonics to manipulate the effective index and mode distribution, the transverse magnetic mode is reflected and absorbed, while the TE mode passes through with relatively low propagation loss. For a 6-μm-long device, the measurement result shows that the extinction ratio in the wavelength range of 1.52 to 1.58 μm varies from 24 to 33.7 dB and the insertion loss is 2.8–4.9 dB. Moreover,the structure exhibits large alignment tolerance and is compatible with silicon-on-insulator fabrication technology.

关键词： TE-pass polarizer using silicon hybrid plasmonic grating transverse magnetic mode

Higher-order topological in-bulk corner state in pure diffusion systems

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liu, Zhoufei Cao, Pei-Chao Xu, Liujun Xu, Guoqiang Li, Ying Huang, Jiping Department of Physics State Key Laboratory of Surface Physics Key Laboratory of Micro and Nano Photonic Structures MOE Fudan University Shanghai200438 China State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Haining314400 China Shaoxing Institute Zhejiang University Shaoxing312000 China Graduate School China Academy of Engineering Physics Beijing100193 China Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Singapore

Compared with conventional topological insulator that carries topological state at its boundaries, the higher-order topological insulator exhibits lower-dimensional gapless boundary states at its corners and hinges. Leveraging the form similarity between Schrödinger equation and diffusion equation, researches on higher-order topological insulators have been extended from condensed matter physics to thermal diffusion. Unfortunately, all the corner states of thermal higher-order topological insulator reside within the band gap. Another kind of corner state, which is embedded in the bulk states, has not been realized in pure diffusion systems so far. Here, we construct higher-dimensional Su-Schrieffer-Heeger models based on sphere-rod structure to elucidate these corner states, which we term "in-bulk corner states". Due to the antiHermitian properties of diffusive Hamiltonian, we investigate the thermal behaviour of these corner states through theoretical calculation, simulation, and experiment. Furthermore, we study the different thermal behaviours of in-bulk corner state and in-gap corner state. Our results would open a different gate for diffusive topological states and provide a distinct application for efficient heat dissipation. © 2022, CC BY.

关键词： Topology

Multi-label Classification with High-rank and High-order Label Correlations

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

A Learning-based Supervisory Control Architecture for Electric Vehicle Charging System Paired with Energy Storages

学校读者我要写书评

暂无评论

A Learning-based Supervisory Control Architecture for Electr...

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Hamed Nademi Bo Zhang Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Department of Energy Storage and Advanced Transportation Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9781728146294

ISBN: (纸本)9781728146300

A Machine Learning-based predictive model is developed for optimal dispatching energy storage system integrated with Electric Vehicle battery charger. The model is effectively trained using transfer learning algorithm and successfully validated via measurement data to achieve high fidelity and accuracy. The study findings provide possibilities for future development of the presented approach to implement computational heavy predictive algorithms with multi-step predictions in emerging modular bidirectional charger topologies to realize vehicle-to-grid (V2G) technology. The online tuning can be explored using the new measurements obtained during the operation, henceforth, enhancing the controller performance for integrating multiple DERs into distribution power networks that shows huge interest for the utilities and regulatory agencies. Multiple operation scenatios have been evaluated and simulation results are discussed to verify intended performance of the proposed control solution.

关键词： Batteries Charging stations Voltage control Vehicle-to-grid Predictive control

Hybrid Multi-User Precoding for mmWave Massive MIMO in Frequency-Selective Channels

学校读者我要写书评

暂无评论

Hybrid Multi-User Precoding for mmWave Massive MIMO in Frequ...

IEEE Conference on Wireless Communications and Networking

作者： Shijian Gao Xiang Cheng Liuqing Yang Department of Electrical & Computer Engineering Colorado State University CO USA State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

This paper investigates the transceiver design for downlink hybrid mmWave multi-user multi-carrier massive MIMO systems. In order to balance the processing complexity and the design flexibility, we adopt a prevalent hybrid precoding technique named hybrid block diagonalization (HBD) for downlink multi-user transmission. Aimed at maximizing the end-to-end mutual information (EEMI), a novel virtual EEMI assisted two-stage HBD scheme is judiciously devised. Apart from a low implementing complexity, the developed scheme is a more generic HBD solution, as it not only takes the frequency selectivity into account, but also removes the reliance on the high-resolution analog network. Simulations show that, even when applied with an inferior hardware configuration, the proposed HBD could still remarkably outperform its counterparts in terms of the EEMI performance at different levels of channel sparsity.

关键词：

Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Yunfei Liang, Chao Gao, Jin Zhang, Zhipeng Hu, Weiming Maybank, Stephen Zhou, Xue Li, Liang School of Information Science and Technology Shanghaitech University Shanghai201210 China State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China KargoBot China Department of Computer Science and Information Systems Birkbeck College LondonWC1E 7HX United Kingdom School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Basic Medical Sciences Beijing100850 China

Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as auxiliary tasks through embedding Re-Identification task (ReID) into the detector, achieving a balance between inference speed and tracking performance. However, solving the competition between the detector and the feature extractor has always been a challenge. Meanwhile, the issue of directly embedding the ReID task into MOT has remained unresolved. The lack of high discriminability in appearance features results in their limited utility. In this paper, a new learning approach using cross-correlation to capture temporal information of objects is proposed. The feature extraction network is no longer trained solely on appearance features from each frame but learns richer motion features by utilizing feature heatmaps from consecutive frames, which addresses the challenge of inter-class feature similarity. Furthermore, our learning approach is applied to a more lightweight feature extraction network, and treat the feature matching scores as strong cues rather than auxiliary cues, with an appropriate weight calculation to reflect the compatibility between our obtained features and the MOT task. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically, on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA, making it the best online tracker capable of achieving real-time performance. Comparative evaluations with other trackers prove that our tracker achieves the best balance between speed, robustness and accuracy. Code is available at https://***/yfzhang1214/TCBTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

Voltage-insensitive stochastic magnetic tunnel junctions with double free layers

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ota, Rikuto Kobayashi, Keito Hayakawa, Keisuke Kanai, Shun Çamsarı, Kerem Y. Ohno, Hideo Fukami, Shunsuke Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Graduate School of Engineering Tohoku University Sendai980-0845 Japan Center for Science and Innovation in Spintronics Tohoku University Sendai980-8577 Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan PRESTO Japan Science and Technology Agency Kawaguchi332-0012 Japan Division for the Establishment of Frontier Sciences Tohoku University Sendai980-8577 Japan National Institutes for Quantum Science and Technology Takasaki370-1207 Japan Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Center for Innovative Integrated Electronic Systems Tohoku University Sendai980-0845 Japan Inamori Research Institute for Science Kyoto600-8411 Japan

Stochastic magnetic tunnel junctions (s-MTJ) is a promising component of probabilistic bit (p-bit), which plays a pivotal role in probabilistic computers. For a standard cell structure of the p-bit, s-MTJ is desired to be insensitive to voltage across the junction over several hundred millivolts. In conventional s-MTJs with a reference layer having a fixed magnetization direction, however, the stochastic output significantly varies with the voltage due to spin-transfer torque (STT) acting on the stochastic free layer. In this work, we study a s-MTJ with a "double-free-layer" design theoretically proposed earlier, in which the fixed reference layer of the conventional structure is replaced by another stochastic free layer, effectively mitigating the influence of STT on the stochastic output. We show that the key device property characterized by the ratio of relaxation times between the high- and low-resistance states is one to two orders of magnitude less sensitive to bias voltage variations compared to conventional s-MTJs when the top and bottom free layers are designed to possess the same effective thickness. This work opens a pathway for reliable, nanosecond-operation, high-output, and scalable spintronics-based p-bits. © 2024, CC BY.

关键词： Tunnel junctions

Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

学校读者我要写书评

暂无评论

IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

学校读者我要写书评

暂无评论

Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management