Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Secure quantum private comparison

Abstract: We propose a two-party quantum private comparison protocol using single photons, in which two distrustful parties can compare whether their secrets are equal with the help of a third party (TP). Any information about the values of their respective secrets will not be leaked out even with a compromised TP. Security is also discussed.

Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning

Abstract: Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained promising performance, they have some limitations: 1) they use either the Gaussian scale mixture model or generalized Gaussian density to predict the nonGaussian marginal distribution of wavelet, Gabor, or discrete cosine transform coefficients. The prediction error makes the extracted features unable to reflect the change in nonGaussianity (NG) accurately. The existing algorithms use the joint statistical model and structural similarity to model the local dependency (LD). Although this LD essentially encodes the information redundancy in natural images, these models do not use information divergence to measure the LD. Although the exponential decay characteristic (EDC) represents the property of natural images that large/small wavelet coefficient magnitudes tend to be persistent across scales, which is highly correlated with image degradations, it has not been applied to the universal blind IQA metrics; and 2) all the universal blind IQA metrics use the same similarity measure for different features for learning the universal blind IQA metrics, though these features have different properties. To address the aforementioned problems, we propose to construct new universal blind quality indicators using all the three types of NSS, i.e., the NG, LD, and EDC, and incorporating the heterogeneous property of multiple kernel learning (MKL). By analyzing how different distortions affect these statistical properties, we present two universal blind quality assessment models, NSS global scheme and NSS two-step scheme. In the proposed metrics: 1) we exploit the NG of natural images using the original marginal distribution of wavelet coefficients; 2) we measure correlations between wavelet coefficients using mutual information defined in information theory; 3) we use features of EDC in universal blind image quality prediction directly; and 4) we introduce MKL to measure the similarity of different features using different kernels. Thorough experimental results on the Laboratory for Image and Video Engineering database II and the Tampere Image Database2008 demonstrate that both metrics are in remarkably high consistency with the human perception, and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes for various types of distortions.

Implementation and investigation of U-shaped aperture UWB antenna with dual band-notched characteristics

Abstract: The design and analysis of an ultra-wideband (UWB) aperture antenna with dual band-notched characteristics are presented. The antenna consists of a circular exciting stub on the front side and a U-shaped aperture on the back ground plane. By inserting a slot and a parasitic strip to the antenna, dual notched frequency bands are achieved. A conceptual circuit model, which is based on the measured impedance of the proposed antenna, is also shown to investigate the dual band-notched characteristics. The measured impedance bandwidth defined by VSWR<2 of 9.0 GHz (2.2-11.2 GHz), with the dual bands of 3.25-4.25 and 5.0-6.05 GHz notched, is obtained.

Non-Negative Spectral Learning and Sparse Regression-Based Dual-Graph Regularized Feature Selection

Abstract: Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed, but most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and do not make full use of the characteristics of the data. To overcome this problem, this paper proposes a new unsupervised feature selection algorithm, called non-negative spectral learning and sparse regression-based dual-graph regularized feature selection (NSSRD). NSSRD is based on the feature selection framework of joint embedding learning and sparse regression, but extends this framework by introducing the feature graph. By using low dimensional embedding learning in both data space and feature space, NSSRD simultaneously exploits the geometric information of both spaces. Second, the algorithm uses non-negative constraints to constrain the low-dimensional embedding matrix of both feature space and data space, ensuring that the elements in the matrix are non-negative. Third, NSSRD unifies the embedding matrix of the feature space and the sparse transformation matrix. To ensure the sparsity of the feature array, the sparse transformation matrix is constrained using the ${L_{2,1}}$ -norm. Thus feature selection can obtain accurate discriminative information from these matrices. Finally, NSSRD uses an iterative and alternative updating rule to optimize the objective function, enabling it to select the representative features more quickly and efficiently. This paper explains the objective function, the iterative updating rules and a proof of convergence. Experimental results show that NSSRD is significantly more effective than several other feature selection algorithms from the literature, on a variety of test data.

Self-Regulated Evolutionary Multitask Optimization

Abstract: Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independently, assuming if some component tasks are related then the useful knowledge (e.g., promising candidate solutions) acquired during the process of solving one task may assist in (and also benefit from) solving the other tasks. In EMTO, task relatedness is typically unknown in advance and needs to be captured via EA’s population. Since the population of an EA can only cover a subregion of the solution space and keeps evolving during the search, thus captured task relatedness is local and dynamic. The multifactorial EA (MFEA) is one of the most representative EMTO techniques, inspired by the bio-cultural model of multifactorial inheritance, which transmits both biological and cultural traits from the parents to the offspring. MFEA has succeeded in solving various multitask optimization (MTO) problems. However, the intensity of knowledge transfer in MFEA is determined via its algorithmic configuration without considering the degree of task relatedness, which may prevent the effective sharing and utilization of the useful knowledge acquired in related tasks. To address this issue, we propose a self-regulated EMTO (SREMTO) algorithm to automatically adapt the intensity of cross-task knowledge transfer to different and varying degrees of relatedness between different tasks as the search proceeds so that the useful knowledge in common for solving related tasks can be captured, shared, and utilized to a great extent. We compare SREMTO with MFEA and its variants as well as the single-task optimization counterpart of SREMTO on two MTO test suites, which demonstrates the superiority of SREMTO.