Search

In this paper, a new lightning electric field (LEF) signals denoising approach combined with empirical mode decomposition (EMD) and wavelet transform (WT) is proposed. Unlike the conventional EMD or WT denoising approaches, we use EMD to decompose the LEF signal firstly, the continuous mean square error(CMSE) criteria are used to determine a turning point in the original signal energy, then the Birge-Massart threshold wavelet denoising method is employed to denoise the high frequency component which contains lots of noise. Finally, the clean high frequency component and the remaining low frequency intrinsic mode function, and the residual of the EMD operation are employed to synthesize a cleaner LEF signal. The method is illustrated on real data, and the performance of the proposed method is evaluated in terms of several standard metrics. The results show that the proposed method is able to reduce noise from the noisy LEF signals more accurately and effectively in comparison to EMD filtering and Wavelet filtering methods.

In this paper, a novel method for face recognition, called two-dimensional weighted and locality preserved discriminant analysis (2D-WLPDA) is proposed. The new algorithm is developed based on three techniques: (1) locality preserved embedding, by embedding nearest-neighbor graphs which characterize the within-class compactness of the same class samples, 2D-WLPDA discovers the submanifold of images space; (2) image based projection which can avoids the small sample problem and improves the computation efficiency;(3) weighting contributions of individual class pairs which alleviates the overlap of neighboring classes in Fisher criterion for a k-class problem with k>2. We experimentally compare 2D-WLPDA to other feature extraction methods, such as 2D-LDA, 2D-PCA and 2D-DLPP, 2D-WLPDA has better recognition performance.

Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.