Sita Road, Pakistan
Sita Road, Pakistan

Bahria University , is a public research university primarily located in Islamabad, Pakistan. The university maintains campuses in Karachi and Lahore.Established by the Pakistan Navy in 2000, its status is granted as civilian. It offers programmes in undergraduate, post-graduate, and doctoral studies. Its research is directed towards the development of engineering, philosophy, natural, social, and medical science. The university is one of the top institution of higher learning in the country and secured its ranking in among country's top ten and most notable universities in "general category" by the HEC, as of 2013. The university is a member of the Association of Commonwealth Universities of the United Kingdom.The university research institutes offer scientific research in the development of medical, environmental, natural science as well as in the engineering and philosophy. Wikipedia.

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Mudakkar S.R.,Lahore School of Economics | Zaman K.,COMSATS Institute of Information Technology | Khan M.M.,COMSATS Institute of Information Technology | Ahmad M.,Bahria University
Renewable and Sustainable Energy Reviews | Year: 2013

This study investigates the causal relationship between energy consumption (i.e., nuclear energy consumption, electricity power consumption and fossil fuels energy consumption) and economic growth; energy consumption and industrialization (i.e., industrial GDP, beverages and cigarettes); energy consumption and environmental degradation (i.e., carbon dioxide emissions, population density and water resources); and finally, energy consumption and resource depletion (i.e., mineral depletion, energy depletion, natural depletion and net forest depletion) in Pakistan over a period of 1975-2011. The Granger causality (GC) test in the frequency domain using the Pierce framework has been employed. This GC test in the frequency domain relies on a modified version of the coefficient of coherence, which they estimate in a nonparametric fashion and for which they derive the distributional properties. The results infer that there exists uni-directional causality running from nuclear energy to industrial GDP, nuclear energy to water resources; and nuclear energy to carbon dioxide emissions but not vice versa. Similarly, electric power consumption Granger cause agriculture GDP but not other way around, further, there is a bi-directional causality running between electric power consumption to population density in Pakistan. Fossil fuel Granger cause industrial GDP and there is a bidirectional causality running between fossil fuel and population density. Moreover, the findings show that the nature of causality among nuclear energy consumption & agriculture; nuclear energy consumption & population density; electric power consumption & cigarettes production; fossil fuel & cigarettes; and fossil fuels and agriculture value added are in favour of the neutrality hypothesis in Pakistan. The conclusion has been strengthen and have a very strong implications in the context of Pakistan, where we have economic and financial constraints, and thus agreeing the bottom line, "living with the just enough". © 2013 Elsevier Ltd.


Akram M.U.,National University of Sciences and Technology | Khalid S.,Bahria University | Khan S.A.,National University of Sciences and Technology
Pattern Recognition | Year: 2013

Diabetic retinopathy is a progressive eye disease which may cause blindness if not detected and treated in time. The early detection and diagnosis of diabetic retinopathy is important to protect the patients vision. The accurate detection of microaneurysms (MAs) is a critical step for early detection of diabetic retinopathy because they appear as the first sign of disease. In this paper, we propose a three-stage system for early detection of MAs using filter banks. In the first stage, the system extracts all possible candidate regions for MAs present in retinal image. In order to classify a candidate region as MA or non-MA, the system formulates a feature vector for each region depending upon certain properties, i.e. shape, color, intensity and statistics. We present a hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The proposed system is evaluated using publicly available retinal image databases and achieved higher accuracy which is better than previously published methods. © 2012 Elsevier Ltd All rights reserved.


Khalid S.,Bahria University
2012 International Conference on Computing, Networking and Communications, ICNC'12 | Year: 2012

The fundamental ingredient of content-based image retrieval is the selection of appropriate features to describe the content of the image. Shape of an object, represented by its contour, is the most important visual feature that is thought to be used by humans to determine the similarity of objects. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Encoding contour representation of shapes in this manner leads to efficiency gains over existing approaches such as structural shape representation and techniques that use discrete point-based flow vectors to represent the contour. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is effective, efficient and robust to different arbitrary and affine distortions. © 2012 IEEE.


Zaman K.,COMSATS Institute of Information Technology | Mushtaq Khan M.,COMSATS Institute of Information Technology | Ahmad M.,Bahria University
Renewable and Sustainable Energy Reviews | Year: 2013

The purpose of this study is to identify major macroeconomic factors that enhance energy consumption for Pakistan through the cointegration, error correction model and Granger causality tests over a 32-year time period, i.e., between 1980 and 2011. The study employed the bivariate cointegration technique to estimate the long-run relationship between the variables; an error correction model was used to determine the short-run dynamics of the system, while Granger causality test was used to find the directions between these variables. The study investigates the relation between four energy consumption variables (i.e., oil/petroleum consumption, gas consumption, electricity consumption and coal consumption) and four macroeconomic factors which have further sub-classifications, i.e., balance of payment (BOP) factors (i.e., exports, imports, trade deficit, worker's remittances and current account balance), fuel factors (i.e., carbon dioxide emissions, natural resource depletion and net forest depletion), agricultural crops yield per hectare (i.e., wheat, rice, sugarcane, maize and cotton) and industrial production items (i.e., beverages, cigarettes, motor tyres, motor tubes, cycle tyres and cycle tubes) in order to manage robust data analysis. The result confirms the long-run relationship between total commercial energy consumption and macroeconomic factors in Pakistan, as oil/petroleum consumption increases exports, fuel factors, agricultural crops yield per hectare and industrial items; however, the intensity of these factors are different in nature. Carbon dioxide emissions, net forest depletion, beverages, motor tyres and motor tubes are more elastic with oil/petroleum consumption. However, oil/petroleum consumption decreases trade deficit and workers' remittances in Pakistan. Gas, electricity and coal consumption increases agricultural crops yield per hectare and industrial production which shows that as agriculture and industry modernizes, energy demand increases. Energizing the food production chain is an essential feature of agricultural development which is a prime factor in helping to achieve food security in Pakistan. The empirical results only moderately support the conventional view that energy consumption has significant long-run casual effect on macroeconomic variables in Pakistan. The present study finds evident of unidirectional causality between the commercial energy consumption factors and macroeconomic factors in Pakistan. However, there is some bidirectional causality exist which is running between electricity consumption (EC) and exports, EC to imports, EC to carbon emissions, EC to natural resource depletion (NRD) and EC to wheat. The results conclude that macroeconomic variables tend to positively respond to total primary energy consumption. This indicates that increasing total commercial energy consumption may cause growth variables which show that Pakistan is an input-driven economy. © 2012 Elsevier Ltd.


Khalid S.,Bahria University | Razzaq S.,National University of Sciences and Technology
Pattern Recognition | Year: 2012

This paper presents an extension of m-mediods based modeling technique to cater for multimodal distributions of sample within a pattern. The classification of new samples and anomaly detection is performed using a novel classification algorithm which can handle patterns with underlying multivariate probability distributions. We have proposed two frameworks, namely MMC-ES and MMC-GFS, to enable our proposed multivarite m-mediods based modeling and classification approach workable for any feature space with a computable distance metric. MMC-ES framework is specialized for finite dimensional features in Euclidean space whereas MMC-GFS works on any feature space with a computable distance metric. Experimental results using simulated and complex real life dataset show that multivariate m-mediods based frameworks are effective and give superior performance than competitive modeling and classification techniques especially when the patterns exhibit multivariate probability density functions. © 2011 Elsevier Ltd. All rights reserved.


Mukati A.,Bahria University
Journal of Network and Computer Applications | Year: 2011

Computer systems failure due to hard or soft memory errors is very common. Hard errors are caused due to any permanent fault in the memory chips whereas soft errors in memory chips, generally transients or intermittent in nature, are caused due to alpha particles or cosmic rays. Non-critical systems may not require serious attention for such failures where simple, cost-effective, little-overhead techniques may be considered enough. However, semi/fully critical systems do require a careful treatment, keeping aside all other factors, but the reliability and serviceability during the intended period of time. A number of monolithic and hybrid techniques have been developed over the years. This paper aims to expose the concerns related to increasing memory and logic errors as an off-shoot due to the advancement in technologies. A survey is presented regarding the techniques being used to deal with such errors. © 2010 Elsevier Ltd. All rights reserved.


Akram M.U.,Bahria University | Khan S.A.,National University of Sciences and Technology
Engineering with Computers | Year: 2013

Diabetic retinopathy screening involves assessment of the retina with attention to a series of indicative features, i.e., blood vessels, optic disk and macula etc. The detection of changes in blood vessel structure and flow due to either vessel narrowing, complete occlusions or neovascularization is of great importance. Blood vessel segmentation is the basic foundation while developing retinal screening systems since vessels serve as one of the main retinal landmark features. This article presents an automated method for enhancement and segmentation of blood vessels in retinal images. We present a method that uses 2-D Gabor wavelet for vessel enhancement due to their ability to enhance directional structures and a new multilayered thresholding technique for accurate vessel segmentation. The strength of proposed segmentation technique is that it performs well for large variations in illumination and even for capturing the thinnest vessels. The system is tested on publicly available retinal images databases of manually labeled images, i.e., DRIVE and STARE. The proposed method for blood vessel segmentation achieves an average accuracy of 94.85% and an average area under the receiver operating characteristic curve of 0.9669. We compare our method with recently published methods and experimental results show that proposed method gives better results. © 2012 Springer-Verlag London Limited.


Khalid S.,Bahria University
Pattern Recognition | Year: 2010

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering and classification of motion. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT (discrete fourier transform)-based coefficient feature space representation. A framework (iterative HSACT-LVQ (hierarchical semi-agglomerative clustering-learning vector quantization)) is proposed for learning of patterns in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is also proposed that models the class containing n members with m Mediods. Once the m-Mediods-based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Our proposed techniques are validated using variety of simulated and complex real life trajectory data sets. © 2009 Elsevier Ltd. All rights reserved.


Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for classification of motion activity and anomaly detection using object motion trajectory. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. A modelling technique, referred to as m-mediods, is proposed that models the class containing n members with m mediods. Once the m-mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Four anomaly detection algorithms using m-mediods based representation of classes are proposed. These includes: (i)global merged anomaly detection (GMAD), (ii) localized merged anomaly detection (LMAD), (iii) global un-merged anomaly detection (GUAD), and (iv) localized un-merged anomaly detection (LUAD). Our proposed techniques are validated using variety of simulated and complex real life trajectory datasets. © 2010 Elsevier Ltd. All rights reserved.


Khalid S.,Bahria University
Multimedia Systems | Year: 2012

Techniques for efficient and effective contentbased image matching are becoming increasingly important with the widespread increase in digital image capturing systems. Shape of an object, represented by its contour, is one of the most important visual feature that is thought to be used by humans to determine the similarity of objects. The selected feature and its distance measure must be robust to different distortions such as noise, articulation, scale and rotation. Existing approaches provides invariance to these distortions at the cost of either the accuracy due to poor discrimination ability or the efficiency. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Shape matching is then carried out in the chosen coefficient feature space resulting in efficient matching. The efficiency of shape matching is further improved by indexing the shape descriptors using hierarchical indexing structure. A novel distributed beam search based technique is proposed that operates on the indexing structure and ensures no false dismissal for a given k-NN query. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is robust, efficient and scalable to very large shape datasets. © Springer-Verlag 2011.

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