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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

电子学报(英文版) 2012年第2期21卷 202-208页

作者： LI Huihui MOU Xuanqin JI Zhen YU Hang LI Yan Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an 710049 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Laboratory of Embedded System Design Shenzhen 518060 China

A novel wideband 5.8GHz CPW-fed an- tenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numeri- cally using the Method of moment (MOM) and the Fi- nite element method (FEM). With the antenna size lim- ited to 30 × 30mm2, the ?10dB bandwidth obtained by MOM is 3.235GHz (5.765～9GHz) and the ?9.5dB band- width obtained by FEM is 2.74GHz (5.32～8.06GHz), cor- responding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured ?10dB bandwidth is 2.68GHz (5.63GHz～8.31GHz), 46.2% of the 5.8GHz frequency. Fur- thermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than ?10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： RFID标签天线尺寸共面波导宽带无线电频率识别有限元分析网络连接数值分析

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Accurate object tracking based on homography matrix

Accurate object tracking based on homography matrix

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2012 International Conference on computer Science and Service System, CSSS 2012

作者： Zhang, Miaohui Hou, Yandong Hu, Zhentao Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai 200240 China School of Computer and Information Engineering Henan University Kaifeng 475004 China

ISBN: (纸本)9780769547190

We present a novel object tracking framework that can efficiently track such 2-D affine motions of the object image. A coarse-to-fine tracking strategy is explored in the tracking framework. Firstly, the object image region is selected from the first frame, the scale-invariant feature (SIFT) extracted from the object image region is utilized as the object model. Secondly, the highest likelihood object region is preliminarily located by the particle filter (PF) algorithm in the following video frame, and the SIFT feature points are also extracted from the highest likelihood object region, matching point set between object model and the highest likelihood region is attained by the scale-invariant feature transform, then the corresponding homography matrix is robustly estimated through the random sample consensus (RANSAC) algorithm. Finally, the affine parameters which determine the location and pose of the object are obtained. Simulation results demonstrate the efficiency of our approach. © 2012 IEEE.

关键词： Tracking (position)

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Contour algorithm based on GVF snake

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2011 International Conference on Electronic engineering, Communication and Management, EECM 2011

作者： Zhang, Fan Li, Shengwei Zheng, Gang Li, Rui College of Computer and Information Engineering Henan University Kaifeng Henan 475001 China Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan 475001 China Kaifeng Electric Power Company Kaifeng 475001 China

ISBN: (纸本)9783642272950

In order to solve the initial active contour problem of Snake model, Contourlet transform is introduced into the GVF Snake model, which will provides a way to set the initial contour, as a result, will improves the edge detection results of GVF Snake model effectively. Firstly, the contours of the object in images can be obtained based on Contourlet Transform, and this contours will be identified as the initial contour of GVF Snake model. Secondly, then GVF Snake model is used to detect the contour edge of human gait motion. Experimental results show that the proposed method can extract the edge feature accurately and efficiently. © 2012 Springer-Verlag.

关键词： Contourlet transform

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Maximum margin clustering on evolutionary data 12

Maximum margin clustering on evolutionary data

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21st ACM International Conference on information and Knowledge Management, CIKM 2012

作者： Fan, Xuhui Zhu, Lin Cao, Longbing Cui, Xia Ong, Yew-Soon Advanced Analytics Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Computer Engineering Nanyang Technological University Signapore Singapore

ISBN: (纸本)9781450311564

Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data. © 2012 ACM.

关键词： Clustering algorithms

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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The Remote Sensing image Enhancement Algorithm Based on NSCT and Grayscale Morphology

The Remote Sensing Image Enhancement Algorithm Based on NSCT...

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2012 4th International Conference on Environmental Science and information Application Technology(ESIAT 2012)

作者： Xiaoting Pu Zhenhong Jia Liejun Wang Jie Yang Raphael Hu College of Information Science and Engineering Xinjiang University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Knowledge Engineering and Discovery Research Institute Auckland University of Technology

In order to restrain pseudo-Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the Nonsubsampled Contourlet Transform (NSCT) and Grayscale Morphology is proposed in this paper. The proposed method utilizes the shift-invariance of NSCT to restrain the pseudo-Gibbs phenomenon. The results obtained with the proposed method are superior to histogram equalization and contourlet method in detail and vision of the image.

关键词： Remote Sensing image image Enhancement Nonsubsampled Contourlet Transform Unsharp Masking

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image Denoising Based on NSCT Domain with an Optimal Threshold and Neighbouring Window

Image Denoising Based on NSCT Domain with an Optimal Thresho...

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2012 4th International Conference on Environmental Science and information Application Technology(ESIAT 2012)

作者： Juan Yang Zhenhong Jia Xizhong Qin Jie Yang Yingjie Hu College of Information Science and Engineering Xinjiang University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Knowledge Engineering and Discovery Research Institute Auckland University of Technology

By considering the advantage of Nonsubsampled Contourlet Transform(NSCT),while Commonly used NeighShrink algorithm uses a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper,A novel image denoising algorithm based on an improved method, which can determine an optimal threshold and neighbouring window size for every NSCT subband by the Stein's unbiased risk estimate (SURE).The proposed method can effectively reduce Gaussian noise in remote sensing image and improve the image of the peak signal-to-noise ratio,This method utilizes the redundant and translation invariant of NSCT transform to inhibit the effect of pseudo Gibbs, and preserves the image texture and edge detail informations, thus obviously ameliorate the visual effect of the image.

关键词： image denoising, redundant, Nonsubsampled Contourlet Transform, adaptive, Neighbourhood

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Target recognition method based on template matching in downward-looking infrared image

Target recognition method based on template matching in down...

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2012 3rd IEEE/IET International Conference on Audio, Language and image processing, ICALIP 2012

作者： Zhu, Hu Deng, Lizhen Zhou, Gang Bai, Xiaodong Wang, Gaihua National Key Laboratory of Science and Technology on Multi-spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China College of Electrical and Electronic Engineering Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China

ISBN: (纸本)9781467301718

Automatic ground target recognition technology in downward-looking infrared imagery is challenging problems due to the complexity of real-world. A robust ground target recognition method is proposed for downward-looking infrared (DLIR) image sequences in this paper. A complete model of the proposed method is designed firstly and the principles of some key technologies are introduced as following. Perspective transformation is used to solve the projective problems of landmark translation, rotation and scale variance in real-time image sequence. The size of landmark is estimated real-timely by using flight parameters and imaging parameters for getting the model with an appropriate scale. Based on the matching results of landmark and flight parameters, target position technology is proposed to identify the position of target by using the position relation between landmark and target. Experimental results using real-world image data with complicated background showed that the proposed method not only causes high precisely locating results, but also has good robustness for target occlusion. © 2012 IEEE.

关键词： Template matching

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Fast ground target detection method in infrared image sequences

Fast ground target detection method in infrared image sequen...

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2012 3rd IEEE/IET International Conference on Audio, Language and image processing, ICALIP 2012

作者： Zhu, Hu Deng, Lizhen Wang, Gaihua Zhou, Gang Bai, Xiaodong National Key Laboratory of Science and Technology on Multi-spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China College of Electrical and Electronic Engineering Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China

ISBN: (纸本)9781467301718

Automatic target detection (ATD) in infrared (IR) imagery is a fundamental and challenging task in computer vision. A fast automatic target detection method in IR image sequence is proposed in this paper. Since the position and scale of target change real-timely, we can predict the target position in real-time image by using the history position of target and flight parameters information of previous and current frames, and then estimate the scale of target depending on flight parameters and imaging parameters for getting the model with the appropriate scale. In order to make the template matching more robust for target rotation, the template matching method based on parametric template vector is used to recognize the position of target. The detection result is identified by using multiframe integration based on recognition information of history and currant frames. Some experimental results using real-world images with complicated background validate the effectiveness and robustness of the proposed method under rotation and scale variance condition. © 2012 IEEE.

关键词： Template matching

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Experimental Comparison of Geometric, Arithmetic and Harmonic Means for EEG Event Related Potential Detection

Experimental Comparison of Geometric, Arithmetic and Harmoni...

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International Conference on Computational Intelligence and Security

作者： Jarno M.A. Tanskanen X.Z. Gao Jing Wang Ping Guo Jari A.K. Hyttinen Vassil S. Dimitrov Department of Biomedical Engineering Tampere University of Technology and BioMediTech Finland College of Information Engineering Shanghai Maritime University China Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China ATIPS Laboratory University of Calgary AB Canada

In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP applications, arithmetic mean (AM) is normally employed in processing the ERPs prior to ERP detection, whereas also other averaging methods might have beneficial properties. Fast ERP detection is essential, for example, in brain computer interfaces and during spine surgery. Thus, it is of interest to search for methods to aid in detecting ERPs with as few stimulus repetitions as possible. Here, noise reduction properties of AM, geometric mean (GM), and harmonic mean (HM) are demonstrated with simulations, and ERP processing by the three methods is illustrated by processing real visual evoked potentials (VEPs).

关键词： Electroencephalography Gaussian noise Educational institutions Electrodes Visualization Harmonic analysis

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