Ahmar Rashid

Unscented Kalman filter approach to tracking a moving interfacial boundary in sedimentation processes using three-dimensional electrical impedance tomography

The monitoring of solid–fluid suspensions under the influence of gravity is widely used in industrial processes. By considering sedimentation layers with different electrical properties, non-invasive methods such as electrical impedance tomography (EIT) can be used to estimate the settling curves and velocities. In recent EIT studies, the problem of estimating the locations of phase interfaces and phase conductivities has been treated as a nonlinear state estimation problem and the extended Kalman filter (EKF) has been successfully applied. However, the EKF is based on a Gaussian assumption and requires a linearized measurement model. The linearization (or derivation of the Jacobian) is possible when there are no discontinuities in the system. Furthermore, having a complex phase interface representation makes derivation of the Jacobian a tedious task. Therefore, in this paper, we explore the unscented Kalman filter (UKF) as an alternative approach for estimating phase interfaces and conductivities in sedimentation processes. The UKF uses a nonlinear measurement model and is therefore more accurate. In order to justify the proposed approach, extensive numerical experiments have been performed and a comparative analysis with the EKF is provided.

An oppositional biogeography-based optimization technique to reconstruct organ boundaries in the human thorax using electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive imaging modality which has been actively studied for its industrial as well as medical applications. However, the performance of the inverse algorithms to reconstruct the conductivity images using EIT is often sub-optimal. Several factors contribute to this poor performance, including high sensitivity of EIT to the measurement noise, the rounding-off errors, the inherent ill-posed nature of the problem and the convergence to a local minimum instead of the global minimum. Moreover, the performance of many of these inverse algorithms heavily relies on the selection of initial guess as well as the accurate calculation of a gradient matrix. Considering these facts, the need for an efficient optimization algorithm to reach the correct solution cannot be overstated. This paper presents an oppositional biogeography-based optimization (OBBO) algorithm to estimate the shape, size and location of organ boundaries in a human thorax using 2D EIT. The organ boundaries are expressed as coefficients of truncated Fourier series, while the conductivities of the tissues inside the thorax region are assumed to be known a priori. The proposed method is tested with the use of a realistic chest-shaped mesh structure. The robustness of the algorithm has been verified, first through repetitive numerical simulations by adding randomly generated measurement noise to the simulated voltage data, and then with the help of an experimental setup resembling the human chest. An extensive statistical analysis of the estimated parameters using OBBO and its comparison with the traditional modified Newton-Raphson (mNR) method are presented. The results demonstrate that OBBO has significantly better estimation performance compared to mNR. Furthermore, it has been found that OBBO is robust to the initial guess of the size and location of the boundaries as well as offering a reasonable solution when the a priori knowledge of the conductivity of the organs is not very accurate.

EM algorithm applied for estimating non-stationary region boundaries using electrical impedance tomography

EIT has been used for the dynamic estimation of organ boundaries. One specific application in this context is the estimation of lung boundaries during pulmonary circulation. This would help track the size and shape of lungs of the patients suffering from diseases like pulmonary edema and acute respiratory failure (ARF). The dynamic boundary estimation of the lungs can also be utilized to set and control the air volume and pressure delivered to the patients during artificial ventilation. In this paper, the expectation-maximization (EM) algorithm is used as an inverse algorithm to estimate the non-stationary lung boundary. The uncertainties caused in Kalman-type filters due to inaccurate selection of model parameters are overcome using EM algorithm. Numerical experiments using chest shaped geometry are carried out with proposed method and the performance is compared with extended Kalman filter (EKF). Results show superior performance of EM in estimation of the lung boundary.

A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography.

Dynamic electrical impedance tomography-based image reconstruction using conventional algorithms such as the extended Kalman filter often exhibits inferior performance due to the presence of measurement noise, the inherent ill-posed nature of the problem and its critical dependence on the selection of the initial guess as well as the state evolution model. Moreover, many of these conventional algorithms require the calculation of a Jacobian matrix. This paper proposes a dynamic oppositional biogeography-based optimization (OBBO) technique to estimate the shape, size and location of the non-stationary region boundaries, expressed as coefficients of truncated Fourier series, inside an object domain using electrical impedance tomography. The conductivity of the object domain is assumed to be known a priori. Dynamic OBBO is a novel addition to the family of dynamic evolutionary algorithms. Moreover, it is the first such study on the application of dynamic evolutionary algorithms for dynamic electrical impedance tomography-based image reconstruction. The performance of the algorithm is tested through numerical simulations and experimental study and is compared with state-of-the-art gradient-based extended Kalman filter. The dynamic OBBO is shown to be far superior compared to the extended Kalman filter. It is found to be robust to measurement noise as well as the initial guess, and does not rely on a priori knowledge of the state evolution model.

Efficient clustering of large uncertain graphs using neighborhood information

Abstract This work addresses the problem of clustering large uncertain graphs. The data is represented as a graph where the proposed solution uses the neighborhood information for the purpose of clustering. The proposed approach converts an uncertain graph to a certain graph by predicting about the existence of the edges in the uncertain graph. For the purpose of prediction, a classifier is used. The proposed approach is compared with baseline approaches for clustering graphs having uncertainties over the edges; uncertain k -means (UK-Mean) and Fuzzy-DBSCAN (FDBSCAN). Additionally, the results are also compared with two state-of-the-art approaches namely, CUDAP (clustering algorithm for uncertain data based on approximate backbone) and PEEDR (partially expected edit distance reduction). Experiments are conducted using two natively uncertain and nine synthetically converted uncertain benchmark datasets. The results are compared with the baseline and the state-of-the-art methods using Davies–Bouldin index, Dunn index and Silhouette coefficient, widely used cluster validity indices. The results show that the proposed approach performs better than the other four methods.

Parallel stabilized mixed Galerkin method for three-dimensional Darcy flow using OpenMP

This paper presents a parallel stabilized mixed Galerkin method for three-dimensional steady-state Darcy flow using OpenMP for shared memory architecture. In finite element problems, the computational complexity increases with increasing number of elements needed for large and complex geometry. Shared memory architecture offers a platform for solving such large and complex problems for practical applications. In parallel solvers, the mesh reordering has proved to be an important preprocessing operation to enhance performance. The objective of this study is to compare the performance of octree-based mesh reordering with multilevel-based reordering in the parallel solution. The reported results include execution time, speedup and efficiency of the solution using hexahedral and tetrahedral meshes of various sizes. The numerical results suggest that the octree-based mesh reordering outperforms multilevel-based method with tetrahedral meshes.

Analysing the performance of EIT images using the point spread function

Electrical impedance tomography (EIT) is a noninvasive medical imaging technique in which a small current is applied to the electrodes attached to the surface of a subject body and a cross-sectional image of the resistivity (or conductivity) distribution inside the body is reconstructed using an inverse algorithm. Due to the ill-posed nature of EIT inverse problem, EIT bears poor spatial resolution and behaves non-linearly in nature. Point spread function (PSF), which is calculated over the whole domain as responses to a small circular anomaly moving around the entire domain, is a characteristic parameter to estimate the performance of imaging systems. In order to analyze the quality of EIT reconstructed image, PSF is employed in this work. PSF incorporates the key imaging attributes, comprising amplitude response, resolution, position error, shape deformation and ringing effect. This paper presents a numerical study on the use of PSF for static and dynamic EIT image reconstruction. The static image reconstruction is done using the modified Newton Raphson (mNR) algorithm whereas the dynamic image reconstruction is done with extended Kalman filter (EKF). A detailed analysis of the performance of mNR and EKF has been carried out based upon on the imaging attributes gathered using the said algorithms. The results are convincing and provide a fresh perspective to use the PSF in order to analyze the performance of EIT image reconstruction.

Profiling Players Using Real-World Datasets: Clustering the Data and Correlating the Results with the Big-Five Personality Traits

Computer games provide an ideal test bed to collect and study data related to human behavior using a virtual environment having real-world-like features. Studies regarding individual players’ actions in a gaming session and how this correlates with their real-life personality have the potential to reveal great insights in the field of affective computing. This study profiles players using data collected from strategy games. This is done by taking into account the gameplay and the associations between the personality traits and the subjects playing the game. This study uses two benchmark strategy game datasets, namely, StarCraft and World of Warcraft. In addition, the study also uses the Age of Empire-II game data, collected using 50 participants. The IPIP-NEO-120 personality test is conducted using these participants to evaluate them on the Big-Five personality traits. The three datasets are profiled using four clustering techniques. The results identify two clusters in each of these datasets. The quality of cluster formation is also evaluated through the cluster evaluation indices. Using the clustering results, the classifiers are then trained to classify a player, after a gameplay, into one of the two profiles. Results show that the gameplay can be used to predict various personality features using strategy game data.

A Differential Evolution Based Approach to Estimate the Shape and Size of Complex Shaped Anomalies Using EIT Measurements

EIT image reconstruction is an ill-posed problem, the spatial resolution of the estimated conductivity distribution is usually poor and the external voltage measurements are subject to variable noise. Therefore, EIT conductivity estimation cannot be used in the raw form to correctly estimate the shape and size of complex shaped regional anomalies. An efficient algorithm employing a shape based estimation scheme is needed. The performance of traditional inverse algorithms, such as the Newton Raphson method, used for this purpose is below par and depends upon the initial guess and the gradient of the cost functional. This paper presents the application of differential evolution (DE) algorithm to estimate complex shaped region boundaries, expressed as coefficients of truncated Fourier series, using EIT. DE is a simple yet powerful population-based, heuristic algorithm with the desired features to solve global optimization problems under realistic conditions. The performance of the algorithm has been tested through numerical simulations, comparing its results with that of the traditional modified Newton Raphson (mNR) method.

Numerical Solution and Analysis of Three-Dimensional Transient Darcy Flow

AbstractThis paper presents a detailed analysis of a numerical solution of three-dimensional transient Darcy flow. The numerical solution of the governing parabolic partial differential equations is obtained by using stabilized mixed Galerkin method and backward Euler method for the discretization of space and time, respectively. The resulting well-posed system of algebraic equations is subsequently solved using conjugate gradient method. The proposed model is validated against Mongan’s analytical model for underground water flow using a set of hexahedral and tetrahedral meshes. The model is used to analyze the transient behavior by simulating the Darcy flow through homogeneous and heterogeneous as well as isotropic and anisotropic media. For large meshes, a parallel algorithm of the transient Darcy flow is also developed for shared memory architecture using OpenMP library. For structured meshes, a speedup of over 22 is obtained on dual AMD Opteron processors. The proposed numerical method for transient Darcy flow offers stability, ease of implementation in higher dimensions and parallel solution for large and complex geometry using standard finite element spaces.