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16th International conference on Signal processing systems, ICSPS 2024

作者： Jing, Zehuan Mu, Hao Li, Peng Yin, Xuefeng Zhang, Jiamiao Li, Jingyi Wang, Zhao School of Electronic Engineering Xidian University Xi’an710071 China National Key Laboratory of Electromagnetic Space Security Chengdu610036 China

ISBN: (纸本)9781510689251

With the rapid advancement of modern electronic warfare, the volume of signals intercepted by electronic reconnaissance equipment is increasing exponentially, yet the quality of these signals remains inconsistent. Low-quality signals hinder radar signal recognition tasks, particularly those employing deep learning algorithms. Despite this challenge, there has been limited research focused on cleaning radar emitter signal data to enhance the quality of radar signal datasets. To address this issue, we propose a data cleaning method that automatically detects and removes low-quality signal samples from radar emitter signal datasets. By analyzing the characteristics of the received radar signals, we extract resemblance coefficients from their envelopes, effectively distinguishing noise signals from pulse signals in the feature space. We then apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster different types of signals. This approach enables the automatic detection and removal of noise data from the dataset. Extensive experiments on both simulated and experimental data demonstrate that the proposed method significantly improves data quality and enhances the recognition rate of deep learning networks for emitter recognition. © 2025 SPIE.

关键词： Radar signal processing

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A study on advancing Edge Detection and Geometric Analysis through image processing 24

A study on advancing Edge Detection and Geometric Analysis t...

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8th International conference on Graphics and Signal processing

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ Heilbronn Germany Heilbronn Univ Professorship Embedded Syst & Commun Engn Heilbronn Germany

ISBN: (纸本)9798400717024

In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, often fall short of achieving the high degree of accuracy and precision necessary for today's complex PCB designs. This research presents a novel approach that significantly enhances measurement accuracy through the application of optimized image processing techniques. By leveraging advanced algorithms within the OpenCV library, we introduce a methodology that accurately transforms pixel coordinates into real-world measurements, crucial for precise PCB alignment. Our technique employs edge detection algorithms such as Canny, Sobel, and Prewit filters, combined with a machine learning model that adapts to variations in real-time imaging conditions. The study delineates the development of a user-friendly graphical interface that streamlines the measurement process, making it accessible for practical industrial application. Results from experimental validations indicate a substantial improvement in measurement precision, with a demonstrable reduction in alignment errors compared to conventional methods. This leap forward not only promises to elevate the standards of PCB manufacturing but also opens avenues for similar advancements in other domains where image measurement is essential. The implications of this work are far-reaching, with the potential to significantly boost the efficiency and reliability of electronic manufacturing processes globally.

关键词： image measurement techniques microns edge detection algorithms real-world coordinate transformation machine learning in imaging graphical user interface (GUI) Printed Circuit Board (PCB)

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High-Fidelity Audio Compression with Improved RVQGAN 37

High-Fidelity Audio Compression with Improved RVQGAN

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37th conference on Neural Information processing systems (NeurIPS)

作者： Kumar, Rithesh Seetharaman, Prem Luebs, Alejandro Kumar, Ishaan Kumar, Kundan Descript Inc San Francisco CA 94118 USA

ISBN: (纸本)9781713899921

Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves 90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.

关键词： image enhancement

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

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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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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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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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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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