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arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina CharlotteNC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://***/hellloxiaotian/LESRCNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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An overview of signal processing techniques for RIS/IRS-aided wireless systems

arXiv

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arXiv 2021年

作者： Pan, Cunhua Zhou, Gui Zhi, Kangda Hong, Sheng Wu, Tuo Pan, Yijin Ren, Hong Renzo, Marco Di Lee Swindlehurst, A. Zhang, Rui Zhang, Angela Yingjun National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Information Engineering School of Nanchang University Nanchang330031 China Department of Information Engineering The Chinese University of Hong Kong Shatin Hong Kong Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France Center for Pervasive Communications and Computing University of California IrvineCA92697 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating at two endpoints (i.e., transmitter and receiver) only are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and revolutionary technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next-/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective. We also highlight promising research directions that are worthy of investigation in the future. © 2021, CC BY.

关键词： Channel estimation

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TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization

TZC: Efficient Inter-Process Communication for Robotics Midd...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yu-Ping Wang Wende Tan Xu-Qiang Hu Dinesh Manocha Shi-Min Hu BNRist Ministry of Education Tsinghua University and the Key Laboratory of Pervasive Computing Beijing China Department of Computer Science and Electrical & Computer Engineering University of Maryland USA

Inter-process communication (IPC) is one of the core functions of modern robotics middleware. We propose an efficient IPC technique called TZC (Towards Zero-Copy). As a core component of TZC, we design a novel algorithm called partial serialization. Our formulation can generate messages that can be divided into two parts. During message transmission, one part is transmitted through a socket and the other part uses shared memory. The part within shared memory is never copied or serialized during its lifetime. We have integrated TZC with ROS and ROS2 and find that TZC can be easily combined with current open-source platforms. By using TZC, the overhead of IPC remains constant when the message size grows. In particular, when the message size is 4MB (less than the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2 IPC from hundreds of milliseconds to less than 1 millisecond. We also demonstrate the benefits of TZC by integrating it with TurtleBot2 to be used in autonomous driving scenarios. We show that by using TZC, the braking distance can be 16% shorter than with ROS.

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Calibration of flux crosstalk in large-scale flux-tunable superconducting quantum circuits

arXiv

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arXiv 2021年

作者： Dai, Xi Tennant, D.M. Trappen, R. Martinez, A.J. Melanson, D. Yurtalan, M.A. Tang, Y. Novikov, S. Grover, J.A. Disseler, S.M. Basham, J.I. Das, R. Kim, D.K. Melville, A.J. Niedzielski, B.M. Weber, S.J. Yoder, J.L. Lidar, D.A. Lupascu, A. Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Northrop Grumman Corporation LinthicumMD21090 United States MIT Lincoln Laboratory 244 Wood Street LexingtonMA02421 United States Departments of Electrical & Computer Engineering Chemistry and Physics Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada

Magnetic flux tunability is an essential feature in most approaches to quantum computing based on superconducting qubits. Independent control of the fluxes in multiple loops is hampered by crosstalk. Calibrating flux crosstalk becomes a challenging task when the circuit elements interact strongly. We present a novel approach to flux crosstalk calibration, which is circuit model independent and relies on an iterative process to gradually improve calibration accuracy. This method allows us to reduce errors due to the inductive coupling between loops. The calibration procedure is automated and implemented on devices consisting of tunable flux qubits and couplers with up to 27 control loops. We devise a method to characterize the calibration error, which is used to show that the errors of the measured crosstalk coefficients are all below 0.17%. Copyright © 2021, The Authors. All rights reserved.

关键词： Crosstalk

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Valley population of donor states in highly strained silicon

arXiv

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arXiv 2021年

作者： Voisin, B. Ng, K.S.H. Salfi, J. Usman, M. Wong, J.C. Tankasala, A. Johnson, B.C. McCallum, J.C. Hutin, L. Bertrand, B. Vinet, M. Valanoor, N. Simmons, M.Y. Rahman, R. Hollenberg, L.C.L. Rogge, S. Silicon Quantum Computing SydneyNSW2052 Australia School of Physics Sydney The University of New South Wales SydneyNSW2052 Australia Centre for Quantum Computation and Communication Technology School of Physics The University of New South Wales SydneyNSW2052 Australia 5. Physikalisches Institut and Center for Integrated Quantum Science and Technology Universität Stuttgart Stuttgart70569 Germany Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Centre for Quantum Computation and Communication Technology School of Physics The University of Melbourne ParkvilleVIC3010 Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne ParkvilleVIC3010 Australia School of Materials Science and Engineering The University of New South Wales SydneyNSW2052 Australia Electrical and Computer Engineering Department Purdue University West LafayetteIN United States Quantum Photonics Laboratory School of Engineering RMIT University MelbourneVIC3000 Australia Université Grenoble Alpes CEA LETI Grenoble38000 France

Strain is extensively used to controllably tailor the electronic properties of materials. In the context of indirect band-gap semiconductors such as silicon, strain lifts the valley degeneracy of the six conduction band minima, and by extension the valley states of electrons bound to phosphorus donors. Here, single phosphorus atoms are embedded in an engineered thin layer of silicon strained to 0.8% and their wave function imaged using spatially resolved spectroscopy. A prevalence of the out of plane valleys is confirmed from the real-space images, and a combination of theoretical modelling tools is used to assess how this valley repopulation effect can yield isotropic exchange and tunnel interactions in the xy-plane relevant for atomically precise donor qubit devices. Finally, the residual presence of in-plane valleys is evidenced by a Fourier analysis of both experimental and theoretical images, and atomistic calculations highlight the importance of higher orbital excited states to obtain a precise relationship between valley population and strain. Controlling the valley degree of freedom in engineered strained epilayers provides a new competitive asset for the development of donor-based quantum technologies in silicon. Copyright © 2021, The Authors. All rights reserved.

关键词： Landforms

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UAV-assisted intelligent reflecting surface symbiotic radio system

arXiv

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arXiv 2020年

作者： Hua, Meng Yang, Luxi Wu, Qingqing Pan, Cunhua Li, Chunguo Swindlehurst, A. Lee School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau Macao999078 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Center for Pervasive Communications and Computing University of California at Irvine IrvineCA92697 United States

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum-rate maximization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the problem of maximizing the minimum rate of the IRSs. We find that the relaxation-based method cannot solve this max-min problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Fast ORB-SLAM without Keypoint Descriptors

arXiv

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

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A telemedicine application for remote diagnosis and assessment of mood disorders 9th

A telemedicine application for remote diagnosis and assessme...

引用

9th International EAI Conference on Broadband Communications, Networks, and Systems, Broadnets 2018

作者： Konstantopoulou, Georgia Panagiotakopoulos, Theodor Mandellos, George J. Asimakopoulos, Konstantinos Lymberopoulos, Dimitrios K. Special Office for Health Consulting Services University of Patras Patras Greece Mobile and Pervasive Computing Quality and Ambient Intelligence Laboratory School of Science and Technology Hellenic Open University Patras26335 Greece Wire Communication Laboratory Electrical and Computer Engineering Department University of Patras Rion26504 Greece Department of Psychiatry School of Medicine University of Patras Patras Greece

ISBN: (纸本)9783030051945

Depression in its various forms is a widespread phenomenon in modern societies. Its high prevalence, associated costs, the chronic nature it develops and the challenges in diagnosing it put a lot of pressure on public healthcare systems. In response to these challenges, ICT-based approaches are increasingly implemented to support effective patient management and discovery. This paper presents a web application named "feeldistress", which is based on a novel distress evaluation framework to enable remote diagnosis of anxiety and depression, facilitate continuous evaluation of patients and assist prevention of suicide. The developed application was used and evaluated from a qualitative perspective by 117 students (47% women) who had visited the Special Office for Health Consulting Services of the University of Patras between 2014 and 2017. The majority of the users were very satisfied by the functionality, usability and appearance of the application showing it can be extremely useful tool for someone before hitting the door of a mental health specialist. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Telemedicine

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Prototype Framework of Low-Latency Collection of Multi-Point Video Stream over Multi-Hop Multi-Channel Wireless Network

Prototype Framework of Low-Latency Collection of Multi-Point...

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作者： Aung Myo Htut Juseong Kim JongWon Kim Chaodit Aswakul Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Networked Computing Systems (NetCS) Laboratory School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology

Big cities are facing heavy road traffic congestion. The main reason is the increasing number of vehicles beyond the road network capacity, which forces traffic police at times to be incapable of controlling the inevitable traffic jams. To resolve those traffic jams, traffic police need the near real-time information of the road network e.g. from traffic cameras. In this paper, a prototype framework of low-latency collection of multi-point video streams over a multi-hop multi-channel wireless network is proposed. Video frames are captured by Raspberry Pi cameras and sent by the wireless link to the herein-called IoT Concentrators. IoTCloud Hub then pulls and stores those video frames from the IoT Concentrators. Users（traffic police） can watch traffic videos from the display screen of the IoT-Cloud Hub. As a proof-of-concept implementation, two Pi Cameras have been set up with Apache Kafka Producers and two additional Raspberry Pi's have been set up as the IoT Concentrators, which run the leader and follower Apache Kafka Brokers. To complete our system, an Intel NUC as IoT-Cloud Hub and another Intel NUC as Orchestration-Hub have been also set up. We assume the available wired communication linkage between the Orchestration-Hub and the other devices. However, the links between the Pi Cameras, IoT Concentrators and IoT-Cloud Hub are wireless with Wi-Fi. To achieve a high performance, multiple Wi-Fi channels can be dynamically configured by the Orchestration-Hub to steer the VDO traffic（based on TCP） away from the congested channels. Based on the implementation prototype, the average TCP throughputs over all the wireless links have been improved by 56.046% on average, compared with the case without the dynamic Wi-Fi channel selection.

关键词： Apache Kafka Raspberry Pi video streaming wireless

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