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arXiv 2020年

作者： Li, You Zhuang, Yuan Hu, Xin Gao, Zhouzheng Hu, Jia Chen, Long He, Zhe Pei, Ling Chen, Kejie Wang, Maosong Niu, Xiaoji Chen, Ruizhi Thompson, John Ghannouchi, Fadhel El-Sheimy, Naser Department of Geomatics Engineering University of Calgary State Key Laboratory of Surveying Mapping and Remote Sensing Wuhan University School of Electronic Engineering Beijing University of Posts and Telecommunications Department of Land Sciences China University of Geosciences Beijing China Department of Computer Science University of Exeter School of Data and Computer Science Sun Yat-sen University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Earth and Space Sciences Southern University of Science and Technology College of Intelligence Science and Technology National University of Defense Technology NSS Research Center Wuhan University School of Engineering University of Edinburgh

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for massmarket localization applications. However, there are various error sources that have limited the localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review-localization data sources-localization algorithms-localization error sources and mitigation-localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors. Copyright © 2020, The Authors. All rights reserved.

关键词： Wide area networks

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Erratum to "Predict sample's line positions of absorption peaks in terahertz band with the forced radiation intensity of molecular electric dipoles" [Opt. Commun. 458 (2020) 124848](S0030401819309782)(10.1016/***.2019.124848)

引用

Optics Communications 2020年 483卷

作者： Zhang, Zhongwei Zhu, Zhi Yuan, Minghui Minghui Li You, Guanjun Chen, Lin Zhu, Yiming Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology No. 580 Jun Gong Road Shanghai200093 China Department of Optical Engineering School of Sciences Zhejiang A&F University Zhejiang ProvinceHangzhou311300 China

The publisher regrets that there is minor error in grant number. The updated acknowledgement is as below: Acknowledgments: This work is financially supported by theNatural Science Foundation of China [grant numbers61722111,11904231,61671302];Shanghai Pujiang Program, China [grant number17PJD028];the 111 Project [grant numberD18014];Shanghai Sailing Program [grant number19YF1434100];Zhejiang Agriculture and Forestry University Teaching Reform Project [grant number2013110150];Zhejiang Science and technology department Project [grant number2017C37015];the Shuguang Program [grant number18SG44]. The publisher would like to apologise for any inconvenience caused. © 2019 Elsevier B.V.

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Reply to comment on "Thermal effects - an alternative mechanism for plasmon-assisted photocatalysis"

arXiv

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arXiv 2020年

作者： Dubi, Yonatan Un, Ieng Wai Sivan, Yonatan Department of Chemistry Ben-Gurion University Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel Joan and Irwin Jacobs TIX Institute National Tsing Hua University Taiwan Ilse Katz Center for Nanoscale Science and Technology Ben-Gurion University Israel

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Weak (approximate) detectability of labeled Petri net systems with inhibitor arcs

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IFAC-PapersOnLine 2018年第7期51卷 167-171页

作者： Zhang, Kuize Giua, Alessandro College of Automation Harbin Engineering University Harbin150001 China ACCESS Linnaeus Center School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Department of Electrical and Electronic Engineering University of Cagliari Cagliari09123 Italy

Weak (approximate) detectability of a labeled Petri net (LPN) system (with inhibitor arcs) is a property such that if the property is satisfied then there exists an infinite label sequence generated by the system such that all markings after a time step can determined (in a prescribed subset of reachable markings) by the label sequence. Specifically, we prove that the problems of deciding weak detectability of LPN systems with inhibitor arcs and weak approximate detectability of LPN systems are both undecidable. © 2018

关键词： Petri nets Computability and decidability Detectability Inhibitor arcs Petri net systems Time step

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Stabilizing deep tomographic reconstruction

arXiv

引用

arXiv 2020年

作者： Wu, Weiwen Hu, Dianlin Cong, Wenxiang Shan, Hongming Wang, Shaoyu Niu, Chuang Yan, Pingkun Yu, Hengyong Vardhanabhuti, Varut Wang, Ge Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY United States The Laboratory of Image Science and Technology Southeast University Nanjing China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong SAR Hong Kong

Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missing in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. On the other hand, compressed sensing (CS) inspired reconstruction methods do not suffer from these instabilities because of their built-in kernel awareness. For deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is thus critically important to meet this challenge by stabilizing deep reconstruction networks. Here we propose an Analytic Compressed Iterative Deep (ACID) framework to address this challenge. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the deep reconstruction using ACID is accurate and stable, and sheds light on the converging mechanism of the ACID iteration under a Bounded Relative Error Norm (BREN) condition. In particular, the study shows that ACID-based reconstruction is resilient against adversarial attacks, superior to classic sparsity-regularized reconstruction alone, and eliminates the three kinds of instabilities. We anticipate that this integrative data-driven approach will help promote development and translation of deep tomographic image reconstruction networks into clinical applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Compressed sensing

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Regular perturbation characterization of transient MHD dissipation penetrable flow with thermal radiative flux

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Journal of Radiation Research and Applied Sciences 2025年第3期18卷

作者： Syed M. Hussain Hina Zahir Muhamamd Taskeen Raza Bandar M. Fadhl Mohamed R. Eid Wasim Jamshed Siti Suzilliana Putri Mohamed Isa Kamel Guedri Department of Mathematics Faculty of Science Islamic University of Madinah Madinah 42351 Saudi Arabia Department of Mathematics Benazir Bhutto Shaheed University (BBSU) Peshawar 25000 Pakistan Department of Electrical Engineering Lahore College for Women University Lahore Pakistan Mechanical Engineering Department College of Engineering and Architecture Umm Al-Qura University P. O. Box 5555 Makkah 21955 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Mathematics Capital University of Science and Technology (CUST) Islamabad 44000 Pakistan College of Engineering Al-Ayen Iraqi University An Nasiriyah 64001 Iraq Department of Computer Engineering Biruni University Topkapi Istanbul Turkey Centre for Foundation Studies in Science of Universiti Putra Malaysia Universiti Putra Malaysia UPM Serdang 43400 Selangor Malaysia Institute for Mathematical Research Universiti Putra Malaysia UPM Serdang 43400 Selangor Malaysia

This study investigates the transient magnetohydrodynamic (MHD) flow and heat and mass transfer over an inclined, radiative, and permeable surface under the influence of viscous dissipation and Ohmic heating in a porous medium. A regular perturbation technique is employed to solve the governing coupled, nonlinear partial differential equations that describe the velocity, temperature, and concentration fields. The analysis focuses on key physical parameters, including the skin friction coefficient, Nusselt number, and Sherwood number, along with the corresponding field profiles. The results highlight the significant impact of inclination angle, porosity, and thermal radiation on the transport behavior. Specifically, thermal radiation is found to reduce both the temperature distribution and the Nusselt number, indicating a damping effect on thermal transport. Conversely, the presence of a heat source elevates the temperature while enhancing heat transfer. Additionally, the Soret number contributes to an increase in both temperature and solute concentration due to thermal diffusion effects. The findings correspond well with previously published studies. In other words, when the inclined angle, porous parameter, and radiation parameter are not considered, all outcomes closely resemble those of the previously published works. This study offers valuable insights applicable to various industrial processes, including chemical manufacturing and thermal management systems.

关键词： Unsteady convection MHD Dissipation Porous media Thermal radiation Perturbation technique

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Repetitive single electron spin readout in silicon

arXiv

引用

arXiv 2019年

作者： Yoneda, J. Takeda, K. Noiri, A. Nakajima, T. Li, S. Kamioka, J. Kodera, T. Tarucha, S. RIKEN Center for Emergent Matter Science RIKEN Saitama351-0198 Japan Center for Quantum Computation and Communication Technology School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo152-8550 Japan

Single electron spins confined in silicon quantum dots hold great promise as a quantum computing architecture with demonstrations of long coherence times1, high-fidelity quantum logic gates2-4, basic quantum algorithms5 and device scalability6. While single-shot spin detection is now a laboratory routine1-7, the need for quantum error correction in a large-scale quantum computing device demands a quantum non-demolition (QND) implementation8-10. Unlike conventional counterparts, the QND spin readout imposes minimal disturbance to the probed spin polarization and can therefore be repeated to extinguish measurement errors. However, it has remained elusive for an electron spin in silicon as it involves exquisite exposure of the system to the external circuitry for readout while maintaining the coherence and integrity of the qubit. Here we show that an electron spin qubit in silicon can be measured in a highly non-demolition manner by probing another electron spin in a neighboring dot Ising-coupled to the qubit spin. The high nondemolition fidelity (99% on average) enables over 20 readout repetitions of a single spin state, yielding an overall average measurement fidelity of up to 95% within 1.2 ms. We further demonstrate that our repetitive QND readout protocol can realize heralded high-fidelity (> 99.6%) ground-state preparation. Our QND-based measurement and preparation, mediated by a second qubit of the same kind, will allow for a new class of quantum information protocols with electron spins in silicon without compromising the architectural homogeneity. Copyright © 2019, The Authors. All rights reserved.

关键词： Silicon

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Adversarial learning of disentangled and generalizable representations of visual attributes

arXiv

引用

arXiv 2019年

作者： Oldfield, James Panagakis, Yannis Nicolaou, Mihalis A. Department of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Informatics and Telecommunications University of Athens Greece Computation-based Science and Technology Center Cyprus Institute Cyprus

Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems such as multi-domain or multi-attribute transfer. The vast majority of such works leverages the strengths of adversarial learning and deep convolutional autoencoders to achieve realistic results by well-capturing the target data distribution. Nevertheless, the most prominent representatives of this class of methods do not facilitate semantic structure in the latent space, and usually rely on binary domain labels for test-time transfer. This leads to rigid models, unable to capture the variance of each domain label. In this light, we propose a novel adversarial learning method that (i) facilitates the emergence of latent structure by semantically disentangling sources of variation, and (ii) encourages learning generalizable, continuous, and transferable latent codes that enable flexible attribute mixing. This is achieved by introducing a novel loss function that encourages representations to result in uniformly distributed class posteriors for disentangled attributes. In tandem with an algorithm for inducing generalizable properties, the resulting representations can be utilized for a variety of tasks such as intensity-preserving multi-attribute image translation and synthesis, without requiring labelled test data. We demonstrate the merits of the proposed method by a set of qualitative and quantitative experiments on popular databases such as MultiPIE, RaFD, and BU-3DFE, where our method outperforms other, state-of-the-art methods in tasks such as intensity-preserving multi-attribute transfer and synthesis. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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