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Institution

Xidian University

EducationXi'an, China
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.


Papers
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Journal ArticleDOI
TL;DR: This paper proposes a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem, and takes advantage of the complementary benefits of RGB and thermal infrared images.
Abstract: RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast.

141 citations

Journal ArticleDOI
Kangqi Fan1, Shaohua Liu1, Haiyan Liu, Yingmin Zhu1, Weidong Wang1, Daxing Zhang1 
TL;DR: In this article, a bi-directional hybrid energy harvester (HEH) is presented to scavenge energy from ultra-low frequency mechanical excitations, which consists of two piezoelectric cantilever beams, a suspended magnet, and a set of coil.

141 citations

Journal ArticleDOI
TL;DR: The results prove that the proposed recommender algorithm performs more accurately than the traditional slope one algorithm when dealing with personalized recommendation tasks that concern the relationship among users.
Abstract: The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

141 citations

Journal ArticleDOI
TL;DR: Benefitting from the tiny intra-cavity energy change, repeatable interconversion between the chaotic modulation instability and stable soliton crystal states can be successfully achieved via simple tuning of the temperature or pump power, showing the easy accessibility and excellent stability of such soliton crystals.
Abstract: We demonstrate robust soliton crystals generation with a fixed frequency pump laser through a thermoelectric-cooler-based thermal-tuning approach in a butterfly-packaged complementary-metal-oxide-semiconductor-compatible microresonator. Varieties of soliton crystal states, exhibiting "palm-like" optical spectra that result from the strong interactions between the dense soliton ensembles and reflect their temporal distribution directly, are experimentally observed by sweeping one cavity resonance across the pump frequency from the blue-detuned side by reducing the operating temperature of the resonator. Benefitting from the tiny intra-cavity energy change, repeatable interconversion between the chaotic modulation instability and stable soliton crystal states can be successfully achieved via simple tuning of the temperature or pump power, showing the easy accessibility and excellent stability of such soliton crystals. This work could facilitate microresonator-based optical frequency combs towards a portable, adjustable, and low-cost system while avoiding the requirements of delicate frequency-sweeping pump techniques.

141 citations

Journal ArticleDOI
TL;DR: A statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms is developed, which has better recognition performance but also is more robust to noises than the two existing statistical models.
Abstract: In the statistical target recognition based on radar high-resolution range profile (HRRP), two challenging tasks are how to deal with the target-aspect, time-shift, and amplitude-scale sensitivity of HRRP and how to accurately describe HRRPs statistical characteristics. In this paper, based on the scattering center model, range cells are classified, in accordance with the number of predominant scatterers in each cell, into three statistical types. After resolving the three sensitivity problems, this paper develops a statistical model comprising two distribution forms, i.e., Gamma distribution and Gaussian mixture distribution, to model echoes of different types of range cells as the corresponding distribution forms. Determination of the type of a range cell is achieved by using the rival penalized competitive learning (RPCL) algorithm, while estimation for the parameters of Gamma distribution and Gaussian mixture distribution by the maximum likelihood (ML) method and the expectation-maximization (EM) algorithm, respectively. Experimental results for measured data show that the proposed statistical model not only has better recognition performance but also is more robust to noises than the two existing statistical models, i.e., Gaussian model and Gamma model.

140 citations


Authors

Showing all 32362 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Jie Zhang1784857221720
Bin Wang126222674364
Huijun Gao12168544399
Hong Wang110163351811
Jian Zhang107306469715
Guozhong Cao10469441625
Lajos Hanzo101204054380
Witold Pedrycz101176658203
Lei Liu98204151163
Qi Tian96103041010
Wei Liu96153842459
MengChu Zhou96112436969
Chunying Chen9450830110
Daniel W. C. Ho8536021429
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023117
2022529
20213,751
20203,816
20194,017
20183,382