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Optical neural networks:progress and challenges

Light(science & Applications) 2024年第11期13卷 2511-2535页

作者： Tingzhao Fu Jianfa Zhang Run Sun Yuyao Huang Wei Xu Sigang Yang Zhihong Zhu Hongwei Chen College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangshaChina Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices National University of Defense TechnologyChangshaChina Nanhu Laser Laboratory National University of Defense TechnologyChangshaChina Department of Electronic Engineering Tsinghua UniversityBeijingChina Beijing National Research Center for Information Science and Technology(BNRist) BeijingChina

Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources,advanced algorithms,and high-performance electronic ***,conventional computing hardware is inefficient at implementing complex tasks,in large part because the memory and processor in its computing architecture are separated,performing insufficiently in computing speed and energy *** recent years,optical neural networks(ONNs)have made a range of research progress in optical computing due to advantages such as subnanosecond latency,low heat dissipation,and high *** are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing ***,we first introduce the design method and principle of ONNs based on various optical ***,we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip ***,we summarize and discuss the computational density,nonlinearity,scalability,and practical applications of ONNs,and comment on the challenges and perspectives of the ONNs in the future development trends.

关键词： networks neural hardware

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Underwater images enhancement using contrast limited adaptive parameter settings histogram equalization

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Multimedia Tools and Applications 2024年 1-15页

作者： Chen, Yahui Liang, Yitao College of Information Science and Engineering Henan University of Technology Henan Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan Zhengzhou China

CLAHE is widely used in underwater image processing because of its excellent performance in contrast enhancement. The selection of the clip point formula is the core problem of the CLAHE methods, and the selection of suitable clipping value has become the focus of some extended methods. In this paper, an automatic CLAHE underwater image enhancement algorithm is proposed. The method determines the clipping value according to the high-order moment dynamic features of each block of the underwater image. By quantifying the dynamic features of each block in the image more precisely, and then adding it to the clipping value formula, the contrast and details of the underwater image can be effectively enhanced. In order to effectively improve the saturation and brightness of underwater images, this paper chooses a more accurate and intuitive HSV model. Experimental results show that our methods enhance the contrast subjectively, while suppressing the amplification of noise very well, and also increase the saturation of underwater images. In objective metrics, our method obtains the best values in underwater quality assessment (UIQM), SSIM, and PSNR. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Underwater imaging

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Global output feedback adaptive stabilization for systems with long uncertain input delay

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science China(information sciences) 2023年第1期66卷 326-327页

作者： Zhuping WANG Jingcheng LIU Chao HUANG Hao ZHANG Huaicheng YAN School of Electronics and Information Engineering Tongji University Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology

Dear editor,The uncertain input delay is frequently encountered in engineering control systems. Adaptation is indispensable when the uncertain input delay is significant. In existing delayadaptive controllers [1–6], the actuator state must be measured to achieve global stability. Recently, a logic-based switching delay-adaptive state-feedback controller [7] was proposed to realize global stability without measuring the actuator state.

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A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

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International Journal of Digital Earth 2023年第1期16卷 272-288页

作者： Chunmei He Jia Sun Yuwen Chen Lunche Wang Shuo Shi Feng Qiu Shaoqiang Wang Torbern Tagesson Hubei Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China Department of Physical Geography and Ecosystem Science Lund UniversityLundSweden State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China International Institute for Earth System Science Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingPeople’s Republic of China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to ***,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their *** study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and *** sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT *** VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent *** ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl *** with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield *** new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

关键词： Leaf carotenoids content leaf chlorophyll content PROSPECT model ratio of Car to Chl vegetation index

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Terahertz parametric upconversion detection based on a KTiOAsO4 crystal

Terahertz parametric upconversion detection based on a KTiOA...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Ming, Na Fan, Shuzhen Zhang, Xingyu Chen, Xiaohan Cong, Zhenhua Liu, Zhaojun Guo, Quanxin Guo, Liyuan Jiao, Binzhe Yuan, Jiasheng Wang, Kaiyu Liu, Naichang Center for Optics Research and Engineering Key Laboratory of Laser and Infrared System Ministry of Education Shandong University Qingdao266237 China School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao266237 China

Highly sensitive terahertz frequency upconversion detection was demonstrated with a KTiOAsO4 crystal. The detectable THz frequency ranges were 3.6-3.9, 4.2-4.4, and 4.8-5.0 THz. The minimum detectable terahertz energy...

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年第2期12卷 1488-1499页

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

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Plant Phenomics 2023年第3期5卷 642-656页

作者： Yapeng Wu Wenhui Wang Yangyang Gu Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng National Engineering and Technology Center for Information Agriculture(NETCIA) MARA Key Laboratory for Crop System Analysis and Decision MakingMOE Engineering Research Center of Smart AgricultureJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityOne WeigangNanjingJiangsu 210095PR China Langfang Normal University 100 Aimin West RoadLangfangHebei 065000PR China

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final *** accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were *** study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before *** effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and ***,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural ***,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each *** findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

关键词： heading Wheat indices

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In situ X-ray imaging and numerical modeling of damage accumulation in C/SiC composites at temperatures up to 1200℃

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Journal of Materials science & technology 2024年第30期197卷 65-77页

作者： Weijian Qian Wanen Zhang Shengchuan Wu Yue Hu Xiangyu Zhang Qiaodan Hu Shaoming Dong Shantung Tu State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610031China State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of SciencesShanghai 200050China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Pressure Systems and Safety Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Carbon fiber reinforced silicon carbide matrix composites(C/SiC)have emerged as key materials for ther-mal protection systems owing to their high strength-to-weight ratio,high-temperature durability,resis-tance to oxidation,and outstanding ***,manufacturing defects deteriorate the mechani-cal response of these composites under extreme thermal-force coupling conditions,prompting significant research *** study demonstrates a customized in situ loading device compatible with syn-chrotron radiation facilities,enabling high spatial and temporal resolution recording of internal material damage evolution and failure behavior under thermal-force coupling *** thermal radia-tion units in a confocal configuration were used to create ultra-high-temperature environments,offering advantages of compactness,rapid heating,and *** situ tensile tests were conducted on C/SiC samples in a nitrogen atmosphere at both room temperature and 1200℃.The high-resolution image data demonstrate various failure phenomena,such as matrix cracking and pore ***-based fi-nite element simulations indicate that the temperature-dependent variation of the failure mechanism is attributable to thermal residual stresses and defect-induced stress *** work seamlessly integrates extreme mechanical testing methods with in situ observation techniques,providing a compre-hensive solution for accurately quantifying crack initiation,pore connection,and failure behavior of C/SiC composites.

关键词： Ceramic matrix composites Extreme environments X-ray computed tomography Internal damage evolution Image-based finite element method

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Test Suite Generation Based on Context-Adapted Structural Coverage for Testing DNN 24

Test Suite Generation Based on Context-Adapted Structural Co...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kong, Wei Du, Qianjin Cao, Huayang Li, Hu Wang, Tong Tian, Jianwen Kuang, Xiaohui National Key Laboratory of Science and Technology on Information System Security Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9788995004395

Deep learning (DL) introduces a novel data-driven programming paradigm, where the system logic is constructed through data training. However, this approach poses challenges in terms of system analysis and defect detection. To address this issue, recent efforts have focused on testing deep learning systems using intuitive criteria known as Neuron Coverage (NC) and its variants. These criteria measure the activation proportion of neurons in a neural network. Unfortunately, recent DL applications have largely overlooked the importance of context during testing. To address this gap, this paper first incorporates the context of the DL pipeline deployed before test execution, such as medical diagnosis and Android Malware detection. Next, we formulate structural coverage criteria to guide test suite generation based on the properties of DL pipeline in different contexts. Furthermore, we proposed a coverage-guided search algorithm to efficiently generate test suites. Experimental results highlight the effectiveness of our approach in uncovering numerous erroneous behaviors in contexts such as medical image diagnosis and Android malware detection. This approach significantly enhances the robustness of DL models by considering the structural aspects of the DL pipeline. Copyright 2023 KICS.

关键词： Deep learning

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