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Camera-Based Channel Modeling and Symbol Error Rate Analysis of CSK Modulation for Outdoor OCC systems

IEEE ACCESS 2022年 10卷 50254-50264页

作者： Dong, Ke Ke, xizheng Li, Hui Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

A camera with an image sensor is an important alternative device to a photodiode-based receiver of visible-light communication in outdoor scenarios. The intrinsic color separation capability of a camera qualifies color shift keying (CSK) modulation as an intuitive solution to enhance the achievable data rate. The symbol error rate (SER) of CSK modulation is considerably important to system design and performance evaluation, and has not been extensively investigated for outdoor optical camera communication systems from the viewpoint of camera-based channel and image processing-based demodulation. In this study, a two-level channel model is proposed to characterize CSK transmission in a single pixel and in the entire image. A general framework of SER analysis for arbitrary CSK constellations was proposed by directly calculating the upper bounds from the empirical distribution of the noise light in the CIE 1931 color space. Through numerical simulations, the influence of the image detector on CSK demodulation was evaluated. The results indicated that an accurate target region is important for maintaining the SER, and an enlarged target region is beneficial when the maximum ratio combination and selective combination algorithms are used in pixel combination.

关键词： image color analysis Modulation Cameras Colored noise Symbols Visible light communication Light sources Color shift keying image detector optical camera communication pixel combination symbol error rate visible light communication

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Empirical Analysis of Denoising algorithms for CCTV Face images

Empirical Analysis of Denoising Algorithms for CCTV Face Ima...

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International conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Bhagwat, Sumedha Rahul Ragha, Leena Ramrao Adik Inst Technol Dept Comp Engn Navi Mumbai India

ISBN: (纸本)9789819984756;9789819984763

image denoising is a critical task in image processing that involves the removal of noise or unwanted distortions from an image while preserving its essential features. Most of the commonly captured pictures are obtained using mobile cameras or CCTV surveillance cameras producing video footage of the activities of people who are stationary or in motion. There is a need to restore such captured footage from noise so that it can become evidence for different criminal cases. Denoising face images captured using CCTV is a challenging task due to fine details being affected by noise. In this paper, we evaluate three image denoising techniques Block-Matching and 3D (BM3D), k-Means Singular Value Decomposition (KSVD), and Weighted Nuclear Norm Minimization (WNNM). The performance of these methods is analyzed using Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Visual Information Fidelity (VIF). It is observed that the overall performance of KSVD is better for a Gaussian and Salt and Pepper noise.

关键词： image denoising BM3D WNNM KSVD Video surveillance images CCTV images

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A wandb based corrosion detection of metals through live video analytics 2

A wandb based corrosion detection of metals through live vid...

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2nd IEEE International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： John Deva Prasanna, D.S. Sai Neeraj, Kuruvella Jaya Krishna, G. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of DSBS India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

Detection of corrosion in moving objects like ships is challenging due to the dynamic nature of the input image. Existing machine learning techniques are suitable for static images and the algorithms suffer in performance when is a live video. In this paper, image processing for detecting corrosion using YOLOv8 which more suitable for processing live videos as speed and accuracy is better. This makes YOLO v8 for corrosion detection in live videos. In addition, Weights and Biases (W&B). is used in the algorithm as it is pivotal in establishing the connections between neurons and biases helps in circumventing flexible inputs. By combining YOLOv8's and W&B approach the accuracy and efficiency of corrosion detection systems is improved. This can ultimately assist in better maintenance and preservation of essential infrastructure resources. © 2024 IEEE.

关键词： Video signal processing

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Danger Detection and Cloud-Based Vocal Assistance System for Visually Impaired Users Using Meta Quest 3 25

Danger Detection and Cloud-Based Vocal Assistance System for...

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16th ACM Multimedia systems conference, MMSys 2025

作者： De Fiore, Fabrizio Murray, Niall Barrett, John Keighrey, Conor Technological University of the Shannon: Midlands Midwest Athlone Ireland

ISBN: (纸本)9798400714672

Individuals who experience a significant loss of vision or blindness often face challenges navigating the world. In such scenarios, there is often a dependence on various types of support or aids to enable individuals to move through their surroundings. Examples of this include guide dogs and assistive white canes. Those who suffer from blindness also rely on a heightened sense of auditory processing. Despite these solutions, navigating the modern world is challenging from a safety *** recent years, computer vision algorithms have been developed to interpret and report the contents of image streams. Applications of these algorithms have often focused on automated labeling of data (image analysis and audio processing etc.) within vast media databases;however, they can be leveraged to operate with live camera streams. In this research, the authors present an overview of a proof-of-concept technology stack to implement a danger detection system for individuals with significant visual impairment. The system integrates an advanced cloud driven approach employing a fine-tuned YOLO model for hazard detection, performs obstacle avoidance, segmentation processes for scene understanding, text recognition through optical character recognition (OCR), and visual question answering (VQA) for user queries. These technologies collectively address critical issues such as dynamic hazard recognition, spatial awareness, and personalized assistance. © 2025 Copyright held by the owner/author(s).

关键词： image segmentation

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Nearly Tight Bounds For Differentially Private Multiway Cut 37

Nearly Tight Bounds For Differentially Private Multiway Cut

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37th conference on Neural Information processing systems (NeurIPS)

作者： Dalirrooyfard, Mina Mitrovic, Slobodan Nevmyvaka, Yuriy Morgan Stanley Machine Learning Res New York NY 10036 USA Univ Calif Davis Dept Comp Sci Davis CA USA

ISBN: (纸本)9781713899921

Finding min s-t cuts in graphs is a basic algorithmic tool, with applications in image segmentation, community detection, reinforcement learning, and data clustering. In this problem, we are given two nodes as terminals and the goal is to remove the smallest number of edges from the graph so that these two terminals are disconnected. We study the complexity of differential privacy for the min s-t cut problem and show nearly tight lower and upper bounds where we achieve privacy at no cost for running time efficiency. We also develop a differentially private algorithm for the multiway k-cut problem, in which we are given k nodes as terminals that we would like to disconnect. As a function of k, we obtain privacy guarantees that are exponentially more efficient than applying the advanced composition theorem to known algorithms for multiway k-cut. Finally, we empirically evaluate the approximation of our differentially private min s-t cut algorithm and show that it almost matches the quality of the output of non-private ones.

关键词： image segmentation

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Research on the detection of dislodged fault in photovoltaic modules based on line scanning

Research on the detection of dislodged fault in photovoltaic...

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2024 International conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Zhao, Desheng Gao, Dedong Su, Weihong Zhang, Shuai Meng, xiangchun School of Mechanical Engineering Qinghai University Qinghai Xining810016 China

ISBN: (纸本)9781510685840

This study addresses the detection of dislodged fault in photovoltaic (PV) power stations by proposing a visible light image processing method utilizing line scanning. The method employs HSV (Hue-Saturation-Value) color space conversion and morphological processing to preprocess images, which achieves precise segmentation and shape simplification of PV module areas through flood fill and polygon contour fitting techniques. For fault detection, the study introduces a line-scanning-based algorithm. By setting appropriate thresholds and continuous occurrence parameters, the algorithm effectively identifies and marks dislodged areas of PV modules in the original image. Experimental results show that this method significantly improves detection accuracy and computational efficiency, providing technical support and solutions for the safe operation and maintenance of PV systems. © 2024 SPIE.

关键词： image segmentation

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Target Localization in ISAC System Based on BP Neural Network

Target Localization in ISAC System Based on BP Neural Networ...

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2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： He, Hangchuan Tang, Yupeng Guo, Wensheng School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Electrical and Information Engineering Yunnan Minzu University Kunming China School of Information Engineering Chang'an University Xi'an China

ISBN: (纸本)9798350314670

In this paper, the problem of Integrated Sensing and Communications (ISAC) system is considered to achieve an overall improvement in positioning accuracy. To achieve this goal, two optimization algorithms are proposed and evaluated. The first involves a direct positioning calculation based on distance, while the second is to use the correlation curve as the input of the Back-Propagation (BP) neural network for localization calculation. The simulation results verify the assistance by increasing perception, which can effectively improve the accuracy of positioning. This conclusion is drawn from the observation of the change in positioning accuracy under varying Signal-to-Noise Ratios (SNR). The results indicate that an increase in SNR results in a corresponding improvement in positioning accuracy. © 2023 IEEE.

关键词： Backpropagation

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Heart Diseases Classification by ECG Trace images Using Deep Learning 4th

Heart Diseases Classification by ECG Trace Images Using Deep...

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4th International conference on Digital Technologies and Applications (ICDTA)

作者： Ali, Sajad M. Daway, Hazim G. Kadhim, Ahlam M. Mustansiriyah Univ Coll Sci Dept Phys Baghdad Iraq

ISBN: (纸本)9783031686498;9783031686504

Recently, many deep learning algorithms have emerged as advanced techniques in the medical field for diagnosing diseases, including heart disease. In this study, an approach was followed that is based on electrocardiogram (ECG) images to detect different heart diseases. Pre-processing was performed for the data images using morphology technology to remove lines from the background ECG paper image to obtain an image containing only the changes of electrical activity for the potion's heart. The pre-processed data images are trained at a rate of 80% of each class data image in the training stage and 20% of each class image used for the testing stage in the efficiency evaluating stage of each model. Seven classification models have been proposed in binary classification. Models 1-7 have been trained to classify the natural ECG case (Nrm) with the other diseases. Models' efficiency is calculated using four measures, where the accuracy reaches 100%, the precision reaches 100%, the specificity is 100%, and the f1-score is 100%. For models 6 and 7, the results of the accuracy reached (88.1366 and 91.0978)%, precision (80.7443 and 91.0834)%, specificity (79.1734 and 88.8665)%, and f1-score (79.4476 and 89.8999) %. The proposed diagnostic system is fast, accessible, more sensitive, and harmless. It is also more cost-effective than any other diagnostic method.

关键词： CNN Deep Learning ECG images pre-processing data

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INFRARED image-ENHANCED CONVOLUTIONAL NEURAL NETWORK FOR SOLID-STATE POWER CONTROLLERS FAULT DIAGNOSIS

INFRARED IMAGE-ENHANCED CONVOLUTIONAL NEURAL NETWORK FOR SOL...

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2024 CSAA/IET International conference on Aircraft Utility systems, AUS 2024

作者： Chen, Fangyuan Shi, Jian Wang, Shaoping Cao, Qiuhan School of Automation Science and Electrical Engineering Beihang University Beijing100083 China Ningbo Innovation Research Institute Beihang University Ningbo315800 China

Aiming at the problem that the temperature information of the solid-state power controllers (SSPCs) contains both fault features and a large amount of redundant data, a convolutional neural network (CNN) fault diagnosis algorithm based on information fusion is proposed. The quality of infrared image is optimised by image pre-processing algorithm, the fault features in multiple information domains are extracted by combining the electrical signal features, and the high-dimensional feature data are down-scaled by principal component analysis (PCA) technique. On this basis, a SSPCs fault diagnosis model is constructed with the outstanding feature learning capability of CNN, and its effectiveness is verified through experiments. The proposed method is superior in both diagnostic accuracy and efficiency, and is of great significance to ensure the safe and reliable operation of SSPCs. © The Institution of Engineering & Technology 2024.

关键词： Convolutional neural networks

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algorithms of Signal processing in the System for High-Speed Corrugation Monitoring of Rails 6

Algorithms of Signal Processing in the System for High-Speed...

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6th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2024

作者： Koluchkin, Vasiliy Ya. Marenov, Nikita E. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350382891

In real operating conditions of the control systems based on the parallax method with structured laser illumination, due to background solar illumination, nonlinear distortions of signals, known as the blooming effect, may occur. Such distortions lead to a decrease in the accuracy of the systems of operational control of straightness of railroad rails based on the above method. The purpose of the research presented in this paper is to evaluate the effectiveness of the subpixel peak position refinement algorithm under normal conditions and under background illumination conditions in an operational high-precision inspection system. As a result of numerical experiments of the rail surface straightness control system model, the effectiveness of subpixel refinement algorithms is investigated in the presence of internal noise of the image registration device, as well as in the presence of distortions caused by the blooming effect. © 2024 IEEE.

关键词： Geometrical optics

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