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Image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology

Image Recognition and Analysis of Computer Interlock Interfa...

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IEEE Advanced information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Xuanmo Chen Qiang Li College of Computer Science and Technology Lanzhoujiaotong University Lanzhou China Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Province Lanzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces.

关键词： visualization Technological innovation Image recognition Machine learning algorithms optical character recognition Software algorithms Clustering algorithms

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A Real-Time Attack Detection Scheme Based on Multi-Source information with False Flag Data

A Real-Time Attack Detection Scheme Based on Multi-Source In...

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2023 International conference on Networking and Network Applications, NaNA 2023

作者： Li, Han Zhang, Zhiwei Duan, Hao He, Yi Xi, Ning School of Computer Science and Technology Xidian University Shaanxi Xian710071 China Institute of Network Information Academy of Systems Engineering Academy of Military Sciences Beijing100141 China Tencent Technology Co. Ltd. Chengdu610095 China School of Cyber Engineering Xidian University Shaanxi Xi'an710071 China

ISBN: (纸本)9798350327380

In recent years, the issue of cyber security has received unprecedented widespread attention and in-depth research. Network false flag attacks have characteristics of stealthiness and persistence, which leads to the static feature matching-based methods misjudging the attack category. At present, the dynamic behavior detection methods based on audit logs have the disadvantages of low reliability, poor real-time performance and low efficiency. Therefore, in this paper, we propose a multidimensional information-based network false flag attacks detection method, which generates state subgraphs by extracting and processing real-time operation information and audit logs of the system. Then, by comparing the clustering results of the current system operation with the historical state, we determine whether the behavior is malicious or not. For malicious behaviors, we cluster the behaviors with the same purpose into groups, and the security analysts label the attack type of each group. The subsequent behaviors can be automatically labeled with attack type according to their clustering results and therefore realize semi-supervised classification. We demonstrate the effectiveness of our method in an attack scenario, our approach achieves an accuracy of 94.55% for false flag attacks detection and an accuracy of 89.62% for semi-supervised classification. © 2023 IEEE.

关键词： Clustering algorithms

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Intelligent Microbial Growth Monitoring: A Parallel OD600 Detection System Based on Artificial Intelligence

Intelligent Microbial Growth Monitoring: A Parallel OD600 De...

引用

Machine vision, Image processing and Imaging Technology (MviPIT), International conference on

作者： Libing Xu Fei Ren Bonifacio T. Doma Nanjing SuperKong Electronics Co. Ltd. China Nan Jing China School of Information Technology Mapua University Philippines School of Graduate Studies Mapua University Manila Philippines

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Microbial $\mathbf{OD}_{\mathbf{600}}$ measurement is a crucial experimental technique in microbiological and molecular biological research, evaluating microbial growth by measuring optical density at 600 nm wavelength. While convenient, this method has limitations. For instance, different microbial species and growth stages may absorb light differently, potentially leading to biases when measuring multiple strains simultaneously. This paper presents an innovative parallel $\mathbf{OD}_{\mathbf{600}}$ detection system capable of simultaneously monitoring growth curves of multiple microbial strains. Our research integrates advanced optical systems, precise mechanical control, and artificial intelligence algorithms, not only improving the accuracy and efficiency of bacterial growth measurements but also achieving parallel processing and analysis of multiple samples. We discuss in detail the hardware design, software architecture, and core algorithms, with particular emphasis on the application of machine learning in multi-strain data analysis and prediction. Experimental results demonstrate that this system significantly outperforms traditional methods in measurement accuracy, automation level, multi-sample processing capability, and data analysis.

关键词： Temperature measurement Accuracy Current measurement Wavelength measurement Machine learning optical variables measurement Prediction algorithms Real-time systems Monitoring Strain

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Utilizing 1D FMCW Radar Data for Distance Estimation to Port Infrastructure

Utilizing 1D FMCW Radar Data for Distance Estimation to Port...

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International conference on information Fusion

作者： Mirjam Bogner Fynn Pieper Christian Steger Matthias Steidel Janusz A. Piotrowski Sebastian Feuerstack German Aerospace Center (DLR) Institute of Systems Engineering for Future Mobility Escherweg 2 Oldenburg Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Assistance systems play an important role in the proceeding transition of surface vessels towards highly automated operations. Particularly when navigating through congested areas like harbors, exact knowledge of distances to nearby obstacles is essential for collision avoidance. This paper applies a combined filtering and clustering approach in order to utilize 1D FMCW radar data for distance estimation to nearby obstacles in the harbor environment. The data processing aims at clearing the raw sensor data from unwanted signals caused by environmental influences like rain or waves and determines a reliable distance from the relevant signals. We evaluate our approach using sea trial data from a research vessel, comparing processed radar distances with a DGPS-based ground truth. The study assesses the performance of three density-based clustering algorithms-DBSCAN, HDBSCAN, and OPTICS-in this context. All of these algorithms show a good performance for processing the 1D FMCW data for our use case, enabling a reliable distance determination to a static obstacle. OPTICS performs slightly better in terms of eliminating disturbing signals than the remaining two algorithms. The processing times of all algorithms were found to be sufficient for online application of the proposed approach.

关键词： Sea surface Clustering algorithms Radar Seaports Data processing Optics Reflection Reliability Surface treatment optical surface waves

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Pre-processing Techniques for Improving a Topological Map-Matching Algorithm 41

Pre-Processing Techniques for Improving a Topological Map-Ma...

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41st International conference of the Chilean Computer Science Society, SCCC 2022

作者： Blazquez, Carola León, Roberto Pérez, Felipe Delgado, Luis Universidad Andres Bello Faculty of Engineering Viña del Mar Chile

ISBN: (纸本)9781665456746

In Intelligent Transportation systems (ITS) applications, GPS measurements captured by vehicles are commonly integrated in a geographic information system (GIS) environment to determine vehicle routes. However, the map-matching problem arises when GPS measurements are associated to incorrect road segments on a digital map. Topological map-matching algorithms (TMMAs) have been widely used to successfully solve this problem. This study proposes two pre-processing techniques that are implemented prior to executing a TMMA in an offline context for improving the quality solution and execution times of the algorithm. The first pre-processing technique finds the optimal buffer size for selecting potential road segment for snapping locations of the GPS points. The second pre-processing technique compares the vehicle heading and the direction of the road segments candidates and selects those candidates that are within a heading tolerance from the vehicle heading. Both pre-processing techniques are tested and compared using GPS measurements collected by cargo vehicles in the commune of Renca in Santiago, Chile. Overall, results show that the identification of an optimal buffer size for each vehicle route and the use of a heading tolerance of 20° improve solution quality and computing times. Finally, the pre-processing techniques yield improve results for different sampling intervals of the GPS measurements. © 2022 IEEE.

关键词： Vehicles

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

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13th International conference on Signal processing systems, ICSPS 2021

作者： Guo, Li Xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： Image enhancement

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Review of ECG Data Feature processing and Classification

Review of ECG Data Feature Processing and Classification

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2022 International conference on Biomedical and Intelligent systems, IC-BIS 2022

作者： Zhang, Zijie Yang, Xinyi Electrical and Electronics Engineering Department The University of Manchester EEE UoM China Electrical and Electronics Engineering Department North China University of Technology EEE NCUT Beijing China

ISBN: (纸本)9781510660212

According to statistics, about one-third of the world's deaths are caused by cardiovascular diseases, which have a significant health impact on modern young people. Therefore, the most critical issues in the medical field are the prevention and diagnosis of heart system diseases. Electrocardiogram (ECG) is currently one of the most common diagnostic techniques for cardiovascular diseases. This article reviews some denoising methods of ECG signals, including traditional denoising methods, wavelet transform denoising methods, based on FIR Denoising method, and elaborates the classification technology and improvement strategies of related ECG features, such as Random Forest(RF), Back Propagation(BP), and analyzes the advantages and disadvantages of various algorithms. Finally, all the preprocessing algorithms and classification methods of ECG are summarized, and future development has prospected. © 2022 SPIE. All rights reserved.

关键词： Electrocardiograms

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A Parametric State-Space Network for Global Impairments Compensation in optical systems

A Parametric State-Space Network for Global Impairments Comp...

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International conference on Communications (COMM)

作者： Alexandru Frunză vincent Choqueuse Flavius Miron Stéphane Azou Military Technical Academy Communications and Information Technology Bucharest Romania École Nationale D’Ingénieurs de Brest Lab-STICC CNRS UMR 6285 France

ISBN: (数字)9798350355956

ISBN: (纸本)9798350355963

This paper presents a novel compensation approach based on a parametric State-Space Network that aims to compensate for multiple linear imperfections in coherent optical communications. A communication impaired by residual in-phase/quadrature imbalance, chromatic dispersion, and laser phase noise is considered. The State-Space Network is composed of multiple parametric layers that mitigate the imperfections impact in a global manner. The proposed technique benefits from the augmentation of some conventional compensation algorithms by transforming some fixed parameters into learnable ones. For example, the phase noise compensation layer consists of an modified Kalman filter that has the ability to automatically update its process and observations noises, leading to a better resilience to improper initial tuning. The proposed method outperforms a conventional digital signal processing compensation scheme in certain scenarios, making it promising for future extensions.

关键词： Phase noise optical filters Laser noise Signal processing algorithms optical fiber networks Laser modes State-space methods Kalman filters Tuning Resilience

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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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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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