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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： Image reconstruction

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Learning convolutional neural network to maximize Pos@Top performance measure 25

Learning convolutional neural network to maximize Pos@Top pe...

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25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Geng, Yanyan Liang, Ru-Ze Li, Weizhi Wang, Jingbin Liang, Gaoyuan Xu, Chenhao Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Suning Commerce RandD Center USA Inc. Palo AltoCA94304 United States Information Technology Service Center Intermediate People's Court of Linyi City Linyi China Jiangsu University of Technology Jiangsu213001 China New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9782875870391

In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a training set to learn the filters of CNN and the classifier parameter. The classifier parameter vector is solved by the Lagrange multiplier method, and the filters are updated by the gradient descent method alternately in an iterative algorithm. Experiments over benchmark data sets show that the proposed method outperforms the state-of-the-art Pos@Top maximization methods. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Convolution

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A Privacy Policy of P3P Based on Relational Database

A Privacy Policy of P3P Based on Relational Database

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International Multi-Symposiums on computer and Computational Sciences (IMSCCS)

作者： Bin Jiang Da-fung Zhang Chao Yang Guangxue Yue Fei Yu Software School Hunan University Changsha Hunan China College of Computer and Communication Hunan University Changsha Hunan China Provincial Key Laboratory of Computer Information Processing Technology Jiangsu Jiangsu Province China

P3P is an industry standard method that enables users to gain powerful control over their processing of personal information on Web sites being visited. Traditional methods of database access control places great dependence on the use application of statically defined views. In this paper, we propose the procedure by which current RDBMS can be transformed into their privacy-preserving equivalents. In addition, architectural for implementing P3P based on RDBMS is dissected and a server-centric implementation that reuses database querying technology is suggested

关键词： Data privacy Relational databases Industrial control Automatic control Guidelines Legislation Chaotic communication Educational institutions laboratories information processing

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An Algorithm for 3D Image Segmentation

An Algorithm for 3D Image Segmentation

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International Conference on Image and Graphics (ICIG)

作者： Ding Zhi Dong Yu-ning College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Jiangsu Provincial Key Lab of Computer Information Processing Technology Suzhou University Suzhou China

We propose a new 3D image segmentation method based on prediction, block-matching and partial 3D constraint in this paper. The algorithm only needs to set a few key points in the first image. We use intensity information and block-matching to optimize the initial condition, and consider the 3D object's characteristics at the same time. By using the partial 3D constraints we can get the result of 2D and 3D smoothness. Experimental results validate its usefulness in 3D image segmentation.

关键词： Image segmentation Active contours Image motion analysis Shape Educational technology information processing Prediction methods Image converters Image sequences computer graphics

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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Linear support vector machine based on kernel function

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Journal of Computational information Systems 2009年第4期5卷 1089-1095页

作者： Gao, Shang Hu, Xuekun Zhang, Zaiyue Cao, Cungen School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 China

Support vector machine is kind of novel machine learning method, based on statistical learning theory, which becomes the hotspot of machine learning because of its excellent learning performance. The method of support vector machine has been developed for solving classification and regression problems. Training a support vector machine requires the solutions of a very large quadratic programming problem. The algorithm is usually slow when there are many data. In order to improve the running speed, three regression models based on kernel function are introduced. One of three models is changed into a system of linear equations problem, the other models are changed into linear programming problems and their dual problems are given. Simulation results show the effectiveness of the methods, and the computation complexity are largely reduced. The kernel function and the parameter of models selection are compared and analyzed by means of an example. © 2009 Binary information Press August, 2009.

关键词： Support vector machines

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Generalized geometry-based optimal power control in wireless networks

Generalized geometry-based optimal power control in wireless...

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2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012

作者： Wang, Wei Shin, Kang G. Zhang, Zhaoyang Wang, Wenbo Peng, Tao Department of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Network Technology Zhejiang University Hangzhou 310027 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2121 United States Wireless Signal Processing and Networking Lab Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9781467319058

Geometry-based optimal power control was proposed in [14] to transform the power-control problem to a new geometrical problem on the position relationship between a line and some points. This scheme provides a novel visual perspective and lowers the complexity of optimization. We generalize this scheme to a larger class of power-control optimization problems so as to maximize the network utility with multiple average and peak power constraints in wireless networks. To facilitate the handling of the geometrical model, we define a subset of geometrical models with specified characteristics, called a regular geometrical model, and derive the type of power-control problems eligible for the regular geometrical model. For such a type of problems, two strategies are proposed for the construction of the regular geometrical model. Utilizing geometrical properties, we propose a novel geometry-based optimization scheme for the general power-control problem. Its computational complexity is significantly lower than the conventional algorithms. We also provide a further discussion on irregular geometrical model cases. Finally, we provide two examples of deploying the proposed geometry-based power-control scheme. © 2012 IEEE.

关键词： Geometry

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Intrusion Detection for High-Speed Networks Based on Producing System

Intrusion Detection for High-Speed Networks Based on Produci...

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International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Ken Chen Fei Yu Cheng Xu Yan Liu School of Computer &Information Engineering Hunan Agricultural University Changsha China College of Computer & Communication Hunan University Changsha China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou China

This paper firstly analyzed the front pattern matching algorithm of intrusion detection system, and improved on the pattern matching algorithm. Having immediately analyzed Rete algorithm and aimed at its shortage, the paper introduced the FRete net algorithm on the basis of Rete net algorithm constituting an inference machine based on FRete matching algorithm, which could infer logical suspicious facts. Then the authors discussed and designed an intrusion detection system based on high-speed networks, and finally established a simulation and experiment platform to test the performance of IDS.

关键词： Intrusion detection High-speed networks Inference algorithms computer networks Data security Pattern matching Algorithm design and analysis computer architecture Protection Data mining

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Multiple SVM-RFE for Multi-class Gene Selection on DNA Microarray Data

Multiple SVM-RFE for Multi-class Gene Selection on DNA Micro...

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International Joint Conference on Neural Networks

作者： Li Zhang Xiaojuan Huang School of Computer Science and Technology & Collaborative Innovation Center of Novel Software Technology and Industrialization Soochow University Suzhou 215006 Jiangsu China School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou 215006 Jiangsu China

ISBN: (纸本)9781479919611

This paper presents a new multi-class gene selection and classification method based on multiple support vector machine recursive feature elimination (SVM-RFE). For a multi-class DNA microarray problem, we solve it as multiple binary classification problems. First, the one-versus-all method is used to decompose the multi-class task into multiple binary problems. Second, an SVM-RFE is adopted to select genes for each binary problem. Then, an SVM classifier is used to train the selected gene data for a binary problem. Finally, we combine the outputs of multiple SVM classifiers. Experimental results on three DNA Microarray datasets show that the proposed method achieves higher classification accuracy.

关键词： support vector machine feature selection recursive feature elimination multi-class classification DNA microarray data Support Vector Network Feature extraction multi-class DNA Microarrays Phyla Genes Binary Classification Methods

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Regional coverage monitoring planning technology for multi-UAV based on pruning PSO 2nd

Regional coverage monitoring planning technology for multi-U...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Wang, KaiXuan Shen, YuTing Zhang, FuQuan Liang, Zhuo Song, ZhiGuo Pan, YanPeng China Academy of Launch Vehicle Technology Beijing100076 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050000 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Digital Performance and Simulation Technology Lab School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030045845

In recent years, with the application and gradual popularization of UAV technology in many fields, the normalization of UAV aerial photography has become a common phenomenon. There are few studies on Multi-UAV regional coverage monitoring. In this study, PSO algorithm based on pruning can be used to solve optimization problem, the multi-agent technology solve the problem of UAV group for self-perception and decision-making, the UAV group, namely multi-agent particles, can automatically initialize the perception agent particle whether meet the requirements of the iteration, whether to need to be pruned, and decide to optimize the iteration or increase the agent number, Finally, simulation verification shows that the optimized algorithm can finish the work faster under the premise of guaranteeing the effect. © Springer Nature Switzerland AG 2019.

关键词： Unmanned aerial vehicles (UAV)

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