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The generation and measurement of shear waves are critical in ultrasonic elasticity imaging. Generally, the resulting wavefront direction is very important for accurately measuring the shear speed and estimating the medium elasticity. In this article, the proposed method can generate a compound shear wavefront with the same direction as speed reconstruction and zero angles between the wavefront and the focus direction, which can improve the estimation accuracy of shear wave velocity. Also, this method, called time-division multipoint excitation image fusion (TDMPEIF), can reconstruct the shear wave propagation images acquired at different depths of a medium according to the frame sequence to produce the shear waves front with a regulable angle. Moreover, the shear wave speed and the elasticity of a medium can be mapped quantitatively with this method. The results demonstrate that the TDMPEIF can improve the quality of the shear wave velocity images, which has wide application value and good promotion prospects for quantitative evaluation of tissue elasticity. [ABSTRACT FROM AUTHOR]

Network-assisted full-duplex (NAFD) distributed massive multiple-input multiple-output (MIMO) systems enable simultaneous uplink and downlink communications by dynamically allocating the numbers of uplink and downlink remote antenna units (RAUs), which potentially improve the spectral efficiency in wireless communications. In such systems, channel state information (CSI) plays a critical role in uplink reception and downlink transmission, as well as the cross link interference cancelation caused by downlink RAUs to uplink RAUs. Moreover, downlink terminals need to estimate CSI to reliably decode the received signals due to the reduced channel hardening effect. However, high training overhead makes it generally impossible to directly estimate CSI. This paper proposes to estimate effective CSI (inner products of beamforming and channel vectors) instead based on beamforming training scheme. Under this scheme, we derive closed-form expressions for uplink and downlink achievable rates with different receivers and beamforming. Given these expressions, we propose an efficient power allocation scheme which is only dependent on slowly varying large-scale fading from the perspective of multi-objective optimization. Numerical results verify the accuracy of the derived closed-form expressions and effectiveness of beamforming training based CSI estimation. Moreover, trade-off regions between the considered optimization objectives under various system parameters offer numerous flexibilities for system optimization. [ABSTRACT FROM AUTHOR]

Social platforms are full of rumors (i.e., unverified contents). Naturally, it is imperative but challenging to effectively determine the veracity of these rumors on popular social platforms. Previously deep learning based rumor verification schemes usually treat the issue as an independent and single task. Considering the rumor verification and stance classification are relevant tasks, we propose an effective Rumor verification scheme based on Multi-task learning Model, RV-ML, in which the shared long-short term memory (LSTM) layer for both rumor verification and stance classification can effectively deal with the sequential information for the original input, and generate macro-level virtual features, and the convolution neural network (CNN) layer uniquely designed for rumor verification task is used to mine local features from shared LSTM layer. Comparisons between our RV-ML and several typical rumor verification schemes on the real RumourEval and PHEME datasets demonstrate that our proposed scheme gains better performance for the task of rumor verification. [ABSTRACT FROM AUTHOR]

For nonuniformly rotating target in the low signal-to-noise ratio (SNR) environment, the inverse synthetic aperture radar (ISAR) imaging is a challenging task due to the Doppler spread induced by time-varying rotation. In this paper, an effective modified bilinear parameter estimation algorithm is proposed and applied to reconstruct the ISAR image of nonuniformly rotating targets with low SNR. First, azimuth echoes of a range cell are modeled as multicomponent cubic phase signals (multi-CPSs) after range alignment and phase adjustment. Then, to estimate the parameter of the CPS (the chirp rate and the quadratic chirp rate, which are identified as causes of the target image defocus), a novel integrated parametric cubic phase function (IPCPF) and a reversing Wigner–Ville distribution processing are developed. Compared to other four representative algorithms, the bilinearity of the IPCPF guarantees a higher antinoise performance and a better suppression on cross terms. Moreover, by utilizing the nonuniform fast Fourier transform and the generalized scaled Fourier transform, the brute-force searching is eliminated and the computational cost is reduced. Finally, a new cross-range scaling method based on a regression analysis is proposed for the ISAR imaging. With the synthetic data and the real radar data, several simulation examples and ISAR imaging results demonstrate the effectiveness and the superiority of the proposed ISAR imaging algorithm. [ABSTRACT FROM AUTHOR]

Neuroimaging techniques can measure structural and functional brain connectivity with unprecedented detail in vivo. This so-called brain connectome can be represented as high dimensional matrices corresponding to edge weights in graphs. After measuring the matrices of two cohorts (i.e., patients and healthy controls), one is often required to formulate computational network models for effective feature engineering to draw discriminative distinctions between the cohorts, as well as estimate the associated statistical significance. We designed a novel method to reveal the intrinsic features of functional matrices of discriminative power for group comparison. More specifically, by encouraging co-selection of edges connected to the same node, we preserved the discriminative edges to maximum extent. To reduce the false positive rate of the extracted discriminative edges, an optimization procedure was developed to evaluate the significance of these edges and remove trivial ones. We validated the proposed method using both synthetic data and real benchmarks, and compared it to \ell1 regularized logistic regression, univariate t-test and stability selection. The experimental results clearly showed that the proposed approach outperformed the three competing methods under various settings. In addition to increasing the F-measure of feature selection, our approach captured the endogenous, discriminative connectivity patterns consistent with recent findings in biomedical literature. This data-driven method paves a new avenue of enquiry into the inherent nature of network models for functional brain connectomes. [ABSTRACT FROM AUTHOR]