Institution
Xidian University
Education•Xi'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 published on a yearly basis
Papers
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19 Jun 2004TL;DR: A new discrete particle swarm optimization algorithm based on quantum individual is proposed, which is simpler and more powerful than the algorithms available.
Abstract: The particle swarm optimization algorithm is a new methodology in evolutionary computation. It has been found to be extremely effective is solving a wide range of engineering problems, however, it is of low efficiency in dealing with the discrete problems. In this paper, a new discrete particle swarm optimization algorithm based on quantum individual is proposed. It is simpler and more powerful than the algorithms available. The simulation experiments and its application in the CDMA also prove its high efficiency.
218 citations
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TL;DR: A device-to-device communication-based load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions is proposed.
Abstract: In LTE-Advanced networks, besides the overall coverage provided by traditional macrocells, various classes of low-power nodes (e.g., pico eNBs, femto eNBs, and relays) can be distributed throughout the macrocells as a more targeted underlay to further enhance the area?s spectral efficiency, alleviate traffic hot zones, and thus improve the end-user experience. Considering the limited backhaul connections within lowpower nodes and the imbalanced traffic distribution among different cells, it is highly possible that some cells are severely congested while adjacent cells are very lightly loaded. Therefore, it is of critical importance to achieve efficient load balancing among multi-tier cells in LTEAdvanced networks. However, available techniques such as smart cell and biasing, although able to alleviate congestion or distribute traffic to some extent, cannot respond or adapt flexibly to the real-time traffic distributions among multi-tier cells. Toward this end, we propose in this article a device-to-device communicationbased load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions. Besides identifying the research issues that deserve further study, we also present numerical results to show the performance gains that can be achieved by the proposed algorithm.
217 citations
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TL;DR: Li et al. as discussed by the authors proposed a self-supervised adversarial hashing (SSAH) approach, which leveraged two adversarial networks to maximize the semantic correlation and consistency of the representations between different modalities.
Abstract: Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (\textbf{SSAH}) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial networks are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.
216 citations
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TL;DR: This paper utilizes the sparse matrix to propose a new secure outsourcing algorithm of large-scale linear equations in the fully malicious model and shows that the proposed algorithm is superior in both efficiency and checkability.
Abstract: With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. In this paper, we investigate secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. For the first time, we utilize the sparse matrix to propose a new secure outsourcing algorithm of large-scale linear equations in the fully malicious model. Compared with the state-of-the-art algorithm, the proposed algorithm only requires ( optimal ) one round communication (while the algorithm requires $L$ rounds of interactions between the client and cloud server, where $L$ denotes the number of iteration in iterative methods). Furthermore, the client in our algorithm can detect the misbehavior of cloud server with the ( optimal ) probability 1. Therefore, our proposed algorithm is superior in both efficiency and checkability. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of our algorithm.
216 citations
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TL;DR: An AI system that automatically analyzes CT images to detect COVID-19 pneumonia features and was able to overcome a series of challenges in this particular situation and deploy the system in four weeks.
Abstract: The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.
216 citations
Authors
Showing all 32362 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Jie Zhang | 178 | 4857 | 221720 |
Bin Wang | 126 | 2226 | 74364 |
Huijun Gao | 121 | 685 | 44399 |
Hong Wang | 110 | 1633 | 51811 |
Jian Zhang | 107 | 3064 | 69715 |
Guozhong Cao | 104 | 694 | 41625 |
Lajos Hanzo | 101 | 2040 | 54380 |
Witold Pedrycz | 101 | 1766 | 58203 |
Lei Liu | 98 | 2041 | 51163 |
Qi Tian | 96 | 1030 | 41010 |
Wei Liu | 96 | 1538 | 42459 |
MengChu Zhou | 96 | 1124 | 36969 |
Chunying Chen | 94 | 508 | 30110 |
Daniel W. C. Ho | 85 | 360 | 21429 |