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) & Computer science. 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.

Superpixel-Based CFAR Target Detection for High-Resolution SAR Images

Abstract: In this letter, a new superpixel-based constant-false-alarm-rate (CFAR) target detection algorithm for high-resolution synthetic aperture radar (SAR) images is proposed. The detection algorithm consists of three stages, i.e., segmentation, detection, and clustering. In the segmentation stage, a superpixel-generating algorithm is utilized to segment the SAR image. In the detection stage, based on the superpixels generated, the clutter distribution parameters for each pixel can be adaptively estimated, even in the multitarget situations. Then, the two-parameter CFAR test statistic can be adopted for detection. In the clustering stage, the hierarchical clustering is used to cluster the detected superpixels to get the candidate targets. The effectiveness of the proposed algorithm is demonstrated using the miniSAR data.

Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

Abstract: Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise is a challenging problem due to its difficulties in an accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image in painting techniques from an incomplete image corrupted by AWGN. However, it is quite challenging to accurately detect the locations of the impulse noise when the mixture noise is strong. In this paper, we propose an effective mixture noise removal method based on Laplacian scale mixture (LSM) modeling and nonlocal low-rank regularization. The impulse noise is modeled with LSM distributions, and both the hidden scale parameters and the impulse noise are jointly estimated to adaptively characterize the real noise. To exploit the nonlocal self-similarity and low-rank nature of natural image, a nonlocal low-rank regularization is adopted to regularize the denoising process. Experimental results on synthetic noisy images show that the proposed method outperforms existing mixture noise removal methods.

Privacy-Preserving Reputation Management for Edge Computing Enhanced Mobile Crowdsensing

Abstract: Mobile crowdsensing (MCS) has gained popularity for its potential to leverage individual mobile devices to sense, collect, and analyze data instead of deploying sensors. As the sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with the edge computing paradigm to reduce time delays and high bandwidth costs. The sensing data may reveal personal information, and thus it is of great significance to preserve the privacy of the participants. However, preserving privacy may hinder the process of handling malicious participants. In this paper, we propose two privacy preserving reputation management schemes for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. In the basic scheme, a novel reputation value updating method is designed based on the deviations of the encrypted sensing data from the final aggregating result. The basic scheme is efficient at the expense of revealing the deviation value of each participant to the reputation manager. To conquer this drawback, we propose an advanced scheme by updating the reputation values utilizing the rank of deviations. Extensive experiments demonstrate that both these two schemes have high cost efficiency and are effective to deal with malicious participants.

Cell-free circulating tumor DNA in cancer

Abstract: Cancer is a common cause of death worldwide. Despite significant advances in cancer treatments, the morbidity and mortality are still enormous. Tumor heterogeneity, especially intratumoral heterogeneity, is a significant reason underlying difficulties in tumor treatment and failure of a number of current therapeutic modalities, even of molecularly targeted therapies. The development of a virtually noninvasive “liquid biopsy” from the blood has been attempted to characterize tumor heterogeneity. This review focuses on cell-free circulating tumor DNA (ctDNA) in the bloodstream as a versatile biomarker. ctDNA analysis is an evolving field with many new methods being developed and optimized to be able to successfully extract and analyze ctDNA, which has vast clinical applications. ctDNA has the potential to accurately genotype the tumor and identify personalized genetic and epigenetic alterations of the entire tumor. In addition, ctDNA has the potential to accurately monitor tumor burden and treatment response, while also being able to monitor minimal residual disease, reducing the need for harmful adjuvant chemotherapy and allowing more rapid detection of relapse. There are still many challenges that need to be overcome prior to this biomarker getting wide adoption in the clinical world, including optimization, standardization, and large multicenter trials.