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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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RECURRENT 3-D MULTI-LEVEL VISUAL TRANSFORMER FOR JOINT CLASSIFICATION OF HETEROGENEOUS 2-D AND 3-D RADIOGRAPHIC DATA 31

RECURRENT 3-D MULTI-LEVEL VISUAL TRANSFORMER FOR JOINT CLASS...

引用

2024 international conference on image Processing

作者： Owais, Muhammad Zubair, Muhammad Hassan, Taimur Velayudhan, Divya Hussain, Irfan Werghi, Naoufel Khalifa Univ Dept Mech & Nucl Engn KUCARS Abu Dhabi U Arab Emirates Univ Cent Punjab Fac IT & Comp Sci Punjab Pakistan Abu Dhabi Univ Dept Elect Comp & Biomed Engn Abu Dhabi U Arab Emirates Khalifa Univ Dept Comp Sci C2PS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350349405;9798350349399

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of 96.67%, F1-score of 96.88%, mean average precision of 96.75%, and mean average recall of 97.02%. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： lung infection R3DM-ViT CBMIR Computer-aided diagnosis Medical image retrieval

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Research on English Customized Distance Assisted Curriculum System based on Computer 'internet +' technology 3

Research on English Customized Distance Assisted Curriculum ...

引用

2023 ieee 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Song, Xiaoying Shenyang Aerospace University School of Foreign Languages Shenyang110136 China

ISBN: (纸本)9798350341546

This manuscript introduces a new English customized distance education teaching method based on computer network technology. This manuscript first discusses the theory of distance teaching in English classroom based on 'internet +' technology. Establish three levels of online education platforms in English courses. This software is developed based on Mutagen Creator. This manuscript proposes a network 'internet +' technology based on digital image processing technology. A virtual teaching platform that can interact with people has been established using the virtual technology of 3DSMAX. The functions of the simulation device were implemented through VRML programming, and the triggering, data transmission, and page conversion of the demonstration program were implemented based on the response function. Thus, the design of a teaching system based on virtual reality has been achieved. Practice has proven that this method has better human-computer interaction effects than traditional English remote multimedia teaching, and has increased students' learning enthusiasm. © 2023 ieee.

关键词： auxiliary modeling English distance teaching human-computer interaction internet + virtual reality

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Artificial Intelligence-based image and Data Analysis in the Industrial internet in Digital Economy Era 5

Artificial Intelligence-based Image and Data Analysis in the...

引用

2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Liu, Yaqi Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350323313

This paper mainly proposes the combination of deep learning in image recognition and target detection in the traditional industrial internet, and proposes a multi-dimensional branching convolutional neural network model and a target detection improvement model based on deep learning, respectively. Firstly, for the pre-processing of the sample data, the network model is constructed by using AlexNet model and Normalization algorithm with 'enhanced feature extraction' as the starting point, and the SENet sub-framework model is introduced in the network model. Secondly, the latest YOLO algorithm model is used to explore various improvement methods for the segmentation problem and multi-scale problem in the target detection field. The experimental results show that the problem of industrial internet in image recognition and object detection is solved, and the technology of Industrial internet and deep learning is combined effectively. © 2023 ieee.

关键词： image recognition

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Computer Network Information Security Supervision and Protection Strategy with internet of Things Information technology 9

Computer Network Information Security Supervision and Protec...

引用

9th international conference on Intelligent Computing and signal Processing, ICSP 2024

作者： Nan, Xiang Parthasarathy, Rajamohan Wang, Chu School of Information Technology NanTong Normal College Jiangsu Nantong226000 China SeGi University Faculty of Engineering Built Environment and Information Technology Kota Damansara Malaysia School of Microelectronics Xidian University Shaanxi Xi'an710000 China

ISBN: (纸本)9798350376548

With the continuous development and popularisation of internet of Things (IoT) technology, the regulation and protection of computer network information security has become increasingly important. The application of IoT information technology enables various devices and systems to communicate and interact with each other, however, it also brings new security challenges and risks. In this context, it is particularly urgent and necessary to develop effective regulation and protection strategies. This paper focuses on computer network information security in the internet of Things environment, considering the rapid development of the internet of Things and its unique security challenges, to study the regulation and protection strategies to adapt to this field. First, this content investigates the shortcomings of traditional information security strategies in dealing with the diversity, large-scale deployment, data privacy protection, real-time monitoring and cross-border integration of the IOT, and discusses the important exploration results and existing defects in the field of IOT security in the current academia and industry. based on a step-by-step exploration of existing references and methods, this content designs a multi-level security protection system that covers security requirements from the device layer to the application layer. In threat detection, the system shows an average of 95% threat detection accuracy, ensuring efficient security monitoring. The system stability and performance evaluation experiment shows that the CPU usage maintenance rate is about 65% on average, and the memory usage is about 55% on average. The above data indicates that the system maintains good stability while operating efficiently. In the adaptive defense mechanism effect test, the system shows an average of about 3 defense mechanism adjustments per day, indicating its high adaptability in a dynamic security environment. The above experimental results jointly verify the effectiv

关键词： Computer Network Protection internet of Things technology Multilayer Security System Protection Mechanism Safety Supervision Strategy

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Enhanced Detection of Oral Squamous Cell Carcinoma from Histopathological images using Convolutional Neural Network Architectures 2

Enhanced Detection of Oral Squamous Cell Carcinoma from Hist...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kaur, Gurjot Sharma, Neha Gupta, Rupesh Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

A major worldwide health issue marked by high morbidity and death;oral squamous cell carcinoma is caused mostly by delayed detection. Early identification of oral cancer using immediate medical intervention improves patient survival rates and therapy results. In this work, we built an image classification system to detect oral squamous cell carcinoma (OSCC) in histopathology images using a customized convolutional neural network (CNN). Normal and OSCC classes comprised the dataset's 4,946 training images, 126 test images, and 120 validation images. The model's resilience was improved using rescaling, shearing, zooming, and horizontal flipping among data augmentation methods. Comprising many convolutional, pooling, and dropout layers, the CNN model was trained using the Adam optimizer and category cross-entropy loss function. Following thirty epochs, the model attained 86.67% validation accuracy and 94.64% training accuracy. The model's performance on the test set was further evaluated generating an overall accuracy of 72%, a recall of 92%, and a precision of 76%. These results highlight the potential of the CNN-based approach in supporting pathologists in early oral cancer detection, hence improving clinical judgment and patient treatment. The outstanding accuracy highlights how well deep learning models analyze medical images and their possible use in clinical operations to raise diagnosis accuracy and results. The CNN-based image classification system heralds a revolution in early oral cancer detection by providing a consistent and fast tool for doctors fighting this common illness.

关键词： Oral squamous cell carcinoma (OSCC) Histopathology images Convolutional Neural Network (CNN) Medical image Classification Early Detection

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Digital image Semantic Communications with Joint Source-Channel Coding and Constellation Optimization 16

Digital Image Semantic Communications with Joint Source-Chan...

引用

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

作者： Shen, Linzi Ma, Nan Chen, Jianqiao Song, Mengshu Dong, Chen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Zgc Institute of Ubiquitous-X Innovation and Applications The 6G R&d Department Beijing100876 China

ISBN: (纸本)9798350390643

Recently developed semantic communication facilitates an intelligent and minimalist approach to meet the growing demands for future sixth-generation (6G) communication sys-tems. However, since the constraints of backpropagation during neural network training, the predominant approach of most extant studies are based on differentiable channel inputs, which is difficult to match with existing digital communication systems effectively. Meanwhile, the research on modulation techniques considering constellation optimization for digital semantic communication is still in the early stage. To deal with these issues, we propose a novel deep joint source-channel coding (DJSCC)-based digital semantic communication with considering constellation optimization, which is referred to as DJSCC-C. Firstly, the digital semantic communication system is introduced. Secondly, the DJSCC-C system is proposed for image transmission, which is trained with a newly-constructed loss function, aiming to improve the transmission rate in the digital semantic communication system as well as optimize the joint source-channel coding capability. Finally, simulation results are provided to verify the superiority of the proposed scheme for future design of digital semantic communication systems. © 2024 ieee.

关键词： Channel coding

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Reliable Federated Learning with Mobility-Aware Reputation Mechanism for internet of Vehicles 26

Reliable Federated Learning with Mobility-Aware Reputation M...

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Chang, Xing Xue, Xiaoping Yao, Baojing Li, Aojie Ma, Jingxiao Yu, Yantao Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Shanghai Software Ind Assoc Shanghai 200436 Peoples R China

ISBN: (纸本)9798350399462

With the widespread deployment of multi-sensors on vehicles, a significant amount of data is generated that exhibits the characteristics of massive volume, wide variety, and privacy sensitivity. The private information protection poses immense challenges to intelligent applications in intelligent transportation systems (ITS). Federated learning (FL) has been introduced to support advanced ITS applications with minimum data exchanges and privacy disclosure. However, besides achieving data privacy in such a system, the malicious behavior or unintentional misbehavior of participating vehicles caused by high vehicle mobility and limited resources are critical considerations that may hinder the adoption of FL in ITS. In this paper, the concept of reputation is introduced as a metric to evaluate the reliability and trustworthiness of vehicles' behavior. Additionally, the reputation evaluation mechanism of the joint vehicle mobility metric and local model update performance metric is designed to calculate the reputation score of the vehicle at each model aggregation round. Furthermore, considering the case that well-behaved vehicles are misjudged due to mobility, we propose a spot-check strategy to verify and employ low-reputation but reliable misjudged vehicles with a certain probability based on their reputation value to improve the model training efficiency. Extensive experiments are conducted on real traffic signal datasets to demonstrate the effectiveness of our proposed scheme.

关键词： Vehicles

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Fault time detection method of active distribution network based on Prony fitting and pixel comparison 3

Fault time detection method of active distribution network b...

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3rd international conference on Energy, Power and Electrical technology, ICEPET 2024

作者： Yan, Yan Jian, Wenhao State Grid of China Technology College Jinan China Shandong University School of Electrical Engineering Jinan China

ISBN: (纸本)9798350352658

In the application of relay protection in active distribution network, it is of great significance to accurately identify the fault time for system protection. However, the detection criteria of traditional fault time detection algorithms are mostly complex and easy to be affected by sampling frequency. Therefore, a fault time detection method based on pixel value comparison is proposed. Firstly, the fitting algorithm is used to fit the non-fault part of the fault recording signal, and the fitting expression is obtained and extended to the length of the original fault recording signal. Then, the extended signal is compared with the original signal image information to obtain the fault occurrence time. Finally, the simulation results show that the fault time detection algorithm has intuitive criterion, high accuracy and improves the time resolution. In the face of different fault types and fault positions, the detection error can be much lower than a sampling interval, so that the detection error can be controlled to the pixel level, and a certain transition resistance can be tolerated. It also has high detection accuracy for low sampling frequency. © 2024 ieee.

关键词： image enhancement

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Shared Waveform Design for Integrated Detection and Jamming signal based on Smart Noise Jamming of Convolution Modulation 8

Shared Waveform Design for Integrated Detection and Jamming ...

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

作者： Li, Chunyang Wu, Guangxin Li, Gui Xue, Hui Nanjing Research Institute of Electronics Technology Nanjing China

ISBN: (纸本)9798350397932

The integrated detection and jamming signal, which can realize radar detection and jamming functions at the same time, is an important research content of multifunctional RF systems. In this paper, we propose an integrated detection and jamming signal with convolution modulation of LFM signal. This integrated signal can form coherent smart noise jamming to the enemy and achieve radar detection by deconvolution and matched filtering of the echo signal. By optimizing the convolution sequence, the performance of the integrated signal after deconvolution and matched filtering is similar to or even better than that of the original LFM signal. Finally, the simulation results verify the performance of the integrated signal. © 2023 ieee.

关键词： signal detection

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Vision Mamba-based Approach for Incomplete Boundary Document image Rectification

Vision Mamba-Based Approach for Incomplete Boundary Document...

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

作者： Zhang, Weihao Xia, Xin Li, Maopeng Zhao, Yunbo HiThink RoyalFlush Information Network Co. Ltd. Hangzhou China University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Capturing document images using handheld mobile devices often results in geometric deformations, which adversely affect the accuracy of Optical Character Recognition (OCR) and document understanding. However, existing transformer-based methods face significant computational costs when processing document images on resource-constrained devices. This study proposes an enhanced Vision Mamba architecture to learn the structural information of document images, thereby rectifying deformed images while reducing computational resource consumption. Additionally, owing to the relative positioning between the document and the imaging device, captured images may exhibit incomplete boundaries. Conventional learning-based methods are primarily designed for images with complete boundaries, which can diminish correction effectiveness. To address this issue, mask consistency loss and preprocessing techniques are introduced to improve the rectification of document images with incomplete boundaries. Experimental results demonstrate the effectiveness and superiority of this method, highlighting its significant value for intelligent document processing. © 2025 ieee.

关键词： Document image rectification image deformation vision mamba

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