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Improved nn-UNet: Generalizable multi-scale Attention-Driven Segmentation of multi-sequence Myocardial Pathology 1st

Improved nn-UNet: Generalizable Multi-scale Attention-Driven...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Tang, Mengshi Li, Nuoxi Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou China School of Medical Imaging Fujian Medical University Fuzhou China

ISBN: (纸本)9783031870088

multi-sequence cardiac magnetic resonance (MS-CMR) images are capable of providing myocardial pathology information for patients with myocardial infarction. Precise myocardial structure and pathology segmentation hold significant importance for subsequent diagnosis and treatment. Nevertheless, traditional manual myocardial structure and pathology segmentation is not only time-consuming and labor-intensive but also has a low accuracy rate, particularly when identifying pathology such as scars and edema that are small in volume and have low contrast with the surrounding tissues, it becomes even more challenging. To address this issue, this paper proposes an improved nn-UNet for fully automatic segmentation of myocardial pathologies. In this network, based on nn-Unet, we use multi-modal data as input to make up for the lack of information in a single mode. For multi-modal data, we utilize cross normalization to improve the generalization performance. Meanwhile, multi-scale attention modules are integrated to process features at different resolutions, thus improving the feature representation capability of neural networks. Through feature fusion and attention weighting, the model can better capture the global and local information of myocardial pathologies, and achieve more accurate segmentation of myocardial pathologies. To verify the effectiveness of the proposed method, we conducted an evaluation using five-fold cross-validation in the dataset of the MyoPS++ challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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L-SPADNet: a target detection and classification network for high-resolution synthetic aperture radar (SAR) imagery directed toward ships 24

L-SPADNet: a target detection and classification network for...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Sun, Qiyang College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798400710094

Synthetic aperture radar (SAR) imagery possesses significant applications across multiple domains owing to its superior resolution. and all-weather observation capability. However, ship detection in SAR images faces challenges such as direction arbitrariness, mutual occlusion, dense alignment, and multi-scale. The common scientific problem of these challenges lies in how to effectively extract and express ship features to realize accurate target detection and classification. To address this core problem, this paper proposes a directed ship classification and detection network (L-SPADNet), which optimizes multi-scale feature processing and enhances the ability to capture and express deep features through an innovative space-to-feature computation module, separable large-size cascade convolution, and angularly-distributed focal-loss joint network. In particular, L-SPADNet improves the training for the rotating bounding box task by introducing Angle DFL (Distributed Focus Loss with Angle Factor) and realizes the accurate detection of ships with arbitrary directions in SAR collections. The experimental findings indicate that L-SPADNet demonstrates outstanding performance on the SRSDD and SSDD+ datasets. Meanwhile, the internet has been lightweight, and the model size is only 5.08MB, which is convenient for practical deployment and application. © 2024 Copyright held by the owner/author(s).

关键词： Synthetic aperture radar

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Design of Obstacle Avoidance and Tracking System for Elderly Assistance Intelligent Car Under computer Vision and multi-Sensor Fusion 14th

Design of Obstacle Avoidance and Tracking System for Elderly...

引用

14th international conference on Frontier Computing, FC 2024

作者： Chen, Yunsheng Cai, Qian Bao, Weixun Sun, Lingzhen Guangzhou Huali Science and Technology Vocational College Guangdong Guangzhou511325 China

ISBN: (纸本)9789819627936

With the increase of age, some elderly people's mobility problems have caused certain obstacles to daily life, so intelligent devices for helping the elderly are becoming more and more popular. The obstacle avoidance and tracking system of the elderly assistance intelligent car in this article adopts computer vision and multi-sensor fusion technology, which can achieve autonomous driving. The tracking module in the system consists of 5 TCRT5000 infrared detectors. The signal collected by the TCRT5000 infrared detector is amplified through an integrated circuit and sent to the microcontroller. The white and black lines are then determined to correspond to the five TCRT5000 infrared detectors, thereby determining the current position of the car. By adjusting the size of the resistance, an appropriate voltage can be obtained compared to the output signal. The minimum number of successful attempts in tracking and obstacle avoidance tests is 37, but overall it still maintains a high level and can meet basic needs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrared detectors

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AquaVision Dehaze and Enhancement: A Robust Algorithm for Improving Underwater Image Quality in Aquatic Environments 2

AquaVision Dehaze and Enhancement: A Robust Algorithm for Im...

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2nd international conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Vijayalakshmi, M. Sasithradevi, A. Spoorthi, J.S. Prakash, P. School of Electronics Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics Engineering Madras Institute of Technology Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

Underwater imaging is frequently challenged by light scattering, leading to haze, color distortion, and visibility loss. To address these issues, we introduce the AquaVision Dehaze and Enhancement algorithm to improve underwater image quality. Extensive testing on a dataset from an aquaculture environment demonstrates that the algorithm significantly enhances image clarity. Quantitative evaluations reveal high performance across various image quality metrics, with scores of UICM (19.6463), UISM (18.1490), UIConM (26.7463), and UIQM (13.3312). Furthermore, the algorithm delivers competitive results in no-reference image quality metrics, including NIQE (3.6780), Entropy (7.5942), PIQE (52.1444), and BRISQUE (35.9847). These results highlight the algorithm's effectiveness in addressing the challenges of underwater imaging and illustrate the effectiveness through an ablation study. The AquaVision Dehaze and Enhancement algorithm offers a robust solution for improving the quality making it a valuable tool for advancing research in aquatic environments. © The Institution of engineering & Technology 2024.

关键词： Aquaculture

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Automated Object Detection and Recognition in Satellite Imagery Using CNNs 1

Automated Object Detection and Recognition in Satellite Imag...

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1st international conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Shanthi, H.J. Shobana, G. Kamalanaban, E. Murugan, J. Senthil Nandhinieswari, S. Stalinbabu, R. Deemed to be University Hindustan Institute of Technology and Science Department of Computer Applications Padur India Takshashila University School of Computer Science Ongurdt Villupuram India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College India Kongunadu Arts and Science College Gounder Mills Coimbatore India M. Kumarasamy College of Engineering Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798350376517

As increasingly high-resolution satellite photography becomes available, it has led to a growing need for efficient and accurate methods to analyse large databases and extract valuable information. This research presents a comprehensive approach for automated item identification and detection in satellite data using Convolutional Neural Networks (CNNs). This research abstract outlines the critical steps involved in a suggested technique. These steps encompass data cleaning and organisation, model architecture development, training and evaluation, post- processing, deployment, and subsequent management. This study investigates the ability of CNNs to learn intricate spatial characteristics. The research focuses on carefully selecting and annotating training and validation datasets incorporating items of interest. This study uses data augmentation methods to enhance the generalisation capabilities of the model. This research abstract discusses the importance of the preprocessing stage in ensuring the consistent performance of a model by normalising the input data. This research abstract discusses the components of the CNN architecture, namely convolutional, pooling, and fully connected layers. The CNN architecture is specifically designed to capture hierarchical characteristics. This study investigates the utilisation of a cutting-edge optimiser in the training process to optimise a meticulously constructed loss function. This study examines the integration of the model into real-world applications, considering the trade-off between processing speed and resource constraints as crucial considerations for successful deployment. The findings suggest that this methodology holds promise as a valuable tool in various fields. © 2024 IEEE.

关键词： Satellite imagery

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A Terse Review of Heart Diseases Estimation Using Machine Learning Practices

A Terse Review of Heart Diseases Estimation Using Machine Le...

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Pawar, Rahul Dash, Amit Kumar Mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Electronical & Electronics Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

Heart diseases, a primary cause of global fatalities, represent a growing social matter on a worldwide scale. The integration of machine learning with advancing technology has greatly benefited the medical field, resulting in the provision of seamless solutions characterized by high accuracy, consistency and toughness. These advancements help the patient society through early detection and reduced medical care costs. They also offer the medical fraternity capable, accessible, precise and consistent prediction systems. This paper provides a comprehensive enquiry of machine learning algorithms engaged for the prediction and classification of several cardiovascular diseases, we explore several modalities, including physiological signals, heart sounds and electronic health records, to achieve effective heart disease recognition. We also provide insights into popular machine learning systems, hybrid systems and fuzzy systems in this context. It is noted in this review that Support Vector Machines (SVM) have gained popularity and widespread usage, with neural networks and ensemble techniques following closely behind in terms of their application. All these approaches are compared based on accuracy metrics. © 2024 IEEE.

关键词： Hybrid systems

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Research on Key Technologies of Network Security Situational Awareness Based on Big Data 2

Research on Key Technologies of Network Security Situational...

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2nd IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Li, Yuxiang Guangdong University of Science and Technology Department of Computer Science Dongguan523083 China

ISBN: (纸本)9798350368208

This paper delves into the critical technologies for cybersecurity situational awareness in big data environments and proposes a comprehensive framework that integrates feature extraction, assessment and prediction. Firstly, in the feature extraction phase, autoencoders are employed to efficiently extract features from complex network data and identify potential security anomalies. Secondly, an improved Dempster-Shafer (D-S) evidence theory is used to assess cybersecurity threats. By introducing an expectation deviation function for uncertainty variables, the credibility of experts is optimized, and local conflicts among evidence are resolved. Finally, a hybrid model combining uncertain evidence theory with deep learning methods is constructed to predict potential attack scenarios. The model's predictive capabilities and computational efficiency are enhanced through optimization. The research results demonstrate that this framework significantly improves the accuracy and real-time performance of cybersecurity situation assessment, offering substantial social value in addressing cybersecurity challenges in big data environments. © 2024 IEEE.

关键词： Cybersecurity

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Design method of aircraft water system architecture based on model

Design method of aircraft water system architecture based on...

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2023 international conference on computer Network Security and Software engineering, CNSSE 2023

作者： Bao, Jiejie Zhang, Jun Requirements Engineering and Aircraft Development Department Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

The implementation of water system function in aircraft involves several systems, and the comprehensive system architecture has high complexity. The number of interfaces between multiple systems is huge and the interface forms are diverse, and the working logic between multiple systems is complex. To handle this complexity, a model-based system architecture design method is proposed;SysML modeling language is applied into the architecture design and behavior analysis of aircraft water system, which can realize the unified definition and decomposition of system architecture elements, the unified interface definition and analysis of multi-systems integrated architecture. At the same time, it also can realize the synthetic design of water system architecture. The architecture model and behavior model of aircraft level and system level are established, and the analysis of water system function and the multi-systems integration design is effectively realized. The practice shows that the architecture design method based on SysML can effectively deal with the complexity of multi-systems integration design, and improve efficiency and quality of integrated design. © 2023 SPIE.

关键词： computer architecture

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Design of Upper computer for multi-channel Data Acquisition System Based on LabVIEW Serial Communication 3

Design of Upper Computer for Multi-channel Data Acquisition ...

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3rd international conference on Testing Technology and Automation engineering, TTAE 2023

作者： Zhang, Yan Xi'an Shiyou University Key Laboratory of Optoelectronic Oil and Gas Logging and Testing Ministry of Education Xi'an710065 China

ISBN: (纸本)9781510674769

With the rapid development of computer technology and intelligent instrumentation in China, data acquisition and data processing technology have become one of the preliminary work for digital signal processing, which is widely used in many fields of production and life. LabVIEW through the virtual system front panel and system block diagram to establish virtual instrument graphic programming system, has been regarded as the standard data acquisition instrument control software, widely used in academia, industry and research room. Based on LabVIEW development platform, a set of data acquisition and processing system is designed, and serial communication, data acquisition, processing, storage, display and other functions are realized through simulation. © 2024 SPIE.

关键词： Data acquisition

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Navigating the VPN Landscape: A Comparative Study of L2TP, IPSec, and MPLS VPN Technologies 4

Navigating the VPN Landscape: A Comparative Study of L2TP, I...

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Chen, Yungui Li, Qian Tian, Liwei Jiang, Yuxiao School of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798331531409

With the rapid advancement of internet technology and deepening globalization trends, Virtual Private Network (VPN) technology has become a crucial tool in ensuring the security and privacy of enterprise data transmission. Amidst complex network environments and escalating cybersecurity threats, VPNs address geographical limitations and information security by establishing encrypted channels over public networks. This study conducts a detailed comparative analysis of three mainstream VPN technologies - L2TP, IPsec, and MPLS. Through theoretical discussions and experimental validation, the study comprehensively compares these technologies across metrics such as transmission efficiency, security, cost-effectiveness, session setup times, and latency. It provides a comprehensive evaluation and application guide for businesses choosing VPN technologies. © 2024 IEEE.

关键词： HTTP

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