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学者姓名:辛景民

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Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator EI SCIE
期刊论文 | 2019 , 67 (2) , 415-430 | IEEE TRANSACTIONS ON SIGNAL PROCESSING
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Abstract :

This paper investigates the localization of multiple near-field narrowband sources with a symmetric uniform linear array, and a new linear prediction approach based on the truncated singular value decomposition (LPATS) is proposed by taking an advantage of the anti-diagonal elements of the noiseless array covariance matrix. However, when the number of array snapshots is not sufficiently large enough, the "saturation behavior" is usually encountered in most of the existing localization methods for the near-field sources, where the estimation errors of the estimated directions-of-arrival (DOAs) and ranges cannot decrease with the signal-to-noise ratio. In this paper, an oblique projection based alternating iterative scheme is presented to improve the accuracy of the estimated location parameters. Furthermore, the statistical analysis of the proposed LPATS is studied, and the asymptotic mean-square-error expressions of the estimation errors are derived for the DOAs and ranges. The effectiveness and the theoretical analysis of the proposed LPATS are verified through numerical examples, and the simulation results show that the LPATS provides good estimation performance for both the DOAs and ranges compared to some existing methods.

Keyword :

source localization oblique projection near-field uniform linear array Linear prediction

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GB/T 7714 Zuo, Weiliang , Xin, Jingmin , Liu, Wenyi et al. Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator [J]. | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2019 , 67 (2) : 415-430 .
MLA Zuo, Weiliang et al. "Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator" . | IEEE TRANSACTIONS ON SIGNAL PROCESSING 67 . 2 (2019) : 415-430 .
APA Zuo, Weiliang , Xin, Jingmin , Liu, Wenyi , Zheng, Nanning , Ohmori, Hiromitsu , Sano, Akira . Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator . | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2019 , 67 (2) , 415-430 .
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A Novel Method of Detecting Vulnerable Coronary Plaques in OCT Images with Deep Learning CPCI-S SCIE
会议论文 | 2018 , 72 (16) , C97-C97 | 29th Great Wall International Congress of Cardiology (GW-ICC), China-Heart-Society, and Beijing-Society-of-Cardiology
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GB/T 7714 Deng, Yangyang , Liu, Sijie , Shi, Peiwen et al. A Novel Method of Detecting Vulnerable Coronary Plaques in OCT Images with Deep Learning [C] . 2018 : C97-C97 .
MLA Deng, Yangyang et al. "A Novel Method of Detecting Vulnerable Coronary Plaques in OCT Images with Deep Learning" . (2018) : C97-C97 .
APA Deng, Yangyang , Liu, Sijie , Shi, Peiwen , Xin, Jingmin , Zheng, Nanning , Wu, Yue et al. A Novel Method of Detecting Vulnerable Coronary Plaques in OCT Images with Deep Learning . (2018) : C97-C97 .
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Point-wise saliency detection on 3D point clouds via covariance descriptors EI SCIE Scopus
期刊论文 | 2018 , 34 (10) , 1325-1338 | VISUAL COMPUTER
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Abstract :

In the human visual system, visual saliency perception is a rapid pre-attention processing mechanism, which can benefit myriad visual tasks, such as segmentation, localization, and detection. While most research is devoted to saliency detection on 2D images and 3D meshes, little work has been performed for efficient saliency detection on 3D point clouds. In this paper, we present a novel point clouds saliency detection method by employing principal component analysis (PCA) in a sigma-set feature space. In this method, we first construct local shape descriptors based on covariance matrices for saliency detection, considering that covariance matrices can naturally model nonlinear correlations of different low-level compact and rotational-invariant features. Secondly, we transform these covariance matrices to vector descriptors in Euclidean vector space by applying the sigma-point technique, which keeps the inner statistics of regions of 3D point clouds. Based on our informative descriptors, PCA is employed in the descriptor space for identifying saliency patterns in a point cloud. Our method shows its advantages of being structure-sensitive, capturing geometry information and computationally efficient. Experimental results demonstrate that our method achieves good performance without using any topological information.

Keyword :

Point clouds saliency Keypoint detection Smoothing Principal component analysis Sigma sets Covariance descriptor

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GB/T 7714 Guo, Yu , Wang, Fei , Xin, Jingmin . Point-wise saliency detection on 3D point clouds via covariance descriptors [J]. | VISUAL COMPUTER , 2018 , 34 (10) : 1325-1338 .
MLA Guo, Yu et al. "Point-wise saliency detection on 3D point clouds via covariance descriptors" . | VISUAL COMPUTER 34 . 10 (2018) : 1325-1338 .
APA Guo, Yu , Wang, Fei , Xin, Jingmin . Point-wise saliency detection on 3D point clouds via covariance descriptors . | VISUAL COMPUTER , 2018 , 34 (10) , 1325-1338 .
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BeiDou Satellites Multi-GNSS Precise Orbit Determination with Ambiguity Fixed EI Scopus CSCD PKU
期刊论文 | 2018 , 47 (3) , 341-347 | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica
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Abstract :

Using the global distributed IGS and Multi-GNSS experiment(MGEX) data, a method of multi-GNSS integrated double-difference precise orbit determination(POD) of BeiDou navigation satellites is studied. Then a specific double-difference ambiguity fixing strategy for BDS is proposed. The orbit determination precision of using single system and multi-GNSS, as well as ambiguity-fixed solution and ambiguity-free solution, are compared based on real observation data. The results show that, compared with single system processing, the multi-GNSS integrated method can effectively improve the orbit precision of IGSO and MEO, but except for GEO. The result of IGSO and MEO ambiguity fixing by the proposed strategy is well. The ambiguity fixing success rate has been improved significantly, especially for long baselines. The ambiguity fixed success rateimproves form 40% to 60% or more on the whole. Ambiguity fixing shows a positive contribution to orbit accuracy of IGSO and MEO from the overlapping RMS with that of the free solution for comparison. The 3D-RMS of IGSO could reach 0.048 m, and that of MEO could reach 0.066 m. © 2018, Surveying and Mapping Press. All right reserved.

Keyword :

Ambiguity fixing Beidou navigation satellite systems Double differences Integrated method Navigation satellites Orbit determination Precise orbit determination Precision analysis

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GB/T 7714 Fang, Yanan , Xin, Jingmin , Zeng, Guang et al. BeiDou Satellites Multi-GNSS Precise Orbit Determination with Ambiguity Fixed [J]. | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica , 2018 , 47 (3) : 341-347 .
MLA Fang, Yanan et al. "BeiDou Satellites Multi-GNSS Precise Orbit Determination with Ambiguity Fixed" . | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 47 . 3 (2018) : 341-347 .
APA Fang, Yanan , Xin, Jingmin , Zeng, Guang , Wang, Jiasong , Li, Jie . BeiDou Satellites Multi-GNSS Precise Orbit Determination with Ambiguity Fixed . | Cehui Xuebao/Acta Geodaetica et Cartographica Sinica , 2018 , 47 (3) , 341-347 .
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Subspace-Based localization of near-field signals in unknown nonuniform noise EI Scopus
会议论文 | 2018 , 2018-July , 247-251 | 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
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Abstract :

In this paper, we consider the problem of estimating the directions-of-arrival (DOAs) and ranges of multiple nearfield narrowband signals impinging on a symmetric uniform linear array (ULA) in nonuniform noise in practical applications. By forming a Toeplitz-like correlation matrix from the anti-diagonal elements of the array covariance matrix to convert the nonuniform noise to a uniform one, a new subspace-based localization method is proposed, where the null space is obtained through eigendecomposition of the resultant Toeplitz-like matrix, and the MUSIC method is used to estimate the location parameters, while a new pairing scheme is presented as well. Additionally, an oblique projector based alternating iteration is presented to improve the estimation accuracy of the location parameters, where the 'saturation behavior'encountered in most of localization methods is solved effectively. Furthermore, the Cramér-Rao lower bound (CRB) for the near-field signals in unknown nonuniform noise is also derived explicitly. Finally, the effectiveness of the proposed method is verified through numerical examples. ©2018 IEEE.

Keyword :

Near fields Non-uniform noise Oblique projector Source localization Uniform linear arrays

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GB/T 7714 Zuo, Weiliang , Xin, Jingmin , Zheng, Nanning et al. Subspace-Based localization of near-field signals in unknown nonuniform noise [C] . 2018 : 247-251 .
MLA Zuo, Weiliang et al. "Subspace-Based localization of near-field signals in unknown nonuniform noise" . (2018) : 247-251 .
APA Zuo, Weiliang , Xin, Jingmin , Zheng, Nanning , Sano, Akira . Subspace-Based localization of near-field signals in unknown nonuniform noise . (2018) : 247-251 .
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SrCNN: Cardiovascular vulnerable plaque recognition with salient region proposal networks EI
会议论文 | 2018 , 38-45 | 2nd International Conference on Graphic and Signal Proceesing, ICGSP 2018
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Abstract :

Vulnerable plaques recognition from IVOCT images is a valuable yet challenging task for computer-aided diagnosis and treatment of cardiovascular diseases. However, most existing supervised methods only used one kind of annotation information, and so they didn't fully and effectively utilize biomedical image information. In this paper, we propose a single, unified salient-regions-based convolutional neural network (SRCNN) to address this challenging task. The proposed SRCNN takes advantage of multi-annotation information (i.e., classification labels and segmentation labels) and combines prior knowledge of cardiologists. Our contributions in this paper are as follows: (i) We employ a bi-branch network combining the annotation information of classification and segmentation to recognize vulnerable plaques in IVOCT images. (2) According to prior knowledge of cardiologists, we construct a salient region proposal network (SRPN) that can propose irregular salient regions different from bounding boxes. (3) We embed SRPN in the bi-branch network through an appropriate merging strategy, and call this new bi-branch network SRCNN. Our proposed SRCNN is evaluated on the 2017 CCCV-IVOCT Challenge dataset. And ablation experiments demonstrate that compared to separate networks, the bi-branch network can improve the performance of classification and segmentation simultaneously. Furthermore, they also show SRPN contributes to extracting more discriminative features and boosting the whole performance of recognizing vulnerable plaques in IVOCT images greatly. © 2018 Association for Computing Machinery.

Keyword :

Ablation experiments Cardio-vascular disease Classification labels Convolutional neural network Discriminative features Salient regions Supervised methods Vulnerable plaques

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GB/T 7714 Liu, Sijie , Deng, Yangyang , Xin, Jingmin et al. SrCNN: Cardiovascular vulnerable plaque recognition with salient region proposal networks [C] . 2018 : 38-45 .
MLA Liu, Sijie et al. "SrCNN: Cardiovascular vulnerable plaque recognition with salient region proposal networks" . (2018) : 38-45 .
APA Liu, Sijie , Deng, Yangyang , Xin, Jingmin , Zuo, Weiliang , Shi, Peiwen , Zheng, Nanning . SrCNN: Cardiovascular vulnerable plaque recognition with salient region proposal networks . (2018) : 38-45 .
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Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception EI
会议论文 | 2018 , 2018-June , 591-596 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic environment with high accuracy and robustness. In this paper, we propose two effective methods, i.e. spatio-temporal evidence generating and independent vision channel, to improve multi-sensor track-level fusion for vehicle environmental perception. The spatio-temporal evidence includes instantaneous evidence, tracking evidence and tracks matching evidence to improve existence fusion. Independent vision channel leverages the specific advantage of vision processing on object recognition to improve classification fusion. The proposed methods are evaluated by using the multi-sensor dataset collected from real traffic environment. Experimental results demonstrate that the proposed methods can significantly improve the multi-sensor track-level fusion in terms of both detection accuracy and classification accuracy. © 2018 IEEE.

Keyword :

Classification accuracy Classification fusion Detection accuracy Environmental perceptions Multi-sensor fusion Track level fusion Traffic environment Vision processing

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GB/T 7714 Shi, Juwang , Wang, Wenxiu , Wang, Xiao et al. Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception [C] . 2018 : 591-596 .
MLA Shi, Juwang et al. "Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception" . (2018) : 591-596 .
APA Shi, Juwang , Wang, Wenxiu , Wang, Xiao , Sun, Hongbin , Lan, Xuguang , Xin, Jingmin et al. Leveraging Spatio-Temporal Evidence and Independent Vision Channel to Improve Multi-Sensor Fusion for Vehicle Environmental Perception . (2018) : 591-596 .
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Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection EI
会议论文 | 2018 , 2018-June , 2111-2116 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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Abstract :

Vehicle detection is an important research topic for autonomous driving community. Since the great success of deep learning on object detection, almost all vehicle detection methods go along with this line. However, deep learning methods heavily rely on the training data, and the whole mechanism is like a 'black box' Therefore, in this paper, we explore a vehicle detection method using traffic semantic context and human common sense instead of relying on the training data. To verify our idea, we compare our method with two classic machine learning methods as well as three state- of-the-art deep learning methods on a dataset collected in real traffics. The results show that our method outperforms others on this dataset. The deep learning methods may exceed ours after enlarging the training data or testing on more complicated datasets. However, the main contribution of this paper is providing inspiration for learning methods, and we believe their performance can be greatly improved after considering the idea of this paper. © 2018 IEEE.

Keyword :

Autonomous driving Learning methods Machine learning methods Research topics Road vehicles Semantic context Training data Vehicle detection

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GB/T 7714 Nan, Zhixiong , Pan, Menghan , Wang, Xiao et al. Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection [C] . 2018 : 2111-2116 .
MLA Nan, Zhixiong et al. "Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection" . (2018) : 2111-2116 .
APA Nan, Zhixiong , Pan, Menghan , Wang, Xiao , Wei, Ping , Xu, Linhai , Sun, Hongbin et al. Exploring the Potential of Using Semantic Context and Common Sense in On-Road Vehicle Detection . (2018) : 2111-2116 .
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Subspace-Based Localization of Far-Field and Near-Field Signals Without Eigendecomposition EI SCIE Scopus
期刊论文 | 2018 , 66 (17) , 4461-4476 | IEEE TRANSACTIONS ON SIGNAL PROCESSING
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We propose a new subspace-based localization of far-field (FF) and near-field (NF) narrowband signals (LOFNS) without eigendecomposition impinging on a symmetrical uniform linear array, where the oblique projection operator is utilized to isolate the NF signals from the FF ones, and the procedures of computationally burdensome eigendecomposition are not required in the estimation of the NF and FF location parameters and the computation of oblique projection operator. As a measure against the impact of finite array data, an alternating iterative scheme is presented to improve the estimation accuracy of the oblique projection operator and, hence, that of the NF location parameters, where the "saturation behavior" encountered in most of localization methods is overcome. Furthermore, the statistical analysis of the proposed LOFNS is studied, and the asymptotic mean-squared-error expressions of the estimation errors are derived for the FF and NF location parameters. Finally, the effectiveness and the theoretical analysis of the proposed LOFNS are substantiated through numerical examples, and the simulation results demonstrate that the LOFNS provides remarkable and satisfactory estimation performance for both the NF and FF signals compared with some existing localization methods even with eigendecomposition.

Keyword :

oblique projection source localization uniform linear array far-field Direction-of-arrival near-field

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GB/T 7714 Zuo, Weiliang , Xin, Jingmin , Zheng, Nanning et al. Subspace-Based Localization of Far-Field and Near-Field Signals Without Eigendecomposition [J]. | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2018 , 66 (17) : 4461-4476 .
MLA Zuo, Weiliang et al. "Subspace-Based Localization of Far-Field and Near-Field Signals Without Eigendecomposition" . | IEEE TRANSACTIONS ON SIGNAL PROCESSING 66 . 17 (2018) : 4461-4476 .
APA Zuo, Weiliang , Xin, Jingmin , Zheng, Nanning , Sano, Akira . Subspace-Based Localization of Far-Field and Near-Field Signals Without Eigendecomposition . | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2018 , 66 (17) , 4461-4476 .
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A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network EI
会议论文 | 2018 , 2018-June , 1464-1469 | 2018 IEEE Intelligent Vehicles Symposium, IV 2018
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Abstract :

Road detection is one of the most basic tasks of autonomous driving systems. At present, researches on this issue mainly take two kinds of data as input, i.e., LIDAR point clouds and RGB images from cameras. To make best use of the advantages and bypass the disadvantages of these two kinds of data, we propose a novel network, namely two- stream fusion fully convolutional network (TSF-FCN), which can take advantage of both the accurate location information from LIDAR point clouds and rich appearance information from RGB images. One stream of this network is LIDAR stream which aggregates multi-scale contextual information from LIDAR point clouds. The other stream is RGB stream which is used for extracting features from RGB images. To fuse the two streams, the feature maps of RGB stream are converted to a bird-view representation to concatenate with that of LIDAR stream. In this way, the two kinds of data can complement each other for detecting road. To verify the efficacy of our TSF-FCN, experiments are carried on KITTI- ROAD benchmark and competitive performance is achieved compared with state-of-the-art methods. © 2018 IEEE.

Keyword :

Accurate location Autonomous driving Competitive performance Contextual information Convolutional networks Extracting features Lidar point clouds State-of-the-art methods

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GB/T 7714 Lv, Xin , Liu, Ziyi , Xin, Jingmin et al. A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network [C] . 2018 : 1464-1469 .
MLA Lv, Xin et al. "A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network" . (2018) : 1464-1469 .
APA Lv, Xin , Liu, Ziyi , Xin, Jingmin , Zheng, Nanning . A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network . (2018) : 1464-1469 .
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