Xi'an, China
Xi'an, China

Xidian University, also known as University of Electronic Science and Technology at Xi'an, is a university located in Xi'an, Shaanxi, People's Republic of China. The university is regarded as having strong science and engineering programs, and is particularly famous in Information Technology related disciplines in China. Wikipedia.

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News Article | April 28, 2017
Site: www.cemag.us

A “chemical imaging” system that uses a special type of laser beam to penetrate deep into tissue might lead to technologies that eliminate the need to draw blood for analyses including drug testing and early detection of diseases such as cancer and diabetes. The system, called stimulated Raman projection microscopy and tomography, makes possible “volumetric imaging” without using fluorescent dyes that might affect biological functions and hinder accuracy, said Ji-Xin Cheng, a professor in Purdue University’s Weldon School of Biomedical Engineering, Department of Chemistry and Birck Nanotechnology Center. “Volumetric chemical imaging allows a better understanding of the chemical composition of three-dimensional complex biological systems such as cells,” he said. The technology uses a type of laser beam called a Bessel beam, which maintains focus for a longer distance than a traditional “Gaussian beam” used in other imaging technologies, making it possible to penetrate deep into tissue. Stimulated Raman spectroscopy eliminates the need for fluorescent dyes. The technology yields more accurate data than other methods because it allows imaging of the entire cell by “adding up” signals produced from the scanning beam, Cheng said. Because the Bessel beam makes possible deep-tissue imaging, it could lead to systems that eliminate the need to draw blood for analyses such as drug testing and detection of biomarkers for non-invasive early diagnosis of diseases, Cheng said. “This is a long-term goal,” he said. “In the meantime, much more research is needed to improve the system.” The researchers proved the concept by imaging fat storage in living cells. Findings are detailed in a research paper appearing on April 24 in the journal Nature Communications. The reported technology yields information about chemical composition, collecting a series of images while rotating the sample and reconstructing the 3-D structure through image reconstruction algorithms. The Bessel beam is produced using a pair of cone-shaped “axicon” lenses and is combined with a microscope objective. Its use for volumetric fluorescence imaging was previously demonstrated by physicist Eric Betzig, who won the Nobel Prize in chemistry in 2014 for his pioneering contribution to super-resolution fluorescence microscopy. Super-resolution technology allows researchers to resolve structural features far smaller than the wavelength of visible light, sidestepping the “diffraction limit” that normally prevents imaging of features smaller than about 250 nanometers, which is large compared to certain biological molecules and structures in cells. However, fluorescence microscopy usually requires the use of fluorescent tags, which may interfere with biological processes and hinder accuracy for determining chemical structure. Future research will include work to increase the detection sensitivity of the system and improve the imaging quality and speed. “There is plenty of room for improvement,” Cheng said. “The system is based on a bulky and relatively expensive femtosecond laser, which limits its potential for broad use and clinical translation. Nevertheless, we anticipate that this limitation can be circumvented through engineering innovations to reduce the cost and size of our technology. We also note that the Bessel beam can be produced using fibers, which could simplify the system and enable endoscopic applications.” The paper was authored by Xueli Chen, a visiting scholar from Xidian University in China; Purdue postdoctoral research associate Chi Zhang; Purdue doctoral students Peng Lin and Kai-Chih Huang; Xidian University researchers Jimin Liang and Jie Tian; and Cheng. The research was supported by funds from the Keck Foundation and National Institutes of Health.


News Article | April 27, 2017
Site: phys.org

The system, called stimulated Raman projection microscopy and tomography, makes possible "volumetric imaging" without using fluorescent dyes that might affect biological functions and hinder accuracy, said Ji-Xin Cheng, a professor in Purdue University's Weldon School of Biomedical Engineering, Department of Chemistry and Birck Nanotechnology Center. "Volumetric chemical imaging allows a better understanding of the chemical composition of three-dimensional complex biological systems such as cells," he said. The technology uses a type of laser beam called a Bessel beam, which maintains focus for a longer distance than a traditional "Gaussian beam" used in other imaging technologies, making it possible to penetrate deep into tissue. Stimulated Raman spectroscopy eliminates the need for fluorescent dyes. The technology yields more accurate data than other methods because it allows imaging of the entire cell by "adding up" signals produced from the scanning beam, Cheng said. Because the Bessel beam makes possible deep-tissue imaging, it could lead to systems that eliminate the need to draw blood for analyses such as drug testing and detection of biomarkers for non-invasive early diagnosis of diseases, Cheng said. "This is a long-term goal," he said. "In the meantime, much more research is needed to improve the system." The researchers proved the concept by imaging fat storage in living cells. Findings are detailed in a research paper appearing on April 24 in the journal Nature Communications. The reported technology yields information about chemical composition, collecting a series of images while rotating the sample and reconstructing the 3-D structure through image reconstruction algorithms. The Bessel beam is produced using a pair of cone-shaped "axicon" lenses and is combined with a microscope objective. Its use for volumetric fluorescence imaging was previously demonstrated by physicist Eric Betzig, who won the Nobel Prize in chemistry in 2014 for his pioneering contribution to super-resolution fluorescence microscopy. Super-resolution technology allows researchers to resolve structural features far smaller than the wavelength of visible light, sidestepping the "diffraction limit" that normally prevents imaging of features smaller than about 250 nanometers, which is large compared to certain biological molecules and structures in cells. However, fluorescence microscopy usually requires the use of fluorescent tags, which may interfere with biological processes and hinder accuracy for determining chemical structure. Future research will include work to increase the detection sensitivity of the system and improve the imaging quality and speed. "There is plenty of room for improvement," Cheng said. "The system is based on a bulky and relatively expensive femtosecond laser, which limits its potential for broad use and clinical translation. Nevertheless, we anticipate that this limitation can be circumvented through engineering innovations to reduce the cost and size of our technology. We also note that the Bessel beam can be produced using fibers, which could simplify the system and enable endoscopic applications." The paper was authored by Xueli Chen, a visiting scholar from Xidian University in China; Purdue postdoctoral research associate Chi Zhang; Purdue doctoral students Peng Lin and Kai-Chih Huang; Xidian University researchers Jimin Liang and Jie Tian; and Cheng. Explore further: Imaging uses 'photothermal effect' to peer into living cells More information: Xueli Chen et al. Volumetric chemical imaging by stimulated Raman projection microscopy and tomography, Nature Communications (2017). DOI: 10.1038/ncomms15117


Wu S.-L.,Xidian University
Nonlinear Analysis: Real World Applications | Year: 2012

This paper is concerned with entire solutions of a bistable reactiondiffusion system modeling manenvironmentman epidemics, i.e., solutions defined for all times t ∈ R and for all points x ∈ R. It is known that the system has an increasing traveling wave solution with nonzero wave speed under some reasonable conditions. Using the comparison argument and sub-super-solution method, we construct some new entire solutions for the system which behave like two increasing traveling wave solutions propagating from both sides of the x-axis and annihilating at a finite time. © 2011 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved.


Li J.,Xidian University
IET Control Theory and Applications | Year: 2013

In this study, a new consensus problem is introduced for leader-follower multi-agent systems with non-linearity and a distributed adaptive iterative learning control is presented for the consensus problem. With the dynamic of the leader unknown to any of the agent, the proper protocol guarantees that the follower agents can track the leader. The consensus is analysed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed method in this study. © 2013 The Institution of Engineering and Technology.


Sun P.G.,Xidian University
Physica A: Statistical Mechanics and its Applications | Year: 2014

Link weights have the equally important position as links in complex networks, and they are closely associated with each other for the emergence of communities. How to assign link weights to make a clear distinction between internal links of communities and external links connecting communities is of vital importance for community detection. Edge centralities provide a powerful approach for distinguishing internal links from external ones. Here, we first use edge centralities such as betweenness, information centrality and edge clustering coefficient to weight links of networks respectively to transform unweighted networks into weighted ones, and then a weighted function that both considers links and link weights is adopted on the weighted networks for community detection. We evaluate the performance of our approach on random networks as well as real-world networks. Better results are achieved on weighted networks with stronger weights of internal links of communities, and the results on unweighted networks outperform that of weighted networks with weaker weights of internal links of communities. The availability of our findings is also well-supported by the study of Granovetter that the weak links maintain the global integrity of the network while the strong links maintain the communities. Especially in the Karate club network, all the nodes are correctly classified when we weight links by edge betweenness. The results also give us a more comprehensive understanding on the correlation between links and link weights for community detection. © 2013 Elsevier B.V. All rights reserved.


Sun P.G.,Xidian University
New Journal of Physics | Year: 2015

Controllability of a single network often focuses on the determination of the network's minimum dominating set, which aims to elaborate how to control the whole network with minimum driver nodes. This paper proposes a new framework, co-controllability of multiple networks, which stresses the control of one network by another network as well as the mutual control characteristics of multiple networks based on minimum dominating sets. We take a drug-disease-gene network that consists of a drug-drug network, a disease-disease network and a gene-gene network as an example to study co-controllability of multiple networks. The results show that driver nodes tend to be conserved, e.g. diseases highly associated with driver nodes of the drug-drug network tend to be driver nodes in the disease-disease network compared with random networks. In addition, co-controllability of multiple networks is probably associated with the networks' node degree, which is more stringent than controllability of a single network that is mainly determined by the network's degree distribution. We also find that diseases and drugs tend to be mapped as two different subnetworks of human protein-protein interaction (PPI) network, drugs are inclined to dominate diseases by controlling the PPI network, and the coded proteins of disease-related genes exhibit a low tendency to be drug targets for the control of diseases. The results in this paper not only play an important role in understanding co-controllability of multiple networks, but also are helpful for understanding the mechanisms of drug-disease-gene, disease treatments and drug design in a network-based framework. © 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.


Wang G.,Xidian University
IEEE Transactions on Vehicular Technology | Year: 2011

In this paper, the energy-based localization problem in wireless sensor networks is addressed. We focus on the weighted least squares (WLS) estimation of the source location. Due to the nonconvex nature of the WLS formulation, its global solution is hard to obtain without a good initial estimate. We propose a semidefinite relaxation method for this localization problem. To do so, we transform the original WLS formulation into a nonconvex approximate WLS (AWLS) formulation, which is then relaxed as a semidefinite programming (SDP). We show that it is possible for the SDP to be tight, i.e., the SDP solves the original AWLS problem. For the cases where the SDP is not tight, a procedure called Gaussian randomization is applied to further refine the SDP solution. Simulation results show that the proposed method can outperform the existing methods at high noise levels. © 2011 IEEE.


Qing-Dao-Er-Ji R.,Xidian University | Wang Y.,Xidian University
Computers and Operations Research | Year: 2012

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm. © 2011 Elsevier Ltd. All rights reserved.


Gao W.-F.,Xidian University | Liu S.-Y.,Xidian University
Computers and Operations Research | Year: 2012

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms. © 2011 Elsevier Ltd. All rights reserved.


A compressive sensing-based multispectral video imager comprises a beamsplitter, a first light channel, a second light channel, and an image reconstruction processor; the beamsplitter is configured to divide the beam of an underlying image into a first light beam and a second light beam; the first light beam enters the first light channel, processed and sampled in the first light channel, to obtain a first mixing spectral image which is transferred to the image reconstruction processor; the second light beam enters the second light channel, processed and sampled in the second light channel, to obtain a second mixing spectral image which is transferred to the image reconstruction processor; the image reconstruction processor is configured to reconstruct the underlying spectral image based on the first mixing spectral image and the second mixing spectral image by using non-linear optimization method.

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