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1. The new model of semi-tensor compressive sensing | |||
Liu Lifei, Li Lixiang, Peng Haipeng, Yang Yixian | |||
Electrics, Communication and Autocontrol Technology 14 January 2019 | |||
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Abstract:Compressive sensing is introduced at first. Compressive sensing not only can compress signal, but also can encrypt signal. But the constraint is that the column of measurement matrix is equal to the row of the signal. In this paper, the model of semi-tensor compressive sensing is given. The model is composed of measurement matrix and auxiliary matrix. The measurement matrix is generated by Logistic chaotic system. The auxiliary matrix is generated by Tent chaotic system. The proposed model breaks the restriction of matrix multiplication, so the matrix can be multiplied when their dimensions do not match. Moreover, the size of measurement matrix of semi-tensor compressive sensing is smaller than compressive sensing, which reduces the transmission overhead and storage space greatly. The experimental simulations show the factor that affect the successful recovery of the signal, including sparsity and the number of measurements. When compression ratio is fixed, the larger the sparsity is, the smaller the percentage of successful recovery is. When sparsity is fixed, the more the number of measurements is, the larger the percentage of successful recovery is. In the experiment, the compression ratio is 0.5, when sparsity is less than 15, the percentage of successful recovery is equal to 1 regardless of the size of measurement matrix. | |||
TO cite this article:Liu Lifei, Li Lixiang, Peng Haipeng, et al. The new model of semi-tensor compressive sensing[OL].[14 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747069 |
2. 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 |
3. Near-Field Source Localization in Unknown Nonuniform Noise | |||
ZUO Weiliang,XIN Jingmin | |||
Electrics, Communication and Autocontrol Technology 29 June 2017 | |||
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Abstract:In this paper, we consider the source localizationfor the multiple near-field narrowband signals impinging on a symmetricuniform linear array (ULA) in nonuniform noise. We introduce the matrix completion (MC) approach to reconstruct the noise-freecovariance matrix for estimating the signal subspace. Then, some existingsource localization methods can be applied immediately. Finally, the effectivenessof the proposed method is verified through numerical examples. | |||
TO cite this article:ZUO Weiliang,XIN Jingmin. Near-Field Source Localization in Unknown Nonuniform Noise[OL].[29 June 2017] http://en.paper.edu.cn/en_releasepaper/content/4738005 |
4. Efficient Method for Localization of Mixed Far-Field and Near-Field Signals | |||
ZUO Weiliang,XIN Jingmin | |||
Electrics, Communication and Autocontrol Technology 29 June 2017 | |||
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Abstract:In this paper, we deal with the problem of localizing mixed far-field (FF)and near-field (NF) sources impinging on a uniform linear arraywith the symmetrical geometric configuration.An efficient method for localization of the mixed FF and NF sources is proposed,where the direction-of-arrivals (DOAs) of the mixed sources are estimated separately by using the oblique projection,and then the ranges of the NF sources are obtained through a polynomial rooting,while the computationally burdensome eigendecomposition and pair-matching are avoided.The effectiveness of the proposed method is verified through numerical examples,and the simulation results show that the proposed method has better estimation performancethan some existing methods. | |||
TO cite this article:ZUO Weiliang,XIN Jingmin. Efficient Method for Localization of Mixed Far-Field and Near-Field Signals[OL].[29 June 2017] http://en.paper.edu.cn/en_releasepaper/content/4738008 |
5. 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 |
6. 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 |
7. 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 |
8. 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 |
9. Time-Reversal-Based Underwater Target Detection System and Its Experimental Verification | |||
Jiang Zhe,Zhang Zhichen,Haiyan Wang | |||
Electrics, Communication and Autocontrol Technology 18 April 2017 | |||
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Abstract:In this paper, we investigate the time-reversal-based underwater target detection system. Three different time reversal processing (TRP) techniques, including active TRP, passive TRP and active virtual TRP, have been discussed and analyzed. Considering these three different TRP techniques, we have designed and constructed an underwater TRP system, which could employ these three different TRP techniques. The performance of constructed TRP system was demonstrated through an outfield lake experiment in Danjiangkou Reservoir. The experimental results showed the effectiveness of TRP technique as well as our constructed system. | |||
TO cite this article:Jiang Zhe,Zhang Zhichen,Haiyan Wang. Time-Reversal-Based Underwater Target Detection System and Its Experimental Verification[OL].[18 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726251 |
10. Advanced Quantum Evolutionary Algorithm for QoS Multicast Routing optimization in Electric Power Communication Network | |||
HUAN Hai,HUANG Min | |||
Electrics, Communication and Autocontrol Technology 17 March 2017 | |||
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Abstract:This thesis studies the requirements of electric power communication system, and classifies traffic in the electric power system. An Advanced Quantum Evolutionary Algorithm (AQEA) for QoS multicast routing optimization is proposed to fulfill the requirements of multicast traffic in electric power communication network, e.g. bandwidth, delay, packet loss, etc. Our approach combines Quantum Evolutionary Algorithm and Minimum Spanning Tree algorithm. First, the current location of individual is represented by probability amplitudes of quantum bits. Quantum crossover is implemented in quantum individuals to keep better gene. Second, quantum gates update and adaptive adjustment of the searching area is achieved according to the phase of quantum bit. A dynamic adjusting mechanism of rotation angle is designed to update the individual pheromone, which can guarantee the strong population diversity and quickly find out the feasible solutions that satisfy all constraints as well. It overcomes the restriction of local optimization in traditional algorithms. Thirdly, Steiner minimal tree is generated with OMST (Optimized Minimum Spanning Tree) algorithm, which ensures a better performance of solutions in precision and speed. Simulations show AQEA has better optimization quality and efficiency in comparing with traditional Ant Colony Algorithm and Quantum Evolutionary Algorithm. Simulation results manifest that the cost and convergence time of the multicast tree obtained by AQEA superior to other evolutionary algorithms. This trend will be more distinct as the nodes increase. Simulations also declare the validity of the strategies in AQEA. | |||
TO cite this article:HUAN Hai,HUANG Min. Advanced Quantum Evolutionary Algorithm for QoS Multicast Routing optimization in Electric Power Communication Network[OL].[17 March 2017] http://en.paper.edu.cn/en_releasepaper/content/4722218 |
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