In this paper, a new approach for human recognition using heart sounds is proposed. The new approach is based mainly on extracting features from heart sounds using wavelet packet decomposition. Different linear and non-linear filter banks at different decomposition levels are designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. Automatic wavelet de-noising and linear discriminant analysis are adopted for pre-processing and classification stages, respectively. The proposed system is tested using an open database for heart sounds known as HSCT-11 which contains data collected from 206 subjects. Based on the achieved results, the proposed system can identify subjects with best accuracy of 91.05% and verify them with an equal error rate of 3.2%. The obtained results in this paper show that wavelet packet based features are appropriate for human recognition task using heart sounds.

In this paper, a new approach for human recognition using heart sounds is proposed. The new approach is based mainly on extracting features from heart sounds using wavelet packet decomposition. Different linear and non-linear filter banks at different decomposition levels are designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. Automatic wavelet de-noising and linear discriminant analysis are adopted for pre-processing and classification stages, respectively. The proposed system is tested using an open database for heart sounds known as HSCT-11 which contains data collected from 206 subjects. Based on the achieved results, the proposed system can identify subjects with best accuracy of 91.05% and verify them with an equal error rate of 3.2%. The obtained results in this paper show that wavelet packet based features are appropriate for human recognition task using heart sounds.

This paper aims to turn complex groundwater data into comprehensible information by indexing the different factors numerically comparative to the standards of World Health Organization (WHO) to produce Water Quality Index (WQI). Water Quality Index (WQI) has been used to assess groundwater quality and Geographic Information Systems (GIS) has been used to create maps representing the spatial distribution of groundwater categories in Assiut governorate, Egypt. Water Quality Index has been computed by Un-weighted Arithmetic Water Quality Index (WQIUA) method and applied on 796 wells over eight years from 2006 to 2013. The results showed that WQIUA values for drinking purposes were high and most of them reached higher or close to 100, which indicated that the groundwater was polluted and unsafe for drinking. On the other hand, the quality index of groundwater for irrigation purposes in most of the study area ranges between 55.78 and 78.38 (poor and very poor category); this means that groundwater is moderately polluted and rather suitable for irrigation.

This paper aims to turn complex groundwater data into comprehensible information by indexing the different factors numerically comparative to the standards of World Health Organization (WHO) to produce Water Quality Index (WQI). Water Quality Index (WQI) has been used to assess groundwater quality and Geographic Information Systems (GIS) has been used to create maps representing the spatial distribution of groundwater categories in Assiut governorate, Egypt. Water Quality Index has been computed by Un-weighted Arithmetic Water Quality Index (WQIUA) method and applied on 796 wells over eight years from 2006 to 2013. The results showed that WQIUA values for drinking purposes were high and most of them reached higher or close to 100, which indicated that the groundwater was polluted and unsafe for drinking. On the other hand, the quality index of groundwater for irrigation purposes in most of the study area ranges between 55.78 and 78.38 (poor and very poor category); this means that groundwater is moderately polluted and rather suitable for irrigation.

This paper aims to turn complex groundwater data into comprehensible information by indexing the different factors numerically comparative to the standards of World Health Organization (WHO) to produce Water Quality Index (WQI). Water Quality Index (WQI) has been used to assess groundwater quality and Geographic Information Systems (GIS) has been used to create maps representing the spatial distribution of groundwater categories in Assiut governorate, Egypt. Water Quality Index has been computed by Un-weighted Arithmetic Water Quality Index (WQIUA) method and applied on 796 wells over eight years from 2006 to 2013. The results showed that WQIUA values for drinking purposes were high and most of them reached higher or close to 100, which indicated that the groundwater was polluted and unsafe for drinking. On the other hand, the quality index of groundwater for irrigation purposes in most of the study area ranges between 55.78 and 78.38 (poor and very poor category); this means that groundwater is moderately polluted and rather suitable for irrigation.

This paper aims to turn complex groundwater data into comprehensible information by indexing the different factors numerically comparative to the standards of World Health Organization (WHO) to produce Water Quality Index (WQI). Water Quality Index (WQI) has been used to assess groundwater quality and Geographic Information Systems (GIS) has been used to create maps representing the spatial distribution of groundwater categories in Assiut governorate, Egypt. Water Quality Index has been computed by Un-weighted Arithmetic Water Quality Index (WQIUA) method and applied on 796 wells over eight years from 2006 to 2013. The results showed that WQIUA values for drinking purposes were high and most of them reached higher or close to 100, which indicated that the groundwater was polluted and unsafe for drinking. On the other hand, the quality index of groundwater for irrigation purposes in most of the study area ranges between 55.78 and 78.38 (poor and very poor category); this means that groundwater is moderately polluted and rather suitable for irrigation.

Two DRASTIC models have been used in this study, which are generic and pesticide to get the groundwater vulnerable levels to pollution in the Nile aquifer along Assiut governorate. Groundwater vulnerability maps were produced using Geographic Information Systems (GIS). It has been found in map of generic DRASTIC model that the most of the study area is covered by moderate vulnerable and high vulnerable; where 55.2 % of the area is moderately vulnerable and 35.4 % has high level of vulnerability. However in the vulnerability map generated by pesticide DRASTIC model, the results concluded that about 64% of the study area has an extreme to high vulnerability to contamination, 34.6% has a moderate vulnerability and small areas occupy about 1.4% and has a low vulnerability.

Two DRASTIC models have been used in this study, which are generic and pesticide to get the groundwater vulnerable levels to pollution in the Nile aquifer along Assiut governorate. Groundwater vulnerability maps were produced using Geographic Information Systems (GIS). It has been found in map of generic DRASTIC model that the most of the study area is covered by moderate vulnerable and high vulnerable; where 55.2 % of the area is moderately vulnerable and 35.4 % has high level of vulnerability. However in the vulnerability map generated by pesticide DRASTIC model, the results concluded that about 64% of the study area has an extreme to high vulnerability to contamination, 34.6% has a moderate vulnerability and small areas occupy about 1.4% and has a low vulnerability.

Two DRASTIC models have been used in this study, which are generic and pesticide to get the groundwater vulnerable levels to pollution in the Nile aquifer along Assiut governorate. Groundwater vulnerability maps were produced using Geographic Information Systems (GIS). It has been found in map of generic DRASTIC model that the most of the study area is covered by moderate vulnerable and high vulnerable; where 55.2 % of the area is moderately vulnerable and 35.4 % has high level of vulnerability. However in the vulnerability map generated by pesticide DRASTIC model, the results concluded that about 64% of the study area has an extreme to high vulnerability to contamination, 34.6% has a moderate vulnerability and small areas occupy about 1.4% and has a low vulnerability.

Two DRASTIC models have been used in this study, which are generic and pesticide to get the groundwater vulnerable levels to pollution in the Nile aquifer along Assiut governorate. Groundwater vulnerability maps were produced using Geographic Information Systems (GIS). It has been found in map of generic DRASTIC model that the most of the study area is covered by moderate vulnerable and high vulnerable; where 55.2 % of the area is moderately vulnerable and 35.4 % has high level of vulnerability. However in the vulnerability map generated by pesticide DRASTIC model, the results concluded that about 64% of the study area has an extreme to high vulnerability to contamination, 34.6% has a moderate vulnerability and small areas occupy about 1.4% and has a low vulnerability.