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Zhou X.,Shanghai University | Fang Y.,Shanghai University | Fang Y.,Key Laboratory of Advanced Display and System Applications | Wang M.,Shanghai University
Journal of Systems Engineering and Electronics | Year: 2010

A compressed sensing (CS) based channel estimation algorithm is proposed by using the delay-Doppler sparsity of the fast fading channel. A compressive basis expansion channel model with sparsity in both time and frequency domains is given. The pilots in accordance with a novel random pilot matrix in both time and frequency domains are sent to measure the delay-Doppler sparsity channel. The relatively nonzero channel coefficients are tracked by random pilots at a sampling rate significantly below the Nyquist rate. The sparsity channels are estimated from a very limited number of channel measurements by the basis pursuit algorithm. The proposed algorithm can effectively improve the channel estimation performance when the number of pilot symbols is reduced with improvement of throughput efficiency. Source


Zhang C.-Q.,Shanghai University | Guan Y.-P.,Shanghai University | Guan Y.-P.,Key Laboratory of Advanced Display and System Applications
Guangdianzi Jiguang/Journal of Optoelectronics Laser | Year: 2015

How to efficiently realize automatic recognition of abnormal behavior for intelligent video surveillance is a key problem. A method has been developed that the dynamic particle flow field from video is got based on the Lagrange dynamic system equation and self-adaptive determination of time interval in the equation. Some motion behaviors for motion objects in video are mapped to the dynamic particle flows which can be used to describe their motion variation states. Some significant motion features for abnormal behavior with different motion styles from different scenes have been extracted to classify and recognize the abnormal behaviors. Some open video test sequences from different scenarios with different behavioral patterns are selected to perform experimental verifications and comparisons. Experimental results show that abnormal behavior can be automatically recognized efficiently in various conditions where it is not necessary to track motion object or collect abnormal behavior sample in advance for learning and training. © 2015, Board of Optronics Lasers. All right reserved. Source


Fang Y.,Shanghai University | Fang Y.,Key Laboratory of Advanced Display and System Applications | Zhou X.,Shanghai University | Wang M.,Shanghai University
High Technology Letters | Year: 2011

A compressed sensing (CS) based channel estimation algorithm is proposed in the fast moving environment. A sparse basis expansion channel model in both time and frequency domain is given. Pilots are placed according to a novel random unit pilot matrix (RUPM) to measure the delay-Doppler sparse channel. The sparse channels are recovered by an extension group orthogonal matching pursuit (GOMP) algorithm, enjoying the diversity gain from multi-symbol processing. The relatively nonzero channel coefficients are estimated from a very limited number of pilots at a sampling rate significantly below the Nyquist rate. The simulation results show that the new channel estimator can provide a considerable performance improvement for the fast fading channels. Three significant reductions are achieved in the required number of pilots, memory requirements and computational complexity. Copyright © by HIGH TECHNOLOGY LETTERS PRESS. Source


Duan X.,Shanghai University | Duan X.,Henan Normal University | Fang Y.,Shanghai University | Fang Y.,Key Laboratory of Advanced Display and System Applications
Gaojishu Tongxin/Chinese High Technology Letters | Year: 2012

This paper presents a new blind separation algorithm for permuted alias images based on sparse decomposing, aiming at a type of permuted alias images with morphological diversity. The method uses the Contourlet dictionary and the local discrete cosine transform dictionary respectively as the characteristic fields of separation so as to use the sparsity diversity existing in sparse representation of the permuting and permuted regions of a permuted alias image. And then the texture image is separated from the permuted alias image by decomposing the permuted alias image on the two characteristic fields. The simulation results show that the proposed algorithm can separate effectively texture images from the permuted alias images regardless of size, location number and types of texture image for a permuted alias image comprising the piecewise smooth part and the texture part. Source


Zhou X.-P.,Shanghai University | Fang Y.,Shanghai University | Fang Y.,Key Laboratory of Advanced Display and System Applications | Wang M.,Shanghai University
Dianbo Kexue Xuebao/Chinese Journal of Radio Science | Year: 2010

To avoid estimating a large number of coefficients of the traditional channel estimation methods, a compressed sensing (CS) based channel estimation algorithm is proposed in fast fading environment. An angle-delay Doppler spread sparse channel model in space, time and frequency domain is presented. A structurally random pilot matrix is given to measure the angle-delay Doppler sparse multiple-input multiple-output (MIMO) Orthogonal frequency division multiplexing (OFDM) channels. The relatively nonzero channel coefficients are tracked and estimated from a very limited number of channel measurements at a sampling rate significantly below the Nyquist rate. The simulation results show that the new channel estimator can provide a considerable performance improvement in estimating fast fading channels when the significant reduction is achieved in the required number of pilots and computational complexity. © 2010 by Editorial Department of Chinese Journal of Radio Science. Source

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