Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

In this paper we present an approach to optimistic concurrency control where the write phase occurs before the validation phase (RWV instead of RVW). Our work extends earlier research into real-time databases that utilized virtual execution to lower disk access overheads. Such work demonstrated that virtual execution allowed an increase in transactions completing before deadlines expired and improvements in overall throughput. However, we demonstrate that only with the reversal of the write and validation phases can virtual execution reach its full potential.

Abstract: The igneous rock unit of Al-Haruj Al-Aswad were interpreted using satellite image. The satellite image of Land sat bands from one tosevenhas captured by the Thematic Mapper (TM7) scanner. These imageswere processed and interpreted with following standard technique. Karhunen– Loèvetransform were employed to produce principalcomponent images. Then, they were paintedby using RGB color system to produce false color images from principal component images and ratio images. The resulted images were interpreted in the matter of geology of the area.Theprincipalcomponent transformation is helpful in mapping different lava flow phases in the area. This transform have used the correlation between the corresponding pixels in images of different bands. Six lava flow phases was interpreted depends on their color and shade in the processed satellite images. 

Abstract

The rock units of Wadi El-shati were classified to group of clusters to explain the concentration of iron ore by using enhanced satellite image. The several images of single band of Thematic Mapper (TM7) of Landsat were combined to produce multispectral image bands. It was processed by group of filters after the false color image has been made. The filtered image was classified with unsupervised classification after the image ratio has been done by dividing band 3 on band1. Finally, we reached the classified image has concentration of iron tone.

This paper presents a new high-capacity algorithm for audio watermarking for the purpose of embedding a watermark audio signal into an original audio signal. This algorithm uses DCT (discrete cosine transform) in combination with SVD (singular value decomposition), DWT (discrete wavelet transform), and CZT (chirp ztransform). For the purpose of ensuring high levels of imperceptibility and robustness, a low-frequency sub band of 1-level DWT is selected to embed the watermark, followed by DCT, CZT, and SVD. As a result, the proposed algorithm achieves a high capacity of 352800 bits per second while a good quality audio signal is maintained (having an objective difference grade of more than-0.04). It also provides high imperceptibility (a signal to noise ratio of more than 58 dB) and it is extremely resistant to common audio attacks, including requantizing, resampling, additive white Gaussian noise or AWGN, MP3 compression, and low-pass filtering. The proposed algorithm presents much better compared to the audio watermarking algorithms already existent.

Prosopis farcta (Banks & Sol.) J. F. Macbr. has been reported for the first time from Libya. This species is native in Asia. Flowering specimens of Prosopis farcta collected from Sokna Region about 650 Km south-east Tripoli. It is presented with updated nomenclature, taxonomic description, geographical distribution, place of occurrence in Libya, and field colored photographs are also provided. The voucher specimens were deposited in the Herbarium of Botany Department (ULT), University of , Tripoli, Libya.

A novel non-blind audio watermarking algorithm, using chirp z-transform in combination with discrete wavelet transform and singular value decomposition, is presented in this paper. In the proposed method, a watermark audio signal (about 13 seconds in length) is hidden into an original audio signal (about 51 seconds in length) by applying discrete wavelet transform, chirp z-transform, and singular value decomposition. The effectiveness of the proposed algorithm has been tested using signal-to-noise ratio, objective difference grade (ODG), and normalised cross- correlation of watermarked audio signal under various common audio attacks. Experimental results demonstrate that the algorithm has a high capacity (352.8 kbps), without significant perceptual distortion (ODG > -0.29) and provides good robustness against common audio attacks, including: re-sampling, echo addition, MP3 compression, additive white Gaussian noise, re- quantisation, cutting, and low-pass filtering. This algorithm has a high capacity and excellent audio quality compared with other audio watermarking algorithms.

Biosynthesis of the metallic nanoparticle is gaining importance because it’s a single-step process, nontoxic, environmentally acceptable, and easily scaled up. The present study investigated the biosynthesis of silver nanoparticles (AgNPs) via reacting olive wastewater filtrate (OWF) with silver nitrate, and the formation of AgNPs was confirmed by a color change of the reaction mixture and visible spectrophotometry. Additionally, the influence of pH, reaction time, AgNO₃ concentration, temperature, and OWF volume on the proposed method was investigated. It was found that with increasing the previously mentioned parameters, the formation of the AgNPs was increased under the experimental conditions. The results showed that OWF represented a promising material for the biosynthesis of AgNPs.