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Two-dimension high efficiency beam steering based on liquid crystal polarization gratings

Optics Express 2025年第10期33卷 21425-21440页

作者： Li, Zifan Yu, Bijun Gong, Junhao Liu, Yutong Fan, Lina Gao, Xin Yin, Jiahang Ding, Tongshan Zhang, Dawei Wang, Jun Hong, Ruijin Mao, Hongmin Lu, Huanjun Cao, Zhaoliang Key Laboratory of Intelligent Optoelectronic Devices and Chips of Jiangsu Higher Education Institutions School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Advanced Technology Research Institute of Taihu Photon Center School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optoelectronic Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electronics and Information Engineering Suzhou University of Science and Technology Jiangsu Suzhou215009 China

Cascaded liquid crystal polarization grating (LCPG) structure is commonly used to realize two-dimension large angle optical beam steering. In this architecture, different LCPGs have different incident angles. As a liquid crystal half-wave plate (LC HWP) is placed in front of the LCPG to control steering direction, the different incident angles will affect the phase modulation of LC HWP and then, decrease the steering efficiency. In this paper, the steering efficiency of cascaded LCPGs under tilt incidence is considered and a compensation method is proposed to increase the steering efficiency. A simple analysis model is established to calculate the steering efficiency of cascaded LCPG structure for tilt incidence with arbitrary direction. Further, the effect of tilt incidence on the phase modulation of LC HWP is compensated and a simple formula is given to compute the compensation voltage. A validation experiment is performed to verify the proposed method. All the simulated and experimental results indicate that the steering efficiency of cascaded LCPGs can be improved for two-dimension beam scanning. This work is helpful for high efficiency beam steering based on cascaded LCPGs. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Bragg gratings

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Research on Lightweight Sensing Technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年第12期12卷 20678-20694页

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State Information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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Thermal Simulation Computing Network Based on Deep Learning

Thermal Simulation Computing Network Based on Deep Learning

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2024 International Symposium on Digital Home, ISDH 2024

作者： Wang, Ruomei Teng, Yi Zhang, Yaoquan Zhou, Fan School of Software and Engineering Guangzhou510303 China School of Computer Science Guangdong University of Education Guangzhou510303 China School of Computer Science Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou510006 China

ISBN: (纸本)9798331509873

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency. © 2024 IEEE.

关键词： Physiological models

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Ultra-flexible graphene/nylon/PDMS coaxial fiber-shaped multifunctional sensor

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Nano Research 2023年第4期16卷 5541-5547页

作者： Peddathimula Puneetha Siva Pratap Reddy Mallem Sung Cheol Park Seoha Kim Dong Hun Heo Cheol Min Kim Jaesool Shim Sung Jin An Dong-Yeon Lee Kwi-Il Park Department of Robotics and Intelligent Machine Engineering/College of Mechanical and IT Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea School of Materials Science and Engineering Kyungpook National UniversityDaegu 41566Republic of Korea Advanced Material Research Center Kumoh National Institute of TechnologyGumi 39177Republic of Korea School of Mechanical Engineering Yeungnam UniversityGyeongsan 38541Republic of Korea Department of Materials Science and Engineering Kumoh National Institute of TechnologyGumi 39177Republic of Korea

The development of flexible and wearable devices is mainly required for tactile sensing;as such devices can adapt to complicated nonuniform surfaces,they can be applied to the human ***,it remains necessary to simultaneously achieve small-scale,portable,and stable developments in such ***,this work aims at fabricating a novel,lightweight,ultra-flexible,and fiber-shaped coaxial structure with a diameter of 0.51 mm using polydimethylsiloxane/graphene/nylon material,based on piezoresistive and triboelectric *** piezoresistive-based robotic-hand-controlled sensor thus realized exhibits a response time of 120 ms and a fast recovery time of 55 ***,the piezoresistive-based sensors effectively feature whisker/joystick-guided behaviors and also sense the human finger *** to the triboelectric-based selfpowered nanogenerator behavior,the resulting sensor can convert mechanical motion into electrical energy,without adversely affecting human ***,this triboelectric-based human finger sensor can be operated under different bending modes at specific ***,this multifunctional sensor is cost-effective and suitable for various applications,including robotichand-controlled operations in medical surgery,whisker/joystick motions in lightweight drone technology,and navigation with highsensitivity components.

关键词： extremely lightweight ultra-flexible piezoresistive/triboelectric sensor human-machine whisker

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Edge Coalition Construction Based on a Novel Market Equilibrium Price

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China Communications 2025年第3期22卷 288-305页

作者： Wang Xiaolong Dang Jianwu Zhao Shuxu Wang Yangping Zhang Zhanping Hao Zhanjun School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China Big Data Center of Gansu Province Lanzhou 730000China School of Mathematics and Information Engineering Longdong UniversityLanzhou 745000China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The emergence of multi-access edge computing(MEC)aims at extending cloud computing capabilities to the edge of the radio access *** the large-scale internet of things(IoT)services are rapidly growing,a single edge infrastructure provider(EIP)may not be sufficient to handle the data traffic generated by these *** of the existing work addressed the computing resource shortage problem by optimizing tasks schedule,whereas others overcome such issue by placing computing resources on ***,when considering a multiple EIPs scenario,an urgent challenge is how to generate a coalition structure to maximize each EIP’s gain with a suitable price for computing resource block corresponding to a *** this end,we design a scheme of EIPs collaboration with a market price for containers under a scenario that considers a collection of service providers(SPs)with different budgets and several EIPs distributed in geographical ***,we bring in the net profit market price model to generate a more reasonable equilibrium price and select the optimal EIPs for each SP by a convex *** we use a mathematical model to maximize EIP’s profits and form stable coalitions between EIPs by a distributed coalition formation *** results demonstrate that our proposed collaborative scheme among EIPs enhances EIPs’gain and increases users’surplus.

关键词： coalition formation computing resource block market equilibrium multi-access edge computing surplus

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A Time Window Sequence-Based Evolutionary Algorithm for Solving Large-Scale Daily Task Planning Problems 13

A Time Window Sequence-Based Evolutionary Algorithm for Solv...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Peng Shao, Shuai Technology and Engineering Center for Space Utilization Chinese Academy of Science China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350308365

The daily task planning problem (DTPP) in the ground control of satellites has been a significant focus for space scientists. Although various optimization methods have been devoted to solving this problem, most of them can only deal with simple DTPPs with a few variables and naive objectives, which cannot obtain multiple optimal solutions that meet the complex requirements of real-world scenarios. To address this issue, this paper formulates DTPPs into a large-scale multi-objective optimization problem and solves it using an evolutionary algorithm embedded with heuristic strategies. The proposed algorithm can quickly approach the optimal solution of DTPPs and can be seamlessly embedded into various evolutionary algorithms. In order to evaluate the performance of the proposed algorithm, statistical experiments are carried out on the DTPP with nine real-world datasets. The experimental results demonstrate that the proposed algorithm has significant advantages over several representative evolutionary algorithms. © 2024 IEEE.

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GP-nano: A geometric graph network for nanobody polyreactivity prediction

GP-nano: A geometric graph network for nanobody polyreactivi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Xiaolong Yuan, Qianmu Zheng, Shuangjia Wang, Yu Yang, Yuedong Sun Yat-sen University Pengcheng Laboratory School of Computer Science and Engineering Guangzhou China Sun Yat-sen University National Supercomputing Center in Shenzhen School of Computer Science and Engineering Guangzhou China China Peng Cheng Laboratory Shenzhen China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350386226

Nanobodies are emerging therapeutic antibodies with more simple structure, which can target antigen surfaces and tissue types not accessible to conventional antibodies. However, nanobodies exhibit polyreactivity, binding non-specifically to off-target proteins and other biomolecules. This uncertainty can affect the drug development process and pose significant challenges in clinical development. Existing computational polyreactivity prediction methods focus solely on the sequence or fail to fully utilize structural information. In this study, we propose GP-nano, a geometric graph network based model for nanobody polyreactivity using predictive structure. GP-nano starts from sequences, predicts protein structures using ESMfold, and fully utilizes structural geometric information via graph networks. GP-nano can accurately classify the polyreactivity of nanobodies (AUC=0.91). To demonstrate GP-nano's generalizability, we also trained and tested it on monoclonal antibodies (mAbs). GP-nano outperforms the best methods on both datasets, indicating the contribution of structural information and geometric features to antibody polyreactivity prediction. © 2024 IEEE.

关键词： Nanoclay

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MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning 24

MICM: Rethinking Unsupervised Pretraining for Enhanced Few-s...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zhenyu Chen, Guangyao Zou, Yixiong Huang, Zhimeng Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

ISBN: (纸本)9798400706868

Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Unsupervised Few-Shot Learning (U-FSL) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases. In this work, we first quantitatively assess the impacts of Masked Image Modeling (MIM) and Contrastive Learning (CL) on few-shot learning tasks. Our findings highlight the respective limitations of MIM and CL in terms of discriminative and generalization abilities, which contribute to their underperformance in U-FSL contexts. To address these trade-offs between generalization and discriminability in unsupervised pretraining, we introduce a novel paradigm named Masked Image Contrastive Modeling (MICM). MICM creatively combines the targeted object learning strength of CL with the generalized visual feature learning capability of MIM, significantly enhancing its efficacy in downstream few-shot learning inference. Extensive experimental analyses confirm the advantages of MICM, demonstrating significant improvements in both generalization and discrimination capabilities for few-shot learning. Our comprehensive quantitative evaluations further substantiate the superiority of MICM, showing that our two-stage U-FSL framework based on MICM markedly outperforms existing leading baselines. The repository of this project is available at https://***/iCGY96/MICM. © 2024 ACM.

关键词： Contrastive Learning

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