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arXiv 2019年

作者： Li, Minghan Cao, Xiangyong Zhao, Qian Zhang, Lei Gao, Chenqiang Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well encodes their temporally dynamic configurations by real-time ameliorated parameters in the model. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the ADMM algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves better performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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A Broadband Dual-Polarized Magneto-Electric Dipole Antenna for 2G/3G/LTE Applications

A Broadband Dual-Polarized Magneto-Electric Dipole Antenna f...

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IEEE Asia-Pacific Conference on Antennas and Propagation

作者： Wensheng Yu Zhongxiang Zhang Aoyun Zhang Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University School of Electronic and Information Engineering Hefei Normal University

ISBN: (纸本)9781538616093

A broadband dual-polarized magneto-electric dipole antenna excited by Γ-shaped strips is presented in this communication. The proposed antenna contains three parts. The radiating element of the proposed antenna is a combination of the vertically oriented shorted patch, the planar dipole and the bottom plate. The two orthogonal Γ-shaped feeding strips connected with 50 Ω SMA launchers are used to excite the proposed antenna. The corner reflector can effectively improve the radiation performance of the proposed antenna. As shown in simulated results, the impedance bandwidth of the proposed antenna is 48% for standing-wave ratio (SWR) ≤2 (0.86-2.47 GHz), the available bandwidth of the antenna covers the 2G/3G/LTE bands, and the antenna obtains a stable gain that the average value is 10 dBi. Meanwhile, the antenna has good performance in radiation characteristics. The proposed antenna can be applied to the base station communication systems.

关键词： Broadband Dual-polarization Corner reflector Magneto-electric dipole

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A Pilot Allocation Method of Jointing Cell Grouping and Alliance Game in Massive MIMO System

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IOP Conference Series: Materials Science and Engineering 2019年第7期490卷

作者： Feiyue Wang Hui Zhi Ziju Huang Key Lab of Computing Intelligent and Signal Processing (Ministry of Education) Anhui University Hefei China

In order to improve the uplink channel estimation performance and reduce pilot contamination (PC), this paper proposes a pilot allocation method of jointing cell grouping and alliance game (JCG-AG) for massive multiple-input multiple-output (MIMO) cellular networks. It can be divided into two steps. First, by properly dividing all cells into different cell-groups, the users in different cell-groups are allocated different orthogonal pilot sets. Second, the users in the same cell-group are further divided into several mutually disjoint sub-alliances. Users belonging to different sub-alliances are allocated different pilots, and users belonging to the same sub-alliance reuse the same pilots. During the second step, the alliance formation algorithm is used to suppress PC. The JCG-AG method takes practical application as the starting point, focuses on the random distribution of users, and uses the idea of alliance game. All these make it more flexible and practical. As demonstrated in the simulation results, the JCG-AG method can greatly mitigate the PC and reduce the average mean square error (MSE) for all channel estimations in the uplink.

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The Downlink Performance of Massive MIMO Systems under Different Uplink Pilot Allocation Schemes

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IOP Conference Series: Materials Science and Engineering 2019年第7期490卷

作者： Ziju Huang Hui Zhi Feiyue Wang Key Lab of Computing Intelligent and Signal Processing (Ministry of Education) Anhui University Hefei China

In the scenario of time division duplexing (TDD) massive multiple-input multiple-output (MIMO) system, when there is pilot contamination (PC) in the uplink, different pilot allocation schemes will affect the results of the uplink channel estimation. When the channel estimation results are used for downlink transmission precoding, which will further affects the downlink performance. This paper analyses the impact of different pilot allocation (PA) schemes on downlink performance. Through theoretical analysis, the system downlink signal to interference plus noise ratio (SINR) and spectral efficiency expressions of different pilot allocation schemes are obtained respectively. The simulation results show that the pilot allocation schemes with cell grouping is better than the schemes without cell grouping. The SINR increases as the number of cell grouping increases, and the spectrum efficiency increases first and then decreases as the number of cell grouping increases.

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A Wideband Dual-Band Magneto-Electric Dipole Antenna With Modified Feed Structure

A Wideband Dual-Band Magneto-Electric Dipole Antenna With Mo...

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IEEE International Conference on Microwave and Millimeter Wave Technology

作者： Fan Li Yufa Sun Haoran Zhu Hongyu Fang Ministry of Education Anhui University Hefei China Ministry of Education Key Lab of Intelligent Computing & Signal Processing Anhui University Hefei China

In this paper, a novel wideband dual-band magneto-electric dipole antenna with modified feed structure is proposed. Double-layer patches are utilized to design a pair of folding metal. The double-layer patches dipole can generate two resonant frequencies, which are employed to form a dual-band magneto-electric dipole antenna. A stair-shaped feeding strip with a hexagonal structure is designed to modify the impedance matching. It is found from simulated results that two operation bands with bandwidths of 60% from 1.61 to 2.99 GHz and 31.9% from 4.99 to 6.89 GHz under the return loss level of 10 dB were achieved. The gain of the antenna ranged from 7.56 to 10.56 dBi. Furthermore, low cross-polarization level and low back-radiation are also obtained with the proposed antenna.

关键词： Dipole antennas Dual band Antenna feeds Broadband antennas Magnetoelectric effects Impedance matching Magnetic resonance

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Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images

arXiv

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arXiv 2024年

作者： Shan, Lianlei Wang, Weiqiang Lv, Ke Luo, Bin The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei230601 China

Semantic segmentation requires pixel-level annotation, which is time-consuming. Active Learning (AL) is a promising method for reducing data annotation costs. Due to the gap between aerial and natural images, the previous AL methods are not ideal, mainly caused by unreasonable labeling units and the neglect of class imbalance. Previous labeling units are based on images or regions, which does not consider the characteristics of segmentation tasks and aerial images, i.e., the segmentation network often makes mistakes in the edge region, and the edge of aerial images is often interlaced and irregular. Therefore, an edge-guided labeling unit is proposed and supplemented as the new unit. On the other hand, the class imbalance is severe, manifested in two aspects: the aerial image is seriously imbalanced, and the AL strategy does not fully consider the class balance. Both seriously affect the performance of AL in aerial images. We comprehensively ensure class balance from all steps that may occur imbalance, including initial labeled data, subsequent labeled data, and pseudo-labels. Through the two improvements, our method achieves more than 11.2% gains compared to state-of-the-art methods on three benchmark datasets, Deepglobe, Potsdam, and Vaihingen, and more than 18.6% gains compared to the baseline. Sufficient ablation studies show that every module is indispensable. Furthermore, we establish a fair and strong benchmark for future research on AL for aerial image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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New Framework for Code-Mapping-based Reversible Data Hiding in JPEG Images

arXiv

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arXiv 2020年

作者： Du, Yang Yin, Zhaoxia Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Anhui Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Communication & Electronic Engineering East China Normal University Shanghai200241 China

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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High-order parabolic equation method for electromagnetic computation

High-order parabolic equation method for electromagnetic com...

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Asia-Pacific Conference on Microwave

作者： Zhi-Xiang Huang Bo Wu Wei Sha Ming-Sheng Xian-liang Wu Hong Dai Key Lab of Intelligent Computing & Signal Processing Anhui University China School of physical science and technology Yunnan University China Department of Physics and Electronic Engineering Hefei Teachers College China Department of Electrical and Electronic Engineering University of Hong Kong China Hefei Normal University Hefei Anhui CN

A high-order PE method has been derived and analyzed for solving multi-object RCS for the first time. Compared with the existing method SPE, the present method has following features: Firstly, boundary conditions including PML and scatterers boundary conditions can be handled as easy as SPE method. Secondly, to obtain the full bistatic RCS, we need only two rotated PE runs for high-order PE while in SPE method the number is twelve. Last but most important of all is that due to the narrow-angle approximation, the SPE cannot handle multi-object scattering problems exactly while the high-order PE method can compute multi-object RCS efficiently. The method is very powerful in deal with objects quite large compared to the wavelength.

关键词： Partial differential equations Electromagnetic scattering Optical scattering Radar scattering Electromagnetic fields Physics computing Finite difference methods Time domain analysis Taylor series signal processing

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Texture classification in extreme scale variations using GANet

arXiv

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arXiv 2018年

作者： Liu, Li Chen, Jie Zhao, Guoying Fieguth, Paul Chen, Xilin Pietikäinen, Matti Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FV-CNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

arXiv

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arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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