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Plasmonically engineered nitrogen-vacancy spin readout

OPTICS EXPRESS 2024年第13期32卷 22352-22361页

作者： Hapuarachchi, Harini Campaioli, Francesco Jelezko, Fedor Cole, Jared h. RMIT Univ ARC Ctr Excellence Exciton Sci & Chem & Quantum Ph Sch Sci Melbourne 3001 Australia Univ Padua Padua Quantum Technol Res Ctr I-35131 Padua Italy Ulm Univ Inst Quantum Opt Albert Einstein Allee 11 D-89081 Ulm Germany

Ultra -precise readout of single nitrogen -vacancy (NV) spins holds promise for major advancements in quantum sensing, computing, and communication technologies. Here we present a rigorous open quantum theory capable of simultaneously capturing the optical, vibronic, and spin interactions of the negatively charged NV center, both in the presence and absence of plasmonic interaction. Our theory is verified against existing experiments in the literature. We predict orders of magnitude brightness and contrast enhancements in optically detected magnetic resonance (ODMR) and NV spin qubit readout arising from plasmonic interaction. Such optimal enhancements occur in carefully engineered parameter regions, necessitating rigorous modelling prior to experimentation. Our theory equips the community with a tool to identify such regions. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Diffraction limit Nitrogen vacancy centers optical properties Quantum information Quantum technology Refractive index

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Applying machine learning to optical metrology: a review

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MEASUREMENT SCIENCE AND technology 2025年第1期36卷 012002-012002页

作者： Xue, Ruidong Hooshmand, Helia Isa, Mohammed Piano, Samanta Leach, Richard Univ Nottingham Fac Engn Mfg Metrol Team Nottingham England

This literature review investigates the integration of machine learning (ML) into optical metrology, unveiling enhancements in both efficiency and effectiveness of measurement processes. With a focus on phase demodulation, unwrapping, and phase-to-height conversion, the review highlights how ML algorithms have transformed traditional optical metrology techniques, offering improved speed, accuracy, and data processing capabilities. Efficiency improvements are underscored by advancements in data generation, intelligent sampling, and processing strategies, where ML algorithms have accelerated the metrological evaluations. Effectiveness is enhanced in measurement precision, with ML providing robust solutions to complex pattern recognition and noise reduction challenges. Additionally, the role of parallel computing using graphics processing units and field programmable gate arrays is emphasised, showcasing their importance in supporting the computationally intensive ML algorithms for real-time processing. This review culminates in identifying future research directions, emphasising the potential of advanced ML models and broader applications within optical metrology. Through this investigation, the review articulates a future where optical metrology, empowered by ML, achieves improved levels of operational efficiency and effectiveness.

关键词： machine learning artificial intelligence neural networks optical metrology

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Design technology Co-Optimization of High-Density RRAM Array for Advanced CMOS technology Node

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IEEE TRANSACTIONS ON ELECTRON DEVICES 2025年第5期72卷 2278-2284页

作者： Bao, Shengyu Wang, Zongwei Yang, Yuhang Wang, Qishen Shan, Linbo Bao, Lin Cai, Yimao Huang, Ru Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Beijing Adv Innovat Ctr Integrated Circuits Beijing 100871 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

In this work, we present a novel n transistor m resistor (nTmR) design that addresses the density limitations of the one-transistor-one-resistor (1T1R) array. The optimal design of nTmR (n = 3 and m = 2) achieves a record-low cell area of 0.0525 mu m(2) in the 40-nm technology node. Fabricated using a commercial 40-nm foundry logic process, the nTmR array demonstrates excellent reliability, including an endurance of 100 000 cycles and a retention capability of 10 years at 150 C-degrees. Furthermore, the proposed 3T2R array is compatible with FinFET technology nodes, enabling even greater density enhancements. This work offers a promising and practical solution for increasing resistance random access memory (RRAM) density with logic processes, facilitating cost-effective, high-performance embedded memory and computing applications.

关键词： Transistors Layout Metals Programming optical switches Resistors Resistance Random access memory Transconductance Three-dimensional displays Emerging memory high density n transistor m resistor (nTmR) resistance random access memory (RRAM)

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Survey on edge computing and its evolution

Survey on edge computing and its evolution

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2024 International Conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Zhu, Hongmei Lin, Yilin Qin, Zeng China Telecom Research Institute Guangdong Guangzhou510630 China

ISBN: (纸本)9781510685437

Edge computing is the key technology for 5G to achieve high bandwidth and low latency. It has been widely applied in vertical industries. However, with the fast development of various intelligent application services, operators put forward new requirements for MEC as an edge cloud. Operators are continuously exploring how to help users find edge nodes more quickly, how to smoothly migrate users within edge nodes, and how to better serve vertical industry customers. This paper first introduced the development history of MEC technology. Then application scenarios of MEC are described. Later functions and architecture of MEC are described with the most popular function. After that 5G System enhancements for edge computing are introduced with R17, R18 and even R19 assumptions. Then the latest Work item ETSI MEC 047 is introduced. Lastly, the conclusion is made about development of MEC in future. © 2024 SPIE.

关键词： Mobile edge computing

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AI-based automated circuit design optimization technology

AI-based automated circuit design optimization technology

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2024 International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2024

作者： Yang, YiLin Sunkayunkwan University Seoul Korea Republic of

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a novel AI-based automated circuit design optimization method aimed at enhancing design quality and efficiency. The proposed modular architecture integrates artificial intelligence with circuit design expertise, comprising key components for data processing, optimization algorithms, circuit simulation, and feedback mechanisms. We implement and test this system on a high-performance computing cluster, utilizing a comprehensive test suite of both analog and digital circuits. Our method demonstrates significant improvements in critical performance metrics, including power consumption, delay, and area, with enhancements ranging from 10% to 30% compared to traditional design approaches. The system's ability to generate multiple optimized design candidates under various constraints provides designers with flexible trade-off options. Furthermore, the high degree of automation in our approach substantially reduces manual effort and accelerates the design process. These results underscore the potential of AI-driven techniques to revolutionize integrated circuit design methodologies. © 2024 SPIE.

关键词： Cluster computing

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Research on improved YOLOv8 lightweight insulator defect detection based on edge computing 4

Research on improved YOLOv8 lightweight insulator defect det...

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4th International Conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Qu, Fengrui Tian, Lianfang Du, Qiliang School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou510640 China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China

ISBN: (纸本)9781510682467

Unmanned Aerial Vehicles (UAVs) are increasingly vital in diagnosing insulator defects, essential for ensuring the stability and safety of electrical transmission lines. In response, we propose a lightweight insulator defect detection model suitable for edge computing environments. Initially, we introduce the yolov8-C2f-RepGhost model, which features a streamlined backbone network. The conventional bottleneck in the C2f module of the backbone network is replaced with a RepGhost bottleneck, utilizing structural re-parameterization to enhance efficiency, thus rebranded as C2f-RepGhost. The RepGhost module significantly boosts detection speed. Furthermore, we employed GridMask data augmentation to expand and diversify the dataset, improving its utility in training and enhancing the model’s generalization capabilities. Our experimental results demonstrate that the yolov8-C2f-RepGhost model achieves notable enhancements in both speed and accuracy when trained on this augmented dataset. © 2024 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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An enhanced hybrid single-image-based structure-guided 0-norm and radiance–reflectance optimization

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Neural computing and Applications 2025年 1-21页

作者： WangNo, Nitit Thaiklang, Saowaluk Major of Computer Science Faculty of Information Technology Roi Et Rajabhat University Roi Et Thailand Major of Computer Science Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand

The reduced visibility during the winter season in an outdoor setting can be attributed primarily to the presence of haze or fog. Despite adjusting the lens of an optical sensor system for various purposes, such as automated driver assistance, remote sensing, and visual surveillance, the visual quality remains compromised. Owing to the overcast and murky atmosphere, it is difficult to remove these haziness emissions from a single image. To address this problem, we present a novel optimization-based dehazing algorithm that combines radiance and reflectance components with an additional refinement via a structure-guided 0-norm filter. We first estimate the poor reflectance map and optimize our transmission maps by using an estimated diffuse map. In addition, we estimate the removal of dehazing artifacts via a structure-guided 0 transmission map. In terms of qualitative and quantitative measures, compared with simulated pairs of images, the experimental results show that the proposed method is superior to the state-of-the-art algorithms. Moreover, the results of real-world enhancements confirm that the proposed method can provide high-quality images with no undesirable effects. In addition, textures may be removed, and edges can be preserved by the guided 0-norm filter, while the general image enhancement algorithms are being applied. The test results indicate that the proposed algorithm effectively generates enhanced images with greater visibility. The proposed approach has demonstrated superior performance metrics, including a 25.17% improvement in the peak signal-to-noise ratio (PSNR) and a 6.51% enhancement in the structural similarity index (SSI), surpassing the average performance of alternative methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Visibility

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Road damage detection algorithm based on GhostNet lightweight YoLov8 4

Road damage detection algorithm based on GhostNet lightweigh...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Liao, Shenglang Guangdong University of Technology Tianhe District GD Guangzhou510520 China

ISBN: (纸本)9781510686106

The detection of road injuries is crucial for preventing traffic accidents. Given the lack of lightweight in existing road damage detection algorithms, we propose an enhanced Yolov8 algorithm based on GhostNet. This approach effectively reduces the complexity of the network, in terms of both computing requirements and the number of parameters. Moreover, the incorporation of the CBAM attention mechanism module has led to notable enhancements in the accuracy and resilience of the detection process. Furthermore, the F-EIOU Loss is employed to further enhance speed of convergence and positioning accuracy of the model. Results on the RDD2022 dataset demonstrate that our method increases precision, recall, and mAP by 6.4%, 3.9%, and 5.4%, respectively. The parameter size is reduced by approximately 25%, thus confirming that our innovative improvements not only enhance detection accuracy significantly but also notably decrease quantity of parameters in road damage detection. © 2024 SPIE.

关键词： Highway accidents

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： optical character recognition

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Automatic Parking System Using Deep Learning and Internet of Things 4

Automatic Parking System Using Deep Learning and Internet of...

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4th International Conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Jyothi, Saddi Rami Reddy, M. Prasad, Chinthalapudi Siva Vara Rishita, Nimmagadda Prasad, Toka Shyam Satya Vara Ali, Shaik Irfan Lakireddy Bali Reddy College of Engineering Department of Information Technology Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Freshman Engineering Department Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350349856

To address the labour - intensive requirements of traditional parking lot operations, we have developed an automated system for access control, occupancy tracking, and revenue processing. The proposed system employs computer vision and deep learning techniques to autonomously handle parking lot ingress and egress while reducing operating costs. Vehicles entering and exiting the automated parking facility will be detected using infrared sensors interfaced to a Raspberry Pi microcomputer. License plate images will be captured by a Pi camera module and processed in real-time using an optical character recognition (OCR) algorithm based on a convolutional recurrent neural network architecture optimized for number plate recognition. Human access control can be handled in parallel by leveraging object detection models like Faster R-CNN or YOLO to differentiate pedestrian from vehicle ingress. The onboard occupancy tracking functionality will alleviate the need for manual counting of available spaces. Additionally, real-time parking availability stats will be published to a monitoring website allowing customers to view open slots prior to arrival. Lot capacity constraints can be configured to automatically disable entrance gates when maximum capacity is reached. The system's sensor fusion and computer vision capabilities eliminate tedious manual tallying and oversight required by legacy parking systems. By autonomously controlling access gates, tracking and publishing occupancy stats, and extracting revenue data from license plates, the proposed automated parking lot system aims to reduce operating costs while creating a scalable solution applicable to facilities ranging from apartment complexes to large shopping malls. The use of a low-cost, open-source development platform promotes maintainability while allowing for customized enhancements. © 2024 IEEE.

关键词： Operating costs

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