drought stress, especially drought at terminal stages, plays an important role in reduction of yield in field crops such as common beans. 50 genotypes of common bean including 3 control genotypes (dehghan, bahman and naz) were used to evaluate and identify the tolerant genotypes. this experiment were performed in a randomized complete block design (rcbd) with three replications under two condit...

the field study was conducted in one district of educational-experimental forest at tehran university (kheirood-kenar forest) in the north of iran. eighty-five soil profiles were dug in the site of study and several chemical and physical soil properties were considered. these factors included: soil ph, soil texture, bulk density, organic carbon, total nitrogen, extractable phosphorus and depth ...

the principle of dimensionality reduction with pca is the representation of the dataset ‘x’in terms of eigenvectors ei ∈ rn  of its covariance matrix. the eigenvectors oriented in the direction with the maximum variance of x in rn carry the most      relevant information of x. these eigenvectors are called principal components [8]. assume that n images in a set are originally represented in mat...

All the human faces are symmetric approximately up to 90% 95% only. No face is 100% symmetric. Based on this property that 10% difference is present in any human face is given as the input to the face recognition system. The intensity variations of the faces are equalized first. Then the left and right face difference is given as the input to the database and to the face recognition system. Thi...

Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a 2D Wavelet based Multi-Band (2D-WMBPCA) recognition method, inspired by PCA techniques for multispectral and hyperspectral images. The proposed 2D-WMBPCA method performs non-decimated wavelet transform on the input image, dividing it into its subbands. Each res...

Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle highdimensional data. However, due to the high computational complexity of its eigen decomposition solution, it hard to apply PCA to the large-scale data with high dimensionality. Meanwhile, the squared L2-norm based objective makes it sensitive to data outliers. In recent research, the L1-norm maximi...