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A Multi-homed VPN Architecture Based on Extended SOCKS+TLS Protocols

Journal of Shanghai Jiaotong university(Science) 2005年第3期10卷 250-255页

作者：张宝良胡汉平陈翔 State Education Ministry Key Lab. of Image Information & Intelligent Control In st. of Pattern Recognition & Artificial Intelligence Huazhong Univ. of Science & Technology Wuhan 430074 China

A multi-homed VPN architecture based on extended SOCKSv5 and TLS was proposed. The architecture employs a dynamic connection mechanism for multiple proxies in the end system,i n which the security-demanded transmission connections can switch smoothly among the multiple proxies by maint aining a coherent connection *** mechanism is transparent to application programs and can support th e building of *** the cooperation of some other security components,the mechanism guarantees the reso urce availab.lity and reliability of the end system against some attacks to the specific ports or hosts.

关键词： multi-homed SOCKSv5 security connection context virtual private network (VPN)

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Knowledge-based segmentation: Using simultaneous shape priori and histogram information to segment brain structures

Knowledge-based segmentation: Using simultaneous shape prior...

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Seventh IASTED International Conference on Signal and image processing, SIP 2005

作者： Batmanghelich, Nematollah Soltanian-Zadeh, Hamid Araabi, Babak Najaar Elec. and Comp. Eng. Dept. Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

In this paper, we propose a new method integrating both a priori shape information and our knowledge about gray levels of the desired structure. We describe an approach inspired from tracking to deal with non-uniform gray levels. We define focus region to consider both interior and exterior of the desired object. We utilize signed distance function to consider shape information. Embedding a priori shape and gray level knowledge in a statistical platform, we use correlation between changes in shape and histogram to improve the results. Our method successfully segments Thalamus and other brain structures.

关键词： image segmentation

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Adversarial semantic data augmentation for human pose estimation

arXiv

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arXiv 2020年

作者： Bin, Yanrui Cao, Xuan Chen, Xinya Ge, Yanhao Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab China

Human pose estimation is the task of localizing body keypoints from still images. The state-of-the-art methods suffer from insufficient examples of challenging cases such as symmetric appearance, heavy occlusion and nearby person. To enlarge the amounts of challenging cases, previous methods augmented images by cropping and pasting image patches with weak semantics, which leads to unrealistic appearance and limited diversity. We instead propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity. Furthermore, we propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamiclly predict tailored pasting configuration. Given off-the-shelf pose estimation network as discriminator, the generator seeks the most confusing transformation to increase the loss of the discriminator while the discriminator takes the generated sample as input and learns from it. The whole pipeline is optimized in an adversarial manner. State-of-the-art results are achieved on challenging benchmarks. The code has been publicly availab.e at https://***/Binyr/ASDA. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Noise suppression of fMRI time-series in wavelet domain

Noise suppression of fMRI time-series in wavelet domain

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Seventh IASTED International Conference on Signal and image processing, SIP 2005

作者： Jahanian, Hesamoddin Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam Ali Elec. and Comp. Eng. Dept. Control and Intelligent Processing Center of Excellence University of Tehran Tehran 14399 Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

Because of poor signal-to-noise ratio (SNR) of the fMRI time series and confounding effects, the results of fMRI analysis are often unsatisfactory. Existence of significant noise and artifacts in fMRI time-series as well as their unknown structure, complicates the problem of activation detection in the time domain. This makes the fMRI noise suppression a challenging problem. Based on some assumptions, different parametric denoising methods such as wavelet based denoising methods have been introduced in the literature. But these assumptions may not necessarily hold for the fMRI data. To remedy this problem, using randomization analysis, we propose a novel wavelet-based denoising method for fMRI analysis. The proposed denoising method is employed to build a feature space for fMRI cluster analysis and its efficiency is shown using simulated and experimental datasets.

关键词： Noise abatement

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： image segmentation

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Statistical inference in fuzzy cluster analysis of functional MRI

Statistical inference in fuzzy cluster analysis of functiona...

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Seventh IASTED International Conference on Signal and image processing, SIP 2005

作者： Jahanian, Hesamoddin Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam Ali Control and Intelligent Processing Center of Excellence Elec. and Comp. Eng. Dept. University of Tehran Tehran 14399 Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

Fuzzy cluster analysis (FCA) of functional magnetic resonance images, suffers from some drawbacks such as a priori definition of number of clusters and unidentified statistical significance of results. Here, we introduce a method to control the rate of false positive detection in FCA which gives a meaningful statistical significance to the results. Using this method, we also derive the optimal number of clusters. In this study by measuring the rate of false alarm detection while analyzing 6 experimental datasets, we evaluate the introduced method for making statistical inference.

关键词： Fuzzy control

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An Approach for National-Scale Accessibility Assessment and Vulnerability Analysis of Integrated Multimodal Transportation Systems

SSRN

引用

SSRN 2021年

作者： Zhang Ouyang, Min Sun, Wenjing Hong, Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

A series of ambitious accessibility-oriented policies have been launched in the world, which center around improving accessibility of the integrated multimodal transportation systems including road, airline, and high-speed rail. The multimodal accessibility assessment is one of the "basic" analyses for the design and implementation of these policies. This paper models the integrated road, high-speed rail, airline, and walking system (IRHAWS) as a four-layer network with the consideration of their interdependencies. An algorithm is then developed to accurately assess accessibility based on the four-layer network with the consideration of daily schedule of high-speed trains and flights, where the accessibility between two nodes is defined as the minimum travel time given a departure time. The proposed approach enables the time-varied accessibility assessment of both off-network and on-network areas at a national or global scale, and is applied to map the multimodal accessibility to 363 city centers in China and analyze the optimal travel modes for on-network travels. Based on the proposed accessibility assessment approach, this paper also investigates the vulnerability of the Chinese integrated multimodal transportation system under the extreme storm event recently occurred in Zhengzhou (July 2021). The findings in this work provide insightful suggestions to plan the road, high-speed rail, and airline systems as a whole. © 2021, The Authors. All rights reserved.

关键词： Railroad transportation

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Fast algorithm of discrete Walsta-Haar transformation

Fast algorithm of discrete Walsta-Haar transformation

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International Conference on Space Information Technology

作者： Lu, Li Shi, Baochang Tian, Jinwen Liu, Jian State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Department of Mathematics Huazhong University of Science and Technology Wuhan Hubei 430074 China

Walsh-Haar function system that was first intruoduced by us is a new kind of function systems, and has a good global/local property. This function system is called Walsh ordering function system since its generation kernel functions belong to Walsh ordering Walsh function system. We worked out a recursive property of the matrix WHKRm+1 corresponding to the first KR m+1 Walsh-Haar functions in Walsh-Haar function system, and we also proved that Walsh-Haar function system is perfect and orthogonal similar to Walsh function system and Haar function system. Thus, discrete Walsh-Haar transformation (DW-HT) is an orthogonal transformation that can be widely used in signal processing. In this paper, using the recursive property of the matrix WHKRm+1 and the fast algorithm of discrete Walsh transformation (DWT) in Walsh ordering, we have designed a fast algorithm of Walsh ordering DW-HT based on the bisection technique. The idea and method used in this paper can be used for designing fast algorithms of other ordering DW-HTs and other discrete orthogonal transformations.

关键词： Mathematical transformations

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Impact of Rail Transit Systems on Transport Accessibility under Road Congestion

Impact of Rail Transit Systems on Transport Accessibility un...

引用

2022世界交通运输大会(WTC2022)

作者： Bo Zhan Hui Zhang Min Ouyang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Lab.for Image Processing and Intelligent Control Huazhong University of Science and TechnologyMinistry of Education

Investment inrail transit systems is widely advocated in large,high-density cities on the basis of its purported role in reducing road congestion,but the impact of rail transit systems on congestion remains a *** paper provides a method for evaluating the impact of rail transit systems on road congestion in terms of transport accessibility,and applies the method to 43 Chinese cities that have opened rail transit *** show that the rail transit systems can reduce road congestion in most cities,but their performance largely depends on the congestion speed and the rail transit network ***,the structural properties analysis of rail transit networks in two typical cities(Dalian and Changchun)indicates that rail transit lines with longer length,smaller detour factor,and larger population coverage rate tend to perform better in congestion *** findings in this paper provide guidance for city authorities regarding current and future investment in rail transit systems.

关键词： Rail transit system Congestion reduction Accessibility Rail transit network size Congestion speed Structural properties

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SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis

arXiv

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arXiv 2022年

作者： Li, Xingyi Hong, Chaoyi Wang, Yiran Cao, Zhiguo Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China S-Lab Nanyang Technological University Singapore

We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is availab.e at: https://***/xingyi-li/SymmNeRF. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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