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	1. Research on image recognition algorithm of mobile devices based on Federated Learning
	Li Jin,Gao Sheng,Zhou Xinya,Zhang Bosen,Xu Kerui
	Electrics, Communication and Autocontrol Technology 26 February 2021
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	Abstract:There are a large number of examples about image recognition applications of mobile devices. Such as the FaceID in Iphones, which can greatly improved convenience. However, after a detailed research, it is obviously that most similar Apps run the risk of compromising users\' privacy, which violates the corresponding data protection law to a certain extent. This paper will introduce a new mobile federated learning image recognition algorithm called GNFedHAtt, which uses the lightweight neural network GhostNet and the new federated aggregation mechanism FedHAtt. And design the smart phone handwriting input method recognition experiments to verify, with the datasets of Mnist/EMnist/HWDB1.0, and compared to the traditional FedAvg and other federated aggregation methods. The results prove that GNFedHAtt method can be effectively applied to the mobile image recognition.
	TO cite this article:Li Jin,Gao Sheng,Zhou Xinya, et al. Research on image recognition algorithm of mobile devices based on Federated Learning[OL].[26 February 2021] http://en.paper.edu.cn/en_releasepaper/content/4753785


	2. A hybrid model for seam-carving and JPEG compression operation chain detection
	BIN Muyu,YANG Gaobo,DONG Xiaoxu
	Electrics, Communication and Autocontrol Technology 22 April 2019
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	Abstract:Seam carving, which is known as content-aware image resizing, leaves no visible traces in resized images. Though several approaches have been presented for seam carving forgery detection, there are few works about the operation chain detection of both seam carving and JPEG compression. In this paper, a hybrid model is proposed to distinguish JPEG-Seam carving-JPEG images from single and double compression JPEG images. Both spatial-domain and transform-domain features are extracted for operation chain detection. Experiment results show that the proposed approach can efficiently detect seam carving operation.
	TO cite this article:BIN Muyu,YANG Gaobo,DONG Xiaoxu. A hybrid model for seam-carving and JPEG compression operation chain detection[OL].[22 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748494

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	3. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks
	Liu Liwei,Peng Haipeng,Li Lixiang,Yang Yixian
	Electrics, Communication and Autocontrol Technology 11 January 2019
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	Abstract:With the rapid development of the applications of wireless sensor networks (WSNs) in various fields, such as internet of things, military collaborative operations, e-government, telemedicine, etc., the security, the energy-efficiency and the storage-saving are undoubtedly highlighted in the research of WSNs. Compressive sensing (CS) can compress and reconstruct sparse or compressible signals with fewer samples than those of Nyquist-Shannon theorem requires. In order to meet the requirements of storage, energy-efficiency and security of WSNs simultaneously, we propose an efficient and secure transmission model based on compressive sensing and hash-chain theory, which is called hash-chain compressive sensing (HCCS). Compared with the traditional compressive sensing, only the initial key and the hash function are used in the sensor node to decrease the storage space. And the characteristics of hash-chain assure the security of data transmission under HCCS. Furthermore, we propose an image encryption method based on HCCS in order to improve the efficiency and security of image transmission. The security of image signal is greatly improved by adopting the double-encryption mechanism, which uses the measurement matrix $\Phi _1$ and the encryption matrix $\Phi _2$. The numerical experiments are performed to show the feasibility of HCCS and the effectiveness of the proposed image transmission model.
	TO cite this article:Liu Liwei,Peng Haipeng,Li Lixiang, et al. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks[OL].[11 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747004


	4. SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach
	Biao Hou,GuiLin Ju,HongXiao Feng,Zhichao Liu
	Electrics, Communication and Autocontrol Technology 02 May 2017
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	Abstract:A new neighborhood-adaptive non-local (NL) despeckling filter is proposed in this paper. An adaptive and point-wise fashion neighborhood that limits the bound of weighted pixels is designed, which is determined by an adaptive directional scales set and a new automatic similarity threshold. The set of adaptive directional scales constructs a rectangular neighborhood and the optimal scale is obtained with the proposed similarity threshold. The presented similarity is based on the probabilistic patch based similarity (PPB-similarity) measurement and deduced with a statistical Monte Carlo method. Experiment results show that our method can not only provide superior speckle removal when compared to probabilistic patch based non-local (PPB-NL) filter with fixed neighborhood, especially for its non-iterative version, but also show good performance in preserving details and texture information.
	TO cite this article:Biao Hou,GuiLin Ju,HongXiao Feng, et al. SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach[OL].[ 2 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4731588


	5. Super-Resolution ISAR Imaging via Cosparse Model
	HOU Biao,LI Zhengwei,ZHANG Guang,JIAO Licheng
	Electrics, Communication and Autocontrol Technology 02 May 2017
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	Abstract:A super-resolution inverse synthetic aperture radar (ISAR) imaging based on cosparse is proposed in this paper. Different from traditional imaging model, we regard the super-resolution imaging process as an analysis model. In order to obtain well-focused and denoised ISAR image, the phase adjustment is realized by analysis operator learning (AOL), and we add a new regularization item and use Augmented Lagrangian (AL) method to approximate the denoised signal. Then we use a modified OMP algorithm to recover the strong scattering coefficients, which can produce a well-focused image. This process can be seen as a multilayer imaging model and the quality of the imaging can be improved step by step. The experimental results show that the proposed method can get higher quality ISAR image than the traditional super-resolution imaging algorithms and is an effective approach to ISAR imaging within a short CPI.
	TO cite this article:HOU Biao,LI Zhengwei,ZHANG Guang, et al. Super-Resolution ISAR Imaging via Cosparse Model[OL].[ 2 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4731585


	6. Exemplar-based Photo Color Enhancement by Exploring Visual Aesthetics
	ZHOU Zhenkun,HAO Shijie,WANG Meng
	Electrics, Communication and Autocontrol Technology 26 April 2017
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	Abstract:With the prevalence of mobile imaging devices, large amount of photos are produced in each day. Automatic image enhancing models, such as exemplar-based color correction model, are highly needed. However, current models do not consider how to obtain reliable exemplars. To address this issue, we proposed a novel approach for image color enhancement, which provides the color correction model with aesthetically good exemplars. Based on feature correspondence between the exemplars and the target photo, the model optimizes the correction parameters by solving a matrix factorization problem. In the model, the exemplars are selected by ranking their aesthetic values, which are produced by a deep CNN model. The selection process makes the exemplars more reliable in the correction model, and thus improves the visual quality of the corrected results. Visual and quantitative comparison in the experiments validate our improvement.
	TO cite this article:ZHOU Zhenkun,HAO Shijie,WANG Meng. Exemplar-based Photo Color Enhancement by Exploring Visual Aesthetics[OL].[26 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4727660


	7. Image Patch Clustering Based on Spectrum Structure and Directionality in Fourier Domain
	BAO Lijun
	Electrics, Communication and Autocontrol Technology 20 April 2017
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	Abstract:Patch clustering is a common issue in image processing and pattern recognition, especially in those patch- based structured sparsity reconstruction problems. Researchers usually adopt the K-means method based on the gray intensity distance or partition according to the edge direction. However, these metrics are not sufficient to help obtaining delicate classification. In this letter, we propose a novel image patch clustering method based on the magnitude spectrum structure and directionality in Fourier domain (SSDF), i.e. the primary direction in the spectrogram, the spectrum structure complexity and components distribution of low, middle and high frequency. Experimental results demonstrate that SSDF method is able to achieve more exquisite classification following three steps of subdivisions with no need to preset the cluster number.
	TO cite this article:BAO Lijun. Image Patch Clustering Based on Spectrum Structure and Directionality in Fourier Domain[OL].[20 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726345


	8. 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


	9. 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


	10. 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

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