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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Fan Xu Jiawei Yao Wenhai Lai Kaiming Shen Xin Li Xin Chen Zhi-Quan Luo Peng Cheng Laboratory Shenzhen China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Huawei Technologies Shenzhen Research Institute of Big Data China

Conventional beamforming methods for intelligent reflecting surfaces (IRSs) typically entail the full channel state information (CSI). However, channel acquisition is costly when IRSs are extensively deployed in the network. To overcome this difficulty, this paper proposes a novel strategy called blind beamforming that coordinates multiple IRSs by means of the received signal statistics without knowing CSI. Blind beamforming just requires measuring the received signal power at the user side with respect to a sequence of randomly generated phase shifts across all the IRSs. Its main idea is to extract the essential statistical information for beamforming by exploring only a small portion of the whole solution space of phase shifts. We show that blind beamforming guarantees a signal-to-noise ratio (SNR) boost of Θ(N 2L ) under certain conditions, where L is the number of IRSs and N is the number of reflecting elements per IRS. The above result significantly improves upon the previous studies (including those with CSI) on multiple-IRS-assisted communication. Furthermore, we demonstrate the effectiveness of the proposed blind beamforming method through field tests at the commercial spectrum band of 2.6 GHz.

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CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fujia Zhou Wei Zhang Guangxu Zhu Hang Li Qingjiang Shi School of Software Engineering Tongji University Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks.

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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Blind Beamforming for Multiple intelligent Reflecting Surfaces

Blind Beamforming for Multiple Intelligent Reflecting Surfac...

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IEEE International Conference on Communications (ICC)

作者： Jiawei Yao Fan Xu Wenhai Lai Kaiming Shen Xin Li Xin Chen Zhi-Quan Luo School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Peng Cheng Laboratory Shenzhen China Huawei Technologies Shenzhen Research Institute of Big Data China

Channel acquisition is a major challenge faced by the conventional beamforming methods when dealing with multiple intelligent reflecting surfaces (IRSs), because the number of unknown channels grows exponentially with the number of IRSs. This work proposes to sidestep channel estimation and to configure the IRSs blindly based on the statistical information which is extracted from a set of random samples of the received signal power. The proposed blind beamforming method has provable performance in terms of the signal-to-noise ratio (SNR) boost. For instance, it yields a quartic SNR boost of $\Theta(N^{4})$ for a double-IRS system under certain condition, where $N$ is the number of reflected elements of each IRS. We remark that the above $\Theta(N^{4})$ result is more sophisticated than the existing ones about the double-IRS system in the literature. Furthermore, we numerically demonstrate the advantage of the proposed blind beamforming method through prototype tests with multiple IRSs.

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization 13

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Yongfeng Zhang, Yuze Ma, Lijia Ji, Junkai Liu, Dugang Leung, Victor C. M. Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518123 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

ISBN: (纸本)9798350308365

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control

arXiv

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arXiv 2024年

作者： Qu, Linzi Shang, Jiaxiang Ye, Hui Han, Xiaoguang Fu, Hongbo The School of Creative Media City University of Hong Kong Hong Kong The Department of Computer Science & Engineering HKUST Hong Kong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

Geometry- and appearance-controlled full-body human image generation is an interesting but challenging task. Existing solutions are either unconditional or dependent on coarse conditions (e.g., pose, text), thus lacking explicit geometry and appearance control of body and garment. Sketching offers such editing ability and has been adopted in various sketch-based face generation and editing solutions. However, directly adapting sketch-based face generation to full-body generation often fails to produce high-fidelity and diverse results due to the high complexity and diversity in the pose, body shape, and garment shape and texture. Recent geometrically controllable diffusion-based methods mainly rely on prompts to generate appearance and it is hard to balance the realism and the faithfulness of their results to the sketch when the input is coarse. This work presents Sketch2Human, the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control). Our solution is based on the latent space of StyleGAN-Human with inverted geometry and appearance latent codes as input. Specifically, we present a sketch encoder trained with a large synthetic dataset sampled from StyleGAN-Human's latent space and directly supervised by sketches rather than real images. Considering the entangled information of partial geometry and texture in StyleGAN-Human and the absence of disentangled datasets, we design a novel training scheme that creates geometry-preserved and appearance-transferred training data to tune a generator to achieve disentangled geometry and appearance control. Although our method is trained with synthetic data, it can handle hand-drawn sketches as well. Qualitative and quantitative evaluations demonstrate the superior performance of our method to state-of-the-art methods. We will release the code upon the acceptance of the paper. Copyright © 2024, The Authors. All rights reserv

关键词： Geometry

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PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming

arXiv

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arXiv 2024年

作者： Li, Bingheng Yang, Linxin Chen, Yupeng Wang, Senmiao Mao, Haitao Chen, Qian Ma, Yao Wang, Akang Ding, Tian Tang, Jiliang Sun, Ruoyu Michigan State University United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center For Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Rensselaer Polytechnic Institute United States

Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance, and logistics. Recently, two distinct approaches have emerged to expedite LP solving: (i) First-order methods (FOMs);(ii) Learning to optimize (L2O). In this work, we propose a FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems. The new architecture PDHG-Net is designed by unrolling the recently emerged PDHG method into a neural network, combined with channel-expansion techniques borrowed from graph neural networks. We prove that the proposed PDHG-Net can recover PDHG algorithm, thus can approximate optimal solutions of LP instances with a polynomial number of neurons. We propose a two-stage inference approach: first use PDHG-Net to generate an approximate solution, and then apply the PDHG algorithm to further improve the solution. Experiments show that our approach can significantly accelerate LP solving, achieving up to a 3× speedup compared to FOMs for large-scale LP problems. © 2024, CC BY.

关键词： Linear programming

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Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/ Victoria during the COVID-19 pandemic in Guangdong, China

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Infectious Diseases of Poverty 2024年第4期13卷 99-100页

作者： Zhiqi Zeng Yong Liu Wenxiang Jin Jingyi Liang Jinbin Chen Ruihan Chen Qianying Li Wenda Guan Lixi Liang Qiubao Wu Yuanfang Lai Xiaoyan Deng Zhengshi Lin Chitin Hon Zifeng Yang State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510180 P.R. China Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory Medicine Guangzhou Medical University Guangzhou People’s Republic of China Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative Engineering Macau University of Science and Technology Macau SAR China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau China Guangzhou Laboratory Guangzhou China Kingmed Virology Diagnostic and Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co. Ltd. Guangzhou China

Background Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019(COVID-19)and ***,with COVID-19 restrictions lifted,an unanticipated outbreak of the influ-enza B/Nictoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong,southern *** mechanism underlying this phenomenon remains *** better prepare for potential influenza out-breaks during COVID-19 pandemic,we studied the molecular epidemiology and phylogenetics of influenza A(H3N2)and BNictoria that circulated during the COVID-19 pandemic in this *** From January 1,2018 to December 31,2022,we collected throat swabs from 173,401 patients in Guang-dong who had acute respiratory tract *** viruses in the samples were tested using reverse tran-scription-polymerase chain reaction,followed by subtype identification and sequencing of hemagglutinin(HA)and neuraminidase(NA)*** and genetic diversity analyses were performed on both genes from 403 samples.A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the *** During the early period of COVID-19 control,various influenza viruses were nearly undetectable in respiratory *** control measures were relaxed in January 2020,the influenza infection rate peaked at 4.94%(39/789)in December 2021,with the influenza B/Victoria accounting for 87.18%(34/39)of the total influenza *** months later,the influenza infection rate again increased and peaked at 11.34%(255/2248)in June 2022;influenza A/H3N2 accounted for 94.51%(241/255)of the total influenza cases in autumn *** diverse geographic distribution of HA genes of B/Nictoria and A/H3N2 had drastically reduced,and most strains originated from *** rate of B/Victoria HA e

关键词： Insidious transmission Influenza COVID-19 control Genetic diversity Evolution

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Investigation of formaldehyde sources and its relative emission intensity in shipping channel environment

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Journal of Environmental sciences 2024年第8期142卷 142-154页

作者： Jiaqi Liu Shanshan Wang Yan Zhang Yuhao Yan Jian Zhu Sanbao Zhang Tianyu Wang Yibing Tan Bin Zhou Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science and EngineeringFudan UniversityShanghai 200433China Institute of Eco-Chongming(IEC) No.20 Cuiniao RoadShanghai 202162China Institute of Atmospheric Sciences Fudan UniversityShanghai 200433China National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Fudan UniversityShanghai 200438China Institute of Digitalized Sustainable Transformation Big Data InstituteFudan UniversityShanghai 200433China

Formaldehyde(HCHO)is considered one of the most abundant gas-phase carbonyl compounds in the atmosphere,which can be directly emitted through transportation ***-Path Differential Optical Absorption Spectroscopy(LP-DOAS)was used to observe HCHO in the river channel of Wusong Wharf in Shanghai,China for the whole year of *** to the impact of ship activity,the annual average HCHO level in the channel is about2.5 times higher than that in the nearby campus *** explain the sources of HCHO under different meteorological conditions,the tracer-pair of CO and O_(x)(NO_(2)+O_(3))was used on the clustered air *** results of the source appointment show that primary,secondary and background account for 24.14%(3.34±1.19 ppbv),44.78%(6.20±2.04ppbv)and 31.09%(4.31±2.33 ppbv)of the HCHO in the channel when the air masses were from the mixed direction of the city and channel,*** performing background station subtraction at times of high primary HCHO values and resolving the plume peaks,directly emitted HCHO/NO_(2)in the channel environment and plume were determined to be mainly distributed between 0.2 and *** cargo ships with higher sailing speeds or main engine powers tend to have higher HCHO/NO_(2)*** the knowledge of NO_(2)(or NO_(x))emission levels from ships,this study may provide data support for the establishment of HCHO emission factors.

关键词： HCHO LP-DOAS Channel environment Ship emissions

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Graded porous scaffold mediates internal fluidic environment for 3D in vitro mechanobiology

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Computers in Biology and Medicine 2025年 186卷 109674-109674页

作者： De Rosa, Chiara Angela Wright, Christopher J. Xiong, Yi Del Giudice, Francesco Zhao, Feihu Department of Biomedical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom Zienkiewicz Institute for Modelling Data and AI Swansea University Swansea United Kingdom School of System Design and Intelligent Manufacturing Southern University of Science and Technology Guangdong Province Shenzhen China Complex Fluid Research Group Department of Chemical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom

Most cell types are mechanosensitive, their activities such as differentiation, proliferation and apoptosis, can be influenced by the mechanical environment through mechanical stimulation. In three dimensional (3D) mechanobiological in vitro studies, the porous structure of scaffold controls the local mechanical environment that applied to cells. Many previous studies have focused on the topological design of homogeneous scaffold struts. However, the impact of scaffold inhomogeneity on the mechanical environment, which is essential in mechanobiological application (e.g. for multi-cells co-culture), remains elusive. In this study, we use a computational fluid dynamics (CFD) approach together with data analysis to study the influence of a porosity gradient (10 %–30 % porosity difference) on the local and global mechanical environment (wall shear stress - WSS) within the commonly used structures of triple periodic minimal surfaces (TPMS). In addition, the anisotropy of internal WSS and scaffold permeability caused by the porosity gradient is investigated. It is found that the influence of anisotropy on the average WSS and permeability is up to 11 % and 31 %, respectively. These results, as theoretical references will be useful to tissue engineers and mechanobiologists for scaffold design and in vitro experiment planning such as integrated use of graded scaffold and bioreactors for specific cell types. © 2025 The Authors

关键词： Shear stress

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