Imran Shafi

A Novel White Space Optimization Scheme Using Memory Enabled Genetic Algorithm in Cognitive Vehicular Communication

AbstractA dedicated single short range communication link is not efficient for an inter-vehicular communication system and results into degraded performance. To address the problem, a cognitive radio site is proposed as an intelligent vehicular device to implement an inter-vehicular communication network using multiple radio access technologies. Further, the whitespace optimization at vehicular speed is achieved by the memory enabled genetic algorithm. The algorithm makes use of four cognitive radio decision variables as genes including frequency, power, data rate and modulation scheme in the chromosome structure. The performance of the proposed approach is validated against the classical genetic algorithm and particle swarm optimization algorithm. In this research, a statistical evaluation is also presented to confirm the potential of cognitive radio paradigm employing multiple radio access technologies as an option to fulfill the increasing bandwidth demand of an inter-vehicular communication system. Experimental results demonstrate the effectiveness of the approach by ensuring efficient bandwidth utilization and fulfilling varying nature of users’ quality of service requirements in real time.

A Methodological Review of 3D Reconstruction Techniques in Tomographic Imaging

Computer Vision has provided immense support to medical diagnostics over the past two decades. Analogous to Non Destructive Testing of mechanical parts, advances in medical imaging has enabled surgeons to determine root cause of an illness by consulting medical images particularly 3-D imaging. 3-D modeling in medical imaging has been pursued using surface rendering, volume rendering and regularization based methods. Tomographic reconstruction in 3D is different from camera based scene reconstruction which has been achieved using various techniques including minimal surfaces, level sets, snakes, graph cuts, silhouettes, multi-scale approach, patchwork etc. In tomography limitations of image aquisition method i-e CT Scan, X Rays and MRI as well as non availability of camera parameters for calibration restrict the quality of final reconstruction. In this work, a comprehensive study of related approaches has been carried out with a view to provide a summary of state of the art 3D modeling algorithms developed over the past four decades and also to provide a foundation study for our future work which will include precise 3D reconstruction of human spine.

Using physical layer clock recovery to augment application layer time synchronization

Achieving same notion of time remains an important task for most distributed systems. Time synchronization requires a unique combination of high accuracy (

Test Case Prioritization for NUnit Based Test Plans in Agile Environment

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Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing: Survey and Future Directions

μs level) and energy efficiency. Several application layer protocols have been developed to meet these requirements. This article proposes that the physical layer clock recovery process can provide application layer clock drift estimate, and the application layer clock can be corrected with the help of this estimate. This eliminates the need of application layer time synchronization protocol i.e. the cross-layer approach reduces the number of message exchanges required by application layer for time synchronization that leads to energy conservation. It argues that such a cross-layer approach can provide a more accurate frequency offset estimation, or can achieve greater energy savings, for a given accuracy, by reducing the message exchanges. Analysis of the proposed method provides concrete bounds on achieved improvement. Experimental evaluation showed that physical layer clock drift can be used to correct application layer clock drift as they are identical.

An architecture to analyze big data in the Internet of Things

Amino acids chains combine to make different type of proteins and protein is a molecular instrument. To make a protein, a cell must put a chain of amino acids together in the right order. Bacteria are small living microscopic organisms, due to its variation in functioning; the treatment and usage require their identification. There are two main groups of their classification i.e. gram negative and gram positive bacteria. Escherichia Coli (E. coli) is a gram-negative bacterium. Bacterial cells can be detected by the signaling data of proteins localization sites in them. These sites are detected by different type of methods like X-ray, ultrasound, nuclear magnetic resonance, electronic microscope and others. However, the data obtained by these methods tend to be complex and sometimes become difficult to classify any specie of bacteria. There are some classification systems available but sometime classification becomes difficult. To answer this problem, three different supervised Artificial Neural Networks (ANN) as follows Feed Forward Neural Network (FFNN), Probabilistic Neural Network (PNN) and Linear Vector Quantization Neural Network (LVQ) are used to classify Escherichia coli data. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity. Most efficient neural network architecture with University of California, Irvine (UCI) database for E. coli has been obtained and result comparison with earlier work is also shown.

Lopez Dahab based elliptic crypto processor (ECP) over GF(2 163 ) for low-area applications on FPGA

Test Case prioritization having a key role to play in prioritizing test scenarios from a pile of scenarios, to best of our knowledge, has not been employed in Agile environment for prioritizing test cases in Automated Test Plans. Considering automated testing in agile environment esp scrum, a prioritized test plan containing high priority test cases is emanated using Genetic Algorithms. This prioritization is courtesy to base factors such as operational profile, test scenario criticality, and faults uncovered by each test case; used to weight test scenarios. Proposed technique exhibits great performance by ameliorating the rate of fault detection by dynamically prioritizing NUnit based test scenarios.

Hardware design and implementation of ECC based crypto processor for low-area-applications on FPGA

Recently genetic algorithm based techniques have been introduced in Femtocells communication systems due to inherent advantages. Higher Quality of Service is the demand of users and service providers in day to day communication. This requires an efficient optimization technique to ensure the best quality. In this paper a genetic algorithm based technique is presented for optimization of coverage, power, and bit error rate for performance optimization of Femtocell. Simulation results demonstrate that genetic algorithm based optimization technique is an efficient technique for achieving batter performance in Femtocell environment