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LVQE: Laparoscopic Video Quality Enhancement Using GAN-based Smoke Elimination Guided by an Embedded Dark Channel

LVQE: Laparoscopic Video Quality Enhancement Using GAN-Based...

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2024 IEEE international conference on signal Processing, Information, Communication and systems, SPICSCON 2024

作者： Islam, Akib Jayed Salehin, Sultanus Alam, Sayem Ul Islam, Kamrul Paul, Shreya Paul, Prithy Norwegian University of Science and Technology Gjøvik Norway Dept. of ECE The University of Alabama Tuscaloosa United States The University of Alabama Tuscaloosa United States National Institute of Technology Tamil Nadu Trichy India East Delta University Chattogram Bangladesh

ISBN: (纸本)9798331510213

In this study, a technique for effectively removing smoke from laparoscopic video recordings—which are often negatively impacted during surgeries—using an image-to-image generative adversarial network (GAN) with a built-in guide mask derived from the dark channel is presented. Smoke from tissue dissection tools can obstruct the surgeon’s view and introduce errors into computer vision-based systems employed for clinical navigation. The GAN addresses this issue by eliminating noise while maintaining perceptually satisfactory image quality. Using the Laparoscopic Video Quality (LVQ) evaluation dataset, the method is trained and assessed. The section on experimental results provides qualitative examples demonstrating the effectiveness of the proposed approach. With a larger, more diverse dataset for training and validation, further improvements are anticipated. The proposed technique generated smoke-free images and videos, surpassing previous methods. © 2024 IEEE.

关键词： Laparoscopy

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Application of Smart Terminals in the Power Industrial internet

Application of Smart Terminals in the Power Industrial Inter...

引用

international Hydropower Development conference, IHDC 2024

作者： Fuqiang, Huang Zhengpu, Chen Jiangtao, Xiao Zhaoan, Jin Jun, Chen Hai, Yang Xiluodu Hydropower Station China Yangtze Power Co. Ltd. Yunnan Province Yongshan County657300 China State Grid International Development Co. Ltd. Hong Kong Shanghai Liqian Power Technology Co. Ltd. Shenzhen China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9789819791835

The Power Industrial internet serves as an essential platform for the digital transformation of the electric power industry, during its implementation, the integration of Information technology (IT) and Operational technology (OT) in power production and maintenance has posed significant challenges, necessitating the development of intelligent equipment based on Cyber-Physical systems (CPS) to bridge this gap. Microcomputer platforms are well-established and reliable systems capable of running various complex algorithms. However, traditional microcomputer-based protection devices face limitations such as firmware programming, limited types of signal sampling, and insufficient data transmission capabilities. In the current era of digital transformation, there is an urgent need for intelligent devices and smart terminals that can overcome these shortcomings through cloud-edge collaboration to assist the digital transformation of the power industry, continuously creating effective applications that enhance quality and efficiency, and fostering new productive forces. This article starts from the strategic orientation of industrial development, the problem orientation of digital transformation, and the demand orientation for quality improvement and efficiency enhancement. It focuses on the key issues in the advancement of the industrial internet, proposes solutions based on microcomputer platforms, and explores new directions in the development of intelligent and digital transformation in the field of relay protection specialization. © The Author(s) 2025.

关键词： Cloud-Edge Collaboration Cyber-Physical systems Industrial internet Microcomputer Platform Power Industry Smart Terminal

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Biometric-based Access Control systems with Robust Facial Recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal Processing, INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 IEEE.

关键词： Biometrics

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Text-to-image Retrieval based on Zero-shot Transfer Learning with CLIP Model and Vector Database 11

Text-to-image Retrieval Based on Zero-shot Transfer Learning...

引用

11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Xie, Junfeng Chen, Junying School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331531904

With the continuous increase in the number of internet users, the explosion of multimodal data on the web has led to a growing demand for image retrieval. Current text-to-image retrieval systems often rely on keyword matching, which frequently fails to accurately capture users' retrieval needs. This work proposes a text-to-image retrieval method based on zero-shot transfer learning with the CLIP model and vector database, designed to be applied in text-to-image retrieval systems. By leveraging the semantic alignment capability of the CLIP model for text and images, this study effectively addresses the semantic gap problem in text-to-image retrieval. Additionally, the method utilizes the Milvus database for efficient storage and retrieval of image feature vectors, thereby enhancing system stability. Experimental results on the Flickr30k dataset demonstrate that this method improves the accuracy of text-to-image retrieval, verifying its effectiveness. Furthermore, this work extends the proposed method by incorporating the iFLYTEK Spark Cognitive Model to support image retrieval using Chinese text descriptions, further broadening the system's applicability and reliability. © 2024 IEEE.

关键词： Zero-shot learning

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Equipment Fault image Recognition Method based on Deep Integrated Learning with Hyperparameter Optimization 6

Equipment Fault Image Recognition Method Based on Deep Integ...

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6th IEEE international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Chen, Pintong Guan, Di Chen, Jing Guangdong University of Technology Guangzhou China Liaoning Technical University Huludao China Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350374315

In order to solve the problem of low precision and low stability of deep learning network for industrial equipment fault recognition under strong background noise, a method of equipment fault image recognition based on deep integrated learning with hyperparameter optimization was proposed. Firstly, the time-frequency image data set is constructed by using two-dimensional wavelet transform of the original vibration signal. Secondly, the noise resistance of each CNN network architecture is tested, and the change of network performance is studied by embedding different noise intensities into the data. Then, group optimization algorithm is used to optimize the learning rate and batch sampling parameters of the optimal architecture model. Finally, decision fusion is used to integrate the trained model, so as to improve the stability and image recognition accuracy of the integrated network. Verified by the bearing data of Western Reserve University, the proposed algorithm has good diagnostic accuracy and stability. © 2024 IEEE.

关键词： Wavelet transforms

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Semantic Communication based on Content Style Adaptation for image Transmission 16

Semantic Communication Based on Content Style Adaptation for...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Song, Mengshu Ma, Nan Liu, Yiming Shen, Linzi Dong, Chen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Peng Cheng Laboratory Department of Broadband Communication Shenzhen China

ISBN: (纸本)9798350390643

The development of semantic communication has promoted the research on image transmission. Content and style are two crucial characteristics of image information. Existing studies have explored image information transmission and recovery methods in semantic communication systems, but do not consider both image style information and content information simultaneously. To address this issue, based on deep joint source-channel coding (JSCC) structure, a novel system named semantic communication based on content style adaption (CSASC) for image transmission is proposed in this paper. Specifically, a novel semantic information extraction method are designed to extract the semantic content information and semantic style information of images, meanwhile an image content-style adaptive reconstruction method is proposed by fusing semantic content and semantic style features to improve the image reconstruction accuracy. In addition, the impact of the weighting of semantic style and content information on image reconstruction quality is analyzed. Results demonstrate that the proposed method has better recovery results compared with existing image semantic communication methods. © 2024 IEEE.

关键词： content information image reconstruction semantic information style information

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An anti-jamming method based on joint spatial-polarimetric processing for FDA-MIMO Radar 4

An anti-jamming method based on joint spatial-polarimetric p...

引用

4th international conference on signal image Processing and Communication, ICSIPC 2024

作者： Zhang, Yunhui Pang, Bo Dai, Dahai Tan, Zhengkuan College of Electronic Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510682467

With the continuous development of radar jamming methods and styles, radar systems are facing more and more complex electromagnetic environment, resulting in limited suppression performance through spatial anti-jamming measures solely. In order to solve the problem, this paper proposes an anti-jamming method based on joint spatial-polarimetric processing for Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) system. Firstly, the signal model of Polarimetric FDA-MIMO (PFDA-MIMO) is established. based on this model, a joint spatial-polarimetric adaptive filtering method is proposed and experiments are carried out to demonstrate the advantages of joint spatial-polarimetric processing in anti-jamming. Compared to the filtering method that only utilizing the spatial information, this method has a better performance dealing with multidimensional jamming and improves the ability of FDA-MIMO in resisting main lobe jamming. © 2024 SPIE.

关键词： Adaptive filtering

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Enhanced Edge Attention-based SRCNN for Retinal image Super-Resolution 1

Enhanced Edge Attention-Based SRCNN for Retinal Image Super-...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Koshika, Manoj Kandukuri, Prabhakar Moppey, Shyam Kumar Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence & Machine Learning Gandipet Hyderabad India

ISBN: (纸本)9798331529246

The early recognition of retinal disorders such as cataracts, glaucoma, and retinal problems is highly dependent on high-resolution retinal imaging, which is a complex issue because of poor image quality and the lack of important features. The proposed Enhanced Edge Attention Super Resolution Convolutional Neural Network (EEASRCNN) is designed to improve the quality of retinal images high precisely. The model preserves important details like blood vessels and the optic disc by using a custom edge loss function, sharpening filters, and combined attention mechanisms. With a high structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) score and an accuracy of 97.16% in preserving structural details, EEASRCNN outperforms traditional super-resolution methods after being trained on a dataset of diverse retinal images. EEASRCNN offers effective image super resolution in real-time medical image diagnosis. © 2025 IEEE.

关键词： Ophthalmology

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Hand Gesture Recognition Method based on mmWave Radar with MobileViT and Knowledge Distillation 16

Hand Gesture Recognition Method based on mmWave Radar with M...

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16th international conference on signal Processing systems, ICSPS 2024

作者： Zhang, Xiangqun Ge, Zhizhou Lu, Kai Du, Genyuan Shen, Jiawen Gao, Xiangqian School of Information Engineering Xuchang University Xuchang461000 China Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing Xuchang461000 China Henan Shengshi Hengxin Technology Co. Ltd Xuchang461111 China

ISBN: (纸本)9781510689251

Human-hand gesture recognition using millimetre wave radar is attractive in human-computer interfaces, industrial internet of Things, and smart home. However, the existing CNN or RNN model is so complex and large that it is hard to apply to mobile vision tasks and embedded devices. This paper proposes dual lightweight convolutional neural networks, MobileViT and knowledge distillation, to solve this problem. Firstly, we acquire the original radar echoes of frequency-modulated continuous wave (FWCW) signal and reshape the three-dimensional matrix by Chirps * Samples * Frames. Then, we perform the signal processing method to eliminate the noise and static background. Secondly, we employ the Fast Fourier Transform to extract the hand gestures feature map of distance and Doppler information. Finally, the feature map is input into the modified MobileViT to recognize dynamic hand gestures, and knowledge distillation models is used to simplify the model structure. The experimental results show that the parameter space complexity of the constructed model is reduced to 0.018 M, and the computational complexity is 0.082 GFLOPs after knowledge distillation *** method is verified in 12 complex gesture. It has the potential to be applied in mobile tasks and embedded devices. © 2025 SPIE.

关键词： Fast Fourier transforms

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IoT based Localization and Tracking by Using MIMO Antenna technology 4

IoT Based Localization and Tracking by Using MIMO Antenna Te...

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4th international conference on signal Processing and Communication, ICSPC 2023

作者： Prakash, P. Manoj, G. Immanuel.j, Samson Karunya Institute of Technology and Sciences Ece Department Coimbatore India

ISBN: (纸本)9798350300772

Multiple aspects of our life could benefit from the IoT. A component of the 'connected house' is the internet of Things (IoT). All the programs below are the ones that need to be localized in some form. When applied to the first, IoT allows for the accurate localization of anything, even living creatures and humans. The second component tracks mobile targets such as toddlers and the elderly. When it comes to home safety and surveillance, smart homes offer features like geolocation and monitoring. A localization system is able to track almost anywhere or anything by combining information from multiple sources, such as wired and wireless networks. As time goes on, the internet of Things (IoT) becomes increasingly important and influences our daily lives in tangible ways. There is growing interest in the field of navigation IoT-based systems due to their widespread use in context-aware and localizationaware applications such as tracking, healthcare, and security. This subject has received a lot of attention as sensor technology and computational capacities improve. This study presents a brief overview of the localization techniques used by internet of Things-based indoor navigation systems at present. © 2023 IEEE.

关键词： internet of things

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