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	1. Low Complexity Method for Spreading Sequence Estimation of DSSS signal in Non-Cooperative Communication Systems
	cang liang,wang fuping,Wang Zanji
	Electrics, Communication and Autocontrol Technology 18 December 2008
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:It is a necessary step to estimate the spreading sequence of direct sequence spread spectrum (DSSS) signal for blind despreading and demodulation in non-cooperative communications. The article proposes two innovative and effective detection statistics to implement the synchronization and spreading sequence estimation procedure. The proposed algorithm also has a low computational complexity with only linear additions and modifications. Theoretical analysis and simulation results show that the algorithm performs quite well in low SNR environment, and is much better than all the existing typical algorithms with a comprehensive consideration both in performance and computational complexity.
	TO cite this article:cang liang,wang fuping,Wang Zanji. Low Complexity Method for Spreading Sequence Estimation of DSSS signal in Non-Cooperative Communication Systems[OL].[18 December 2008] http://en.paper.edu.cn/en_releasepaper/content/26731


	2. Localization in Wireless Sensor Networks Using a Mobile Anchor
	Zhen Hu,Lei Liu,Zhengxun Song,Dongbing Gu
	Electrics, Communication and Autocontrol Technology 12 May 2008
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	Abstract:In wireless sensor networks (WSNs), sensor location plays a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper describes MACL, a mobile anchorCentroid localization method, which uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. The proposed method is radio-frequency based, so no extra hardware or data communication is needed between the sensor nodes. We use simulations and tests from an indoor deployment using the Cricket location system to investigate the performance of MACL, and find that the localization method is principle simple, less computing and communication overhead, low costly, and flexible accuracy.
	TO cite this article:Zhen Hu,Lei Liu,Zhengxun Song, et al. Localization in Wireless Sensor Networks Using a Mobile Anchor[OL].[12 May 2008] http://en.paper.edu.cn/en_releasepaper/content/21314


	3. An Improved Face Hallucination Based PCA
	Xiaoling Wang,Ju Liu,Jianping Qiao,Jinyu Chu
	Electrics, Communication and Autocontrol Technology 21 April 2008
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	Abstract:In this paper, based on Circularly Symmetrical Gabor Transform (CSGT) and Principal Component Analysis (PCA), we propose a face hallucination approach. In this approach, all of the face images (both input face image and original training database) are transformed through CSGT at first and then local extremes criteria is utilized to extract the intrinsic features of the faces. Based on these features, we calculate Euclidean distances between the input face image and every image in the original training database, and then Euclidean distances are used as criteria to choose the reasonable training database. Once the training database is chosen, we use PCA to hallucinate the input face image as the linear combination of the chosen training images. Experimental results show that our approach can choose training database automatically according to the input face image and get high quality super-resolution image.
	TO cite this article:Xiaoling Wang,Ju Liu,Jianping Qiao, et al. An Improved Face Hallucination Based PCA[OL].[21 April 2008] http://en.paper.edu.cn/en_releasepaper/content/20681


	4. Uncoupled Fuzzy-based Geodesic Active Regions Models for Image Segmentation
	Li Canfei,Wang Yaonan
	Electrics, Communication and Autocontrol Technology 30 November 2006
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	Abstract:In this paper we present a novel Uncoupled Fuzzy-based Geodesic Active Regions (UFGAR) framework for dealing with frame partition problems in image segmentation. Like the usually Geodesic Active Regions (GAR) framework presented by N. Paragios and R. Derichhave, our framework unifies boundary and region-based information. The boundary information of the mixture model is determined using a edge detector based on the fuzzy membership functions, and the region information indicates the region intensity properties is estimated by a measure based on the fuzzy membership functions too. The defined objective function is minimized using a gradient descent method where a level set approach is used to implement the resulting PDE system. But unlike GAR, which is based on the Maximum Likelihood Principle for the observed density function image histogram using a mixture of Gaussian elements, our model is based on FCM and fuzzy membership functions. We define a novel region measure based on fuzzy membership functions to measure the property of regions. According to the motion equation, the initial curve is propagated toward the segmentation result under the influence of boundary and region-based segmentation forces, and being constrained by a regularity force. The performance of this method is demonstrated by a synthetic image, and the results of its application to brain images are presented.
	TO cite this article:Li Canfei,Wang Yaonan. Uncoupled Fuzzy-based Geodesic Active Regions Models for Image Segmentation[OL].[30 November 2006] http://en.paper.edu.cn/en_releasepaper/content/10066

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