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image Transmission and Denoising under non-Kolmogorov Turbulence 15

Image Transmission and Denoising under non-Kolmogorov Turbul...

引用

15th International conference on Information Optics and Photonics, CIOP 2024

作者： xu, Meiyong Wu, Dongfang Wang, Yutong Lu, xinning Wan, Jian Wang, Kaimin Zhang, Dawei Beijing University of Posts and Telecommunications Beijing100876 China Jinhua Research Institute of Zhejiang University Jinhua321017 China Ministry of Education Shanghai Key Lab of Modern Optical System Shanghai Environmental Biosafety Instruments and Equipment Engineering Technology Research Center University of Shanghai for Science and Technology Shanghai200093 China Shanghai Pudong New Area People's Hospital Shanghai201399 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

image signal degradation of computational ghost imaging is an important issue in atmospheric turbulence, especially for the images in laboratory safety monitoring. However, the relevant research under non-Kolmogorov turbulence model has been rarely reported. To address this issue, we proposed an efficient image signal processing and transmission scheme tailored for low-sampling conditions to enhance image signal denoising for applications like educational laboratory safety monitoring. Our approach employed computational ghost imaging enhanced by a modified sequence on a Hadamard orthogonal basis. Then we simulated turbulence using a non-Kolmogorov spectrum mode, which a phase screen approach realized. We applied a bucket detector to capture light intensities reflected or scattered from the target, and reconstructed the target image from captured signals with the computational ghost imaging algorithms. The orthogonally modified Hadamard matrix patterns with a compress ratio were applied to reduce noise and enhance image quality. Simulations show significant image quality improvements over traditional schemes. © 2024 SPIE.

关键词： image quality

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Algorithm for detection and classification of small objects on a complicated background to automated robotic complex 10

Algorithm for detection and classification of small objects ...

引用

conference on Optical Metrology and Inspection for Industrial applications x

作者： Semenishchev, Evgenii Urunov, Slavat Ilyukhin, Yuriy Siryakov, Aleksey Voronin, Viacheslav Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first-order finite difference, as well as the average square of the distance difference between the input implementation and the generated data;parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis;the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machine vision system installed on a robotic complex. Data on the required parameters of the formed machine vision systems are given, recommendations on the required parameters of the algorithms are presented.

关键词： image/signal smoothing edge detection preprocessing multicriteria method objects detection

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Application of Artificial Intelligence-based Video image processing Technology in Security Industry

Application of Artificial Intelligence-based Video Image Pro...

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2022 International conference on Cloud Computing, Internet of Things, and Computer applications, CICA 2022

作者： Wang, Fei Jiangxi Police Institute Jiangxi Nanchang330038 China

ISBN: (纸本)9781510656673

In the field of security, video image processing technology plays an important role. An efficient security system based on video processing technology can effectively improve the efficiency of public security, transportation, justice, retail and other industries. Nowadays, with the development of information technology, video image processing technology can gradually replace manual labor in the field of security. This paper analyzes the point processing, area processing and frame processing in video image processing technology in detail, and conducts in-depth investigation and research on the application of video image processing technology in the field of security. © 2022 SPIE.

关键词： Video signal processing

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An Efficient Hardware Accelerator For Lossless Data Compression 5

An Efficient Hardware Accelerator For Lossless Data Compress...

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5th International conference on Communications, signal processing, and their applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless Data Compression Lossless image Compression Hardware Accelerator Compression Algorithms Rice-Golomb Predictive Coding xORing

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Intelligent Broadband Detection Method of Communication signal Based on Deep Learning

Intelligent Broadband Detection Method of Communication Sign...

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2023 International conference on Electronic Information Engineering and Data processing, EIEDP 2023

作者： xu, Yongjian Wang, Keyong Zhang, Zhipeng Wu, xiangyu Ma, Peng Hou, Changbo Harbin Engineering University College of Information and Communication Engineering 145 Nantong Street Nangang District Heilongjiang Province Harbin150001 China

ISBN: (纸本)9781510666160

Aiming at the problem that multi-signal time-frequency domain overlapping is very likely to appear in the actual scene, it is difficult to identify, and a broadband signal intelligent detection method based on time-frequency analysis and target detection network is proposed. The communication signal is transformed into a time-frequency image, and the YOLOv5 network model is improved. Introduce the attention mechanism in the feature extraction network, highlight the signal area information, modify the K-Means clustering rules, recalculate the size of the prior frame, and use the improved model to study the time-frequency diagram of multiple signals overlapping. The results show that under different signal-to-noise ratios, the detection and parameter estimation of six types of random overlapping communication signals are realized. Overall, the signal detection probability reaches 94.74%, the false alarm probability is 2.86%, and the average error of parameter estimation is 2.15%. The analysis results in this paper can provide support for the rapid and effective detection of signals in practical applications. © 2023 SPIE.

关键词： signal detection

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Automatic Data Augmentation Method with Improved Interpretability for image Classification in Computer Vision applications

Automatic Data Augmentation Method with Improved Interpretab...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Ungarbayev, Dair Demirel, Osman Akhtar, Muhammad Tahir Nazarbayev Univ Dept Elect & Comp Engn Sch Engn & Digital Sci Kabanbay Batyr Ave 53 Nur Sultan Kazakhstan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

This paper presents an interpretable automatic data augmentation method. While the mechanism of the existing automatic augmentation methods is not easily understandable, the proposed method seeks to make the process of finding an augmentation policy more clear while maintaining simplicity. The proposed method has been tested on the Fashion-MNIST with a simple convnet and on the CIFAR10 dataset with ResNet50 network. Moreover, it was compared with the RandAugment, Augmix and TrivialAugment methods using benchmark datasets Fashion MNIST-C and CIFAR-10C. The developed method exhibits comparable results to that of existing approaches with the key advantage of interpretability.

关键词： Automatic data augmentation Robustness image classification

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Road Identification Algorithm Based on Tire Acceleration signal Under Multi-Vehicle Conditions

Road Identification Algorithm Based on Tire Acceleration Sig...

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

作者： xia, Peiwen Zhang, Jiayi Nie, Zheng Yao, Wenxin Han, Zongqiao xu, Zhichao Zhang, Shun Nanjing University of Science and Technology Nanjing China Jiangsu Lubida Iot Technology Co. Ltd Nantong China

ISBN: (纸本)9798350314670

In this paper, an intelligent tire algorithm based on back propagation neural network was proposed to identify the type of road peak adhesion coefficient. In the drum test, eight different vehicle conditions were taken into account for each of three simulated road conditions. Tire pressure of 900 or 1000 kPa, speed of 30 or 40 km/h, and load of 500 or 1000 kg were the parameters for the vehicle conditions. The corresponding tire x-acceleration signal was obtained using the tire acceleration sensor. Then, the original signal data was denoised using wavelet transform. The signal data was grouped by tire rotation period and the anomalous data was eliminated using the isolated forest algorithm. Following that, wavelet transform was used once more to extract the time domain and frequency domain characteristics from each batch of data, producing 45-dimensional feature parameters. Principal components analysis was applied to reduce the dimension of feature parameters to obtain 8-dimensional principal components. Finally, to identify the type of road peak adhesion coefficient, the normalized principal components and vehicle condition parameters (tire pressure, speed, load) were used as input to build a four-layer back propagation neural network, while the adam algorithm was used to optimize the learning rate. 10-fold cross validation was used to examine the model effect. The prediction results show that the model has a high identification accuracy for three types of road, with an average accuracy of 99.53%. The prediction accuracy of the models developed in 10-fold cross-validation fluctuates between ±0.2%. Under different vehicle conditions, the proposed road identification perception algorithm establishes the direct correlation between tire x-axis acceleration and road adhesion coefficient. With its high dependability and real-time capabilities, this algorithm has a wide range of possible applications in the areas of automated driving and intelligent tires. © 2023 IEEE.

关键词： Tires

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Asia conference on Electronic Technology, ACET 2024

Asia Conference on Electronic Technology, ACET 2024

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2024 Asia conference on Electronic Technology, ACET 2024

ISBN: (纸本)9781510681361

The proceedings contain 13 papers. The topics discussed include: modulation type recognition of MIMO radar based on FrFT and instantaneous frequency extraction;design based on the health detection technology of young children;a vibration monitoring signal processing method for shunt reactor;a comparative study of deep learning models for image super-resolution;semantic segmentation for scene modeling of mobile robots based on improved DeepLabV3+;fabrication of high-performance broadband photodetectors using 1D-2D hybrid electronic materials for IoT applications;enhancement and optimization of the propulsion system modules of unmanned aerial vehicles;active space debris detection, capture, and storage system;and measurement study of UHF local discharge based on voltage division method.

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Design and implementation of algorithm execution time predictor for remote sensing product workflow 3

Design and implementation of algorithm execution time predic...

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3rd International conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Zhou, Bing Wu, Qiong Li, Jiaguo Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668522

Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-Task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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Coarse x-Ray Lumbar Vertebrae Pose Localization using Triangulation Correspondence

Coarse X-Ray Lumbar Vertebrae Pose Localization using Triang...

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14th Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Yookwan, Watcharaphong Sangrueng, Jiranun Chinnasarn, Krisana Burapha Univ Fac Informat Saen Suk Thailand

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Dual-Energy x-ray Absorptiometry Scanner is essential for medical diagnostics and clinical routine. Typically, this type of device generates two radiography images of the human spine, including the Anteroposterior and Lateral sides. However, these two photographs presented distinct perspectives. The suggested method consists of three fundamental phases. The grayscale lumbar input image was initially projected vertically utilizing its vertical pattern for automated cropping. Then, Delaunay triangulation was done out using the SURF features as triangle nodes. Using the edge density of each node, the posture area of the vertebrae was computed. The proposed method provided an automated estimation of the position of the human lumbar vertebrae, hence reducing the radiologist's workload, computing time, and complexity in numerous bone-clinical applications. The outcome of the proposed method can support numerous applications, including segmentation of lumbar vertebrae pose, bone mineral density examination, and vertebral pose deformation. The proposed approach can estimate the vertebral position with an accuracy of 80.32 percent, a recall of 85.37 percent, a precision of 82.36%, and a false negative rate of 15.42 percent.

关键词： DxA vertebrae pose localization Delaunay triangulation SURF features

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