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< Page ,Total 215 >
Robust point cloud registration based on both hard and soft assignments EI Scopus
期刊论文 | 2019 , 110 , 202-208 | Optics and Laser Technology
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Abstract :

For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments. Given two initially posed clouds, it firstly establishes the forward correspondence for each point in the data shape and calculates the value of a binary variable, which indicates whether this point correspondence is located in the overlapping areas or not. Then, it establishes the bilateral correspondence and computes bidirectional distances for each point in the overlapping areas. Based on the ratio of bidirectional distances, the exponential function is selected and utilized to calculate the probability value, which indicates the reliability of the point correspondence. Subsequently, both the values of hard and soft assignments are embedded into the proposed objective function for registration of partially overlapping point clouds, which then be solved by the proposed variant of ICP algorithm to obtain the optimal rigid transformation. The proposed approach can achieve good registration of point clouds, even when their overlap percentage is low. Experimental results tested on public datasets illustrate its superiority over previous approaches on accuracy and robustness. © 2018 Elsevier Ltd

Keyword :

Bidirectional distances Hard assignment Overlap percentage Point cloud registration Soft assignments

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GB/T 7714 Zhu, Jihua , Jin, Congcong , Jiang, Zutao et al. Robust point cloud registration based on both hard and soft assignments [J]. | Optics and Laser Technology , 2019 , 110 : 202-208 .
MLA Zhu, Jihua et al. "Robust point cloud registration based on both hard and soft assignments" . | Optics and Laser Technology 110 (2019) : 202-208 .
APA Zhu, Jihua , Jin, Congcong , Jiang, Zutao , Xu, Siyu , Xu, Minmin , Pang, Shanmin . Robust point cloud registration based on both hard and soft assignments . | Optics and Laser Technology , 2019 , 110 , 202-208 .
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Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems. EI PubMed Scopus
期刊论文 | 2018 , 80 , 195-202 | ISA transactions
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Abstract :

For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are useful methods to solve the state estimation problems, and both can obtain good performance in Gaussian noises. However, their performances often degrade significantly in the face of non-Gaussian noises, particularly when the measurements are contaminated by some heavy-tailed impulsive noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance instead of traditional minimum mean square error (MMSE) criterion, a new square-root nonlinear filter is proposed in this study, named as the maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filter not only retains the advantage of square-root cubature Kalman filter (SCKF), but also exhibits robust performance against heavy-tailed non-Gaussian noises. A judgment condition that avoids numerical problem is also given. The results of two illustrative examples, especially the SINS/GPS integrated systems, demonstrate the desirable performance of the proposed filter.

Keyword :

Square-root cubature Kalman filter (SCKF) Maximum correntropy criterion (MCC) SINS/GPS integrated systems

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GB/T 7714 Liu Xi , Qu Hua , Zhao Jihong et al. Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems. [J]. | ISA transactions , 2018 , 80 : 195-202 .
MLA Liu Xi et al. "Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems." . | ISA transactions 80 (2018) : 195-202 .
APA Liu Xi , Qu Hua , Zhao Jihong , Yue Pengcheng . Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems. . | ISA transactions , 2018 , 80 , 195-202 .
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PSNet: prostate segmentation on MRI based on a convolutional neural network. PubMed Scopus
期刊论文 | 2018 , 5 (2) , 021208 | Journal of medical imaging
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Abstract :

Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of [Formula: see text] as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.

Keyword :

deep learning magnetic resonance imaging convolutional neural network prostate segmentation

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GB/T 7714 Tian Zhiqiang , Liu Lizhi , Zhang Zhenfeng et al. PSNet: prostate segmentation on MRI based on a convolutional neural network. [J]. | Journal of medical imaging , 2018 , 5 (2) : 021208 .
MLA Tian Zhiqiang et al. "PSNet: prostate segmentation on MRI based on a convolutional neural network." . | Journal of medical imaging 5 . 2 (2018) : 021208 .
APA Tian Zhiqiang , Liu Lizhi , Zhang Zhenfeng , Fei Baowei . PSNet: prostate segmentation on MRI based on a convolutional neural network. . | Journal of medical imaging , 2018 , 5 (2) , 021208 .
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A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image SCIE
期刊论文 | 2018 , 8 (1) , 9-15 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
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Schizophrenia is a severe mental disorder associated with delicate aberrant cortical. Currently, many methods which have been proposed for identifying possible abnormalities of surface area or cortical thickness in the high-risk neonates, have made initial progress driven by the techniques for neonates brain Magnetic Resonance (MR) image processing. However, it is in the initial study of neonates high-risk schizophrenia prediction that their methods have few achieve satisfactory accuracy because they only focus on the cortical reconstructed surface characteristic. The local structural texture can contribute the classification mode for prediction, but little attention to cortical structured MR image based feature was paid. To this end, we present a novel framework that jointed surface feature from reconstructed surface with 3D Haar-like features from structured MR image by SCAD SVM. We used this hybrid feature to train the SVM mode for neonates high-risk schizophrenia prediction. In the experiment, our method shows significant performance advance with an improvement of 8.5% in the accuracy rate on a challenging neonates high-risk schizophrenia prediction application.

Keyword :

Hybrid Features Schizophrenia Neonates Prediction SCAD SVM

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GB/T 7714 Fan, Caili , Jiang, Peilin , Chen, Lei et al. A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image [J]. | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) : 9-15 .
MLA Fan, Caili et al. "A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image" . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 8 . 1 (2018) : 9-15 .
APA Fan, Caili , Jiang, Peilin , Chen, Lei , Wang, Fei , Yang, Haiwei . A Novel Prediction of High-Risk Schizophrenia in Neonates Using Reconstructed Surface and Local Structure in MR Image . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) , 9-15 .
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SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks EI SCIE Scopus
期刊论文 | 2018 , 13 (3) , 559-573 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
WoS CC Cited Count: 3
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Abstract :

Application layer distributed denial of service (DDoS) attacks have become a severe threat to the security of web servers. These attacks evade most intrusion prevention systems by sending numerous benign HTTP requests. Since most of these attacks are launched abruptly and severely, a fast intrusion prevention system is desirable to detect and mitigate these attacks as soon as possible. In this paper, we propose an effective defense system, named SkyShield, which leverages the sketch data structure to quickly detect and mitigate application layer DDoS attacks. First, we propose a novel calculation of the divergence between two sketches, which alleviates the impact of network dynamics and improves the detection accuracy. Second, we utilize the abnormal sketch to facilitate the identification of malicious hosts of an ongoing attack. This improves the efficiency of SkyShield by avoiding the reverse calculation of malicious hosts. We have developed a prototype of SkyShield and carefully evaluated its effectiveness using real attack data collected from a large-scale web cluster. The experimental results show that SkyShield can quickly reduce malicious requests, while posing a limited impact on normal users.

Keyword :

Application layer DDoS attacks intrusion prevention system sketch data structure

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GB/T 7714 Wang, Chenxu , Miu, Tony T. N. , Luo, Xiapu et al. SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2018 , 13 (3) : 559-573 .
MLA Wang, Chenxu et al. "SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13 . 3 (2018) : 559-573 .
APA Wang, Chenxu , Miu, Tony T. N. , Luo, Xiapu , Wang, Jinhe . SkyShield: A Sketch-Based Defense System Against Application Layer DDoS Attacks . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2018 , 13 (3) , 559-573 .
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Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network SCIE
期刊论文 | 2018 , 8 (1) , 50-54 | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
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Abstract :

Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology. The development of automatic medical image analysis of erythrocyte morphology has simplified the diagnosis of many diseases and combined a good reliability with high performance. However, in computational molecular biology, the elaboration of reliable and fully automated image analysis techniques for red blood cells imaging is still quite problematic. In this respect, quite lucrative is the watershed transformation concept, which can be utilized in image segmentation to generate partitions of the image corresponding to objects of interest. This approach is used in the current study, which presents a two-layer red blood cells detection framework, including a full convolutional neural network to extract the candidate cell regions with about 97% accuracy rate, and a novel labeled watershed method based on the morphology label and conditional skeleton extraction for improving the overlapped cells' segmentation. It is shown that the proposed method successfully extracts the red blood candidate regions, including single and overlapped ones. A comparative analysis of the proposed detection method and several other general methods was performed. The experimental results obtained indicate that the proposed framework is robust and accurate, with the cell extracting ratio over 87.76% accuracy.

Keyword :

Full Convolutional Neural Network Red Blood Cells Cell Segmentation Conditional Skeleton Watershed Algorithm

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GB/T 7714 Jiang, Peilin , Zhang, Xuetao , Wang, Fei . Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network [J]. | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) : 50-54 .
MLA Jiang, Peilin et al. "Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network" . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 8 . 1 (2018) : 50-54 .
APA Jiang, Peilin , Zhang, Xuetao , Wang, Fei . Red Blood Cell Detection by the Improved Two-Layer Watershed Segmentation Method with a Full Convolutional Neural Network . | JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS , 2018 , 8 (1) , 50-54 .
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FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC EI SCIE Scopus
期刊论文 | 2018 , 34 (1) , 15-25 | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS
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Recently, system-on-chips (SoCs) are increasingly employed in reliable applications for their high-performance and high-densities. Moreover, the structure shrinking of SoC leads to its proneness to radiation-induced soft errors. This paper presents a fine-grained fault injection framework for SoC (FFI4SoC) to assess the reliability of SoC against soft errors. FFI4SoC facilitates fault injection for SoC by defining the primary components and rules that are required by fine-grained fault injection. Furthermore, based on FFI4SoC, we develop a fine-grained fault injection tool named SSIFFI for bare-metal MicroZed. The design of SSIFFI is presented in order to illustrate the application of FFI4SoC. Finally, SSIFFI is engaged in simulated fault injection experiments to explore the cause of single event functional interrupts (SEFIs) and to validate functional properties of FFI4SoC. The experimental results disclose detailed reasons for SEFI and prove that FFI4SoC can be employed to assess reliability of SoC well with the merit of fine-grained injection.

Keyword :

Fault injection Reliability assessment SEU Systemon Chip (SoC) Fault tolerance assessment

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GB/T 7714 Du, Xiaozhi , Luo, Dongyang , Shi, Kailun et al. FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC [J]. | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2018 , 34 (1) : 15-25 .
MLA Du, Xiaozhi et al. "FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC" . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 34 . 1 (2018) : 15-25 .
APA Du, Xiaozhi , Luo, Dongyang , Shi, Kailun , He, Chaohui , Liu, Shuhuan . FFI4SoC: a Fine-Grained Fault Injection Framework for Assessing Reliability against Soft Error in SoC . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2018 , 34 (1) , 15-25 .
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Capturing Car-Following Behaviors by Deep Learning EI SCIE Scopus
期刊论文 | 2018 , 19 (3) , 910-920 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
WoS CC Cited Count: 3
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Abstract :

In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers' actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

Keyword :

recurrent neural network (RNN) deep learning Microscopic car-following model gated recurrent unit (GRU) neural networks

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GB/T 7714 Wang, Xiao , Jiang, Rui , Li, Li et al. Capturing Car-Following Behaviors by Deep Learning [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (3) : 910-920 .
MLA Wang, Xiao et al. "Capturing Car-Following Behaviors by Deep Learning" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 19 . 3 (2018) : 910-920 .
APA Wang, Xiao , Jiang, Rui , Li, Li , Lin, Yilun , Zheng, Xinhu , Wang, Fei-Yue . Capturing Car-Following Behaviors by Deep Learning . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2018 , 19 (3) , 910-920 .
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Large-scale vocabularies with local graph diffusion and mode seeking EI SCIE Scopus
期刊论文 | 2018 , 63 , 1-8 | SIGNAL PROCESSING-IMAGE COMMUNICATION
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In this work, we propose a large-scale clustering method that captures the intrinsic manifold structure of local features by graph diffusion for image retrieval. The proposed method is a mode seeking like algorithm, and it finds the mode of each data point with the defined stochastic matrix resulted by a same local graph diffusion process. While mode seeking algorithms are normally costly, our method is efficient to generate large-scale vocabularies as it is not iterative, and the major computational steps ere done in parallel. Furthermore, unlike other clustering methods, such as k-means and spectral clustering, the proposed clustering algorithm does not need to empirically appoint the number of clusters beforehand, and its time complexity is independent on the number of clusters. Experimental results on standard image retrieval datasets demonstrate that the proposed method compaies favorably to previous large-scale clustering methods. (C) 2018 Elsevier B.V. All rights reserved.

Keyword :

Image retrieval Large-scale clustering Local graph diffusion Mode-seeking

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GB/T 7714 Pang, Shanmin , Xue, Jianru , Gao, Zhanning et al. Large-scale vocabularies with local graph diffusion and mode seeking [J]. | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2018 , 63 : 1-8 .
MLA Pang, Shanmin et al. "Large-scale vocabularies with local graph diffusion and mode seeking" . | SIGNAL PROCESSING-IMAGE COMMUNICATION 63 (2018) : 1-8 .
APA Pang, Shanmin , Xue, Jianru , Gao, Zhanning , Zheng, Lihong , Zhu, Li . Large-scale vocabularies with local graph diffusion and mode seeking . | SIGNAL PROCESSING-IMAGE COMMUNICATION , 2018 , 63 , 1-8 .
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Towards traffic matrix prediction with LSTM recurrent neural networks EI SCIE Scopus
期刊论文 | 2018 , 54 (9) , 566-567 | ELECTRONICS LETTERS
WoS CC Cited Count: 1
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This Letter investigates traffic matrix (TM) prediction that is widely used in various network management tasks. To fastly and accurately attain timely TM estimation in large-scale networks, the authors propose a deep architecture based on LSTM recurrent neural networks (RNNs) to model the spatio-temporal features of network traffic and then propose a novel TM prediction approach based on deep LSTM RNNs and a linear regression model. By training and validating it on real-world data from Abilene network, the authors show that the proposed TM prediction approach can achieve state-of-the-art TM prediction performance.

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GB/T 7714 Zhao, Jianlong , Qu, Hua , Zhao, Jihong et al. Towards traffic matrix prediction with LSTM recurrent neural networks [J]. | ELECTRONICS LETTERS , 2018 , 54 (9) : 566-567 .
MLA Zhao, Jianlong et al. "Towards traffic matrix prediction with LSTM recurrent neural networks" . | ELECTRONICS LETTERS 54 . 9 (2018) : 566-567 .
APA Zhao, Jianlong , Qu, Hua , Zhao, Jihong , Jiang, Dingchao . Towards traffic matrix prediction with LSTM recurrent neural networks . | ELECTRONICS LETTERS , 2018 , 54 (9) , 566-567 .
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