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9th International Conference on Energy Materials and Electrical Engineering, ICEMEE 2023

作者： Li, Xin Chen, Xiaoke Ou, Lin Wang, Weilin Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou510080 China Zhanjiang Zhongheng Investment Co. Ltd. Guangdong Zhanjiang524000 China

ISBN: (纸本)9781510672765

To meet the input voltage requirement of onboard chips in intelligent accounting devices, Utilizing the STM32 series microcontroller as the central control unit, a novel integrated Buck circuit voltage regulator applying PID control has been developed, where voltage monitoring and temperature detection are included. To simplify the driving circuit, we shift the switching of the Buck circuit;the use of operational amplifier circuits for voltage sampling and scaling, and the subsequent voltage tracking, makes the feedback signal more stable and not be disturbed. Designed interrupt program for microcontroller based on PID control algorithm, and programs to handle feedback voltage and PID control are included. The finalized voltage stabilization circuit design successfully achieves reliable voltage output, over-temperature shutdown circuitry, and alarm functionality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Design and modelling Microcontrollers Circuit switching Control systems Power supplies Transistors Tunable filters Signal processing Detection and tracking algorithms Temperature control

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FPGA-Based Real-Time processing for optical Quantum Measurement and Control

FPGA-Based Real-Time Processing for Optical Quantum Measurem...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Haozhi Wang Yaping Lu Yifei Chen Maojie Ma Jialin Xu School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China Beijing Naishu Electronics Co. Ltd. Beijing China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In the context of rapid technological advancements, progress in chip manufacturing technology is propelling electronic devices toward miniaturization and high efficiency at an unprecedented pace. The fundamental principles of quantum mechanics are beginning to play a crucial role in modern electronic devices, providing the foundation for new technological methods and research directions. optical quantum signal processing, as an important branch of quantum technology, utilizes the quantum properties of photons for information processing and transmission, offering advantages such as high bandwidth, low noise, and strong resistance to interference. However, current optical quantum measurement and control instruments face significant challenges in terms of real-time performance and programming complexity, resulting in reduced data processing efficiency and significantly increased development costs. This study implements a real-time processing algorithm for optical quantum signals on a Field-Programmable Gate Array (FPGA). The algorithm possesses advantages of real-time performance, low power consumption, and high integration. It allows for rapid functionality switching and efficient optical quantum signal processing. The design specifically aims to enhance the signal-to-noise ratio, significantly improving measurement accuracy and reliability. Experimental results indicate that this method substantially enhances the measurement precision and signal-to-noise ratio of optical quantum signals. This research provides an efficient and reliable new method for optical quantum signal processing, laying a solid foundation for further studies in quantum communication, quantum computing, and quantum precision measurement.

关键词： Resistance Quantum computing Signal processing algorithms optical variables measurement Solids Real-time systems optical noise optical signal processing Field programmable gate arrays Signal to noise ratio

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processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames 12

Processing pipeline for automated data mining of the single ...

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12th International Scientific and Practical Conference "information Control systems and Technologies", ICST 2024

作者： Khlamov, Sergii Savanevych, Vadym Vlasenko, Vladimir Hadzhyiev, Emil Bondar, Yehor Netrebin, Yuriy Kharkiv National University of Radio Electronics Nauki avenue 14 Kharkiv61166 Ukraine National Space Facilities Control and Test Center Moskovska street 8 Kyiv01010 Ukraine INTTVE Limited O'Connell Bridge House D'Olier Street 2 DublinD02RR99 Ireland

In this paper the authors presented a sophisticated data mining pipeline, which was designed for restoring the high-quality images from the blurred frames made by the Charge-Coupled Device (CCD) cameras. The developed data mining pipeline leverages the modern informational technologies for the horizontal and vertical scalability. The core methodology integrates the following mathematical methods and algorithms: an inverse median filtration method for the noise reduction and the Lucy-Richardson algorithm for deblurring. The inverse median filtration effectively reduces impulsive noise while preserving edges, and the Lucy-Richardson algorithm iteratively refines the image by correcting for blurring effects encoded in the point spread function (PSF). The proposed system's architecture of the processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames utilizes the following modern technologies: Python programming language, Redis, FastAPI, React, Docker, and Caddy to ensure high performance, scalability, and ease of deployment. This integrated approach significantly enhances the accuracy of astronomical observations, facilitating more precise studies of celestial objects. The proposed pipeline addresses the unique challenges of astronomical image processing, offering a robust solution for automated data mining of single astronomical objects. Our work demonstrates the potential to advance astronomical research by improving image clarity and reliability, contributing to various fields within astronomy. The combination of effective noise reduction and deblurring techniques, along with a scalable and high-performance system architecture, provides a comprehensive solution to the challenges faced in processing astronomical images. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Noise abatement

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High-Speed Data processing System for optical Analog-to-Digital Converters

High-Speed Data Processing System for Optical Analog-to-Digi...

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Electronic information Engineering and Computer Communication (EIECC), International Conference on

作者： Ziqi Zhang Jifang Qiu Qiuyan Li Zirui Zhao Jian Wu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

optical Analog-to-Digital Converters (OADC) are devices that directly convert analog optical signals into digital signals. This research has designed an embedded high-speed data acquisition system for OADC using Field-Programmable Gate Arrays (FPGA). The main control chip utilizes the Xilinx Virtex UltraScale series VU9P FPGA, employs GTY IP cores for high-speed data reception, and implements algorithms for high-speed serial data input alignment. The Adsantec ANST7122-KMA flash ADC chip is used to perform the analog-to-digital conversion function. The acquired data is stored in Block RAM resources of FPGA and then uploaded to the host computer via the UART serial port. The data processing system board can synchronously collect signals from five channels. The experimental results show that the system can realize 10GSps data sampling with a sampling accuracy of 4.3 bit@1.1GHz. and continuously sample 2 16 points. The system integrates data acquisition, data processing, and data transmission onto a single board, offering an architecture with high integration and portability.

关键词： Data acquisition Signal processing algorithms Computer architecture Data processing Real-time systems Synchronization optical signal processing Field programmable gate arrays Analog-digital conversion High-speed optical techniques

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Numerical Performance Evaluation of ℓ1 - ℓ2 Sparse Reconstruction Using optical Analog Circuit

Numerical Performance Evaluation of ℓ1 - ℓ2 Sparse Reconst...

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Asia-Pacific Signal and information processing Association Annual Summit and Conference (APSIPA)

作者： Soma Furusawa Kazunori Hayashi Kaito Kameda Ryo Hayakawa Graduate School of Informatics Kyoto University Kyoto Japan Graduate School of Engineering Science Osaka University Osaka Japan

Iterative shrinkage thresholding algorithm (ISTA), fast ISTA (FISTA) and alternating direction method of multipliers (ADMM) based algorithm are well known convex optimization algorithms based on proximal splitting for ℓ 1 - ℓ 2 optimization problem in compressed sensing. In our previous study, aiming at the implementation with optical analog devices, we have proposed constant inertial FISTA (CIFISTA) and an approximated ADMM based algorithm, which do not require the operation of division. In this paper, we consider to realize those algorithms with optical analog devices and show concrete configurations of analog circuits using optical devices. We have conducted computer experiments to evaluate the performance of each algorithm, taking into account the effects of the additive noise introduced by optical amplifiers assuming the implementation using optical devices for common optical fiber communications systems. From the numerical results, it has been confirmed that both the proposed CIFISTA and the approximated ADMM based algorithm can achieve faster convergence than ISTA, while the degradation of the steady state mean squared error (MSE) due to the circuit noise is much smaller than that of the conventional ADMM algorithm.

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processing pipeline for data mining of the primary orbits of the Solar System objects using the Väisälä method 12

Processing pipeline for data mining of the primary orbits of...

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12th International Scientific and Practical Conference "information Control systems and Technologies", ICST 2024

作者： Troianskyi, Volodymyr Bazyey, Olexander Khlamov, Sergii Trunova, Tetiana Orlov, Stanislav Astronomical Institute of the Slovak Academy of Sciences Vysoké Tatry Tatranska Lomnica05960 Slovakia Odesa I. I. Mechnikov National University Pastera street 42 Odesa65082 Ukraine Vasyl Stefanyk Precarpathian National University Shevchenko Street 57 Ivano-Frankivsk76000 Ukraine Odesa National Maritime University Mechnykov Street 34 Odesa65029 Ukraine Kharkiv National University of Radio Electronics Nauki avenue 14 Kharkiv61166 Ukraine National Aerospace University Kharkiv Aviation Institute Chkalova street 17 Kharkiv61000 Ukraine

In this paper we presented the processing pipeline for data mining of the primary orbits and discovery of the new Solar System objects using the CoLiTec software and classical Väisälä method. The 1st step of a such method is performing the sequential astronomical observations and the classical astrometric data reduction. It is implemented as a combination of the general mathematical algorithms for processing of the astronomical frames incapsulated in the CoLiTec software. It contains the inverse median filter, astronomical calibration with usage of the master-frames, detection of the astronomical objects and its trajectories in series of frames and others features. After that the especial parameters of the object's orbit (right ascension and declination) are calculated at the certain time. When we receive at least two observations of the same object under study at different moments, the classic Väisälä algorithm can be applied. For this, the primary orbit from the two nearest observations is calculated. Then the geocentric rectangular coordinates and the appropriate geocentric velocity components are computed. This give us an opportunity for determining the Keplerian elements of orbit of the investigated object at any time we are interested in. The developed algorithm is implemented as a processing pipeline that includes a combination of the CoLiTec software and created tool with encapsulation of the Väisälä method. The created processing pipeline for data mining of the primary orbits and discovery of the new Solar System objects using the CoLiTec software and classical Väisälä method is practically verified. Using it a few new asteroids are first reported and a few lost small bodies of the Solar System are found in the Kyiv comet station and the Odesa - Mayaky observatory. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Median filters

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Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1253-1270页

作者： Imran Ahmed Sadia Din Gwanggil Jeon Francesco Piccialli Giancarlo Fortino the Center of excellence in Information Technology Institute of Management SciencesPeshawar 25000Pakistan the Department of Information and Communication Engineering Yeungnam UniversitySouth Korea the School of Electronic Engineering Xidian UniversityXi’an 710071China the Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea the Department of Mathematics and Applications“R.Caccioppoli” University of Naples Federico IINapoli 80138Italy the Department of Informatics ModelingElectronics and SystemsUniversity of CalabriaRendeCS 87036Italy

Collaborative Robotics is one of the high-interest research topics in the area of academia and *** has been progressively utilized in numerous applications,particularly in intelligent surveillance *** allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking *** tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are *** order to show the importance of top view surveillance,a collaborative robotics framework has been *** can assist in the detection and tracking of multiple objects in top view *** framework consists of a smart robotic camera embedded with the visual processing *** existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and *** detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and *** algorithms,along with detection models,help to track and predict the trajectories of detected *** pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data *** detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection *** tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

关键词： Collaborative robotics deep learning object detection and tracking top view video surveillance

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FE-YOLOv8n: A YOLOV8 Enhancement Algorithm for Effective Fire Extinguisher Detection

FE-YOLOv8n: A YOLOV8 Enhancement Algorithm for Effective Fir...

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Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Tianyang Lan Hongbo Li College of Electrical Engineering and Information Northeast Agricultural University Harbin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

People often rely on optical devices to identify the source of fire in a timely manner in order to prevent fires, but it is also crucial to promptly activate firefighting equipment. At present, there are models on the market that combine neural networks such as CNN with location algorithms for real-time detection of fire extinguisher positions, which can make real-time detection and alarm through cloud services. However, there is a lack of algorithms that can accurately identify the type of fire extinguisher and specific occlusion situations. Firstly, this article proposes using a model, based on YOLOv8n, to detect several main types of fire extinguishers (which can be determined by shape). Secondly, we propose a target detection algorithm based on CNN (Convolutional Neural Network) that can effectively detect specific fire extinguishers: FE-YOLOv8. The dataset consists of CSDM, Billbill videos, and on-site images, and specific image enhancements have been made for different types of fire *** trained model achieved a mAP of 90%, outperformed the original, and increased by 1.2 percentage points.

关键词： Fire extinguishers Training Shape Refining Object detection Real-time systems Convolutional neural networks Object recognition optical devices Videos

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Development and Characterization of a Hyperspectral LCTF-based Colposcopic System

Development and Characterization of a Hyperspectral LCTF-bas...

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Conference on Liquid Crystals Optics and Photonic devices

作者： Vega, Carlos Marquez-Suarez, Nerea Leon, Raquel Castro-Femandez, Maria Socorro-Marrero, Guillermo, V Fabelo, Himar Rial-Ferrario, Jorge Callico, Gustavo M. Univ Las Palmas de Gran Canaria ULPGC Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain Fdn Inst Invest Sanitaria Canarias FIISC Las Palmas Gran Canaria Spain OPTOMIC Espana SA Madrid Spain

ISBN: (纸本)9781510673519;9781510673502

The early detection of precancerous cervical lesions is essential to improve patient treatment and prognosis. Hyperspectral (HS) imaging (HSI) has demonstrated a high potential to become a new non-invasive and label-free imaging technique in the medical field for performing quick diagnosis of different diseases. This study presents the research and development process to integrate and characterize a KURIOS-XE2 filter (Thorlabs, Inc., NJ, USA), based in a liquid crystal tunable filter (LCTF) technology, into an existing colposcope (C5, OPTOMIC, Spain). The main goal was to capture spectral information in the near infrared range (650-1100 nm) by using a monochrome camera and acquiring 90 spectral wavelengths with a spectral resolution of 5 nm. Two different integration strategies were studied: i) filtering the emitted light by the sensor and ii) filtering the received light by the sensor, evaluating their respective benefits and limitations. Furthermore, a custom software was developed for HS image acquisition, integrating a variable acquisition time per wavelength, which allows improving the signal-to-noise ratio at wavelengths where the system presents lower quantum efficiency. The proposed system simplifies the adaptation of existing optical systems to HSI technology, improving the signal-to-noise ratio in the studied spectral range respect to other approaches. The results were compared against a previous custom implementation based on a Snapscan camera (IMEC, Belgium), covering the visual and near infrared and highlighting the advantages and limitations of both technologies for the development of a HS colposcope system targeting early detection of precancerous cervical lesions during routine clinical practice.

关键词： Hyperspectral imaging liquid crystal tunable filter cervical cancer image processing medical imaging medical instrumentation colposcopy

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Federated learning for IoT devices: Enhancing TinyML with on-board training

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information FUSION 2024年 104卷

作者： Ficco, M. Guerriero, A. Milite, E. Palmieri, F. Pietrantuono, R. Russo, S. Univ Salerno Salerno Italy Univ Napoli Federico II Naples Italy

The spread of the Internet of Things (IoT) involving an uncountable number of applications, combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive and intelligent systems in a variety of domains, including healthcare, environment, railway transportation and Industry 4.0. While this opens up favorable scenarios, it also raises new challenges. The huge amount of data collected and processed by ML applications requires efficient and scalable solutions that contrast with the constrained capabilities of IoT devices as for memory, power consumption, processing and network bandwidth. The TinyML technologies foster the adoption of ML algorithms running locally on IoT devices. However, they typically foresee a remote training process (e.g., on cloud servers) combined with local inference - a strategy not always viable, e.g., for privacy and security *** present a technique to enable the on-board training of ML algorithms on IoT devices, through the combination of federated learning (FL) and transfer learning (TL). We experimentally analyze it in classification and regression problems, comparing it to traditional FL solutions, as well as with a consolidated technique based on Tensorflow Lite. Results show that FL with TL reaches accuracy values better than FL without TL in both classification (86.48%) and regression (0.0201). These results are comparable with a model trained on the full dataset. We further analyze training and inference time and power consumption on various devices. Finally, we evaluate how the performance changes with unbalanced training datasets, showing that although they strongly impact accuracy, FL makes models more robust, letting them achieve accuracy comparable to when trained on balanced datasets.

关键词： Internet of things Federated learning Transfer learning TinyML

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