This project estimates interwell connectivity, a characteristic that is crucial to determine reservoir continuity while developing a waterflooding project. It tests the combination of Fourier transforms (FT’s) of the flow rate data and analytical solutions from analog electrical circuits to infer the inverse diffusivity coefficient (IDC). I solved the transmission line equation analytically for 0D, 1D, and 2D resistance/capacitance (RC) network models and used those solutions to compare with the flow rate FT’s to determine the diffusivity parameters. I used the analogy between the electrical response of RC networks and the fluid response of permeable reservoirs on the basis of the similarities in the governing equations. I conclude that the analogy works accurately in simple reservoirs, where the assumptions of an analytical solution are met, i.e. single-phase fluid and a homogeneous system. For two-phase liquid cases, I determined that the analogy remains applicable because we still could produce accurate interwell connectivity information. When I investigated cases with dissolved-gas production around the wellbore, however, the analogy broke down and the results were not as good as the liquid systems.

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise.

Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.

Thesis (M.S.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 41-42).

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of motor disorders such as Parkinson’s Disease (PD). It is therefore important to develop models for inferring brain functional connectivity from data obtained through non-invasive imaging technologies, such as functional magnetic resonance imaging (fMRI). The complexity of brain activities as well as the dynamic nature of motor disorders require such models to be able to perform complex, large-scale, and dynamic system computation. Traditional models proposed in the literature such as structural equation modeling (SEM), multivariate autoregressive models (MAR), dynamic causal modeling (DCM), and dynamic Bayesian networks (DBNs) have all been suggested as suitable for fMRI data analysis. However, they suffer from their own disadvantages such as high computational cost (e.g. DBNs), inability to deal with non-linear case (e.g. MAR), large sample size requirement (e.g. SEM), et., al. In this research, we propose applying Probabilistic Boolean Network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fIVIRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects enables us to identify statistically significant abnormality in PD connectivity by comparing it with normal subjects. The PBN results also suggest a mechanism of evaluating the effectiveness of L-dopa, the principal treatment for PD. In addition to PBNs’ promising application in inferring brain connectivity, PBN modeling for brain ROTs also enables researchers to study dynamic activities of the system under stochastic conditions, gaining essential information regarding asymptotic behaviors of ROTs for potential therapeutic intervention in PD. The results indicate significant difference in feature states between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamic network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention. Results identified a target ROT with the best intervention performance, and that ROl is a potential candidate for therapeutic exercise. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

This research has three objectives; 1) describe the distribution of footpaths in the outer suburbs of Ottawa, 2) quantify the impact footpaths have on network connectivity in the outer western suburban neighborhoods of Ottawa, 3) quantify the impact of footpaths on destination-based walkability measures in the outer western suburban neighborhoods of Ottawa. The distribution of footpaths is assessed using a principal component analysis on 86 observations (footpaths) and 11 variables (land usage, transit connection, income, population density). Network connectivity is measured using the link-node ratio, the gamma index, and the alpha index, as well a node betweenness centrality. Walkability is measured in ArcGIS through an origin-destination cost matrix. The results show that the distribution of footpaths cannot be explained by the selected variables. Footpaths slightly decrease overall network connectivity and re-work node betweenness centrality. Footpaths have no impact on destination-based walkability. And so, footpaths may serve as better pedestrian routes but not necessarily as faster routes through the outer western suburbs of Ottawa.

In-Flight Entertainment and Connectivity (IFEC) is becoming a key trend and offering in-flight connectivity is one of the most essential demands of commercial airline passengers. A grand challenge is to provide in-flight connectivity in high altitudes and particularly in isolated locations, such as the oceans, where establishing an air-to-ground link is not possible. Moreover, the high speed and dynamic characteristics of such aircraft make this task difficult. Aeronautical Ad-Hoc Networking (AANET) intends to cope with this challenge by forming a network of airplanes having air-to-air (A2A) connections. However, the dynamic nature of such a network is likely to lead to unstable connections. The primary root cause of the majority of these stability issues is known to be the short life of A2A links which is the result of poor topology formation of aircraft. Concentrating on aircraft clustering and making them more stable can improve connection lifetime and improve the stability and performance of the network. Therefore the main objective in making AANETs feasible should be to form the topology as clusters of aircraft. With this in mind, the thesis's proposition is twofold: First, unveil the benefits of density-based clustering to improve the AANET performance. To do so, a modified DBSCAN algorithm is employed for the clustering problem that exploits several features of real flight datasets. This method also includes a weighted scheme to reflect the relative importance of each feature of the final calculation. The proposed method improved the packet delivery ratio and end-to-end latency of the state-of-the-art clustering-based AANET solutions by 51 % and 30 %, respectively. In addition, the proposed approach reduces the number of cluster changes by 22%. Second, selecting a well-connected cluster head is the next stage in enhancing connection and stability. This thesis presents a new cluster head selection technique for AANETs that calculates the Neighbor Nodes within a given distance of each node and selects the node with the most connections as the new cluster head. In instances where a cluster head cannot interact directly with another cluster, a Gateway node is chosen to facilitate connection with other clusters. According to simulations, the suggested method increases packet delivery ratio by 3, end-to-end delay by 9 and throughput by up to 10% compared to the current state of the art. In addition, the proposed method reduces cluster head replacements by 17% and increases cluster head longevity by 8%.

No description available.

Delineating population units is essential for the conservation and management of a species. Applying a genetic approach to delineate units, this study identifies genetic population structure, and landscape resistance to gene flow, of the nationally threatened boreal woodland caribou (Rangifer tarandus caribou) across the ecotypes’ southern range in Saskatchewan. Three genetic clusters were delineated across the study area, with moderate genetic connectivity identified with Manitoba. Isolation-by-distance was found to be significant across Saskatchewan, and within each genetic cluster. Gene flow across clusters in Saskatchewan was high (FST = ~0.01), with genetic connectivity being lowest for the south-central cluster surrounding Prince Albert National Park (FST = ~0.03). Resistance to gene flow was identified with the following landscape variables: water, forestry, roads, wildfire, and low suitability habitat. Careful consideration of these variables in range planning will help to maintain genetic connectivity of boreal caribou across its southern range in Saskatchewan. / May 2016