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Editorial Introduction to JSTQE Special Issue on optical Computing

IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2023年第2期29卷

作者： Shastri, Bhavin J. Buckley, Sonia Van Vaerenbergh, Thomas Lin, Xing Offrein, Bert Queens Univ Dept Phys Engn Phys & Astron Kingston K7L 3N6 ON Canada Natl Inst Stand & Technol Appl Phys Div Boulder CO 80305 USA HPE Belgium Hewlett Packard Labs B-1831 Diegem Belgium Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Zurich Res Lab IBM Res Europe CH-8803 Ruschlikon Switzerland

The twenty-eight papers included in this special section focuses on optical computing. There is a renaissance in the research of photonic computing driven by the demands of artificial intelligence and neuromorphic computing on current digital electronic hardware (which has a central processor and separated memory for sequential processing). optical physics could enable non-von Neumann computing and potentially enable new applications that require low latency, high bandwidth, and low energies. This special issue will cover the current status, prospects, and challenges of the field in using light for neuromorphic computing, machine learning, and quantum information processing. The purpose of this special section is to provide a glimpse of the current status and future trends, as well as provide original results and recent developments in the field of optical computing, from devices, integration technology, system architectures, and algorithms, to applications.

关键词： Special issues and sections optical computing Quantum computing Photonics Neuromorphic engineering Computer architecture systems architecture Semiconductor device manufacture

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Application of artificial intelligence algorithm model in fault diagnosis of interactive information 6

Application of artificial intelligence algorithm model in fa...

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6th International Conference on Image, video processing, and Artificial Intelligence, IVPAI 2024

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk International Institute of Engineering Psychology DenverCO80202 United States Foothill Preparatory School Temple CityCA91780 United States Department of Computer Engineering Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9781510681781

Interactive information fault diagnosis technology is a new type of fault diagnosis technology which is integrated by information fusion, artificial intelligence, computer science and other disciplines. It can extract interactive information data of equipment in real time, analyze the characteristics of fault information, and then find the change and trend of equipment operating status. However, there are still some shortcomings in the practical application of this technology, for example, it is difficult to deal with a large number of complex interactive information data, and it is difficult to carry out effective fault diagnosis. Therefore, an intelligent fault diagnosis method with improved grey relational degree is proposed in this paper. This method can effectively extract the interactive information feature data of the equipment, and carry out the correlation analysis of the fault feature data through the grey correlation analysis algorithm, which provides a new idea for the fault diagnosis of the equipment. © 2024 SPIE.

关键词： Correlation coefficients Evolutionary algorithms Matrices Artificial intelligence Fuzzy logic algorithms Data processing Algorithm development Feature extraction Intelligence systems

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Capturing multiple full-scene images with a single camera via aperture stop exploitation

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JOURNAL OF THE optical SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND viSION 2022年第8期39卷 1456-1461页

作者： Pung, Aaron J. Space Dynam Lab 2340 Alamo Ave SE Albuquerque NM 87106 USA

In an effort to increase the capability of modern camera systems, recent advances in imaging technology have seen the maturation of postprocessing and demosaicing algorithms, multispectral imagers, and scene-splitting techniques. Although highly enabling, each of these methods faces an inherent limitation imposed by the cam-era's geometry. By reevaluating the fundamental components of the camera, this study presents a new method and paradigm in capturing and processing scene information. The proposed camera design is validated and opti-mized using Zemax simulations. The results show that light entering a camera can be split into three independent, spatially separated, full-scene images, wherein each image retains all spectral, polarimetric, and relative intensity information of the original scene. (c) 2022 Optica Publishing Group

关键词： Beam splitters Complementary metal oxide semiconductors Image quality Imaging systems optical systems Point spread function

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Balancing noise and nonlinear distortion by power loading in FBMC OWC

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APPLIED OPTICS 2025年第16期64卷 4589-4598页

作者： Kasjanowicz, Jakub Bojarczuk, Juliusz Araszkiewicz, Piotr Stepniak, Grzegorz Warsaw Univ Technol Inst Telecommun PL-00665 Warsaw Poland

Nonlinear distortion (ND), after intersymbol interference, is the second most significant data rate-limiting factor in LED-based optical wireless communication systems, particularly when using multicarrier modulation formats. Dealing with ND typically requires either power back-off or the application of computationally exhaustive nonlinear equalizers (e.g., Volterra, artificial neural network). In this paper, we demonstrate theoretically and experimentally that in multicarrier modulations, it is possible to mitigate ND by combining subcarrier power loading with either bit or entropy loading (EL). By doing so, ND is suppressed without any additional computational load. We consider the following power loading strategies: uniform power loading, pre-emphasis (application of an inverse channel filter at the transmitter), and the newly, to the best of our knowledge, proposed adaptive nonlinear power loading algorithm (NPL). Formal analysis carried out in a behavioral model of nonlinear LED reveals that uniform loading excels at low input powers and pre-emphasis at the highest. However, for intermediate power levels, neither of the above is optimal, and it is our NPL algorithm that achieves the best performance. We demonstrate that the NPL can be combined with conventional bit and power loading, as well as entropy loading algorithms. To achieve even higher data rates than in orthogonal frequency division multiplexing, we apply offset quadrature amplitude modulated filter bank multicarrier. The experimental results show an improvement of 11% in terms of asymptotic information rates over conventional EL and a gain of 17% compared to the well-known Levin-Campello algorithm. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Frequency division multiplexing Light sources Neural networks optical wireless communication Quadrature amplitude modulation Signal processing

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Design of photovoltaic radio energy transmission system based on NSGA-II

Design of photovoltaic radio energy transmission system base...

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2024 International Conference on Signal processing and Communication Security, ICSPCS 2024

作者： Zhou, Jiacheng Ma, Jun Wang, Hongxiang Chen, Bohang Institute of Computer Science Qinghai Normal University Qinghai China Institute of Physics and Electronic Information Engineering Qinghai Normal University Xining Qinghai China

ISBN: (纸本)9781510681699

In order to efficiently apply Wireless Power Transfer (WPT) technology in photovoltaic (PV) systems, mathematical modeling and mechanism analysis of Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) systems were conducted. By employing the multi-objective genetic algorithm (NSGA-II) and an adjustable coupling distance LCC-S topology, the reliability of power supply was ensured. The results demonstrate stable charging efficiency, strong adaptability, and high potential for widespread application. © 2024 SPIE.

关键词： Genetic algorithms

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A Novel Environment Reconstruction Method based on Distributed NLOS Signals 16

A Novel Environment Reconstruction Method based on Distribut...

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16th International Conference on Signal processing systems, ICSPS 2024

作者： Yu, Fangrui Li, Junlin Xie, Wei Zhang, Kehao Wan, Qun School of Information and Communication Engineering Nation Key Laboratory on Blind Signal Processing University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

Integrated Sensing and Communication (ISAC) is a rapidly evolving field with extensive application prospects. Environmental reconstruction (ER) is an important content of ISAC. Existing ER techniques often require large sample sizes or specific signals, which significantly limit their application. User Equipments (UEs) widely distributed in the environment and their communication signals provide an adequate measurement basis. However, there are challenges in utilizing communication signals due to limited research, and difficulties in identifying and extracting Non-Line-of-Sight (NLOS) signals. This paper proposes a distributed ER algorithm based on Angle of Arrival (AOA) and Time of Flight (TOF) information of NLOS signals, enabling identification and utilization of various order NLOS signals under complex NLOS conditions. A series of simulation delays validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Non line of sight propagation Reflection Reconstruction algorithms Mirror pointing Signal processing Environmental sensing Wireless communications

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Intelligent Edge Caching and Computing for Scalable information systems

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EAI ENDORSED TRANSACTIONS ON SCALABLE information systems 2023年第5期10卷 1-3页

作者： Zhang, Yudong Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia

INTRODUCTION: In scalable information systems, edge computing can help to overcome the challenges of latency, bandwidth, and connectivity in large-scale networks by reducing the amount of data that needs to be transmitted over the ***: The edge devices, such as sensors, cameras, gateways, routers, switches, multiplexers, integrated access devices, etc., can perform initial data processing and filtering, reducing the data volume sent to the central ***: This special issue aims to provide the recent progress of intelligent edge caching and computing for scalable information ***: The guest editors received more than 30 submissions, and finally, ten papers were ***: This special issue has contributed to advancing academic research and practice in intelligent edge caching and computing for scalable information systems.

关键词： edge computing scalable information systems integrated access devices internet of things convolutional neural network signal-to-noise ratio optical fiber composite overhead ground wire

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Deep Learning Process and Application for the Detection of Dangerous Goods Passing through Motorway Tunnels

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algorithms 2022年第10期15卷 370-370页

作者： Sisias, George Konstantinidou, Myrto Kontogiannis, Sotirios Univ Western Macedonia Dept Informat Kastoria 52100 Greece NCSR Demokritos Syst Reliabil & Ind Safety Lab Inst Nucl & Radiol Sci Energy Technol & Safety Athens 15341 Greece Univ Ioannina Dept Math Lab Team Distributed Microcomp Syst Ioannina 45110 Greece

Automated deep learning and data mining algorithms can provide accurate detection, frequency patterns, and predictions of dangerous goods passing through motorways and tunnels. This paper presents a post-processing image detection application and a three-stage deep learning detection algorithm that identifies and records dangerous goods' passage through motorways and tunnels. This tool receives low-resolution input from toll camera images and offers timely information on vehicles carrying dangerous goods. According to the authors' experimentation, the mean accuracy achieved by stage 2 of the proposed algorithm in identifying the ADR plates is close to 96% and 92% of both stages 1 and 2 of the algorithm. In addition, for the successful optical character recognition of the ADR numbers, the algorithm's stage 3 mean accuracy is between 90 and 97%, and overall successful detection and optical Character Recognition accuracy are close to 94%. Regarding execution time, the proposed algorithm can achieve real-time detection capabilities by processing one image in less than 2.69 s.

关键词： risk management transport management systems CNN algorithms image processing algorithms deep learning algorithms distributed management systems

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Application of machine learning to optical sensing and imaging

Application of machine learning to optical sensing and imagi...

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作者： Zhou, Chen The Pennsylvania State University

学位级别：博士

Recent years have witnessed significant advancements in optical sensing and imaging techniques. To effectively interpret complex data acquired through these techniques and accurately extract information from detectors, machine learning has emerged as a promising solution. Machine learning enables automatic learning of the relationship between raw data and desired outputs, without the need for complete and explicit physics-based models. This data-driven approach presents opportunities for making inferences on material properties, solving inverse problems in the area of optical sensing and imaging. However, the current majority of machine learning methods applied in optical systems primarily serve as post-processing tools to enhance automation and improve the signal-to-noise ratio after data acquisition with standard optical systems. This approach often utilizes precision optics that can be bulky and expensive, along with typical machine learning algorithms that may not fully exploit the underlying physics. The separation of optics and algorithms in design and optimization limits the potential for integration and performance improvement. Consequently, an area of fruitful research lies in deeply integrating machine learning into the design of efficient optical hardware systems, optimizing and streamlining their performance. This dissertation aims to demonstrate how machine learning can contribute to the design of cost-effective and portable optical devices by leveraging minimal optical components in conjunction with powerful learning models. The proposed approach adopts a holistic perspective in designing optical sensing systems. By relieving the burden on optics, simpler and more affordable optical components can be utilized. Moreover, optical domain knowledge can be effectively employed to custom design efficient machine learning algorithms. The dissertation is divided into three main parts, exploring different spaces: spectral, polarization, spatial-temporal, and mo

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Machine Learning for Signal processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

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16th International Conference on Signal processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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