In previous studies on heartbeat series, it has been proposed that the healthy heartbeat pattern represents complex nonlinear dynamics, and such cardiac nonlinearity may be used as a clinical indicator for the diagnosis of certain types of heart disease. However, it is still not quite clear whether there is any difference among the heartbeat series of patients with congestive heart failure (CHF), or whether cardiac nonlinearity represents a severe heart disease situation. In the present study, parallel cascade identification (PCI), which frequently requires only short stretches of data to obtain highly promising results, is used to distinguish severe congestive heart failure, a clinical situation associated with a high-risk of sudden death, from low-risk CHF. Parallel cascade identification is an accurate and robust method for identifying dynamic nonlinear systems. The PCI algorithm combined with a specified statistical test may be used as a severe congestive heart failure marker by comparing a nonlinear model with a “linear” model (more precisely, a first-order Volterra series). In this thesis, PCI is applied to distinguish R-R wave intervals of CHF patients who died from those of patients who survived in a 5-year study. The detection accuracy of the PCI detector is evaluated over a first set of 49 patients, and then over a larger set of a further 352 patients, and consistent results are obtained between the two sets. Over the larger set, Matthews' correlation coefficient of nonlinearity with unfavorable outcome (death) is , sensitivity for predicting unfavorable outcome is , while the specificity is . The R-R wave interval exhibits nonlinearity in patients who died during the 5-year study. However, typically nonlinearity cannot be detected in patients who survived during the study. These findings show that for patients with congestive heart failure, nonlinearity is associated with unfavorable outcome (death), while patients for whom nonlinearity cannot be detected overwhelmingly have good outcomes. This is significant for clinical diagnosis and prognosis of severe congestive heart failure. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-09-28 11:54:57.695

With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches, a temporal synthetic dataset is developed. Methods are then applied to this dataset as well as the Brainsim dataset, a popular simulated temporal gene expression data. Furthermore, the methods are also applied to a biological dataset in yeast Saccharomyces Cerevisiae. This dataset includes 14 cell-cycle regulated genes; their known cell cycle pathway is used as the target network structure, and the criteria sensitivity, precision, and specificity are calculated to evaluate the inferred networks through these two methods. Resulting networks are also compared with two previous studies in the literature on the same dataset. / Thesis (Master, Computing) -- Queen's University, 2010-10-18 20:47:36.458

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Multi-modal biometrics is one of the most promising avenues to address the performance problems in biometrics-based personal authentication systems. While uni-modal biometric systems have bolstered personal authentication better than traditional security methods, the main challenges remain the restricted degrees of freedom, non-universality and spoof attacks of the traits. In this research work, we investigate the performance improvement in bi-modal biometrics authentication systems based on the physiological trait, the iris, and behavioral trait, the signature. We investigate a model to detect the largest non-occluded rectangular part of the iris as a region of interest (ROI) from which iris features are extracted by a cumulative-sums-based grey change analysis algorithm and Gabor Filters. In order to address the space complexity of biometric systems, we proposed two majority vote-based algorithms which compute prototype iris features codes as the reliable specimen templates. Experiments obtained a success rate of 99.6%. A text-based directional signature verification algorithm is investigated. The algorithm verifies signatures, even when they are composed of symbols and special unconstrained cursive characters which are superimposed and embellished. The experimental results show that the proposed approach has an improved true positive rate of 94.95%. A user-specific weighting technique, the user-score-based, which is based on the different degrees of importance for the iris and signature traits of an individual, is proposed. Then, an intelligent dual ν-support vector machine (2ν-SVM) based fusion algorithm is used to integrate the weighted match scores of the iris and signature modalities at the matching score level. The bi-modal biometrics system obtained a false rejection rate (FRR) of 0.008, and a false acceptance rate (FAR) of 0.001. / Thesis (Ph.D)-University of KwaZulu-Natal, Westville, 2010.

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Accurate identification of pathogens and vectors is essential in epidemiological studies of mosquito-borne pathogens. However, the members of the communis and punctor complexes are difficult to distinguish because they are highly cryptic species, with little to no species-specific morphological characters. The objective of this thesis is to develop molecular tools, including RFLP and DNA barcoding using cytochrome oxidase I (COI), internal transcribed spacer 2 (ITS2) and the intron of ribosomal protein S12 (RPS12) to facilitate identification of the members of these two complexes in Manitoba. A distinct interspecific distance for COI was found between the members of the communis complex included here, and diagnostic RFLP profiles were developed for Oc. communis and Oc. churchillensis. Relatively low average interspecific genetic distances using COI, ITS2 and RPS12 were observed between the members of the punctor complex, indicates no discernable boundaries between these species based on DNA barcoding.