The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The metho...The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.展开更多
A simple and effective greedy algorithm for image approximation is proposed. Based on the matching pursuit approach, it is characterized by a reduced computational complexity benefiting from two major modifications. F...A simple and effective greedy algorithm for image approximation is proposed. Based on the matching pursuit approach, it is characterized by a reduced computational complexity benefiting from two major modifications. First, it iteratively finds an approximation by selecting M atoms instead of one at a time. Second, the inner product computations are confined within only a fraction of dictionary atoms at each iteration. The modifications are implemented very efficiently due to the spatial incoherence of the dictionary. Experimental results show that compared with full search matching pursuit, the proposed algorithm achieves a speed-up gain of 14.4-36.7 times while maintaining the approximation quality.展开更多
In the research on spatial hearing and realization of virtual auditory space,it is important to effectively model the head-related transfer functions(HRTFs)or head-related impulse responses(HRIRs).In our study,we mana...In the research on spatial hearing and realization of virtual auditory space,it is important to effectively model the head-related transfer functions(HRTFs)or head-related impulse responses(HRIRs).In our study,we managed to carry out adaptive non-linear approximation in the field of wavelet transformation.The results show that the HRIRs’adaptive non-linear approximation model is a more effective data reduction model,is faster,and is 5 dB on average better than the traditional principal component analysis(PCA)(Karhunen-Loève transform)model based on relative mean square error(MSE)criterion.Furthermore,we also discussed the best bases’choice for the time-frequency representation of HRIRs,and the results show that local cosine bases are more propitious to HRIRs’adaptive approximation than wavelet and wavelet packet base.However,the improved effect of local cosine bases is not distinct.Here,for the sake of modeling the HRIRs more truthfully,we consider choosing optimal time-frequency atoms from redundant dictionary to decompose this kind of signals of HRIRs and achieve better results than all the previous models.展开更多
In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms accordin...In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.展开更多
基金This project is supported by the National Natural Science Foundation of China(Grant No.51305211)Natural Science Foundation of Jiangsu(Grant No.BK20160955)Jiangsu Government Scholarship for Overseas Studies,College students practice and innovation training project of Jiangsu province(Grant No.201710300218),and the PAPD。
文摘The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.
文摘A simple and effective greedy algorithm for image approximation is proposed. Based on the matching pursuit approach, it is characterized by a reduced computational complexity benefiting from two major modifications. First, it iteratively finds an approximation by selecting M atoms instead of one at a time. Second, the inner product computations are confined within only a fraction of dictionary atoms at each iteration. The modifications are implemented very efficiently due to the spatial incoherence of the dictionary. Experimental results show that compared with full search matching pursuit, the proposed algorithm achieves a speed-up gain of 14.4-36.7 times while maintaining the approximation quality.
基金supported by the National Basic Research of China(No.2002CB312102).
文摘In the research on spatial hearing and realization of virtual auditory space,it is important to effectively model the head-related transfer functions(HRTFs)or head-related impulse responses(HRIRs).In our study,we managed to carry out adaptive non-linear approximation in the field of wavelet transformation.The results show that the HRIRs’adaptive non-linear approximation model is a more effective data reduction model,is faster,and is 5 dB on average better than the traditional principal component analysis(PCA)(Karhunen-Loève transform)model based on relative mean square error(MSE)criterion.Furthermore,we also discussed the best bases’choice for the time-frequency representation of HRIRs,and the results show that local cosine bases are more propitious to HRIRs’adaptive approximation than wavelet and wavelet packet base.However,the improved effect of local cosine bases is not distinct.Here,for the sake of modeling the HRIRs more truthfully,we consider choosing optimal time-frequency atoms from redundant dictionary to decompose this kind of signals of HRIRs and achieve better results than all the previous models.
基金supported by the National Natural Science Foundation of China(61201134)the 111 Project(B08038)
文摘In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.
