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7th EAI International Conference on Cloud Computing, CloudComp 2016 and 1st EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2016

作者： Zhang, Hao Yin, Zhaoxia Zhang, Xinpeng Chen, Jianpeng Wang, Ruonan Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9783319696041

This paper presents an adaptive reversible information hiding algorithm that can maintain thee JPEG file sizes by using RLC (Run Length Coding) AC coefficient coded for embedding, the key point is to choose the appropriate number of participation to hide information. By calculating the maximum storage capacity of the image at different system, select the appropriate RLC pairs to rotate and embed data. In the extraction stage, by calculating the sequence of the original RLC pairs status, then consult the mapping relationships between the current sequence and the original RLC pairs sequence, we extract the secret message and recover the original image. Test results proved that the proposed method can improve the rate-distortion performance to some extent. © 2018, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Adaptive algorithms

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Realization of fixed-point SAR imaging based on embedded GPU

Realization of fixed-point SAR imaging based on embedded GPU

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： HuiXing Li ZiXin Gao BingYi Li Yi Zhuang Xie Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Radio Measurement Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Aiming at the problem of insufficient memory in the real-time processing of SAR imaging on embedded TX2, this paper studies a design optimization scheme of fixed-point imaging algorithm on TX2. In order to ensure the accuracy, this paper only performs fixed-point processing on the FFT. According to the characteristics of the algorithm, the memory multiplexing method is adopted to effectively reduce the occupation of the work area buffer. A large number of registers are used to improve computing performance. The results show that the fixed-point FFT processing scheme can achieve a hundred times faster than the Intel Xeon CPU, and the memory footprint is less.

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Polarimetric SAR Image Scattering Center Estimation via Atomic Norm Minimization

Polarimetric SAR Image Scattering Center Estimation via Atom...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Ying Xi Yanhua Wang Wei Chen Haibo Liu Yang Li Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Polarization is one of the most important characteristics of electromagnetic wave, which offers another dimension of information to radar automatic target recognition (RATR). The combination of polarization and SAR technology is a vital research direction for RATR. Fusing the polarization information into the scattering center model is able to refine the scattering structural information. This paper proposes a fully-polarimetric SAR images scattering center estimation method based on multiple measurement vector (MMV) atomic norm minimization (ANM) model. The experimental results indicate the superiorities of the proposed method in terms of accuracy and robustness.

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How Powerful are Shallow Neural Networks with Bandlimited Random Weights?

arXiv

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arXiv 2020年

作者： Li, Ming Sonoda, Sho Cao, Feilong Wang, Yu Guang Liang, Jiye Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua China Deep Learning Theory Team RIKEN AIP Tokyo Japan College of Sciences China Jiliang University Hangzhou China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China

We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignment, and only the output layer parameters are trained by loss minimization. Using random weights for a hidden layer is an effective method to avoid non-convex optimization in standard gradient descent learning. It has also been adopted in recent deep learning theories. Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error. In particular, we derive a new nontrivial approximation error lower bound. The proof utilizes the technique of ridgelet analysis, a harmonic analysis method designed for neural networks. This method is inspired by fundamental principles in classical signal processing, specifically the idea that signals with limited bandwidth may not always be able to perfectly reconstruct the original signal. We corroborate our theoretical results with various simulation studies, and generally, two main take-home messages are offered: (i) Not any distribution for selecting random weights is feasible to build a universal approximator;(ii) A suitable assignment of random weights exists but to some degree is associated with the complexity of the target function. © 2020, CC BY.

关键词： Convex optimization

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Face Analysis for Coronary Heart Disease Diagnosis 12

Face Analysis for Coronary Heart Disease Diagnosis

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12th International Congress on Image and signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Lin, Shisong Zhuang, Xiaodong Huang, Shiyun Liu, Yahui Shen, Linlin Liao, Xinxue Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Cardiology department First affiliated hospital of Sun Yat-sen University Guangzhou China NHC Key Laboratory of Assisted Circulation Sun Yat-sen University Guangzhou China Shenzhen National Medical Science and Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781728148526

CHD (Coronary Heart Disease) is one of the leading causes of cardiovascular disease deaths. Invasive coronary arteriography is one of the widely used approaches to diagnose CHD. However, the time cost and expenses for most of the diagnosis methodologies are high and some patients are reluctant to do such a diagnosis. In this paper, we aim to develop a novel low cost method to predict CHD. As suggested by the cardiologists, there are substantial differences between the facial images of patients with CHD and those of healthy subjects. In this paper, we conduct an automatic analysis of the texture features extracted from eight ROIs (Regions of Interests) of the face images. Based on the texture features, random forest and decision tree are used to predict whether the subject has a CHD, or not. The experimental results on a set of 1528 face images collected from 309 subjects suggest that, our approach achieved a promising, i.e. 72.73%, prediction accuracy. © 2019 IEEE.

关键词： Diagnosis

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Geometric Correction of Spaceborne SAR Image Based on DEM and ICESat Database

Geometric Correction of Spaceborne SAR Image Based on DEM an...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Chao Xing Yuekun Wang Zhibin Wang Zhiqiang Liao Feng Tian Zhiyong Suo Zhenfang Li National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Nanjing Research Institute of Electronics Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China Sichuan Aerospace Electronic Equipment Research Institute Sichuan China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The accuracy of geolocation is crucial in the application of remote sensing images. However, the presence of inevitable measurement errors consisting of antenna phase center error, slant range error, and reference height error of ground target, will result in degraded the geolocation accuracy of imaged ground targets. To improve the accuracy of geolocation, the approach of geometric correction for spaceborne SAR image is investigated in this paper. The well-known database contained digital elevation model(DEM) and ICESat data are adopted to estimate and calibrate the aforementioned errors for improving the geolocation accuracy. Validation of the proposed method is demonstrated with GF3 SAR data and high-precision GPS device.

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Learning compact target-oriented feature representations for visual tracking

arXiv

引用

arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

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Nonlinearity in the Dark: Broadband Terahertz Generation with Extremely High Efficiency

引用

Physical Review Letters 2019年第2期122卷 027401-027401页

作者： Ming Fang Nian-Hai Shen Wei E. I. Sha Zhixiang Huang Thomas Koschny Costas M. Soukoulis Ames Laboratory—U.S. DOE and Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Institute of Electronic Structure and Lasers (IESL) FORTH 71110 Heraklion Crete Greece

Plasmonic metamaterials and metasurfaces offer new opportunities in developing high performance terahertz emitters and detectors beyond the limitations of conventional nonlinear materials. However, simple meta-atoms for second-order nonlinear applications encounter fundamental trade-offs in the necessary symmetry breaking and local-field enhancement due to radiation damping that is inherent to the operating resonant mode and cannot be controlled separately. Here we present a novel concept that eliminates this restriction obstructing the improvement of terahertz generation efficiency in nonlinear metasurfaces based on metallic nanoresonators. This is achieved by combining a resonant dark-state metasurface, which locally drives nonlinear nanoresonators in the near field, with a specific spatial symmetry that enables destructive interference of the radiating linear moments of the nanoresonators, and perfect absorption via simultaneous electric and magnetic critical coupling of the pump radiation to the dark mode. Our proposal allows eliminating linear radiation damping, while maintaining constructive interference and effective radiation of the nonlinear components. We numerically demonstrate a giant second-order nonlinear susceptibility ∼10−11 m/V, a one order improvement compared with the previously reported split-ring-resonator metasurface, and correspondingly, a 2 orders of magnitude enhanced terahertz energy extraction should be expected with our configuration under the same conditions. Our study offers a paradigm of high efficiency tunable nonlinear metadevices and paves the way to revolutionary terahertz technologies and optoelectronic nanocircuitry.

关键词： Metamaterials

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Mapping Heavy Metals in Cultivated Soils Based on Land Use Types and Cokriging 5th

Mapping Heavy Metals in Cultivated Soils Based on Land Use T...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zhao, Jinling Liu, Chuang Song, Qixiang Jiang, Yan Hong, Qi Huang, Linsheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Information Engineering Institute Suzhou University Suzhou234000 China

ISBN: (纸本)9789811308925

It is extremely important to explore the heavy metal content and spatial distribution in different cultivated soils. In our study, cokriging (COK) method was used to investigate the relationship between heavy metals and land use types. A total of six heavy metals including Zn, Fe, Cu, Mn, B, S were selected to forecast the heavy metals in cultivated soils. Five types of cultivated land were considered in the study area, including vegetable land, irrigated land, irrigated paddy field, dry land, orchard. Test of normality was firstly carried out to assure that higher prediction accuracy can be obtained, and then correlation analysis and analysis of variance (ANOVA) were performed to find out the most sensitive heavy metals for spatial interpolation. The analysis results showed that Zn, Cu and Fe were the primary heavy metals in soil of the study area. It was obvious that the effect of Mn and Fe were much greater caused by land use types, while they were smaller for B and S. Three errors of mean standard (MS), root mean square error (RMSE), and root mean square standardized error (RMSSE) were derived to assess the prediction accuracy. In comparison with ordinary kriging (OK), the MS of COK approached to 1, the RMSE was much smaller and the RMSSE also approached to 1, which showed that COK can make better predictions than OK. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Heavy metals

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