Photonic Crystal Fiber (PCF) based on the Surface Plasmon Resonance (SPR) effect has been proposed to detect polluted water samples. The sensing characteristics are illustrated using the finite element method. The right hole of the right side of PCF core has been coated with chemically stable gold material to achieve the practical sensing approach. The performance parameter of the proposed sensor is investigated in terms of wavelength sensitivity, amplitude sensitivity, sensor resolution, and linearity of the resonant wavelength with the variation of refractive index of analyte. In the sensing range of 1.33 to 1.3624, maximum sensitivities of 1360.2 nm ∕ RIU and 184 RIU−1 are achieved with the high sensor resolutions of 7 ×10-5 RIU and 5.4× 10−5 RIU using wavelength and amplitude interrogation methods, respectively. The proposed sensor could be established to detect various refractive index (RI) of pollutions in water.
The real and imaginary part of complex dielectric constant for InAs(001) by adsorption of oxsagen atoms has been calculated, using numerical analysis method (non-linear least square fitting). As a result a mathematical model built-up and the final result show a fairly good agreement with other genuine published works.
In this work, strains and dynamic crack growth were studied and analyzed in thin flat plate with a surface crack at the center, subjected to cycling low velocity impact loading for two types of aluminum plates (2024, 6061). Experimental and numerical methods were implemented to achieve this research. Numerical analysis using program (ANSYS11-APDL) based on finite element method used to analysis the strains with respect to time at crack tip and then find the velocity of the crack growth under cycling impact loading. In the experimental work, a rig was designed and manufactured to applying the cycling impact loading on the cracked specimens. The grid points was screened in front of the crack tip to measure the elastic-plas
... Show MoreProfit is a goal sought by all banks because it brings them income and guarantees them survival and continuity, and on the other hand, facing commitments without financial crisis. Hence the idea of research in his quest to build scientific tools and means that can help bank management in particular, investors, lenders and others to predict financial failure and to detect early financial failures. The research has produced a number of conclusions, the most important of which is that all Islamic banks sample a safe case of financial failure under the Altman model, while according to the Springate model all Islamic banks sample a search for a financial failure except the Islamic Bank of Noor Iraq for Investment and Finance )BINI(. A
... Show MoreMedia studies have focused mostly on the issue of the mental image because the image that is formed in the mind has become not only a photo of a human being and having kept for himself. This image has an outside influence which may sometimes up to the formation of the fate of others and it sometimes includes individuals and groups together.
This study comes in the context of identifying the image of Iraqi political parties among Iraqi university students and the nature of the view that students have in their minds about these parties.
Chapter one includes the problem of the research, the importance of the study, the goals and method used. Chapter two is divided into two sections: section one deals with the concept of the mental i
True random number generators are essential components for communications to be conconfidentially secured. In this paper a new method is proposed to generate random sequences of numbers based on the difference of the arrival times of photons detected in a coincidence window between two single-photon counting modules
The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet
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