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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yang Yang Jialiang Chen Xianghan Zheng Ximeng Liu Wenzhong Guo Hairong Lv College of Mathematics and Computer Science Fuzhou University Fuzhou China Guangxi Key Laboratory of Cryptography and Information Security Guangnxi China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology Beijing National Research Center for Information Science and TechnologyDepartment of Automation Tsinghua University Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.

关键词： Blockchain Privacy Smart contracts Authentication Internet Remuneration Hash functions

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Distributed dimensionality reduction of industrial data based on clustering

Distributed dimensionality reduction of industrial data base...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yongyan Zhang Guo Xie Wenqing Wang Xiaofan Wang Fucai Qian Xulong Du Jinhua Du Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing China

Large amounts of data are produced in system operation, and how to extract effective information from these data has become an important research topic in the industrial application. Dimensionality reduction is a way to refine the data. Because of the low efficiency of the existing methods, these methods can't discover the internal structure of the data. Regarding these problems, a distributed method of dimensionality reduction based on clustering is proposed, which includes the following steps:(1) Clustering the data into some small classes according to the similarity between the data variables; (2) reducing the dimension of data in a small class after being clustered respectively; (3) merging the data after being reduced dimension; (4) classifying the data after being merged by support vector machine (SVM). The data in the simulation is the test data, and the results show that the methods proposed in this paper are better than the existing dimensionality reduction methods.

关键词： Dimensionality reduction Support vector machines Clustering algorithms Distributed databases Data mining Principal component analysis Classification algorithms

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Face Analysis for Coronary Heart Disease Diagnosis

Face Analysis for Coronary Heart Disease Diagnosis

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shisong Lin Xiaodong Zhuang Shiyun Huang Yahui Liu Linlin Shen Xinxue Liao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen P.R. China NHC Key Laboratory of Assisted Circulation Sun Yat-sen University Guangzhou China Shenzhen National Medical Science and Technology Co. Ltd. Shenzhen China First affiliated hospital of Sun Yat-sen University Guangzhou China

CHD (Coronary Heart Disease) is one of the leading causes of cardiovascular disease deaths. Invasive coronary arteriography is one of the widely used approaches to diagnose CHD. However, the time cost and expenses for most of the diagnosis methodologies are high and some patients are reluctant to do such a diagnosis. In this paper, we aim to develop a novel low cost method to predict CHD. As suggested by the cardiologists, there are substantial differences between the facial images of patients with CHD and those of healthy subjects. In this paper, we conduct an automatic analysis of the texture features extracted from eight ROIs (Regions of Interests) of the face images. Based on the texture features, random forest and decision tree are used to predict whether the subject has a CHD, or not. The experimental results on a set of 1528 face images collected from 309 subjects suggest that, our approach achieved a promising, i.e. 72.73%, prediction accuracy.

关键词： Feature extraction Faces Decision trees Ear Heart Random forests Diseases

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Tongue image segmentation via color decomposition and thresholding

Tongue image segmentation via color decomposition and thresh...

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作者： Li, Zuoyong Yu, Zhaochai Liu, Weixia Xu, Yong Zhang, Daoqiang Cheng, Yong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China The Fujian College's Research Base of Humanities and Social Science for Internet Innovation Research Center Minjiang University Fuzhou350121 China Straits Institute Minjiang University Fuzhou350121 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Communication Engineering Nanjing Institute of Technology Nanjing211167 China

Tongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method. © 2018 John Wiley & Sons, Ltd.

关键词： Image segmentation

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Restricted linearized augmented lagrangian method for Euler's elastica model

arXiv

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arXiv 2019年

作者： Zhang, Yinghui Deng, Xiaojuan Zhang, Jun Li, Hongwei School of Mathematical Sciences Capital Normal University Beijing100048 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi330099 China

Euler's elastica model has been extensively studied and applied to image processing tasks. However, due to the high nonlinearity and nonconvexity of the involved curvature term, conventional algorithms suffer from slow convergence and high computational cost. Various fast algorithms have been proposed, among which, the augmented Lagrangian based ones are very popular in the community. However, parameter tuning might be very challenging for these methods. In this paper, a simple cutting-off strategy is introduced into the augmented Lagrangian based algorithms for minimizing the Euler's elastica energy, which leads to easy parameter tuning and fast convergence. The cutting-off strategy is based on an observation of inconsistency inside the augmented Lagrangian based algorithms. When the weighting parameter of the curvature term goes to zero, the energy functional boils down to the ROF model. So, a natural requirement is that its augmented Lagrangian based algorithms should also approach the augmented Lagrangian based algorithms formulated directly for solving the ROF model from the very beginning. Unfortunately, this is not the case for certain existing augmented Lagrangian based algorithms. The proposed cutting-off strategy helps to decouple the tricky dependence between the auxiliary splitting variables, so as to remove the observed inconsistency. Numerical experiments suggest that the proposed algorithm enjoys easier parameter-tuning, faster convergence and even higher quality of image restorations. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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A Recurrent Gaussian Process Regression Model with Composite Kernel for Industrial Process Quality Prediction

A Recurrent Gaussian Process Regression Model with Composite...

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International Conference on Networking and Network Applications (NaNA)

作者： Yanping Chen Cong Gao Weihui Yang Zhongmin Wang Zhong Yu Hong Xia School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

With the blooming development of Industry 4.0, the management of industrial processes has drawn a lot of research attention. We employed the Gaussian process regression model to predict the key indicators of an industrial process. To facilitate the description of the data characteristics in an industrial process, the kernel function is reconstructed with respect to different kinds of data. We propose a novel structure based on the Gaussian process regression model which incorporates the previously predicted values into the input data of subsequent prediction. A series of contrastive experiments are conducted on the Tennessee Eastman process simulator. Experimental results show that the proposed method possesses a higher prediction accuracy than several notable prediction methods.

关键词： Ground penetrating radar Kernel Predictive models Gaussian processes Data models Biological neural networks Training

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A Survey of Multimodal Fusion for Identity Verification

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Journal of Physics: Conference Series 2020年第1期1607卷

作者： Yongmin Li Guanyu Li Pengqi Li Sixuan Li Xinyu Yuan Ding Liu Xuebin Yang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou Gansu 730000 China Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Lanzhou Gansu 730000 China

With the rapid development of computer and network technology, the digitization and invisibility of people's identities have become an important feature of today's highly informationized society. This article introduces the knowledge of multimodal fusion, and lists some commonly used multimodal fusion techniques in the field of identity verification, and puts forward some prospects for the future multimodal fusion identity verification.

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Mutual authentication and key distribution protocol based on the Trust-Third-Party in mobile RFID environment 13

Mutual authentication and key distribution protocol based on...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Fangfang, Xu Zhao, Yang Bo, Yang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430065 China College of Science Wuhan University of Science and Technology Wuhan Hubei430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei430065 China

ISBN: (纸本)9781538637579

With the popularity of intelligent terminals, the era that the mobile RFID be extensively used is coming. RFID tags will be present in every aspect of our lives, to help intelligent device recognize and sense the external environment, timely and intelligent execute some commands, so that our lives more convenient. However, these intelligent devices are vulnerable to various adversaries' attacks. To solve these problems, we propose a new simplified cryptographic protocol for mobile RFID environment. The protocol uses a Trust-Third-Party (TTP) to generate and distribute the secret key for authorizing those RFID tags and readers who want to access the database servers, to ensure the security and scalability of the entire system. What's more, the use of XOR algorithm, which has high computational efficiency, guarantees the computation efficiency. Nonetheless, the simplified scheme carries obvious risks from the key disclosure attack which caused by the malicious members inside this system. In order to improve the simplified protocol's deficiencies, we then proposed an enhanced one, which based on the more secure public key cryptography system, to ensure the security of the system under several common attacks. The paper also discusses the two schemes can satisfy the safety requirements when intelligent devices play different roles. Schemes described in this paper may be applicable to some practical applications for it quickly achieves authentication and access authorization as well as the superior flexibility and security. © 2018 IEEE.

关键词： Public key cryptography

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Model Predictive Control for Human Following Rehabilitation Robot

Model Predictive Control for Human Following Rehabilitation ...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Shihao Yan Jing Tao Jian Huang Anshun Xue the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ministry of Civil Affairs of the People's Republic of China National Research Center for Rehabilitation Technical Aids Beijing China the Beijing Advanced Innovation Center of Intelligent Robots and Systems Beijing China

A new type of rehabilitation robot that can follow the user side-by-side is designed for aiding the elderly and disabled. The Human Following Rehabilitation Robot (HFRR) consists of a metal armrest, a set of sensors and a differential mobile base. By predicting the user's speed in the future through the user's current walking speed and the preset path, the problem of human following side-by-side is converted to a problem of keeping a certain lateral distance from the predicted trajectory. Linearizing the tracking error model, the nonholonomic mobile robot (NMR) can maintain a certain lateral distance from the trajectory by using Model Predictive Control (MPC) and then follow the user side-by-side. The simulation and experimental results show the proposed controller can obtain good performance in this problem.

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Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Jia Chen Zihan Ni Nong Sang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper presents a model named Multi-Scale YOLOv2 (MS-YOLOv2) for hand detection in complex scenes. The proposed MS-YOLOv2 is implemented by introducing three modules to YOLOv2, including a Multi-Scale Feature Refinement Module to acquire fine-grained features, a Channel Importance Evaluation Module to recalibrate feature channels and a Hard Example Punishment Module to get rid of hand interference areas. Experiment results show that the proposed MS-YOLOv2 makes much performance improvement to YOLOv2, but with little computational complexity gain. On our dataset, the proposed MS-YOLOv2 can achieve 98.2% of AP and 97.9% of AR. Moreover, on the VIVA challenge, the proposed MS-YOLOv2 achieves AP/AR of 85.1%/45.8% at Level-1 and 80.1%/45.9% at Level-2.

关键词： Feature extraction Detectors Object detection Skin Color Gesture recognition

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