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Research on nonlinear compensation technology in visible light communication system based on improved BP neural network 4

Research on nonlinear compensation technology in visible lig...

引用

4th international conference on Advanced Algorithms and signal image Processing, AASIP 2024

作者： Zheng, Jiangpeng Wang, Hetao Key&Core Technology Innovation Institute of the Greater Bay Area Guangzhou510535 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

Visible Light Communication (VLC) technology solves the problem of limited spectrum in traditional mobile communications, and the data transmission rate can reach hundreds of megabits. However, nonlinear distortion also exists in visible light communication systems. Nonlinear distortion will cause a nonlinear relationship between the transmitted signal and the received signal, resulting in a higher bit error rate when the signal is decoded at the receiving end. In order to improve the performance of the VLC system, it is necessary to suppress the nonlinear distortion generated by the signal during the transmission process. based on the understanding of commonly used VLC system models, LED nonlinear models and two common neural network models, this paper studies a post-distortion compensation algorithm based on a hybrid algorithm of genetics and particle swarm and a Back Propagation (BP) neural network. The received signal of the system is sent to the network, and the original transmitted signal is used as the desired output. The initial weight of the BP network is pre-optimized through a hybrid algorithm of genetics and particle swarm, and then the transmitted signal is estimated with the help of the trained network to achieve distortion compensation. Experimental results show that the improved optimization algorithm. It can effectively suppress the nonlinear characteristics of the VLC system, and appropriately expand the hidden layer of the network to improve the nonlinear mapping ability of the network to a certain extent, so that the compensated signal can better fit the original input signal, which improves the system performance. © 2024 SPIE.

关键词： Genetic algorithms

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Robust and Secure Watermarking Algorithm based on High Frequencies of Integer Wavelet Transform 47

Robust and Secure Watermarking Algorithm Based on High Frequ...

引用

47th international conference on Telecommunications and signal Processing, TSP 2024

作者： Elbasi, Ersin Topcu, Ahmet E. Alzoubi, Yehia Mohammed, Ahmed A. College of Engineering and Technology American University of the Middle East Kuwait College of Business Administration American University of the Middle East Kuwait Ninevah University Computer and Information Department Iraq

ISBN: (纸本)9798350365597

Nowadays, copyright owners, movie studios, photographers, software developers, and other content owners are concerned about copyright protection because of the ease of dissemination with internet technology. Watermarking and cryptography are two critical techniques for copyright protection, authentication, and broadcasting. Encryption protects multimedia content during transmission;however, after decryption, there is no protection. Frequency-based watermarking is an essential method to hide a logo, stamp, image, or text from the protected element. Discrete wavelet transformation and discrete cosine transformation give secure and robust results. However, these methods are not robust for some of the geometric attacks. In this work, we applied the integer wavelet transformation to hide binary watermark into the medical cover images. Cover images are first divided into blocks (4× 4), and then a binary logo is embedded using a secret key. Peak signal-to-noise ratio and similarity ratio values show that the integer wavelet transform based proposed algorithm is more robust, secure, and resistant to attacks than other frequency-based algorithms. © 2024 IEEE.

关键词： Cryptography

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Highway vehicle detection based on Ridge-LASSO regression 4

Highway vehicle detection based on Ridge-LASSO regression

引用

4th international conference on image Processing and Intelligent Control, IPIC 2024

作者： Kou, Xuewei Du, Qingguo Huang, Longting Wang, Honghai Li, Zhengying National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Hubei Wuhan430070 China School of Information Engineering Wuhan University of Technology Hubei Wuhan430070 China

ISBN: (纸本)9781510682313

Distributed acoustic sensing systems can obtain information about road vibration caused by vehicle driving vibration on highways. By characterizing the vehicle driving vibration data, a highway vehicle driving vibration detection scheme based on ridge-LASSO regression is proposed. Firstly, using the random forest's out-of-bag error criterion for vibration signal multi-feature selection, to prove the validity of the feature quantity used to characterize the highway vibration signal, and then the multi-feature quantity values of multiple measurement areas are composed into a data matrix, and then finally the ridge-LASSO regression algorithm is used to detect the vehicles travelling on the highway to obtain the vehicle's trajectory. The experiment proves that the method can effectively detect vehicles on the highway. © 2024 SPIE.

关键词： image coding

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Field weed classification based on modified LBP and local sparse representation

Field weed classification based on modified LBP and local sp...

引用

2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Yang, Huan College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Accurately identifying field weeds is crucial for selecting appropriate agricultural machinery and herbicides. This paper focuses on nine common weed species during the seedling stage in natural backgrounds of field vegetables, proposing a novel weed foreground segmentation method based on image processing and a new weed classification model using sparse representation classification (K_SRC). Initially, K-means clustering and threshold segmentation are applied to the a component of Lab color space to obtain binary weed images, followed by area filtering to remove interference. The binary weed image is then masked with the original image to extract clean weed images. Color features and mLBP texture features are extracted from segmented weed images to form a training sample set. KNN computes a local dictionary, and sparse coefficients are calculated using the OMP algorithm. Experimental results demonstrate a classification accuracy of 95.22% compared to four other algorithms. Additionally, this research provides new insights into the application of sparse representation in agriculture. © 2024 SPIE.

关键词： image segmentation Education and training Feature extraction image classification Detection and tracking algorithms Binary data image processing algorithms and systems Reconstruction algorithms image processing

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Imaging System based on Single Photodiode 10

Imaging System Based on Single Photodiode

引用

10th international conference on Information technology and Nanotechnology, ITNT 2024

作者： Osepan, S.M. Skidanov, R.V. Ivliev, N.A. Khanenko, Yu.V. Markushin, M.A. Samara National Research University Samara Russia Image Processing Systems Institute Nrc 'Kurchatov Institute' Samara National Research University Samara Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372939

A short wave infrared range (SWIR) imaging system using a lens, rotating substrate with applied Walsh matrices, single photodiode and a 10-bit ADC has been proposed. The possibility of using the Walsh function for encoding and restoring an optical signal is shown. A description of the design of the developed system is presented. The parameters of the structure of an encoding disk suitable for production at a circular laser recording station have been calculated. The quality of image restoration was assessed, according to which the structural similarity of the original image and the restored one is quite high, which indicates that the both images are almost identical. © 2024 IEEE.

关键词： Imaging systems

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IoT-based Smart Home Care systems with Location Detection

IoT-based Smart Home Care Systems with Location Detection

引用

2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cheng, Chia-Hsin Huang, Kuo-Ting Chen, Jun-Jie Chiu, Tzu-Yuan National Formosa University Department of Electrical Engineering Yunlin Huwei Taiwan

ISBN: (纸本)9798350324174

In this paper, a home care system with location information is designed. From this we can learn from the physiological information and location information of the elders. The physiological information part includes the measurement of blood oxygen and pulse value and the medication information. The medication information part is realized through image Recognition technology, by using Yolov3 for drug identification. The location information part uses Wi-Fi wireless transmission signals to do indoor positioning (Indoor Positioning), measures the received signal strength (RSSI) of the distance between the elder and the wireless signal sender, and judges the elder's location through the measured results. © 2023 IEEE.

关键词： internet of things

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GNSS signal Spoofing Detection based on Satellite Oscillator Stability

GNSS Signal Spoofing Detection based on Satellite Oscillator...

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2024 international conference on Satellite internet, SAT-NET 2024

作者： Wang, Haiyang Guo, Shichang Liu, Ziqi Zhang, Yuanyu Shen, Yulong Jiang, Xiaohong Xidian University School of Computer Science and Technology Xi'an China Future University Hakodate School of Systems Information Science Hokkaido Hakodate Japan

ISBN: (纸本)9798350375176

Global navigation satellite systems (GNSS) provide all-weather timing, positioning and navigation services, playing a crucial role in both military and civilian applications. Despite their widespread use, GNSS is vulnerable to severe signal spoofing attacks. In this paper, we propose a novel GNSS signal spoofing detection method by exploiting the radio frequency fingerprint (RFF) of GNSS satellites. We introduce a new RFF feature named satellite oscillator stability, which is closely related to the hardware impairments of GNSS satellites and extracted from the GNSS receiver carrier tracking loop. Utilizing this new feature, we propose a GNSS signal spoofing detection method, which takes the extracted satellite oscillator features as input and employs a Residual Network as a classifier to distinguish between legitimate and spoofed signals. For verification, we use the most widely used GPS and Beidou signals, and conducted experiments in two scenarios: GPS generative spoofing and Beidou forwarding spoofing. The experimental results demonstrated that our proposed method achieve a detection accuracy of over 98% in the GPS generative spoofing scenario, and over 93% in the Beidou forwarding spoofing scenario. © 2024 IEEE.

关键词： Global positioning system

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Centralized Monitoring System of Rail Transit Multiple signals based on Bus technology 7th

Centralized Monitoring System of Rail Transit Multiple Signa...

引用

7th EAI international conference on Advanced Hybrid Information Processing, ADHIP 2023

作者： Li, Bo Wuhan Railway Vocational College of Technology Wuhan430000 China

ISBN: (纸本)9783031505485

Conventional rail transit signal monitoring systems are prone to being affected by pooling aggregation during image downsampling processing, resulting in abnormal monitoring functions. Therefore, this study designs a new centralized monitoring system for multi-channel signals in rail transit based on bus technology. In the hardware part of the system, SBMA RF receiver, SZ45XIT magnetic random access memory, and SPACECOM electric zoom monitoring camera are installed to support smooth operation of the system. In the system software section, based on the design of traffic multi-channel monitoring signal processing algorithms, a signal centralized monitoring function module was generated based on bus technology. The test results indicate that the various functions of the system operate in an orderly manner, with reliability and application value. In addition, compared to traditional systems, the signal monitoring delay of this system is lower. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Random access storage

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SAR image Despeckling based on Gradient Domain Convolutional Sparse Coding 15

SAR Image Despeckling based on Gradient Domain Convolutional...

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15th international conference on signal Processing systems, ICSPS 2023

作者： Chen, Gao Li, Zifeng Zhou, Qingfeng Liu, Chanzi Dongguan University Of Technology 1 University Rd Dongguan China

ISBN: (纸本)9781510675056

Sparse regularization is an effective tool for synthetic aperture radar (SAR) image *** effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation. © 2024 SPIE.

关键词： Synthetic aperture radar

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image Segmentation Algorithm of Ischemic Stroke based on Wavelet Pooling Neural Network 2

Image Segmentation Algorithm of Ischemic Stroke based on Wav...

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2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Zhu, Huiyuan Zhai, Yao Wu, Jinghe Lu, Chuwei Zhang, Xianchao Li, Ruide Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China College of Medicine Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Department of Nursing Jiaxing China Beijing Institute of Technology School of Information and Electronics Beijing China Aviation Industry Corporation of China Avic Xi' An Flight Automatic Control Research Institute Xi'an China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China

ISBN: (纸本)9798331515669

Ischemic stroke is a high-risk brain disease that causes disability or death in adults worldwide. Rapid diagnosis and treatment, as well as segmentation of ischemic lesions from stroke medical images so that doctors can evaluate and develop treatment plans, are crucial to reducing brain damage. Currently, doctors' manual segmentation of lesions is time-consuming and labor-intensive and prone to missed or misdiagnosed diagnoses, delaying the best time for treatment. A well-trained neural network is considered as an effective aid for image segmentation tasks. However, in the traditional neural network down-sampling process, the pooling layer loses feature information, and the lost information significantly impacts the edge regions. While for medical images, edge regions are crucial features in the segmentation process. Therefore, to address the above issues, in this paper we improve upon the existing stroke lesion segmentation algorithm U-net, we design a segmentation network based on wavelet pooling and attention mechanisms. The network was tested on ISLES 2018 and ISLES 2022 datasets, and the Dice coefficient improved by 2.13% and 1.93% compared to baseline network, respectively. © 2024 IEEE.

关键词： image segmentation

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