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Industrial Video Surveillance image Denoising based on Non-subsampled Contourlet Transform 4

Industrial Video Surveillance Image Denoising based on Non-s...

引用

4th ieee international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhang, Qingyun Chen, Zhuo Xie, Pengfei Wang, Yingxu Shi, Ruibo Zhou, Qian Pu, Yuduo Shandong University of Science and Technology Dept. of Electronic and Information Eng. Qingdao China Rizhao Guangming Electric Power Service Co. Ltd. Rizhao China State Grid Information & Telecommunication Branch Beijing China Wuhan Fiberhome Technical Services Co. Ltd Wuhan China

ISBN: (纸本)9798350363586

With the continuous expansion of industrial video scale, visualization technology has become an indispensable technical means. However, during the collection and transmission of images, they may be affected by factors such as the device itself and environmental factors, which can lead to noise pollution and a decrease in image quality, which is not conducive to image post-processing. Therefore, industrial video surveillance image denoising has become an important research topic, and using various methods to improve image quality has become an important research direction. In order to address the ringing effect caused by hard threshold denoising, this paper proposes an adaptive smooth non-subsampled contourlet transform (NSCT) denoising algorithm. The proposed method utilizes the correlation of each sub-band to improve the denoising function, making it adaptive. The simulation results show that the algorithm proposed in this paper improves subjective perception such as contrast and detail clarity of remote sensing images, as well as objective evaluation indicators such as peak signal-to-noise ratio, information entropy, and correlation coefficient © 2024 ieee.

关键词： Contourlet transform

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image Fusion Via Slice-based Convolutional Sparse Representation 48

Image Fusion Via Slice-Based Convolutional Sparse Representa...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Xu, Jingchen Zhang, Yali Li, Ze Wang, Jinjia Yanshan University School of Information and Science Engineer China Yanshan University Hebei Key Laboratory of Information Transmission and Singal Processing China

ISBN: (纸本)9781728163277

For pixel-level image fusion, convolutional sparse representation model usually relies on the ADMM in the Fourier domain, generating many iterations and losing the sense of locality, which may result in fused images with blurred texture parts. To extract texture information of images more effectively, a slice-based convolutional sparse representation (SCSR) model is proposed, which is solved by an inertial proximal gradient method with dry friction (IPGM-DF) algorithm in the signal domain. IPGM-DF updates the dictionary and sparse coefficients simultaneously. Experimental results of image fusion show that the proposed method is superior to the convolutional sparse representation-based fusion method in both subjective and objective evaluation, which are comparable to that of SOTA. © 2023 ieee.

关键词： image fusion proximal gradient algorithm slice-based convolutional sparse representation two-scale decomposition

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Research on Snapshot GISC Hyperspectral image Analysis technology based on Mean Filtering Algorithm 2

Research on Snapshot GISC Hyperspectral Image Analysis Techn...

引用

2nd international conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Liu, Zhuo Li, Xinyang Li, Meixuan Changchun College of Electronic Technology Changchun China Jilin Engineering Normal University Changchun China

ISBN: (纸本)9798350386639

With the improvement of the quality of life, people pay more and more attention to the quality of diet, and fruits and vegetables are an important part of a healthy diet, as well as essential nutrients for people to maintain physical health. Therefore, this paper proposes a non-destructive detection method for fruit and vegetable quality-GISC hyperspectral imaging technology. The principle of GISC hyperspectral imaging technology is introduced, and the mean filtering method is used to denoise the characteristic signal data periodically and at high frequencies. The mean and variance of the denoised signal data are calculated to form a feature vector. Finally, the effectiveness of the scheme is verified by using information entropy for image quality evaluation. © 2024 ieee.

关键词： Wiener filtering

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Chameleon: An Adaptive System for Overlapping Keystroke signal Separation and Identification 30

Chameleon: An Adaptive System for Overlapping Keystroke Sign...

引用

30th ieee international conference on Parallel and Distributed systems, ICPADS 2024

作者： Zhao, Jiayi Huang, Yongzhi Xie, Qipeng Wang, Weizheng Wang, Lu Wu, Kaishun Thrust Information Hub Guangzhou China / Guangzhou China Hong Kong University of Science and Technology Hong Kong City University of Hong Kong Computer Science Engineer Hong Kong Shenzhen University China

ISBN: (纸本)9798331515966

Keystroke dynamics has proven to be highly effective, with its applications expanding significantly over the years in areas such as preventing transaction fraud, account takeovers, and identity theft. Key-positioning and feature-learning methods are commonly used to identify keystroke signals. However, the existing methods face challenges in detecting overlapping keystrokes and environmentally changed signals. We propose a solution called Chameleon to address these limitations. Unlike previous signal separation and deep learning methods that are ineffective in keystroke signals and computationally demanding, Chameleon employs a low-computation Ranking Model to separate overlapping keystroke signals. Moreover, our experiments demonstrate that Chameleon separated signals can be recognized with an average accuracy of 92.69%, surpassing the commonly used FastICA method, which only reaches 25% accuracy. To account for environmental changes, we utilize the Fréchet Inception Distance (FID) as a guiding metric for model migration. Additionally, we introduce the Inductive Vector, which enables our key-identifying model to adapt to altered environmental conditions such as environment, phone location, and user variety. The Inductive Vector adjusts the model parameters based on the shift in FID. In scenarios with various phone locations, the Inductive Vector significantly improves recognition accuracy from 61% to 98%, outperforming the best existing keystroke recognition algorithm. In other dynamic environmental conditions, our approach achieves an average accuracy rate of 81.7%, which is at least 1.6 times better than the current state-of-the-art keystroke recognition algorithm. © 2024 ieee.

关键词： Deep learning

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IoT Smart Door System with Motion Sensing and Facial Recognition 20

IoT Smart Door System with Motion Sensing and Facial Recogni...

引用

20th ieee international Colloquium on signal Processing and Its Applications, CSPA 2024

作者： Hamzah, Muhammad Kamil Sulaiman, NorakmarArbain Kassim, Murizah Saaidin, Shuria Kutty, Suhaili Beeran Universiti Teknologi Mara School Of Electrical Engineering College Of Engineering Selangor Shah Alam40450 Malaysia Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350382310

The advancement of technology has brought about significant transformations in various domains, including home security systems. This study has developed a system that enhances the functionality and security of smart doors using internet of Things (IoT) technology called IoT Smart Door System with Motion Sensing and Visual Facial Recognition. The smart door system integrates multiple access control mechanisms, including keypad entry and remote access via a mobile application called Blynk. Additionally, it incorporates a motion sensor that triggers a camera to capture images of individuals approaching the door. These images are sent as notifications to the owner's smartphone, enabling them to visually verify the visitor's identity and the system to inform the condition of the door access. The system enhances home security by providing real-time monitoring and control over door access. The prototype implementation of the system allows for evaluating critical parameters such as response time, security, scalability, and user experience. The results obtained from testing provide valuable insights into the strengths and limitations of the system, enabling further refinement and optimization. This method can transform home security systems, enhancing homeowners' convenience and safety environments. © 2024 ieee.

关键词： internet of things

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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 ieee.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler Vision Transformer

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CurMIM: Curriculum Masked image Modeling

CurMIM: Curriculum Masked Image Modeling

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Liu, Hao Wang, Kun Han, Yudong Wang, Haocong Hu, Yupeng Wang, Chunxiao Nie, Liqiang School of Software Shandong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9798350368741

Masked image Modeling (MIM), following "mask-and-reconstruct" scheme, is a promising self-supervised method to learn scalable visual representation. Studies indicate that selecting an effective mask strategy is vital for MIM. However, existing approaches often rely on static pre-defined priors, which limit their ability to adapt mask strategies dynamically for network optimization. In this paper, we focus on the learning process of the network and introduce human-like curriculum into MIM for dynamic representation refinement, and propose an end-to-end framework Curriculum Masked image Modeling (CurMIM). CurMIM consists of two components: Mask Priority Measurer, which acts as a curriculum learner to determine mask priority values using the network's intrinsic state information, and Dual Adaptive Selector, which serves as a curriculum scheduler to create effective masks based on these values. With negligible extra parameters, our curriculum-based method consistently establishes noticeable improvements across varying model sizes and benchmarks, showing effectiveness and generalization. © 2025 ieee.

关键词： Curriculum Learning image Representation Masked image Modeling Visual Pre-training

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RSA Algorithm for Time-Frequency Assignment in EONs based on signal Overlap 8

RSA Algorithm for Time-Frequency Assignment in EONs Based on...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Li, Weiqi Sun, Qiang Liu, Fengyue Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Ocean University of China Sanya Oceanographic Institution Qingdao China

ISBN: (纸本)9798350397932

In order to expand the network capacity and reduce the blocking rate of services, this paper uses the signal overlapping technology to double the number of available frequency slots in the Elastic Optical Networks (EONs). On this basis, the Routing and Spectrum Allocation (RSA) problem is studied. Considering the time loss caused by signal overlapping technology, this paper designs an RSA algorithm for time-frequency allocation on EONs based on signal overlapping. This algorithm will select the route with the smallest lost time slot. In this paper, the feasibility and low blocking rate of the algorithm are verified by simulation. © 2023 ieee.

关键词： Frequency allocation

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Overview of Automatic Modulation Recognition Methods 2

Overview of Automatic Modulation Recognition Methods

引用

2nd ieee international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Xie, Shaoxiong Ye, Jun Hainan University Key Laboratory of Internet Information Retrieval of Hainan Province School of Cyberspace Security Haikou China

ISBN: (纸本)9798350347456

Blind detection and identification processing such as communication signal detection, modulation identification, and individual radiation source identification under non-cooperative reception conditions is an important basis for radio spectrum monitoring and battlefield communication reconnaissance and confrontation. However, due to adverse factors such as strong interference and fast-changing electromagnetic environment during non-cooperative reception, complex and diverse communication signal systems, and inability to fully grasp signal prior information, blind detection and identification of communication signals are extremely challenging. This article comprehensively sorts out the modulation recognition algorithm. The DL-based AMR algorithm is systematically reviewed from the three levels of the data set, network model, and final performance. Finally, the existing problems, potential research directions, and conclusions of AMR are outlined. © 2023 ieee.

关键词： Decision theory

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Contrast-Enhanced Cardiac Magnetic Resonance image based on Enhanced Deep Residual Networks Modified 5

Contrast-Enhanced Cardiac Magnetic Resonance Image Based on ...

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5th ieee international conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Ulkareem Naser, Maysaa Abd Hassin Al-Asadi, Abbas H. University of Basrah Dept. of Computer Information Systems Basrah Iraq Kufa University Department of Computer Science Kufa Iraq

ISBN: (纸本)9798350352863

Contrast is very important in analyzing medical images, and some contrast enhances the methods that could be very helpful when the image contrast is not good. The enhancement of Deep Super-Resolution (EDSR) is the deep learning (DL) technique that improves the quality and resolution of low-resolution images. Within this study, a transfer learning method grounded in a convolutional neural network (EDSR-Modified) is leveraged for enhancing contrast in cardiac magnetic resonance imaging (CMR). The study conducts experiments at the S7 ISP and ACDC datasets, utilizing the S7 ISP dataset for training and the ACDC dataset for testing the assessment enhancement. This study aims to combine EDSR-Modified with transfer learning to improve CMR image quality, which is used to diagnose cardiovascular diseases. Qualitative comparisons are performed using mean squared error (MSE), peak signal-to-noise ratio (PSNR), and SSIM evaluation metrics. It is observed that the proposed technique outperforms the other techniques. © 2024 ieee.

关键词： Convolutional neural networks

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