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The efficiency to which a wireless multi node network is connected is generally measured by the probability that all the nodes are connected to a master node or connected to a master node via other connected node. The grade of connectivity measures how easily and reliably a packet sent by a node can reach another node. Our thesis work is aimed to find connectivity measurement between the nodes in a wireless multi node network. The result is investigated by randomly placing all the nodes in a given area of 38*38 meters and by estimating the connectivity of the whole network. The sub goals of the thesis are To Design a link metric To Find a Routing algorithm which provides information about neighboring nodesAchieving the expected results from this thesis work, it can be a contribution to the research in the measure of connectivity for a wireless multi-node network. By using the available routing algorithm and by setting up appropriate threshold for (i) Good connectivity (ii) Average connectivity (iii) bad connectivity, the status (connectivity measure) is informed to the master node (teacher node) in the network, so that the life time of the whole network is enhanced. Various results and solutions are provided and discussed for the above stated problem from the practical experiments.

Wireless ad hoc and sensor networks are envisioned to be self-organizing and autonomous networks, that may be randomly deployed where no fixed infrastructure is either feasible or cost-effective. The successful commercialization of such networks depends on the feasible implementation of network services to support security-aware applications. Recently, free space optical (FSO) communication has emerged as a viable technology for broadband distributed wireless optical sensor network (WOSN) applications. The challenge of employing FSO include its susceptibility to adverse weather conditions and the line of sight requirement between two communicating nodes. In addition, it is necessary to consider security at the initial design phase of any network and routing protocol. This dissertation addresses the feasibility of randomly deployed WOSNs employing broad beam FSO with regard to the network layer, in which two important problems are specifically investigated. First, we address the parameter assignment problem which considers the relationship amongst the physical layer parameters of node density, transmission radius and beam divergence of the FSO signal in order to yield probabilistic guarantees on network connectivity. We analyze the node isolation property of WOSNs, and its relation to the connectivity of the network. Theoretical analysis and experimental investigation were conducted to assess the effects of hierarchical clustering as well as fading due to atmospheric turbulence on connectivity, thereby demonstrating the design choices necessary to make the random deployment of the WOSN feasible. Second, we propose a novel light-weight circuit-based, secure and integrated routing and localization paradigm within the WOSN, that leverages the resources of the base station. Our scheme exploits the hierarchical cluster-based organization of the network, and the directionality of links to deliver enhanced security performance including per hop and broadcast authentication, confidentiality, integrity and freshness of routing signals. We perform security and attack analysis and synthesis to characterize the protocol’s performance, compared to existing schemes, and demonstrate its superior performance for WOSNs. Through the investigation of this dissertation, we demonstrate the fundamental tradeoff between security and connectivity in WOSNs, and illustrate how the transmission radius may be used as a high sensitivity tuning parameter to balance there two metrics of network performance. We also present WOSNs as a field of study that opens up several directions for novel research, and encompasses problems such as connectivity analysis, secure routing and localization, intrusion detection, topology control, secure data aggregation and novel attack scenarios.

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Název práce: Grupová souvislost graf· Autor: Lucie Mohelníková Katedra: Informatický ústav Univerzity Karlovy Vedoucí diplomové práce: Mgr. Robert 'ámal,Ph.D., Informatický ústav Univerzi- ty Karlovy Abstrakt: Zabývali jsme se grupovou souvislostí graf·, zejména pak Z2 2- a Z4- souvislostí. Implementovali jsme v jazyce C++ test, zda je graf grupově souvislý a pomocí něho hledáme grafy, které jsou grupově souvislé v jedné ze zkoumaných grup a v druhé nikoliv. Zkoumali jsme grafy, které vzniknou podrozdělením hran několika speciálních graf· např. K4 a krychle. Hlavním přínosem této práce je nalezení dvou graf·, které jsou Z4-souvislé a nejsou Z2 2-souvislé. Pomocí druhé nezávislé implementace testu na grupovou souvislost napsané v jazyce Prolog s využitím CSP jsme ověřili, že tyto grafy jsou Z4-souvislé. Analyticky jsme dokázali, že jeden z nalezených graf· není Z2 2-souvislý. Klíčová slova: grupová souvislost, toky, grupa

This practicum intends to improve the landscape surrounding the Investors Group Field stadium at the University of Manitoba in Winnipeg, Manitoba. It addresses the site’s current challenges, the proposed changes to the neighboring infrastructure, and how these improvements are better-suited to the daily lives of fans, residents, and University of Manitoba students. The design proposal further aims at creating a more welcoming and enjoyable experience for visitors first arriving at the stadium. Moreover, it shows how fans, residents, and University of Manitoba students would benefit from the interrelationship between Investors Group Field, University of Manitoba, and a changing new neighboring infrastructure — a “win-win” situation entitled Doppelpass (“one-two pass”). / May 2016

Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity is an important control on waterflood sweep efficiency and requires evaluation to optimize injection well rates. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior and can be used to estimate interwell connectivity. This study discusses the universal characteristics of different sandbody percolation models and develops an efficient percolation method to estimate interwell connectivity. Using King and others results for fluid travel time between locations in a percolation model, we developed a method to estimate interwell connectivity. Three parameters are needed to use this approach: the sandbody occupied probabilitysandp, the dimensionless reservoir length, and the well spacing. To evaluate this new percolation method, the procedure was coded using Visual Basic and Mathematica and the results compared to those from two other methods, a simple geometrical model and Monte Carlo simulation. All these methods were applied to estimate interwell connectivity for the D1, D2, and D3 intervals in the Monument Butte field. The results suggest that the new percolation method can give reasonable effective-square sandbody dimensions and can estimate the interwell connectivity accurately for thin intervals with sandp in the 60% to 80% range. The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. To extend the method to 3D cases, we propose an approach that can be used to estimate interwell connectivity for reservoirs having multiple, noncommunicating layers, and that considers the weight of each interval for multilayer estimation. This approach is applied to the three-layer case of Monument Butte field and the estimates showed the method gives useful results for well pattern design. For example, water saturation and interval thickness affect the weight of each interval to be included in the multilayer estimation. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new percolation method.

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.

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.

Reservoir connectivity is often an important consideration for reservoir management. For example, connectivity is an important control on waterflood sweep efficiency and requires evaluation to optimize injection well rates. The uncertainty of sandbody distributions, however, can make interwell connectivity prediction extremely difficult. Percolation models are a useful tool to simulate sandbody connectivity behavior and can be used to estimate interwell connectivity. This study discusses the universal characteristics of different sandbody percolation models and develops an efficient percolation method to estimate interwell connectivity. Using King and others results for fluid travel time between locations in a percolation model, we developed a method to estimate interwell connectivity. Three parameters are needed to use this approach: the sandbody occupied probabilitysandp, the dimensionless reservoir length, and the well spacing. To evaluate this new percolation method, the procedure was coded using Visual Basic and Mathematica and the results compared to those from two other methods, a simple geometrical model and Monte Carlo simulation. All these methods were applied to estimate interwell connectivity for the D1, D2, and D3 intervals in the Monument Butte field. The results suggest that the new percolation method can give reasonable effective-square sandbody dimensions and can estimate the interwell connectivity accurately for thin intervals with sandp in the 60% to 80% range. The proposed method requires that the reservoir interval for evaluation be sufficiently thin so that 2D percolation results can be applied. To extend the method to 3D cases, we propose an approach that can be used to estimate interwell connectivity for reservoirs having multiple, noncommunicating layers, and that considers the weight of each interval for multilayer estimation. This approach is applied to the three-layer case of Monument Butte field and the estimates showed the method gives useful results for well pattern design. For example, water saturation and interval thickness affect the weight of each interval to be included in the multilayer estimation. For thick intervals or heterogeneous sandbody distributions, the percolation method developed here is not suitable because it assumes thin layers. Future percolation research will be needed to adapt this new percolation method.