Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%

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.

Soil fertilization with wastewater treatment plant (WWTP) biosolids is associated with the introduction of resistance genes (RGs), mobile genetic elements (MGEs) and potentially selective pollutants (antibiotics, heavy metals, disinfectants) into soil. Not much data are available on the parallel analysis of biosolid pollutant contents, RG/MGE abundances and microbial community composition. In the present study, DNA extracted from biosolids taken at 12 WWTPs (two large-scale, six middle-scale and four small-scale plants) was used to determine the abundance of RGs and MGEs via quantitative real-time PCR and the bacterial and archaeal community composition was assessed by 16S rRNA gene amplicon sequencing. Concentrations of heavy metals, antibiotics, the biocides triclosan, triclocarban and quaternary ammonium compounds (QACs) were measured. Strong and significant correlations were revealed between several target genes and concentrations of Cu, Zn, triclosan, several antibiotics and QACs. Interestingly, the size of the sewage treatment plant (inhabitant equivalents) was negatively correlated with antibiotic concentrations, RGs and MGEs abundances and had little influence on the load of metals and QACs or the microbial community composition. Biosolids from WWTPs with anaerobic treatment and hospitals in their catchment area were associated with a higher abundance of potential opportunistic pathogens and higher concentrations of QACs.

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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. 

Single carrier with frequency domain equalization (SC/FDE) substantially out performs OFDM in BER performance for Ultra Wideband (UWB), a result already reported. In this paper we provide an explanation and justify this superiority. We showed besides, by only a proper selection of both cyclic prefix duration and the period of fast Fourier transform an extra gain is obtainable. This performance enhancement is in favor of SC/FDE's superiority for UWB in the context of internet of things.

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.