The factors and mechanism which control the spatial patterns of heavy metals in groundwater and their effect on human health could be identified with multivariate statistical methods and human health risk assessment. Sampling wells are statistically classified into two cluster based on the similar characters in groundwater quality using Q-mode cluster analysis (Q-mode CA). Two significant factors were extracted by principal component analyses (PCA), explaining 64.19% of the total variance. These factors were in turn described by the clusters 1 and 2, respectively, resulting from the R-mode CA. PCA and CA revealed significant anthropogenic contributions and water-rock interaction effects of the metals in groundwater. Health risk assessment factors including chronic daily intake (CDI) and hazard quotient (HQ) indices were computed for child and adult. The HQ indices of Cd and Pb in the both child and adult cases showed the value greater than the safe limits, which cause the harmful health hazards and potential non-carcinogenic health risks to the human. Spatial variability maps using ordinary kriging show that safe zones are mainly covered the west and south-western parts of the study area, while the contamination zones are found to be concentrated in the east, north, and south-eastern parts of the plain. The indicator kriging maps show highly uneven spatial pattern of Pb and Cd concentrations. The probability maps reveal that more than 50% of the total area possessed the highest probability (0.8–1.0) of exceeding the threshold values for Cd and Pb.

The Removal of noise and restoration of signals has been one of the most interesting researches in the field of signal processing in the past few year. In this paper, we have tested various deconvolution algorithms proposed in literature, using denoised signal (by wavelets techniques in our case) instead of measured one which is the real signal degraded by measurement procedure. It is very difficult to compare algorithms because the results obtained depend heavily on signal quality (signal-to-noise ratio, sampling), and on algorithm parameters and optimizations. Which criteria should be used to compare signals? Our algorithm which based on Tikhonov-Miller regularization and a model of solution, is a iterative algorithm, gives best results without artifacts and oscillations related to noise, and achieves higher-quality denoising and a high restoration ratio for noisy signal than the existing methods.

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.