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ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 203-216页

作者： Siyi XUN Yan ZHANG Sixu DUAN Mingwei WANG Jiangang CHEN Tong TONG Qinquan GAO Chantong LAM Menghan HU Tao TAN Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Jinan Branch of China Telecom Co.Ltd. Jinan 250000China School of Physics and Electronics Shandong Normal UniversityJinan 250000China Department of Dardiovascular Medicine Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310000China Clinical School of Medicine Hangzhou Normal UniversityHangzhou 310000China Hangzhou institute of Cardiovascular Diseases Hangzhou 310000China Shanghai Key Laboratory of Multidimensional information processing Shanghai 200000China School of communication&Electronic Engineering East China Normal UniversityShanghai 200000China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghai 200000China College of Physics and information Engineering Fuzhou UniversityFuzhou 350000China

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue ***,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous *** addition,the labeling of tumor areas is time-consuming and *** To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network *** influence of network normalization,loss function,and network depth on segmentation performance is further *** In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor *** In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

关键词： Brain tumor MRI U-net Segmentation Attention mechanism Deep learning

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots

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Frontiers of information Technology & Electronic Engineering 2018年第3期19卷 459-470页

作者： Xu-guang ZUO Lu YU Zhejiang Provincial Key Laboratory of information processing Communication and Networking IPCANInstitute of Information and Communication Engineering Zhejiang University

The latest video coding standard High Efficiency Video Coding （HEVC） can achieve much higher coding efficiency than previous video coding standards. Particularly, by exploiting the hierarchical B-picture prediction structure, temporal redundancy among neighbor frarnes is eliminated remarkably well. In practice, videos available to consumers usually contain many repeated shots, such as TV series, movies, and talk shows. According to our observations, when these videos are encoded by HEVC with the hierarchical B-picture structure, the temporal correlation in each shot is well exploited. However, the long-term correlation between repeated shots has not been used. We propose a long-term prediction （LTP） scheme to use the long-term temporal correlation between correlated shots in a video. The long-term reference （LTR） frames of a source video are chosen by clustering similar shots and extracting the representative frames, and a modified hierarchical B-picture coding structure based on an LTR frame is introduced to support long-term temporal prediction. An adaptive quantization method is further designed for LTR frames to improve the overall video coding efficiency. Experimental results show that up to 22.86% coding gain can be achieved using the new coding scheme.

关键词： High Efficiency Video Coding(HEVC) Long-term temporal correlation Long-term prediction Hierarchical B-picture structure

Unsupervised Spatio-Temporal Segmentation for Extracting Moving Objects in Video Sequences

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Journal of Shanghai Jiaotong university(Science) 2009年第2期14卷 154-161页

作者：李仁杰余松煜王向文 institute of Image communication and information processing Shanghai Jiaotong University institute of Image communication and information processing Shanghai Jiaotong University

Extraction of moving objects is an important and fundamental research topic for many video applications. This paper addresses an unsupervised spatio-temporal segmentation scheme to extract moving objects from video *** temporal segmentation localizes moving objects by comparing the motion vector of each block in each frame with the corresponding global motion vector estimated by an outlier rejection(OR) based ***,the temporal compensation utilizing the temporal coherence of moving objects is considered in the temporal segmentation to solve the temporarily stopping *** detected moving regions usually have discontinuous boundaries and some *** regions are then compensated in the spatial domain. In the spatial segmentation,the watershed algorithm considering the global information improves the accuracy of segmentation in the spatial *** modified mean filter is presented to suppress some *** using a fusion module,moving objects are *** on various sequences have successfully demonstrated the validity of the proposed scheme.

关键词： segmentation spatio-temporal region growing outlier rejection watershed

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

Mode Multiplication of Cylindrical Vector Beam Using Raytracing Control

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Chinese Physics Letters 2024年第9期 39-44页

作者：王晶曾庆计吴海生谢创鑫叶华朋董泽范滇元陈书青 institute of Microscale Optoelectronics Shenzhen University Guangdong Provincial Key Laboratory of Optical information Materials and Technology and Institute of Electronic Paper Displays South China Academy of Advanced OptoelectronicsSouth China Normal University School of information and Electronics Beijing Institute of Technology

Cylindrical vector beams(CVBs) hold significant promise in mode division multiplexing communication owing to their inherent vector mode orthogonality. However, existing studies for facilitating CVB channel processing are confined to mode shift conversions due to their reliance on spin-dependent helical modulations, overlooking the pursuit of mode multiplication conversion.

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Compressed Multi-image Reconstruction Based on Quantization Noise Distribution

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Journal of Donghua University(English Edition) 2007年第6期24卷 756-761页

作者：程燕方向忠 institute of Image communication and information processing Shanghai Jiaotong University

Because of the quantization noise introduced during the compression,super-resolution reconstruction(SRR)techniques are complicated for the compressed *** paper aims to incorporate the prior knowledge of discrete cosine transform(DCT)coefficients into modeling the quantization *** spatial covariance matrix of the quantization noise is estimated by utilizing the Laplacian distribution of the alternating current(AC)*** estimating the spatial joint covariance of overall noises for the imaging system,we propose a general Bayesian framework to enhance the resolution for compressed *** demonstrate the effectiveness of the proposed algorithm and show the superiority to previous methods in objective and subjective aspects.

关键词： super-resolution quantization noise covariance Bayesian

Spatio-temporal Compensation Based Object Detection for Video Surveillance Systems

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Journal of Donghua University(English Edition) 2008年第2期25卷 123-129页

作者：李仁杰余松煜熊红凯 institute of Image communication and information processing Shanghai Jiaotong University

Moving object detection in video surveillance is an important step. This paper addresses an automatic object detection algorithm based on spatio-temporal compensation for video surveillance. Temporal difference of the pairs of two frames with a k-frame distance is utilized to obtain coarse object masks. Usually, object regions in these coarse masks have discontinuous boundaries and some holes. Region growing with the distance constraint is proposed to compensate these coarse object regions in spatial domain, followed by filling holes. The added distance constraint can prevent object regions from growing infinitely. The proposed filling holes method is simple and effective. To solve the temporarily stopping problem of moving objects, temporal compensation is proposed to compensate the object mask by utilizing temporal coherence of moving objects in temporal domain. The proposed detection algorithm can extract moving objects as completely as possible. Experimental results have successfully demonstrated the validity of the proposed algorithm.

关键词： surveillance temporal mask intensity difference region growing compensation

Mean shift based log-Gabor wavelet image coding

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第4期8卷 620-624页

作者： LI Ji-liang FANG Xiang-zhong HOU Jun institute of Image communication and information processing Shanghai Jiao Tong University Shanghai 200240 China

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

关键词： Sparse approximation Log-Gabor Image coding Mean shift Overcomolete