NoncovANM: Gridless DOA Estimation for LPDF System

Direction of arrival (DOA) estimation is an important research in the area of array signal processing, and has been studied for decades. High resolution DOA estimation requires large array aperture, which leads to the increase of hardware cost. Besides, high accuracy DOA estimation methods usually have high computational complexity. In this paper, the problem of decreasing the hardware cost and algorithm complexity is addressed. First, considering the ability of flexible controlling the electromagnetic waves and low-cost, an intelligent reconfigurable surface (IRS)-aided low-cost passive direction finding (LPDF) system is developed, where only one fully functional receiving channel is adopted. Then, the sparsity of targets direction in the spatial domain is exploited by formulating an atomic norm minimization (ANM) problem to estimate the DOA. Traditionally, solving ANM problem is complex and cannot be realized efficiently. Hence, a novel nonconvex-based ANM (NC-ANM) method is proposed by gradient threshold iteration, where a perturbation is introduced to avoid falling into saddle points. The theoretical analysis for the convergence of the NC-ANM method is also given. Moreover, the corresponding Cram\'er-Rao lower bound (CRLB) in the LPDF system is derived, and taken as the referred bound of the DOA estimation. Simulation results show that the proposed method outperforms the compared methods in the DOA estimation with lower computational complexity in the LPDF system.Comment: 11 pages, 8 figure

Decision Engineering Analysis of Fraud Information Disclosure after China's Share-Splitting Reform

AbstractThis paper outlines a dynamic game model to analyze the fraud information disclosure by listed companies in China since the share-splitting reform in 2005. By analyzing the conditions of coalition-proof Nash equilibrium between large shareholders and the manager, exogenous variables’ effects on the equilibrium as well as the first-order condition of the maximum utility of the supervisory department, it is concluded that efficient capital markets require a high supervising probability and intensity of penalty to the “insider” and shortened the intervals between supervising conducts as well. Moreover, there exists a unique optimum incentive stock option ratio over which fraud information disclosure becomes more rampant. This results in a higher intensity of penalty to the manager given more stock option incentive and, in contrast, a higher intensity of penalty to large shareholders of a well managed and efficiently capital-structured company once fraud information disclosure is detected. The model's conclusions are consistent with the facts of listed companies in China. Finally, the model makes sharp suggestions for the mechanism design of stock option incentive as well as suggestions for the supervisory department to achieve efficiency of capital markets in China

URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming

The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to both the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadrature (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix reconstruction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms are relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining the accuracy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiment demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.Comment: 11 pages, 16 figure

Turning Social Capital into Economic Capital: the Sales Effect of Friendship Group Participation in Social Commerce Websites

Friendship groups have been widely adopted in social commerce platforms because of the powerful and pervasive influence of groups on decision making. Despite their widespread use, the sales effects of seller participation in friendship groups (FGP) have received limited research attention. Using a quasi-experimental design with 373,964 products from 8,250 sellers on a leading social commerce platform, we find that FGP increase sellers\u27 product sales performance through the formation of relational and cognitive capital. In addition, we find that seller guarantee, product guarantee and product rating strengthen the sales effect of FGP, while the number of seller followers weakens the sales effect of FGP. Our study contributes to the literature by examining how, why, and when FGP affect sales performance in social commerce. We also provides guidance for sellers and platforms to use friendship groups and group marketing to improve sales performance in social commerce

Realistic Volume Rendering with Environment-Synced Illumination in Mixed Reality

Interactive volume visualization using a mixed reality (MR) system helps provide users with an intuitive spatial perception of volumetric data. Due to sophisticated requirements of user interaction and vision when using MR head-mounted display (HMD) devices, the conflict between the realisticness and efficiency of direct volume rendering (DVR) is yet to be resolved. In this paper, a new MR visualization framework that supports interactive realistic DVR is proposed. An efficient illumination estimation method is used to identify the high dynamic range (HDR) environment illumination captured using a panorama camera. To improve the visual quality of Monte Carlo-based DVR, a new spatio-temporal denoising algorithm is designed. Based on a reprojection strategy, it makes full use of temporal coherence between adjacent frames and spatial coherence between the two screens of an HMD to optimize MR rendering quality. Several MR development modules are also developed for related devices to efficiently and stably display the DVR results in an MR HMD. Experimental results demonstrate that our framework can better support immersive and intuitive user perception during MR viewing than existing MR solutions.Comment: 6 pages, 6 figure