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Air University may refer to: Henley Putnam University Air University Air University Wikipedia.


Kousar N.,Shanghai JiaoTong University | Kousar N.,Air University | Liao S.,Shanghai JiaoTong University
Nonlinear Analysis: Real World Applications | Year: 2011

The unsteady non-similarity boundary-layer flows caused by an impulsively stretching flat sheet have been investigated. The partial differential equations governing the flows have been solved analytically by means of an analytic technique for strongly non-linear problems, namely the homotopy analysis method (HAM). This analytic approach gives us the convergent series solution uniformly valid for all dimensionless time in the whole spatial region 0≤x<∞ and 0≤y<∞. To the best of our knowledge, such a kind of series solution has never been obtained. The skin friction coefficient and the boundary-layer thickness are also analyzed for different dimensionless time τ. © 2010 Published by Elsevier Ltd. All rights reserved. Source


Ashraf U.,Air University
IEEE Transactions on Mobile Computing | Year: 2015

In this paper, we address the following problem: Given a specific deployment of a wireless mesh network and given a budget to augment the capacity of N links within the network, which links should be selected for capacity augmentation in order to maximize the achievable aggregate throughput for all the flows in the network? Unlike previous research which focuses on installing additional nodes or gateways, we work on an existing deployment of the mesh network and identify optimal link positions for capacity augmentation. We model the capacity augmentation problem in single-radio single-channel mesh networks as a multicommodity maximum flow problem and derive a mixed-integer linear programming model for identifying the optimal links for capacity augmentation. For ensuring fairness among flows, we propose a concurrent flow problem formulation and derive a corresponding mixed-integer linear programming model. We propose greedy algorithms to solve the mixed-integer linear programming formulations. We also extend our analysis to the case of multi-radio multi-channel networks in which additional radios can be deployed on nodes for capacity augmentation. The simulation results of the proposed schemes for several different topologies indicate that the proposed greedy algorithms are correct and provide exact results at a fraction of the cost. © 2014 IEEE. Source


Sadiq M.A.,Air University
Journal of the Physical Society of Japan | Year: 2011

In this paper steady streaming due to a vibrating sphere in an incompressible fluid is considered. The Vishik-Lyusternik method is employed to construct an asymptotic expansion of the solution of the Navier-Stokes equations in the limit of high-frequency vibrations for Reynolds numbers of O(1). The effect of the Stokes drift of fluid particles is also considered. © 2011 The Physical Society of Japan. Source


Ahmed S.,Air University
IEEE Transactions on Aerospace and Electronic Systems | Year: 2014

A novel radar pulse integration technique is proposed in which the received signals in various returns from the target during one scan are multiplied with each other instead of the conventional summing up. Using the Mellin transform, mathematical expressions are derived that pave the way for computation of probability of detection and probability of false alarm in the context of this technique. Analytical and simulation results show that the proposed product-based pulse combining is more resistant to noise jamming, as compared to conventional noncoherent pulse integration, under certain conditions. © 2014 IEEE. Source


Arif M.,Air University
International Journal of Innovative Computing, Information and Control | Year: 2011

In this paper, a novel index called Arif Index is proposed to evaluate the discrimination power of the features in pattern classification. Optimizing the performance of a classifier requires a prior knowledge of maximum achievable accuracy in the pattern classification using a particular set of features. Moreover, it is also desirable to know that this set of features is separable by a decision boundary of any arbitrary complexity or not. Proposed index is model free and requires no clustering algorithm to discover the clustering structure present in the feature space. It is only based on the information of local neighborhood of feature vectors in the feature space. This index can be used to predict the classification accuracy and density of the feature vectors of a class in the feature space. It was found in this paper that predicted accuracy and Arif index are very strongly correlated with each other (R2 = 0.99 with ρ-value nearly equals to zero). This index is designed to predict the maximum achievable accuracy by a particular set of features. Implementation of the index is simple and time efficient. Performance of Arif index on different benchmark physiological data sets is found to he in consistent with the reported accuracies in the literature. Hence, this index will be very useful in providing prior useful information about the quality of features before designing any classifier. ICIC International © 2011. Source

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