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0-minimization methods for image restoration problems based on wavelet frames

Inverse Problems 2019年第6期35卷

作者： Lu, Jian Qiao, Ke Li, Xiaorui Lu, Zhaosong Zou, Yuru Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and Statistics Shenzhen University Shenzhen China College of Mathematics and Statistics Shenzhen University Shenzhen China Department of Mathematics Simon Fraser University Burnaby Canada

In this paper we consider a class of 0-minimization and wavelet frame-based models for image deblurring and denoising. Mathematically, they can be formulated as minimizing the sum of a data fidelity term and the 0-'norm' of the framelet coefficients of the underlying image, and we are particularly interested in three different types of data fidelity forms for image restoration problems. We first study the first-order optimality conditions for these models. We then propose a penalty decomposition (PD) method for solving these problems in which a sequence of penalty subproblems are solved by a block coordinate descent (BCD) method. Under some suitable assumptions, we establish that any accumulation point of the sequence generated by the PD method satisfies the first-order optimality conditions of these problems. Moreover, for the problems in which the data fidelity term is convex, we show that such an accumulation point is a local minimizer of the problems. In addition, we show that any accumulation point of the sequence generated by the BCD method is a block coordinate minimizer of the penalty subproblem. Furthermore, under some convexity assumptions on the data fidelity term, we prove that such an accumulation point is a local minimizer of the penalty subproblem. Numerical simulations show that the proposed 0-minimization methods enjoy great potential for image deblurring and denoising in terms of solution quality and/or speed. © 2019 IOP Publishing Ltd.

关键词： Image reconstruction

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Thickness dependence of VO2 microstructure and phase transition properties deposited on ITO substrate

Thickness dependence of VO2 microstructure and phase transit...

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Pacific-Rim Laser Damage 2023: Optical Materials for High-Power Lasers

作者： Zeng, Lanxuan Lei, Yuxuan Li, Le Liu, Yi Ye, Fan Cai, Xingmin Zhong, Aihua Zhang, Dongping Key Laboratory of Advanced Thin Film Physics & Applications Shenzhen University China Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province China College of Physics & Optoelectronic Engineering Shenzhen University China

ISBN: (纸本)9781510672826

Vanadium dioxide is one of the reversible phase transition materials, which can be induced by temperature, electric field, laser, stress, etc. This characteristic makes thin films have great potential applications in fields of various optoelectronic devices. For electro-optical switch applications, a high extinction ratio and low threshold voltage are required, which are closely related to the VO2 film thickness. In this paper, VO2 thin films with different thicknesses were deposited on ITO substrates by pulsed magnetron sputtering. Microstructure, optical and electrical properties, and phase transition performance of the samples are investigated. The research results have a high reference value for the design of high-performance VO2-based electro-optic switches. © 2023 SPIE.

关键词： Microstructure

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Back contact interfacial modification mechanism in highly-efficient antimony selenide thin-film solar cells

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Journal of Energy Chemistry 2023年第5期80卷 256-264,I0007页

作者： Junhui Lin Guojie Chen Nafees Ahmad Muhammad Ishaq Shuo Chen Zhenghua Su Ping Fan Xianghua Zhang Yi Zhang Guangxing Liang Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060GuangdongChina Institute of Fundamental and Frontier Science University of Electronic Science and Technology of ChinaChengdu 610054SichuanChina Univ Rennes CNRSISCR(Institut des Sciences Chimiques de Rennes)UMR 6226Rennes F-35000France Institute of Photoelectronic Thin Film Devices and Technology and Tianjin Key Laboratory of Thin Film Devices and Technology Nankai UniversityTianjin 300350China

Antimony selenide(Sb_(2)Se_(3))is a potential photovoltaic(PV)material for next-generation solar cells and has achieved great development in the last several *** properties of Sb_(2)Se_(3)absorber and back contact influence the PV performances of Sb_(2)Se_(3)solar ***,optimization of back contact characteristics and absorber orientation are crucial steps in raising the power conversion efficiency(PCE)of Sb_(2)Se_(3)solar *** this work,MoO2was introduced as an intermediate layer(IL)in Sb_(2)Se_(3)solar cells,and comparative investigations were *** growth of(211)-oriented Sb_(2)Se_(3)with large grains was facilitated by introducing the MoO2IL with suitable *** MoO2IL substantially lowered the back contact barrier and prevented the formation of voids at the back contact,which reduced the thickness of the MoSe2interface layer,inhibited carrier recombination,and minimized bulk and interfacial defects in ***,significant optimization enhanced the open-circuit voltage(VOC)of solar cells from 0.481 V to 0.487 V,short-circuit current density(JSC)from 23.81 m A/cm^(2)to 29.29 m A/cm^(2),and fill factor from 50.28%to 57.10%,which boosted the PCE from 5.75%to 8.14%.

关键词： Sb_(2)Se_(3)solar cells MoO_(2)intermediate layer Back contact Defects

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Ion doping simultaneously increased the carrier density and modified the conduction type of Sb_(2)Se_(3) thin films towards quasi-homojunction solar cell

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Journal of Materiomics 2021年第6期7卷 1324-1334页

作者： Guangxing Liang Xingye Chen Donglou Ren Xiangxing Jiang Rong Tang Zhuanghao Zheng Zhenghua Su Ping Fan Xianghua Zhang Yi Zhang Shuo Chen Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China Univ Rennes CNRSISCR(Institut des Sciences Chimiques de Rennes)UMR 6226F-35000RennesFrance Tianjin Key Laboratory of Photoelectronic Thin Film Devices and Technology Renewable Energy Conversion and Storage CenterNankai UniversityTianjin300350China

Antimony selenide(Sb_(2)Se_(3))has drawn tremendous research attentions in recent years as an environment-friendly and cost-efficient photovoltaic ***,the intrinsic low carrier density and electrical conductivity limited its scope of *** this work,an effective ion doping strategy was implemented to improve the electrical and photoelectrical performances of Sb_(2)Se_(3) thin *** Sn-doped and I-doped Sb_(2)Se_(3) thin films with controllable chemical composition can be prepared by magnetron sputtering combined with post-selenization treatment based on homemade plasma sintered *** a result,the Sn-doped Sb_(2)Se_(3) thin film exhibited a great increase in carrier density by several orders of magnitude,by contrast,a less increase with one order of magnitude was achieved for the Idoped Sb_(2)Se_(3) thin ***,such cation or anion doping could simultaneously modify the conduction type of Sb_(2)Se_(3),enabling the first fabrication of a substrate structured Sb_(2)Se_(3)-based quasihomojunction thin film solar cell with configuration of Mo/Sb_(2)Se_(3)-Sn/Sb_(2)Se_(3)-I/ITO/*** obtained power conversion efficiency exceeding 2%undoubtedly demonstrated its attractive photovoltaic application potential and further investigation necessity.

关键词： Sb_(2)Se_(3) Thin film Ion doping Carrier density Quasi-homojunction solar cell

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The 2025 2D Materials Roadmap

arXiv

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Katiyar, Ajit Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Chinese Academy of Sciences China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New HavenCT United States School of Integrated Circuits Nanjing Uni

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Electromagnetic shielding

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Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos

arXiv

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arXiv 2022年

作者： Alfasly, Saghir Lu, Jian Xu, Chen Zou, Yuru Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Pazhou Lab Guangzhou China

With the assumption that a video dataset is multimodality annotated in which auditory and visual modalities both are labeled or class-relevant, current multimodal methods apply modality fusion or cross-modality attention. However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge. To tackle this challenge, we propose a novel audio-visual framework that effectively leverages the audio modality in any solely vision-specific annotated dataset. We adopt the language models (e.g., BERT) to build a semantic audio-video label dictionary (SAVLD) that maps each video label to its most K-relevant audio labels in which SAVLD serves as a bridge between audio and video datasets. Then, SAVLD along with a pretrained audio multi-label model are used to estimate the audio-visual modality relevance during the training phase. Accordingly, a novel learnable irrelevant modality dropout (IMD) is proposed to completely drop out the irrelevant audio modality and fuse only the relevant modalities. Moreover, we present a new two-stream video Transformer for efficiently modeling the visual modalities. Results on several vision-specific annotated datasets including Kinetics400 and UCF-101 validated our framework as it outperforms most relevant action recognition methods. © 2022, CC BY.

关键词： Semantics

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Realizing high thermoelectric performance in n-type Bi_(2)Te_(3)based thin films via post-selenization diffusion

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Journal of Materiomics 2023年第4期9卷 618-625页

作者： Yue-Xing Chen Jun-Ze Zhang Mohammad Nisar Adeel Abbas Fu Li Guang-Xing Liang Ping Fan Zhuang-Hao Zheng Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen518060China

Thermoelectric thin film has attracted a lot of attention due to its potential in fabricating micropower generator in chip sensors for internet of things(IoT).However,the undeveloped performance of n-type thermoelectric thin film limits its widely *** this work,a facile post-selenization diffusion reaction method is employed to introduce Se into Bi_(2)Te_(3)thin films,in order to optimize the carrier transport *** and theoretical calculation results indicate that the carrier concentration decreases and density of states increases after Se doping,leading to the enhancement of Seebeck ***,adjusting the diffusion reaction temperature can maintain the carrier concentration while increasing the mobility simultaneously,resulting in a high power factor of 1.5 mW/(m·K^(2)),which is eight times higher than that of the pristine Bi_(2)Te_(3)thin ***,a thin film device fabricated by the present Se-doped Bi_(2)Te_(3)thin films shows the highest output power of 60.20 nW under the temperature difference of 37 K,indicating its potential for practical use.

关键词： Thermoelectric Thin film Magnetron sputtering Bie-Tee-Se

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An improved group theory-based optimization algorithm for discounted 0-1 knapsack problem

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Advances in Computational Intelligence 2021年第5期1卷 1-11页

作者： Wang, Ran Zhang, Zichao Ng, Wing W. Y. Wu, Wenhui College of Mathematics and Statistics Shenzhen University Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China

Discounted 0-1 knapsack problem (D0-1KP) has been proved to be NP-hard, thus a lot of researches focus on designing non-deterministic algorithms to solve it. Group theory-based optimization algorithm (GTOA), as a recently proposed evolutionary algorithm (EA), can provide satisfactory results to D0-1KP. GTOA introduces important theories of algebra, i.e., group theory, to describe combinatorial optimization problems, and applies the classic operations in group theory to design operators for EA. In order to generate a better solution according to a set of existing solutions during each evolutionary iteration, an important operator called random linear combination operator (RLCO) is designed. However, the practical meaning of applying the operations in group theory is hard to explain, and the proposed RLCO is lack of interpretability, causing difficulties in analyzing and improving the algorithm. In this paper, to improve the interpretability and further enhance the performance, we propose a new operator named random xor operator (RXO), and interpret it from the view point of bitwise operation. By replacing RLCO with RXO, a new GTOA algorithm is realized for D0-1KP. Experimental results demonstrate that it can provide very competitive performance.

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Multi-stage feature learning based object recognition and 3D pose estimation with kinect 6

Multi-stage feature learning based object recognition and 3D...

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6th International Conference on Information Science and Technology, ICIST 2016

作者： Zeng, Wei Liang, Guoyuan Guangdong, Can Wang Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Dept. Mechanical and Automation Engineering Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781509012206

Impressive achievements have been achieved over the years in object recognition and 3D pose estimation while detecting objects and estimating their 3D poses reliably is still a very challenging problem, especially for texture-less objects in heavily cluttered scenes. In this paper, we introduce an improved approach based on multi-stage feature learning to identify object as well as their 3D orientation efficiently. Our approach is motivated by visual neuroscience models. Currently similar architectures have been successfully applied to pedestrian detection, traffic recognition, face representation, etc. We compute the feature descriptors by multi-stage convolutional neural networks (CNNs) which could map the object identity and 3D poses to the Euclidean distance space by evaluating the similarity between descriptors. By contrast with classical approaches, our method relies on fast and approximate Nearest Neighbor (NN) matching to solve the large scale problems instead of one classifier per object or multi-class classifiers whose complexity grows rapidly with the number of objects. The experiments of detecting poorly-textured objects in cluttered scenarios have been carried out to verify the effectiveness and efficacy of the proposed approach. © 2016 IEEE.

关键词： Object recognition

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A motion planning method based on HRL for autonomous exoskeleton

A motion planning method based on HRL for autonomous exoskel...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Dong, Yao He, Yong Su, Zhilong Ni, Jiangpeng Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

With the increase of the world's population and the serious aging phenomenon, more and more people with mobility impairments need technology to assist them in their daily lives. Autonomous Exoskeleton are one of the important technologies. The existing gait generation algorithm are most based on simplified mathematical model calculations, which have the problem of poor accuracy and unfriendliness to the wearer. This research aims to propose a path planning and gait generation policies (HPPO) based on hierarchical reinforcement learning (HRL), so that the wearer can safely avoid obstacles and maintain dynamic balance when using the autonomous exoskeleton controlled by this policy to walk. The high-level controller (HL) is responsible for path planning, it can quickly plan a reasonable exoskeleton footholds in the indoor environment. The low-level controller (LL) can imitate a given reference trajectory, learn a walking policy suitable for the exoskeleton, and it can follow the planned footholds, so as to achieve the purpose of avoiding obstacles for the wearer. The algorithm is verified in simulation environment. © 2021 IEEE.

关键词： Motion planning

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