This paper proposes a new variable order affine projection algorithm for acoustic echo cancellation. By exploiting an efficient voice activity detection technique, the proposed algorithm can distinguish the significant and insignificant input data periods. It can thus switch between the higher and lower algorithm orders in these two situations, respectively. Hence, input data reusing can enhance convergence with more input excitation and the computation cost can be saved when the input is relatively weak. Simulations results verify the effectiveness of the proposed algorithm.

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.