Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noising tool to reduce the effects of both clock skew and queuing delay fluctuations on the decision of congestion type. Since, classical Discrete Wavelet Transform (DWT) is not shift-invariant transform which is a very useful property particularly in signal de-noising problems. Therefore, another transform called Stationary Wavelet Transform (SWT) that possesses shiftinvariant property is suggested and used instead of DWT. The modified technique exhibits a better performance in terms of the time required to correctly detect the state of congestion especially with the existence of clock skew problem. The suggested technique is tested using simulations under different
environments.
There are two ways that the contract might be formed with (contracting between persons who are attended and contracting between absence persons).the need for determining the precise moment of the contract , is so clear because there is a specify period separate between the declaration of acceptance and the knowledge with it .and it is clear from the four theories known for jurisprudence (theory of the declaration of the acceptance, theory of exporting the acceptance , theory of the arrival of the acceptance , theory of the knowledge with the acceptance ) . It is difficult to promote one theory on another one if we look at each one and the justification of its supporters and what the opponents of each theory expose. Legal background and diff
... Show MoreThe research aims to reach a set of objectives concerning creation a clear vision about conceptual, philosophical and practical dimension of relations, and effects between knowledge management, costumer orientation and competitiveness to construct a framework of a pragmatic model as a solution to research problem and its questions which the main one is about the role of knowledge management and costumer orientation in competitiveness of business organizations. To achieving this goal, it was necessary to make, in priory, a review and discussion to the theoretical dimension of research variables to have a clear vision about constructing hypostatical research model implying a set of hypotheses which, by proving them in companies studied, repr
... Show MoreDAIRMD Professor Hayder R. Al-Hamamy, **Professor Adil A. Noaimi, **Dr. Ihsan A. Al-Turfy, IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 2015
Background: During acrylic resin processing, the mold must be separated from the surface of the gypsum to prevent liquid resin from penetrating into the gypsum, and water from the gypsum seeping into the acrylic resin. For many years, tin foil was the most acceptable separating medium, and because it's difficult to apply, a tin-foil substitute is used. In this study, olive oil is used as an alternative to tin foil separating medium for first time, and evaluating its effect as a separating medium on some mechanical properties such as (indentation hardness and transverse strength) of acrylic resins denture base comparing it with those processed using tin-foil and tin foil substitute such as (cold mold seal) separating medium. Materials and M
... Show MoreZernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the s
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Lymphoma is a cancer arising from B or T lymphocytes that are central immune system components. It is one of the three most common cancers encountered in the canine; lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic conditions and to improve decision-making around treating and what treatment type to use. This study aimed to evaluate a potential novel biomarker related to iron metabolism,
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co
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