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	1. SKIN DETECTION BASED ON MULTISPRECTAL IMAGES
	HOU Yali,HAO Xiaoli,GUO Changqing
	Electrics, Communication and Autocontrol Technology 14 December 2016
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	Abstract:Traditional methods of human skin detection are usually based on an RGB camera. To handle the problem of metameric color, multispectral images have attracted more attention. In this paper, a multispectral imaging system is developed for skin detection. In order to simplify the system, a band selection algorithm has been used to choose the effective bands from 31 bands within 400nm to 1000nm. In the test for discriminating skin and skin-like objects, two bands around 800nm and 420nm are selected. A significantly better detection performance has been obtained compared with the a simulated three-band RGB images.Two main contributions in this paper are presented. First, as we know, it is the first time that a band selection algorithm has been used for skin detection. Second, by using a conditional probability scheme, the band selection method can reduce the computational complexity caused by the calculation of high-dimensional distance measures.
	TO cite this article:HOU Yali,HAO Xiaoli,GUO Changqing. SKIN DETECTION BASED ON MULTISPRECTAL IMAGES[OL].[14 December 2016] http://en.paper.edu.cn/en_releasepaper/content/4713091


	2. Highly Restricted Keyword Selection Based on Sparse Analysis for Uyghur Text Categorization
	Dong Wang,Askar Humdulla,Rayilam Parhat,Javier Tejedor
	Electrics, Communication and Autocontrol Technology 03 December 2016
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	Abstract:Text categorization (TC) has achieved significant success in recently years; however, in the case where the text is not well represented, TC performance is usually substantially reduced. A particular example of such a scenario is in the content-aware public telephone network (PTN), where the input speech can be only partially transcribed due to the concern of privacy protection and computational cost. One, therefore, needs an effective approach to selecting a highly restricted group of keywords (less than $100$), by which the spoken content can be well represented and so the TC performance is largely retained.Conventional keyword selection approaches are based on a carefully designed intermediate score, and the keywords are selected according to the score independently. This often leads to suboptimum performance. This paper proposes a novel sparsity-based approach to tackling the highly restricted keyword selection for TC. The idea is to formulate keyword selection as an $l_1$ regularized linear optimization problem. The $l_1$ term drives less important dimensions of the model coefficients to zeros, and so the corresponding words are nullified, leaving only the promising keywords. By this approach, the objective function of keyword selection is more consistent to the one used in TC; more importantly, the keywords are selected jointly as a group, leading to a group-optimized selection. The experiments conducted on an Uyghur TC task demonstrated that the proposed approach is highly effective.
	TO cite this article:Dong Wang,Askar Humdulla,Rayilam Parhat, et al. Highly Restricted Keyword Selection Based on Sparse Analysis for Uyghur Text Categorization[OL].[ 3 December 2016] http://en.paper.edu.cn/en_releasepaper/content/4712789

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	3. A Novel Data Aggregation of Source Node Based on TEEN Protocol in WSN
	WANG Yifan,WANG Chaowei,WANG Weidong
	Electrics, Communication and Autocontrol Technology 15 November 2016
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	Abstract:With the development of wireless communication systems and microelectronics, WSNs have been widely used in various fields. But the survival time is still the main factor restricting the development of wireless sensor networks. In this paper, we focus on the problem of redundant data in the source node, in order to solve the energy consumption problem. Based on the TEEN routing protocol, a novel data aggregation of source node based on the TEEN protocol is proposed by analyzing the energy consumption of the network, and we use a Kalman filter to reduce the data forwarding of the source nodes. Simulation results show that the proposed algorithm can effectively reduce the data from the source node, save network energy and prolong the network lifetime.?????
	TO cite this article:WANG Yifan,WANG Chaowei,WANG Weidong. A Novel Data Aggregation of Source Node Based on TEEN Protocol in WSN[OL].[15 November 2016] http://en.paper.edu.cn/en_releasepaper/content/4709942


	4. Scene Classification Based on minimized Deep Convolutional Neural Networks
	LIU Yu-xuan, DONG Yuan, BAI Hong-liang
	Electrics, Communication and Autocontrol Technology 24 June 2016
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	Abstract: Scene Classification is a subdivision problem of Large-scale classfication problem since the latter has been basically resolved. In this article, several common Scene Classification Data-set and their differences are introduced. Additionally, there are lots of advanced methods of Deep Convolutional Neural Network. Methods for solving Large-scale Classification problems to be used on solving Scene Classification is a very common way. This article summerizes the results of those network structures trained on Scene Data-sets. Therefore, this article introduces some improvement for simply using CNN on Scene Classification and their better result. Since the common network structure is so complicated that it takes a long time to train and test, a method of simplifying these deep networks is raised in this article. Reducing size of input pictures and numbers of convolution kernels could take effect on increasing the speed on both training and testing stages. Finally, this much smaller network got an acceptable result on the data-set. % Reviews: please describe the background, status and application of the research with 150-300 words. I and we can not be used as the subject, % and the abstract must not the same as the sentences of the main text. General research paper: please extracts the key points of the paper, give the main research achievements with object, methods, results and conclusion with 200-400 words. I and we can not be used as the subject, and the abstract must not the same as the sentences of the main text.
	TO cite this article:LIU Yu-xuan, DONG Yuan, BAI Hong-liang. Scene Classification Based on minimized Deep Convolutional Neural Networks[OL].[24 June 2016] http://en.paper.edu.cn/en_releasepaper/content/4698285


	5. Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature
	Chao Ma, Yun Gu, Wei Liu, Jie Yang, Xiangjian He
	Electrics, Communication and Autocontrol Technology 06 June 2016
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	Abstract:Video hashing is a common solution for content-based video retrieval by encoding high-dimensional feature vectors into short binary codes. Videos not only have spatial structure inside each frame but also have temporal correlation structure between frames, while the latter has been largely neglected by many existing methods. Therefore, in this paper we propose to perform video hashing by incorporating the temporal structure as well as the conventional spatial structure. Specifically, the spatial features of videos are obtained by utilizing Convolutional Neural Network (CNN), and the temporal features are established via Long-Short Term Memory (LSTM). The proposed spatio-temporal feature learning framework can be applied to many existing unsupervised hashing methods such as Iterative Quantization (ITQ), Spectral Hashing (SH), and others. Experimental results on the UCF-101 dataset indicate that by simultaneously employing the temporal features and spatial features, our hashing method is able to significantly improve the performance of existing methods which only deploy the spatial feature.
	TO cite this article:Chao Ma, Yun Gu, Wei Liu, et al. Unsupervised Video Hashing by Exploiting Spatio-Temporal Feature[OL].[ 6 June 2016] http://en.paper.edu.cn/en_releasepaper/content/4691838


	6. Invariant Face Representation via Pulse Images
	ZHAN Kun
	Electrics, Communication and Autocontrol Technology 19 May 2016
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	Abstract:This paper show how biologically inspired methods can be applied to a variety of face image analysis. Spiking cortical model (SCM) based on the observation of the visual cortex nerve cell of cats and simulating the activities of the visual nerve cell. It has demonstrated that SCM can effectively extract invariant image feature. The feature is inherent in an image, and describes the regional information, edges, shapes, segments and textures in the image. In this paper, the pulse images, output of the system of SCM, are developed for face image representation. The experimental results show that pulse images very suitable to process face images, and its application on face recognition achieve high accuracy and it is a robust scheme for face modeling systems.
	TO cite this article:ZHAN Kun. Invariant Face Representation via Pulse Images[OL].[19 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4693017


	7. Double Estimator Multipath Mitigation Method for BOC Modulated Signal
	YAO Zheng,GAO Yang
	Electrics, Communication and Autocontrol Technology 18 May 2016
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	Abstract:A multipath mitigation method tailored for Double Estimator structure and SinBOC(kn,n) signal, where is larger than 2, is presented. The discriminators for code and subcarrier loops are designed based on the features of derived 2-dimention correlation. This method can further improve the multipath performance of double estimator, especially for high order SinBOC(kn,n) signal.
	TO cite this article:YAO Zheng,GAO Yang. Double Estimator Multipath Mitigation Method for BOC Modulated Signal[OL].[18 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688210


	8. Multi-frequency Phase Sensitive Detection with Improved Median Filtering
	CUI Zi-Qiang, HAO Zhen-Hua, WANG Hua-Xiang
	Electrics, Communication and Autocontrol Technology 13 May 2016
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	Abstract:Multi-frequency signal detection plays an important role in bio-impedance measurement, EM induction based non-destructive test, etc. The Phase-sensitive detection (PSD) is an effective tool for discriminating the amplitude and phase of each frequency component. The phase sensitive demodulator can be viewed as a matched filter to its source signal and is the optimal linear filter when only Gaussian noise is at presence. In many real applications, however, the noise has also rather distinctive impulsive characteristics. The impulsive noise puts forward a great challenge for the multi-frequency PSD. In this paper, an approach for the reduction of impulsive noise is introduced to improve the performance of multi-frequency PSD. The proposed method is able to achieve better signal-to-noise ratio (SNR) than its linear counterparts in processing digital signal that contaminated by impulsive noise.
	TO cite this article:CUI Zi-Qiang, HAO Zhen-Hua, WANG Hua-Xiang. Multi-frequency Phase Sensitive Detection with Improved Median Filtering[OL].[13 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688108


	9. A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters
	HUANG Yue
	Electrics, Communication and Autocontrol Technology 10 May 2016
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	Abstract:Region of interest segmentation from large histopathology images is an actively researched area given the multitude of applications in pathological research and clinical practice. Here we propose a system to detect regions (objects) of interest in histopathology images using a supervised learning pipeline. Instead of typical k-means in well-used simple linear iterative clustering (SLIC) method, initial superpixel detection is improved by weighted k-means strategy for a better performance at adhering to the object boundary. In each superpixel, multiscale color-texture features are extracted and processed using rolling guidance filters in an effort to reduce inter-class ambiguity and intra-class variation simultaneously. Finally, after feature extraction, a support vector machine (SVM) is trained and applied to segment the testing images. We apply this method to detect pancreatic islets, and in comparison to other approaches, it shows both a dramatic improvement and accuracy compared to existing methods. We envision the system could be used for a variety of other purposes (e.g. tumor detection) in histopathology image analysis.
	TO cite this article:HUANG Yue. A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters[OL].[10 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4687168


	10. Affine Transform of Two Dimensional Iterated Function System Realized by Parallel Optical
	Tian Fengchun,Zhao Zhenzhen,Chen Danyu,Hu Youwen,Han Liang,Zhang Wenli
	Electrics, Communication and Autocontrol Technology 09 May 2016
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	Abstract:An affine transform is a basic transform in fractal Iterated Function System (IFS). The enormous data computing is the bottleneck of traditional IFS. The affine transform of a 2-D image realized in optical way is the key technique for 2-D IFS. An affine transform method based on modern optical switch technique is proposed. It features that the affine transform can be realized by all optical way after parameters are set. It can increase the computing speed. Besides, it can largely depress the crosstalk due to the limited diffraction efficiency of gratings in optical switch. Experimental results verified the effectiveness of this method.
	TO cite this article:Tian Fengchun,Zhao Zhenzhen,Chen Danyu, et al. Affine Transform of Two Dimensional Iterated Function System Realized by Parallel Optical[OL].[ 9 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688168

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