Recently, symmetric linear nonuniform arrays (SLNAs) have been promoted for hybrid near-field (NF) and far-field (FF) source localization. A new SLNA based on symmetric nested array (SNA) is presented in the paper. Th...
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Recently, symmetric linear nonuniform arrays (SLNAs) have been promoted for hybrid near-field (NF) and far-field (FF) source localization. A new SLNA based on symmetric nested array (SNA) is presented in the paper. The proposed array and its difference coarray (DCA) configuration can be expressed in closed-form. Comparing to the existing SLNAs with same sensors number, the proposed array can locate more sources and provide longer consecutive lags, which optimizes the detection results. Simulation results prove that the designed array outperforms traditional compressed symmetric nested array (CSNA).
A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm...
A new dual-band low-profile quadrifilar-helical antenna (QHA) was presented. The spiral radiation arm of QHA is printed to three circular dielectric plates to reduce the antenna height. In this case, the radiation arm of the lower layer is connected to the feed, and the L-shaped branch is connected to the ground through a 50 ohm resistance. The radiation arm in the middle layer adds branches to realize dual frequency. The upper radiation arm rotates inward and folds, further realizing miniaturization. As a result, the height of QHA is only 20mm. Through CST simulation and optimization, The operating frequency bands of QHA are 1GHz - 1.45GHz and 1.54GHz - 1.74GHz. The beam width of QHA is more than 120°, which is to provide service for satellite navigating systems. The AR for the operation bands are less than 3dB.
Historically, the empirical risk of a pattern classifier was asked to be made zero, therefor the default property of training samples were limited to a separable ones. Nowadays on the contrary, the major idea of learn...
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Historically, the empirical risk of a pattern classifier was asked to be made zero, therefor the default property of training samples were limited to a separable ones. Nowadays on the contrary, the major idea of learning classification no longer ask the empirical risk of classifier must be made zero. In this situation, inseparable feature sets may not be detrimental to the performance of classifier. However, so far no experimental studies and analytical results show whether an inseparable feature set is available or not. This paper firstly analyzes the interaction between learning algorithms and feature selection, and gives a proof by both the analytical analysis and experimental studies.
To date, there is only limited research that explicitly exploits the relationships among Action Units and expressions for facial expression recognition. In this paper, we propose an facial expression recognition metho...
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ISBN:
(纸本)9781479947164
To date, there is only limited research that explicitly exploits the relationships among Action Units and expressions for facial expression recognition. In this paper, we propose an facial expression recognition method through modeling the expression-dependent AU relations. First, the incremental association Markov blanket algorithm is adopted to select crucial action units for a certain expression. Second, a Bayesian Network (BN) is constructed to capture the relationships between a certain expression and its crucial action units. Given the learned BNs and measurements of AUs and expression, we can then perform expression recognition within the BN through a probabilistic inference. Experimental results on the CK+ and MMI databases demonstrate the effectiveness and generalization ability of our method.
With the development of computer technology, Reverse Engineering (RE) based on 3D data processing has developed greatly. The whole procedure of laser scanning color 3D data from the aspect of 3D data processing includ...
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With the development of computer technology, Reverse Engineering (RE) based on 3D data processing has developed greatly. The whole procedure of laser scanning color 3D data from the aspect of 3D data processing includes the pre-processing of point clouds and the surface reconstruction. This paper proposes improved point clouds data simplification algorithm in 3D data processing, it is the adjustment of simplification algorithm under the restriction of color boundary compared to the key simplification algorithms of the pre-processing for color 3D point clouds. The experiment result shows that the proposed algorithm is effective.
Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we pre...
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Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.
Based on the QSIM reasoning of Kuipers, comparative constraint is presented to reduce the diagnosis space. The transfer regulation of qualitative constrain is used in the simulation and reasoning of system diagnosis. ...
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Based on the QSIM reasoning of Kuipers, comparative constraint is presented to reduce the diagnosis space. The transfer regulation of qualitative constrain is used in the simulation and reasoning of system diagnosis. This arithmetic takes observed faulty state as the beginning, to diagnosis the discrepancy of variable and location of the causation according to the transfer regulation of qualitative constraint, reasons from faulty diagnosed to examine the result and delete the redundancy of diagnosis result. In the example of Condensation refrigeration system, the constraint relation is built according to qualitative difference equation. Aim at the bad efficiency of refrigeration, the mixture of air or Freon superfluously is diagnosed as the result faulty source, is consistent with the factual system.
Smartphones are becoming increasingly energy-hungry to support feature-rich applications, posing a lot of pressure on battery lifetime and making energy consumption a non-negligible issue. In particular, dynamic rando...
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Parzen windows estimation is one of the classical non- parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation be...
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Parzen windows estimation is one of the classical non- parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation between the kernel density estimation (KDE) and low-pass filtering is well known, it is vary difficult to setting the parameters of the other kinds of density functions. This paper proposes a novel method to deal with the parameters of Laplace kernel through measuring the degree of exchanged information among interpolating points. Experimental results showed that the proposed method can improve the performance of Parzen windows significantly.
parallel framework for NURBS-based isogeometric analysis on multi-core cpus is presented in this *** framework is composed of two parts:(1) construction of the stiffness matrix and right hand side vector; (2) a parall...
parallel framework for NURBS-based isogeometric analysis on multi-core cpus is presented in this *** framework is composed of two parts:(1) construction of the stiffness matrix and right hand side vector; (2) a parallel sparse solver.
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