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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

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Three-step one-way model in terahertz biomedical detection

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PhotoniX 2021年第1期2卷 279-296页

作者： Yan Peng Jieli Huang Jie Luo Zhangfan Yang Liping Wang Xu Wu Xiaofei Zang Chen Yu Min Gu Qing Hu Xicheng Zhang Yiming Zhu Songlin Zhuang Shanghai Key Lab of Modern Optical System Terahertz Technology Innovation Research InstituteUniversity of Shanghai for Science and Technology516 Jungong RoadShanghai 200093China Department of Nephrology Tongji HospitalTongji University School of MedicineShanghai 200065China Laboratory of Artificial-Intelligence Nanophotonics University of Shanghai for Science and TechnologyShanghai 200093P.R.China. Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of TechnologyCambridgeMA 02139USA. The Institute of Optics University of RochesterRochesterNew York 14627USA.

Terahertz technology has broad application prospects in biomedical ***, the mixed characteristics of actual samples make the terahertz spectrumcomplex and difficult to distinguish, and there is no practical terahertz detectionmethod for clinical medicine. Here, we propose a three-step one-way terahertzmodel, presenting a detailed flow analysis of terahertz technology in the biomedicaldetection of renal fibrosis as an example: 1) biomarker determination: screeningdisease biomarkers and establishing the terahertz spectrum and concentrationgradient;2) mixture interference removal: clearing the interfering signals in the mixturefor the biomarker in the animal model and evaluating and retaining the effectivecharacteristic peaks;and 3) individual difference removal: excluding individualinterference differences and confirming the final effective terahertz parameters in thehuman sample. The root mean square error of our model is three orders ofmagnitude lower than that of the gold standard, with profound implications for therapid, accurate and early detection of diseases.

关键词： Terahertz spectroscopy Renal fibrosis Biomedical detection Biomarker Mixture interference Individual difference

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

引用

arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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DSC: Deep scan context descriptor for large-scale place recognition

arXiv

引用

arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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Random Sampling on an High-Dimensional Sphere for Solving SVP

Random Sampling on an High-Dimensional Sphere for Solving SV...

引用

Computing Conference, 2019

作者： Qin, Ling Zhang, Xue Wang, Xiaoyun Zheng, Zhongxiang Qi, Wenfeng National Digital Switching System Engineering & Technological Research Center Zhengzhou China Institute for Advanced Study Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Science and Technology on Communication Security Laboratory Chengdu China

ISBN: (纸本)9783030228675

In this paper, we present a random sampling algorithm to solve the shortest vector problem (SVP). The idea of our algorithm comes from the Gaussian hypothesis, which is one of the most important and commonly used hypotheses in solving SVP. The main idea of our method is to generate the points which approximately satisfy Gaussian hypothesis of a given lattice. To find the shortest vector, we set some rules to make the result of this sampling algorithm get shorter progressively. We evaluate our algorithm by statistical and geometrical tools and give a basic analysis of the efficiency of algorithm. According to our analysis, parameters can be optimized to achieve high efficiency. Practical experiments have been conducted to validate our result where we solve SVP instances with dimension from 80 to 100. © 2019, Springer Nature Switzerland AG.

关键词： Computational geometry

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

引用

Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

Journal of Remote Sensing

引用

Journal of Remote Sensing 2023年第1期3卷 19-34页

作者： Zhongbing Chang Lei Fan Jean-Pierre Wigneron Ying-Ping Wang Philippe Ciais Jérôme Chave Rasmus Fensholt Jing MChen Wenping Yuan Weimin Ju Xin Li Fei Jiang Mousong Wu Xiuzhi Chen Yuanwei Qin Frédéric Frappart Xiaojun Li Mengjia Wang Xiangzhuo Liu Xuli Tang Sanaa Hobeichi Mengxiao Yu Mingguo Ma Jianguang Wen Qing Xiao Weiyu Shi Dexin Liu Junhua Yan Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China University of Chinese Academy of Sciences Beijing 100049China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China INRAE UMR1391 ISPAUniversite de BordeauxF-33140 Villenave d’OrnonFrance CSIRO Oceans and Atmosphere AspendaleVIC 3195Australia Laboratoire des Sciences du Climat et de l’Environnement CEA/CNRS/UVSQ/Universite Paris SaclayGif-sur-YvetteFrance Laboratoire Evolution et Diversite Biologique Universite Paul SabatierToulouseFrance Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark Department of Geography and Program in Planning University of TorontoTorontoON M5S 3G3Canada College of Geographical Science Fujian Normal UniversityFuzhou 3500007FujianChina School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082GuangdongChina Southern Marine Science and Engineering Guangdong Laboratory Zhuhai 519000GuangdongChina Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijing 100101China CAS Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing 100101China Department of Microbiology and Plant Biology Center for Spatial AnalysisUniversity of OklahomaNormanOKUSA LEGOS Universite de ToulouseCNESCNRSIRDUPS-14 avenue Edouard Belin31400 ToulouseFrance State Key Laboratory of Remote Sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Climate Change Research Centre University of N

Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among *** 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and *** results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other *** AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to *** performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to *** the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to *** of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

关键词： estimation Remote microwave

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Fractional-order biological system: chaos, multistability and coexisting attractors

引用

The European Physical Journal Special Topics 2021年第5期231卷 1061-1070页

作者： Debbouche, Nadjette Ouannas, Adel Momani, Shaher Cafagna, Donato Pham, Viet-Thanh Laboratory of Dynamical System and Control University of Larbi Ben M’hidi Oum El Bouaghi Algeria Department of Mathematics and Computer Sciences Larbi Ben M’hidi University Oum El Bouaghi Algeria Nonlinear Dynamics Research Center (NDRC) College of Humanities and Sciences Ajman University Ajman UAE Department of Mathematics Faculty of Science University of Jordan Amman Jordan Department of Innovation Engineering University of Salento Lecce Italy Nonlinear Systems and Applications Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam

In this paper, the nonlinear dynamics of the biological system modeled by the fractional incommensurate order Van der Pol equations are investigated. The stability of the proposed fractional non-autonomous system is analyzed by varying both the fractional order derivative and system parameters. Moreover, very interesting phenomena such as symmetry, multi-stability and coexistence of attractors are discovered in the considered biological system. Numerical simulations are performed by considering the Caputo fractional derivative and results are reported by means of bifurcation diagrams, computation of the largest Lyapunov exponent, phase portraits in 2D and 3D projections.

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

引用

arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO systems with Imperfect CSI

arXiv

引用

arXiv 2021年

作者： Zhi, Kangda Pan, Cunhua Ren, Hong Wang, Kezhi Elkashlan, Maged Di Renzo, Marco Schober, Robert Poor, H. Vincent Wang, Jiangzhou Hanzo, Lajos School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Computer and Information Sciences Northumbria University United Kingdom Université Paris-Saclay CNRS and CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette France Germany Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Engineering and Digital Arts University of Kent United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially-independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially-correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by other devices present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially-independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest. © 2021, CC BY.

关键词： Signal interference

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