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.

A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration

Abstract: Robustness and accuracy are the two main challenging problems in feature-based remote sensing image registration. In this letter, a novel point-matching algorithm is proposed. An improved random sample consensus (RANSAC) algorithm called fast sample consensus (FSC) is proposed. It divides the data set in RANSAC into two parts: the sample set and the consensus set. Sample set has high correct rate and consensus set has a large number of correct matches. An iterative method is put forward to increase the number of correct correspondences. A set of measures has been used to evaluate the registration result. The performance of the proposed method is validated on the evaluation of these measures and the mosaic images. FSC can get more correct matches than RANSAC in less number of iterations, iterative selection of correct matches algorithm and removal of the imprecise points algorithm effectively increase the accuracy of the result. Extensive experimental studies compared with three state-of-the-art methods prove that the proposed algorithm is robust and accurate.

Two-Stage Method for Synthesizing Liveness-Enforcing Supervisors for Flexible Manufacturing Systems Using Petri Nets

Abstract: This paper develops a two-stage approach to synthesizing liveness-enforcing supervisors for flexible manufacturing systems (FMS) that can be modeled by a class of Petri nets. First, we find siphons that need to be controlled using a mixed integer programming (MIP) method. This way avoids complete siphon enumeration that is more time-consuming for a sizable plant model than the MIP method. Monitors are added for only those siphons that require them. Second, we rearrange the output arcs of the monitors on condition that liveness is still preserved. The liveness is verified by an MIP-based deadlock detection method instead of much time-consuming reachability analysis. Experimental studies show that the proposed approach is more efficient than the existing ones and can result in more permissive and structurally simpler liveness-enforcing supervisors than all the known existing methods. This paper makes the application of siphon-based deadlock control methods to industrial-size FMS possible

Enhancing privacy through caching in location-based services

Abstract: Privacy protection is critical for Location-Based Services (LBSs). In most previous solutions, users query service data from the untrusted LBS server when needed, and discard the data immediately after use. However, the data can be cached and reused to answer future queries. This prevents some queries from being sent to the LBS server and thus improves privacy. Although a few previous works recognize the usefulness of caching for better privacy, they use caching in a pretty straightforward way, and do not show the quantitative relation between caching and privacy. In this paper, we propose a caching-based solution to protect location privacy in LBSs, and rigorously explore how much caching can be used to improve privacy. Specifically, we propose an entropy-based privacy metric which for the first time incorporates the effect of caching on privacy. Then we design two novel caching-aware dummy selection algorithms which enhance location privacy through maximizing both the privacy of the current query and the dummies' contribution to cache. Evaluations show that our algorithms provide much better privacy than previous caching-oblivious and caching-aware solutions.

HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network

Abstract: The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of the same coin. How to jointly model them is the key issue for HSIs’ noise removal, including random noise, structural stripe noise, and dead pixels/lines. In this paper, we introduce the deep convolutional neural network (CNN) to achieve this goal. The learned filters can well extract the spatial information within their local receptive filed. Meanwhile, the spectral correlation can be depicted by the multiple channels of the learned 2-D filters, namely, the number of filters in each layer. The consequent advantages of our CNN-based HSI denoising method (HSI-DeNet) over previous methods are threefold. First, the proposed HSI-DeNet can be regarded as a tensor-based method by directly learning the filters in each layer without damaging the spectral-spatial structures. Second, the HSI-DeNet can simultaneously accommodate various kinds of noise in HSIs. Moreover, our method is flexible for both single image and multiple images by slightly modifying the channels of the filters in the first and last layers. Last but not least, our method is extremely fast in the testing phase, which makes it more practical for real application. The proposed HSI-DeNet is extensively evaluated on several HSIs, and outperforms the state-of-the-art HSI-DeNets in terms of both speed and performance.

Smart MoS2/Fe3O4 Nanotheranostic for Magnetically Targeted Photothermal Therapy Guided by Magnetic Resonance/Photoacoustic Imaging.

Abstract: The ability to selectively destroy cancer cells while sparing normal tissue is highly desirable during the cancer therapy. Here, magnetic targeted photothermal therapy was demonstrated by the integration of MoS2 (MS) flakes and Fe3O4 (IO) nanoparticles (NPs), where MoS2 converted near-infrared (NIR) light into heat and Fe3O4 NPs served as target moiety directed by external magnetic field to tumor site. The MoS2/Fe3O4 composite (MSIOs) functionalized by biocompatible polyethylene glycol (PEG) were prepared by a simple two-step hydrothermal method. And the as-obtained MSIOs exhibit high stability in bio-fluids and low toxicity in vitro and in vivo. Specifically, the MSIOs can be applied as a dual-modal probe for T2-weighted magnetic resonance (MR) and photoacoustic tomography (PAT) imaging due to their superparamagnetic property and strong NIR absorption. Furthermore, we demonstrate an effective result for magnetically targeted photothermal ablation of cancer. All these results show a great potential for localized photothermal ablation of cancer spatially/timely guided by the magnetic field and indicated the promise of the multifunctional MSIOs for applications in cancer theranostics.