Amin N.-U.,Abdul Wali Khan University Mardan |
Alam S.,University of Malakand |
Gul S.,UET Peshawar
RSC Advances | Year: 2015
Natural kaolinitic clay from Khyber Pakhtoonkhwa, Pakistan was thermally activated at different temperatures and its pozzolanic behavior was studied. Thermal activation was carried out in an electrical muffle furnace at 200, 400, 600 and 800 °C for two hours. The index of order/disorder and structural changes with thermal treatment were studied using X-ray diffraction (XRD) and Fourier transformed infra-red spectrometry (FTIR). The pozzolanic behavior of all the samples was evaluated using an electrical conductivity test, lime consumption (LC) test and strength activity index (SAI). This study confirmed that natural kaolinitic clay other than pure kaolinite can act as an interesting pozzolana when thermally activated. The best temperature for thermal treatment of natural kaolinitic clay is 800°C, while the permissible replacement in mortar is 25% as confirmed from the compressive strength measurement. This journal is © The Royal Society of Chemistry.
Zain-Ul-Abdein M.,Ghulam Ishaq Khan Institute of Engineering Sciences and Technology |
Azeem S.,UET Peshawar |
Shah S.M.,University of Lyon
International Journal of Engineering Science | Year: 2012
Particulate filled polymeric composites with enhanced thermo-physical properties are highly demanded in electronic industry. This paper presents an experimental and computational investigation of the thermal conductivity enhancement in a bakelite-graphite composite material. The experimental work illustrates an effect of the graphite addition in different volume fractions upon the effective thermal conductivity of the composite. Computational investigation was performed in two parts. The first part explains a development of experimentally validated finite element models for the estimation of effective thermal conductivity, while the second part demonstrates a detailed analysis of the factors affecting thermal conductivity of the composite. The factors that were examined include particle size with individual constituent properties, and air gaps/voids and interface additions in terms of packing density. The findings showed that not only the finite element simulations may be exploited for the prediction of effective thermal conductivity in a composite material; they may also be helpful in suggesting the optimum particle size and packing density factors to suit the industrial design requirements. © 2012 Elsevier Ltd. All rights reserved.
Ullah A.,Loughborough University |
Khan S.W.,Loughborough University |
Shakoor A.,UET Peshawar |
Starov V.M.,UET Peshawar
Separation and Purification Technology | Year: 2013
The model presented in the paper is the continuation of the previous work where a mathematical model was developed for the passage and deformation of micro-sized oil drops through a 4 μm converging slotted pore membrane. In the previous work, it was assumed that drops deform from a spherical shape to a prolate spheroid when pass through a converging slot. In the present study, it has been assumed that drops deform into an oblate spheroid while passing through a non-converging slot and a mathematical model is developed for the deformation of drops through non-converging slots. After extending the idea of static and drag forces, it is readily seen that the magnitude of static force (F cx) for the non-converging slotted pore membrane is higher than the static force (Fcxâ̂ -) for the converging slotted pore membranes. This is because of drops deform suddenly in the non-converging slots, while, in case of converging slots, the drops deform gradually. Micro-sized oil drops of two systems with different interfacial tensions (4 and 9 mN/m) have been used in the study and it is observed that a higher interfacial tension leads to a higher rejection rate for both converging and non-converging slotted pore membranes at various in-pore filtration velocities. © 2013 Elsevier Ltd. All rights reserved.
Khan N.,UET Peshawar |
Fekri S.,Cranfield University |
Ahmad R.,University of Leicester |
Gu D.,University of Leicester
Proceedings of the IEEE Conference on Decision and Control | Year: 2011
In this paper, an integrated attitude estimation and control algorithm is addressed and implemented to a spacecraft dynamical model subject to observation (sensor) losses. Rigid body equations of motion for modeling and control of spacecraft model is obtained from both kinematic and dynamic equations. An earlier version of the so-called closed-loop estimation scheme presented in  is extended and implemented to the spacecraft model subject to observation losses. Compensated observation signals are reconstructed based on linear prediction subsystem and utilized at measurement update steps. Simulation results verify that the proposed robust estimation algorithm applied to the rigid body spacecraft model significantly outperforms existing open-loop filtering algorithms and could attack many other practical applications with intermittent output measurement losses. © 2011 IEEE.
Ali H.,University of Peshawar |
Badshah N.,UET Peshawar |
Chen K.,University of Liverpool |
Khan G.A.,University of Peshawar
Pattern Recognition | Year: 2016
Level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects/regions in a given image, provided that the underlying intensity has homogeneity. The case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well-known work of Chan-Vese (2001) and its many variants. However for images with intensity inhomogeneity or multiphases within the foreground of objects, such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful. To deal with such problems, in this paper, we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images. Tests and comparisons will show that our new model outperforms two previous models. Both synthetic and real life images are used to illustrate the reliability and advantages of our new model. © 2015 Elsevier Ltd.
Gul T.,Abdul Wali Khan University Mardan |
Islam S.,Abdul Wali Khan University Mardan |
Shah R.A.,U.E.T Peshawar |
Khan I.,University of Technology Malaysia |
Shafie S.,Majmaah University
PLoS ONE | Year: 2014
In this work, we have carried out the influence of temperature dependent viscosity on thin film flow of a magnetohydrodynamic (MHD) third grade fluid past a vertical belt. The governing coupled non-linear differential equations with appropriate boundary conditions are solved analytically by using Adomian Decomposition Method (ADM). In order to make comparison, the governing problem has also been solved by using Optimal Homotopy Asymptotic Method (OHAM). The physical characteristics of the problem have been well discussed in graphs for several parameter of interest. © 2014 Gul et al.
Mahsal Khan M.,UET Peshawar |
Masood Ahmad A.,UET Peshawar |
Muhammad Khan G.,UET Peshawar |
Miller J.F.,University of York
Neurocomputing | Year: 2013
A fast learning neuroevolutionary algorithm for both feedforward and recurrent networks is proposed. The method is inspired by the well known and highly effective Cartesian genetic programming (CGP) technique. The proposed method is called the CGP-based Artificial Neural Network (CGPANN). The basic idea is to replace each computational node in CGP with an artificial neuron, thus producing an artificial neural network. The capabilities of CGPANN are tested in two diverse problem domains. Firstly, it has been tested on a standard benchmark control problem: single and double pole for both Markovian and non-Markovian cases. Results demonstrate that the method can generate effective neural architectures in substantially fewer evaluations in comparison to previously published neuroevolutionary techniques. In addition, the evolved networks show improved generalization and robustness in comparison with other techniques. Secondly, we have explored the capabilities of CGPANNs for the diagnosis of Breast Cancer from the FNA (Finite Needle Aspiration) data samples. The results demonstrate that the proposed algorithm gives 99.5% accurate results, thus making it an excellent choice for pattern recognitions in medical diagnosis, owing to its properties of fast learning and accuracy. The power of a CGP based ANN is its representation which leads to an efficient evolutionary search of suitable topologies. This opens new avenues for applying the proposed technique to other linear/non-linear and Markovian/non-Markovian control and pattern recognition problems. © 2013 Elsevier B.V.
Ahmad A.M.,UET Peshawar |
Khan G.M.,UET Peshawar |
Mahmud S.A.,UET Peshawar |
Miller J.F.,University of York
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation | Year: 2012
A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1% for Type-I (classifying benign sample falsely as malignant) and 0.5% for Type-II (classifying malignant sample falsely as benign). © 2012 ACM.
Khan G.M.,UET Peshawar |
Khan S.,UET Peshawar |
Ullah F.,UET Peshawar
International Conference on Intelligent Systems Design and Applications, ISDA | Year: 2011
Load forecasting has been an inevitable issue in electric power supply in past. It is always desired to predict the load requirements in order to generate and supply electric power efficiently. In this research, a neuro-evolutionary technique known as Cartesian Genetic Algorithm evolved Artificial Neural Network (CGPANN) has been deployed to develop a peak load forecasting model for the prediction of peak loads 24 hours ahead. The proposed model presents the training of all the parameters of Artificial Neural Network (ANN) including: weights, topology and functionality of individual nodes. The network is trained both on annual as well as quarterly bases, thus obtaining a unique model for each season. © 2011 IEEE.
Afridi M.A.,UET Peshawar |
Ullah S.,UET Peshawar
Jurnal Teknologi | Year: 2016
In this paper, a 2.42 GHz micro-strip patch antenna is designed and analyzed using a conventional and a metamaterial (artificial) based Electromagnetic Bandgap (EBG) ground planes. The directivity, return loss and VSWR of the conventional 2.42 GHz patch antenna were found to be 5.23dB, -13.2dB, and 1.5 respectively. The proposed antenna then being mounted on a Mushroom-type EBG structures (artificial ground plane) produced better far-field performance as compared to conventional counterpart i.e. the return loss, directivity and VSWR were improved by 80.3%, 58.5% and 24.6%. The WLAN antenna was designed and tested on a miniaturized slotted EBG structure. The slotted EBG was 11.4 % compact as compared to the mushroom structure. The directivity, return loss and VSWR of the antenna using the slotted EBG are improved by be 51%, 31.8%, 15.4% respectively as compared to the patch conventional WLAN patch antenna. The antenna can be used for WLAN applications. © 2016 Penerbit UTM Press. All rights reserved.