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arXiv 2020年

作者： Tang, Wenming Gong, Yuanhao Liu, Kanglin Liu, Jun Pan, Wei Liu, Bozhi Qiu, Guoping College of Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Mechanical & Automotive Engineering South China University of Technology Department of Research and Development OPT Machine Vision Tech Co. Ltd Jinsheng Road Changan Dongguan Guangdong523860 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method, which states that for any point on a developable surface there must be another point that lives on its tangent plane. Then, based on this theoretical insight, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and is 20 times faster than the classical standard Gaussian curvature flow method. We present extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, geometric feature preserving smoothing and mesh noise removal. This method can be used in a large range of applications that involve Gaussian curvature. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

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Segmenting medical MRI via recurrent decoding cell

arXiv

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arXiv 2019年

作者： Wen, Ying Xie, Kai He, Lianghua School of Communication and Electronic Engineering Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science and Technology Tongji University Shanghai

The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. An encoder-decoder network, named Convolutional Recurrent Decoding Network (CRDN), is also proposed based on RDC for segmenting multi-modality medical MRI. CRDN adopts CNN backbone to encode image features and decode them hierarchically through a chain of RDCs to obtain the final high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and HVSMR datasets demonstrate that the introduction of RDC effectively improves the segmentation accuracy as well as reduces the model size, and the proposed CRDN owns its robustness to image noise and intensity non-uniformity in medical MRI. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

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Design of Distributed Multi-Parameter Measurement Control System Based On CAN Bus 3

Design of Distributed Multi-Parameter Measurement Control Sy...

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2018 3rd International Conference on Insulating Materials, Material Application and Electrical Engineering, IMMAEE 2018

作者： Guo, Hong Hu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

This paper introduces a multi-parameter distributed measurement control system based on CAN bus network and uses C8051F040 MCU as measurement control node, and corresponding hardware and software design to realize simultaneous detection of multiple measurement nodes. This paper described the overall structure of the system, introduced the hardware design and system software design of the data acquisition and control processing module based on C8051F040 microcontroller, and verified the correctness and feasibility of the system design by experimental tests. © 2018 Published under licence by IOP Publishing Ltd.

关键词： Software design

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Lightweight Protection of User Identity Privacy Based on Zero-knowledge Proof

Lightweight Protection of User Identity Privacy Based on Zer...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhengwei Ren Xianye Zha Kai Zhang Jing Liu Heng Zhao College of Computer Science and Technology Wuhan University of Science and Technology and Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan Digital and Engineering Institute Wuhan China

A number of solutions have been proposed to tackle the user privacy-preserving issue. Most of existing schemes, however, focus on methodology and techniques from the perspective of data processing. In this paper, we propose a lightweight privacy-preserving scheme for user identity from the perspective of data user and applied cryptography. The basic idea is to break the association relationships between User identity and his behaviors and ensure that User can access data or services as usual while the real identity will not be revealed. To this end, an interactive zero-knowledge proof protocol of identity is executed between CSP and User. Besides, a trusted third-party is introduced to manage user information, help CSP to validate User identity and establish secure channel between CSP and User via random shared key. After passing identity validation, User can log into cloud platform as usual without changing existing business process using random temporary account and password generated by CSP and sent to User by the secure channel which can further obscure the association relationships between identity and behaviors. Formal security analysis and theoretic and experimental evaluations are conducted, showing that the proposal is efficient and practical.

关键词： Proposals Data privacy Privacy Protocols Password Authentication

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Spatial Pyramid Pooling based Convolutional Autoencoder Network for Loop Closure Detection

Spatial Pyramid Pooling based Convolutional Autoencoder Netw...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Rong Xiang Yuan Liu Qieshi Zhang Jun Cheng School of Computer Science and Information Security Guilin University of Electronic Technology GuangXi China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection are vulnerable to environmental variations as they mainly rely on hand-crafted features. The convolutional neural networks (ConvNets) can automatically learn feature representation from original image, and it is more robust to illumination changes. However, the ConvNets methods may fail when the viewpoint changes significantly due to it extract global features. In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the proposed method on several open datasets using precision-recall metric. The results show that our method is feasible for detecting loops and is more robust than state-of-the-art methods.

关键词： Feature extraction Convolution Lighting Visualization Training Simultaneous localization and mapping Network architecture

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Velocity Coordination of Multi-vehicle Systems via Distributed Neighbor Selection

Velocity Coordination of Multi-vehicle Systems via Distribut...

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International Conference on Intelligent Transportation

作者： Haibin Shao Lulu Pan Yugeng Xi Dewei Li Shu Lin Department of Automation and the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538670255

In this paper, we examine a data-driven control approach to coordination of multi-vehicle systems based on the distributed neighbor selection. The core of the proposed approach is to utilize a recently developed metric called relative tempo for distributed coordination which is computable from local measurable data for each vehicle in the network. The relative tempo between each agent and its neighbors is shown to be closely related to the directed spanning tree of the underlying network in a quantitative manner. Based on this fact, a local neighbor selection protocol is subsequently provided to construct the global refined communication structure which can both maintain the connectivity and increase the efficiency of the multi-vehicle coordination. The numerical study is finally provided to demonstrate the effectiveness of our approach.

关键词： Protocols Vehicle dynamics Control systems Topology Intelligent transportation systems Measurement Heuristic algorithms

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Performance analysis of mobile video networks 11

Performance analysis of mobile video networks

引用

11th EAI International Conference on Mobile Multimedia Communications, MOBIMEDIA 2018

作者： Xu, Lingwei Tao, Ye Li, Ye Wang, Jingjing Cao, Conghui Tian, Changfeng Department of Information Science and Technology Qingdao University of Science and Technology Qingdao266061 China Shandong Provincial Key Laboratory of Computer Networks Jinan250101 China College of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (纸本)9781631901645

With the tremendous growth of mobile videos, mobile video communication is becoming increasingly important to influence users browsing and searching *** this paper, we present the approximate average symbol error probability (ASEP) and outage probability (OP) performance for mobile video networks. We derive the exact closed-form OP and ASEP expressions for amplify-and-forward (AF) relaying. The analytical results match perfectly with the simulation results. Further, we evaluate the impact of power allocation on OP performance. © 2018 ACM.

关键词： Probability

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Industrial Air Pollution Prediction Using Deep Neural Network 1

引用

13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Pengfei, Yu Juanjuan, He Xiaoming, Liu Kai, Zhang School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In this paper, a deep neural network model is proposed to predict industrial air pollution, such as PM2.5 and PM10. The deep neural network model contains 9 hidden layers, each layer contains 45 neurons. The output of the hidden layer neurons is calculated using the ReLU activation function, which can effectively reduce the gradient elimination effect of the deep neural network. Twelve air pollutant indicators from industrial factories are collected as the input data, such as CO, NO2, O3, and SO2. About 180,000 real industrial air pollution data from Wuhan City are used to train and test the DNN model. Furthermore, the performance of our approach is compared with the SVM and Artificial neural network methods, and the comparison result shows that our algorithm is accurate and competitive with higher prediction accuracy and generalization ability. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Efficient trajectory data privacy protection scheme based on laplace's differential privacy

Efficient trajectory data privacy protection scheme based on...

引用

作者： Gu, Ke Yang, Lihao Liu, Yongzhi Yin, Bo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Central South University Changsha410083 China

Now many applications of trajectory (location) data have facilitated people's daily life. However, publishing trajectory data may divulge individual sensitive information so as to influence people's normal life. On the other hand, if we cannot mine and share trajectory data information, trajectory data will lose its value to serve our society. Currently, because the records of trajectory data are discrete in database, some existing privacy protection schemes are difficult to protect trajectory data. In this paper, we propose a trajectory data privacy protection scheme based on Laplace's differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user's trajectory data;secondly, the algorithm builds the polygons according to the protected points and the adjacent and high frequent accessed points selected from the accessed point database, then the algorithm calculates the polygon centroids;finally, the noises are added to the polygon centroids by the Laplace's differential privacy method, and the new polygon centroids are used to replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher. © 2018 Slovene Society Informatika. All rights reserved.

关键词： Trajectories

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An efficient interval uncertainty optimization approach based on quasi-sparse response surface

arXiv

引用

arXiv 2019年

作者： Wang, Kefeng Li, Pu Zhang, Yanfeng Huang, Yunbao Provincial Key Laboratory of Computer Integrated Manufacturing Guangdong University of Technology Guangzhou Guangdong China School of Physics and Electrical Engineering Shaoguang University Shaoguan Guangdong China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China

The structure uncertainty optimization problem is usually treated as double-loop optimization process, which is computation-intensive. In this paper, an efficient interval uncertainty optimization approach based on Quasi-sparse response surface (QSRS) is proposed for structure uncertainty optimization. In which, 1) with ll1 norm and ll2 norm penalty method, a few appropriate basis functions are selected form a large number of basis functions to construct QSRS accurately and only a few sampling points is required, 2) as the orthogonal chebyshev polynomials is employed as QSRS basis functions, the local uncertainty can be evaluated by the combination of QSRS and interval arithmetic. Hence, the inner optimization process is eliminated. One mathematical problem and one engineering problem are used to validate the efficiency of the proposed approach. The results show that only 25% sampling points is needed than recently published approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Surface properties

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