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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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Semantic cohesion model for phrase-based SMT

Semantic cohesion model for phrase-based SMT

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24th International Conference on Computational Linguistics, COLING 2012

作者： Feng, Minwei Sun, Weiwei Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany MOE Key Laboratory of Computational Linguistics Institute of Computer Science and Technology Peking University Beijing China

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicateargument structures as soft constraints and plays the role as a reordering model in the phrasebased statistical machine translation system. We build a translation system with GALE data. Experimental results on the NIST02, NIST03, NIST04, NIST05 and NIST08 Chinese-English tasks show that our model improves the baseline system by 0.93 BLEU 0.98 TER on average. We also compare our method with a syntax-augmented model (Cherry, 2008), and demonstrate the importance of predicate-argument semantics in machine translation. © 2012 The COLING.

关键词： Semantics

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Online non-feedback image re-ranking via dominant data selection

Online non-feedback image re-ranking via dominant data selec...

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20th ACM International Conference on Multimedia, MM 2012

作者： Cao, Chen Chen, Shifeng Li, Yuhong Liu, Jianzhuang Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab. Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450310895

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches. © 2012 ACM.

关键词： Supervised learning

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Locating high-density clusters with noisy queries

Locating high-density clusters with noisy queries

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International Conference on pattern recognition

作者： Chen Cao Shifeng Chen Changqing Zou Jianzhuang Liu Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong China

ISBN: (纸本)9781467322164

Semi-supervised learning (SSL) relies on a few labeled samples to explore data's intrinsic structure through pairwise smooth transduction. The performance of SSL mainly depends on two folds: (1) the accuracy of labeled queries, (2) the integrity of manifolds in data distribution. Both of these qualities would be poor in real applications as data often consist of several irrelevant clusters and discrete noise. In this paper we propose a novel framework to simultaneously remove discrete noise and locate the high-density clusters. Experiments demonstrate that our algorithm is quite effective to solve several problems such as non-feedback image re-ranking and image co-segmentation.

关键词： Noise Noise measurement Clustering algorithms Databases Vectors Manifolds Semisupervised learning

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization computer vision Histograms Windows

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QT-based monitoring system of multi-functional laboratory

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Research Journal of Applied Sciences, Engineering and Technology 2012年第21期4卷 4480-4483页

作者： Xiaoling, Li Jimin, Yuan School of Computer Science and Technology Chengdu University Chengdu 610106 China Key University Key Laboratory of Pattern Recognition and Intelligent information Processing Sichuan Province China School of Computer Science and Technology Panzhihua University Panzhihua 617000 China

Regarding the embedded processor as the core, this study utilizes various cutting-edge technologies such as wireless LAN, USB interface, Bluetooth, multimedia, etc., to propose the design program of QT-based security monitoring system. Taking the lab environment in school as an example, this system has achieved the security monitoring, information transmission and control of certain equipment. Besides, it has cut the Linux kernel module reasonably and explored the touch screen, serial port, wireless LAN, Bluetooth, USB and other resources, thus realizing various functions, such as collection and processing of audio, video and security information and wireless communication. Thereby, users can carry out real-time monitoring for multiple locations through the wireless LAN. © Maxwell Scientific Organization, 2012.

关键词： Bluetooth

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Image super-resolution reconstruction based on self-similarity and neural networks

Image super-resolution reconstruction based on self-similari...

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2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012

作者： Xu, Yan Li, Xue M. Gao, Tian Suen, Ching Y. Beijing Key Laboratory of Network System and Network Culture University of Posts and Telecommunications Beijing 100876 China Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QC H3G 1M8 Canada School of Computer Shandong Jianzhu University Jinan 250101 China

ISBN: (纸本)9781467303828

A novel super-resolution approach is presented. An image pyramid has been built based on the framework of wavelet transform, and the detailed coefficients are explored for training the neural networks. The initial high resolution image is estimated by the trained networks and the inverse wavelet transform, and then is constrained with prior knowledge of the error function by iteration. For a factor of 2n, repeat this process and update the networks. The experimental results show that our method reconstructs the more reliable image without obvious visual artifacts. © 2012 IEEE.

关键词： Wavelet transforms

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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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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

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作者： Gass, Tobias Pishchulin, Leonid Dreuw, Philippe Ney, Hermann Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781424491407

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape. © 2011 IEEE.

关键词： Face recognition

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Clustering-based Discriminative Locality Alignment for Face Gender recognition

Clustering-based Discriminative Locality Alignment for Face ...

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IEEE/RSJ International Conference on Intelligent Robotics and Systems

作者： Duo Chen Jun Cheng Dacheng Tao College of Communication Engineering Chongqing University Chongqing 400044 China. He is also with the Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. He is also with the Chinese University of Hong Kong and Guangdong Provincial Key Laboratory of Robotics and Intelligent System. Center for Quantum Computation and Intelligent System Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia.

ISBN: (数字)9781467317368

ISBN: (纸本)9781467317375

To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance. In particular, CDLA exploits the global geometry through k-means clustering, extracts the discriminative information through margin maximization and explores the local geometry through intra cluster sample concentration. These three properties uniquely characterize CDLA for face gender recognition. The experimental results obtained from the FERET data sets suggest the superiority of the proposed method in terms of recognition speed and accuracy by comparing with several representative methods.

关键词： FERET CDLA comparing recognition (Psychology) Gender Global geometry Compare Face Face

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