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Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machine-learning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for fibrosis classification with the selected features, based on the results of which the optimal classifier was established. The performance of the proposed MP-QUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82%, and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24%, and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects. [ABSTRACT FROM AUTHOR]

The analysis of electromagnetic (EM) scattering from a target located in the near zone of an electrically large near-field-focused (NFF) array of antennas is presented. The analysis first examined the EM scattering from a metal sphere, attempting to create a reference for comparison with other targets in a fashion analogous to conventional far-field analysis of radar cross section (RCS). Realistic targets were afterward examined and analyzed to exhibit their RCS characteristics. A numerical simulation-based maximization of EM scattering fields for effective target detection in the near zone of antenna was developed, which allows one to obtain a set of optimum element excitation weightings by applying the steepest ascent method (SAM). RCS phenomena were validated by full-wave simulations and experimental measurements to justify the feasibility of the proposed concept. [ABSTRACT FROM AUTHOR]

Kalman filter is widely used in precise point positioning (PPP) process to achieve optimal positioning solution. However, the positioning accuracy and stability are closely related to the covariance matrix or weight matrix of observations used in the filtering process. Typically, the weights of pseudorange and carrier-phase types of observations in the PPP are determined based on a priori assumption of noise variance. These imprecise weight values significantly degrade the performance of the filter. In global positioning system (GPS)/BDS PPP, there is an additional challenge to set the weights of GPS and BDS groups of observations since the observation quality of GPS and BDS systems are not the same. In this paper, a novel weighting approach based on the combination of modified variance component estimation (MVCE) and Helmert variance component estimation (HVCE) methods is proposed. The MVCE method is developed to adjust the weights of pseudorange, and carrier-phase types of observations from the same GNSS and HVCE are customized to adjust the weights of GPS and BDS groups of observations. An additional sliding window averaging filter is used in the MVCE to obtain a more precise and stable weight ratio solutions between carrier-phase and pseudorange types of observations. Both static and kinematic tests are conducted to verify the effectiveness and to assess the performance of the proposed weighting approach. The results indicate that the proposed scheme can significantly improve the positioning accuracy and stability as well as reducing the convergence time in GPS/BDS PPP. [ABSTRACT FROM AUTHOR]

For precise point positioning (PPP), Kalman filter is usually used to estimate the position and velocity parameters. But, the positioning accuracy and convergence time are highly susceptible to gross errors in observations and dynamic model errors in motion states. In addition, the weights of different types of observation are generally determined by the prior variance are usually inaccurate, which affects the performance of PPP. In order to achieve a more desirable positioning solutions, an improved robust adaptive filtering method is proposed. First in this method, classification robust equivalent weight model based on t-test statistic is developed to construct the equivalent weight matrix for each types of observation separately. Second, variance component estimation weighting method based on innovations is proposed to determine the scale factor of each types of observation. Finally, adaptive factor function model based on innovations is adopted to acquire the optimal adaptive factor. The availability of the proposed classification robust equivalent weight model and variance component estimation weighting method are verified with the static test data, and the performance of the improved robust adaptive filtering method is further validated with the dynamic test data. The results indicate that compared with the conventional robust adaptive filter, the improved one has a better performance in positioning accuracy, convergence time, and the stability of PPP. [ABSTRACT FROM AUTHOR]

This study presents a new approach for classification of building type in complex urban scene. The approach consists of two parts: extended multiresolution segmentation (EMRS) and soft classification using BP network (SBP). The technology scheme is referred to here as EMRS-SBP. EMRS is used to guide the design of descriptor. A descriptor is a feature expression or a symbolized algorithm to systematically promote the expressing capability of image features. A classifier can perform far better to discern complex pattern of combining pixels working in an EMRS-based feature space constructed by a number of such descriptors. SBP serves as a classifier model to generate natural clusters of member which refers to here as both pixels and image patches. Class-mark ensured member is denoted as sure member and the rest as unsure (fuzzy) members. The latter can be relabeled through recursive defuzzifying according to the information carried by the gradually increased sure members. By using EMRS-SBP, three building types, i.e., old-fashioned courtyard dwellings, multistorey residential buildings, and high-rise buildings, can be accurately classified from high spatial resolution imagery in a feature space constructed with fifteen descriptors including nine EMRS-based ones. There is evidence that the mean overall accuracy using SBP in the EMRS-based feature space is 19.8% higher than that using the hard classification with BP network in a single resolution segmentation space and meanwhile, the mean kappa statistic value (κ) is 25.1% higher. [ABSTRACT FROM PUBLISHER]

A theoretical physical model of axial-flux-type superconducting eddy-current coupling is systematically investigated. A mathematical description of a high-field superconducting bulk magnet used as the magnetic field source is modeled based on the physical superconducting mechanism. Thereafter, a practical two-dimensional (2-D) magnetic equivalent circuit model for the analytical calculation of magnetic field distribution in this coupling design using Schwarz–Christoffel mapping is proposed to predict the air gap magnetic density and the transferred torque, taking nonlinear effects of the magnetic materials into consideration. Finally, a 1/6 section 3-D model of the proposed superconducting eddy-current coupling is designed and analyzed via finite element method. The calculation results by the proposed analytical model are verified with those issued from the finite element method. Also, the efficiency of superconducting eddy current couple is analyzed theoretically. [ABSTRACT FROM PUBLISHER]