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Learning to see speckle in the weak laser field through multimode fiber

Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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A Copyright Authentication Method Balancing Watermark Robustness and Data Distortion 26

A Copyright Authentication Method Balancing Watermark Robust...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Chundong Li, Yue Tianjin University of Technology Tianjin300384 China National Engineering Laboratory for Virus Prevention and Control Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (纸本)9798350331684

Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion. © 2023 IEEE.

关键词： Copyrights

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A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

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Frontiers of Information Technology & Electronic engineering 2024年第5期25卷 664-684页

作者： Xiaojun BAI Yang ZHANG Haixing WU Yuting WANG Shunfu JIN School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research *** this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance *** to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant *** meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,*** establish a four-dimensional continuous-time Markov chain as the system *** using the Gauss-Seidel method,we derive the stationary probability distribution of the system ***,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of *** experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading ***,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

关键词： Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function

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Battery Degradation Modeling in Hybrid Power Plants: An Island system Unit Commitment Study 4

Battery Degradation Modeling in Hybrid Power Plants: An Isla...

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4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Huang, Jianqiao Fang, Xin Zhou, Xinyang Tan, Jin Dong, Shuan Hoke, Andy National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States Illinois Institute of Technology Electrical and Computer Engineering Chicago United States Mississippi State University Electrical and Computer Engineering Department StarkvilleMS United States

ISBN: (纸本)9798350335408

As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs. © 2023 IEEE.

关键词： Cost functions

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Unlocking the Potential of Large Language Models in Legal Discourse: Challenges, Solutions, and Future Directions

Unlocking the Potential of Large Language Models in Legal Di...

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International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： M'Rhar Kaoutar Ben Jaafar Chaima Bencharef Omar Bourkoukou Outmane Computer and system engineering laboratory FSTG Cadi Ayyad University Marrakesh Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

The rise of Large Language Models (LLMs) has been remarkable, especially exemplified by the achievements of systems such as ChatGPT and Google's Bard. Both specialized and general users are warmly embracing these potent tools, indicating their increasing integration into everyday life. Nevertheless, challenges persist in their widespread adoption, particularly within specialized fields where they necessitate meticulous fine-tuning and access to high-quality data. Additionally, their lack of interpretability further complicates matters, often relegating them to the status of “black boxes”. Within the legal domain, LLMs harbor transformative potential but encounter obstacles due to legal hallucinations. This research delves into these hallucinations through a distinct set of legal queries pertaining to Canadian tax law, drawing comparisons between state-of-the-art LLMs. Its objective is to illuminate their efficacy in legal discourse and specialized domains, capitalizing on their broad knowledge base. The research advocates for fine-tuning as a potential solution, stressing the significance of domain-specific LLMs and delineating methods for their development. This includes considerations such as dataset curation, preprocessing techniques, model selection, and adherence to regulatory requirements, encompassing the creation of domain-specific vocabularies. Practical implementation entails the generation of domain-specific LLMs tailored for legal tasks such as research, information retrieval, and question answering. Despite inherent limitations, the study proposes avenues for enhancement and underscores the significance of LLMs utilization in legal services. This contributes to the evolution of natural language processing technology within the legal realm.

关键词： Productivity Vocabulary Technological innovation Law Navigation Large language models Semantics Knowledge based systems Finance Question answering (information retrieval)

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Three-dimensional reconstruction based on three-dimensional phase mapping using single-pixel imaging

Three-dimensional reconstruction based on three-dimensional ...

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2024 International Conference on Optoelectronic Information and Optical engineering, OIOE 2024

作者： Liu, Yemei Li, Yanfei Yu, Jing Miao, Yupei Liu, Xiaoli Peng, Xiang Tang, Qijian Key Laboratory of Optoelectronic Devices and System of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

ISBN: (数字)9781510680470

ISBN: (纸本)9781510680463

The single-pixel imaging technique has garnered widespread attention and research due to its low requirements on detector resolution and equipment cost. In this study, a laser point scanning single-pixel imaging system is constructed. It utilizes a grating installed in front of the detector to capture a single-frame deformed fringe pattern, combining Fourier transform profilometry with three-dimensional phase mapping to ultimately achieve three-dimensional data acquisition. Following the calibration of the system, the positions of each light ray are calibrated using the intrinsic and extrinsic parameters obtained through camera calibration. Consequently, this paper proposes a single-pixel three-dimensional reconstruction method based on three-dimensional phase mapping, establishing a correlation between three-dimensional points and phases to realize three-dimensional phase mapping. Experimental results demonstrate that the model can achieve high reconstruction accuracy, presenting a novel solution for the field of single-pixel three-dimensional reconstruction. © 2024 SPIE.

关键词： Calibration

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Multi-view 3D reconstruction based on speckle correlation 8

Multi-view 3D reconstruction based on speckle correlation

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Zeng, Xiaoxian Jing, Yu Tang, Qijian Liu, Xiaoli Peng, Xiang Key Laboratory of Optoelectronic Devices System of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Three-dimensional (3D) reconstruction based on digital speckle correlation has gained widespread usage in dynamic scenarios due to its ability to achieve single-frame reconstruction. Traditional binocular speckle structured light systems often face challenges such as insufficient surface texture richness and excessive curvature, leading to limitations in the accuracy of corresponding point localization during the matching process. Furthermore, partial point cloud loss caused by varying viewpoints can impact the precision and completeness of 3D reconstruction. This paper introduces a Multiview speckle correlation-based approach for 3D reconstruction. By establishing multi-view stereo camera epipolar rectification, complex speckle matching issues are transformed into one-dimensional searches along unified epipolar lines, simplifying the matching process and enhancing efficiency. Additionally, a digital speckle correlation computational model and subpixel interpolation algorithm based on Newton-Raphson iteration are employed to realize multi-view subpixel-level corresponding point localization. Ultimately, the reconstruction point cloud data is obtained through the intersection of three-ray least squares method. Experimental results demonstrate that this method achieves higher accuracy and better model completeness in 3D data reconstruction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Speckle

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A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage system

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 627-636页

作者： Shaojian Song Huangjiao Wei Yuzhang Lin Cheng Wang Antonio Gómez-Expósito IEEE School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Laboratory of Engineering for Energy and Environmental Sustainability University of SevilleSpain

Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are *** on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also *** simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.

关键词： Active distribution network(ADN) anomaly detection battery energy storage system(BESS) Kalman filtering situational awareness state estimation state of charge(SOC)

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