Magister Scientiae - MSc / This thesis demonstrates that a situation-aware algorithm improves quality of service on small mesh networks running BATMAN-adv with some mobile nodes. BATMAN-adv is a proactive mesh routing protocol that counts beacons as a link quality metric. BATMAN-adv was modi ed to give more recently received beacons more weight, thereby calculating a more precise indication of the current state of a link that BATMAN-adv can use to forward packets. BATMAN-adv `original' was compared with a situation-aware version in two laboratory test beds with the same voice traffic profile on actual hardware with a realistic voice traffic profile; with controlled transmission rates and buffer sizes to simulate congestion. The second test bed included mesh potatoes, PCs and laptops as mobile nodes. BATMAN-adv achieved better jitter and packet loss than the situation-aware version in the initial, smaller test bed, and average throughput for both versions was almost identical. However, in the second slightly larger test bed, with additional mobile nodes, the situation-aware algorithm performed better than the original BATMAN-adv algorithm for all quality of service metrics, including throughput. Thus the thesis concludes that a situation-aware protocol offers a promising solution to address issues pertaining to mobility, congestion and scalability for voice traffic in mesh networks with mobile nodes. / South Africa

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Master of Science / Department of Statistics / Perla Reyes / Networks are defined as sets of items and their connections. Interconnected items are represented by mathematical abstractions called vertices (or nodes), and the links connecting pairs of vertices are known as edges. Networks are easily seen in everyday life: a network of friends, the Internet, metabolic or citation networks. The increase of available data and the need to analyze network have resulted in the proliferation of models for networks. However, for networks with billions of nodes and edges, computation and inference might not be achieved within a reasonable amount of time or budget. A sampling approach seems a natural choice, but traditional models assume that we can have access to the entire network. Moreover, when data is only available for a sampled sub-network conclusions tend to be extrapolated to the whole network/population without regard to sampling error. The statistical problem this report addresses is the issue of how to sample a sub-network and then draw conclusions about the whole network. Are some sampling techniques better than others? Are there more efficient ways to estimate parameters of interest? In which way can we measure how effectively my method is reproducing the original network? We explore these questions with a simulation study on Mesa High School students' friendship network. First, to assess the characteristics of the whole network, we applied the traditional exponential random graph model (ERGM) and a stochastic blockmodel to the complete population of 205 students. Then, we drew simple random and stratified samples of 41 students, applied the traditional ERGM and the stochastic blockmodel again, and defined a way to generalized the sample findings to the population friendship network of 205 students. Finally, we used the degree distribution and other network statistics to compare the true friendship network with the projected one. We achieved the following important results: 1) as expected stratified sampling outperforms simple random sampling when selecting nodes; 2) ERGM without restrictions offers a poor estimate for most of the tested parameters; and 3) the Bayesian stochastic blockmodel estimation using a strati ed sample of nodes achieves the best results.

This work presents a new approach for detecting and isolating faults in nonlinear processes using independent neural network models. In this approach, an independent neural network is used to model the proton exchange membrane fuel cell nonlinear systems using a multi-input multi-output structure. This research proposed the use of radial basis function network and multilayer perceptron network to perform fault detection. After training, the neural network models can give accurate prediction of the system outputs, based on the system inputs. Using the residual generation concept developed in the model-based diagnosis, the difference between the actual and estimated outputs are used as residuals to detect faults. When the magnitude of these residuals exceed a predefined threshold, it is likely that the system is faulty. In order to isolate faults in the system, a second neural network is used to examine features in the residual. A specific feature would correspond to a specific fault. Based on features extracted and classification principles, the second neural network can isolate faults reliably and correctly. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations.

Submitted by Nayara Passos (nayara.passos@ufpe.br) on 2015-03-13T12:04:17Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE Max Santana Rolemberg Farias.pdf: 3331146 bytes, checksum: aafe22682c1e4d4144f19615252785b9 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:23:12Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE Max Santana Rolemberg Farias.pdf: 3331146 bytes, checksum: aafe22682c1e4d4144f19615252785b9 (MD5) / Made available in DSpace on 2015-03-13T13:23:12Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE Max Santana Rolemberg Farias.pdf: 3331146 bytes, checksum: aafe22682c1e4d4144f19615252785b9 (MD5) Previous issue date: 2014 / CNPq, FACEPE / O crescente número de tarefas em execução em plataformas Multiprocessor Systemson- Chips (MPSoC) exige mais e mais processadores e as plataformas MPSoC que utilizam o meio de comunicação tradicional (barramento) possui uma largura de banda limitada e não são escaláveis para projetos de alta performance. Nesse sentido, os MPSoC baseados em Networkon- Chip (NoC) foram propostos para resolver estas limitações. Um dos principais problemas em plataformas MPSoC baseado em NoC é o custo de comunicação, pois esse custo de comunicação depende do mapeamento de tarefas nos processadores. Este trabalho apresenta uma abordagem que utiliza uma meta-heurística para atribuir um conjunto de tarefas a um conjunto de Processing Element (PE) em um MPSoC baseado em NoC. Esta abordagem proposta avalia e otimiza o mapeamento de tarefas de aplicações e, em alguns experimentos, os resultados foram comparados com o pior e o melhor mapeamento do espaço de projeto. Os resultados encontrados durante os experimentos mostram uma redução média de energia de 47% quando é utilizado o mecanismo de agrupamento de tarefas e 44% quando o mecanismo de agrupamento é desligado.

RCC 106 Chapter 10 has established the standard for interoperable flight test recording on the DOD ranges. The growth of network and distributed technologies in flight test instrumentation (FTI) has led to the recent adoption of Chapters 21 through 28 to provide standards for implementing interoperable telemetry networks. However, the new standards have led to confusion and concern that the investment in Chapter 10 recorders will be lost. This paper first clarifies the complementary nature of the RCC 106 chapters and proposes one possible path to extending the current capability of a Chapter 10 recorder with telemetry network capability while minimizing impact to existing recording and support systems.

Master of Science / Department of Geology / Matthew W. Totten / The Meramec member of Mississippian age is a proficient oil and gas producing formation within the midcontinent region of the United States. It is produced in Kansas, Oklahoma, and Texas. In Kansas, 12% of the state’s petroleum production comes from Mississippian-aged rocks. Bindley Field, located in central west Kansas, has produced 3,669,283 barrels of oil from one facies within the M2 interval of the Meramec formation. This facies is a grain-supported echinoderm/bryozoan dolostone, of variable thickness. Its sporadic occurrence in the subsurface has made exploring Bindley Field and the surrounding area difficult. The challenge in finding oil in this area is in locating a producible zone of this productive facies. Previously, Bindley Field has been the subject of detailed reservoir characterization studies (Ebanks et al., 1977; Johnson, 1990; Johnson, 1994). These studies helped to contribute to a better understanding of Meramecian stratigraphy in Kansas. The Meramec was divided into four major depositional sequences, with some of those sequences nonexistent in the subsurface, due to aerial exposure and erosion post-deposition. The Meramecian units were further separated into parasequence-scale chronostratigraphic units based on marine flooding events. The primary producing interval in Bindley Field is the Meramec 2 interval which consists of seven lithotypes, and is recognized to have six, meter-scale depositional cycles (Johnson, 1990). As production from this interval increased, more information became available about controls on reservoir quality. There are still areas, however, where core data do not exist, and predicting the productive facies remains challenging. The aim of this study is to create a workflow for evaluating the subsurface using regional core and log data from Bindley Field to create a model of the subsurface distribution of the reservoir facies, which could be extended to data poor areas. Geophysical logs (neutron, gamma ray, guard) along with an artificial neural network (ANN), was used to create an accurate prediction of producing intervals within the subsurface. Values are derived from wire line log data and used to develop the ANN definition of facies distribution within Bindley Field. The ANN model was examined for accuracy and precision using core description and well cuttings from wells within Bindley Field and the surrounding area. Correlations were found between the subsurface geometry of the study area, and the production of oil and gas within the study area. An ANN model with an accuracy of 72% was achieved and applied to wells surrounding the Bindley Field, where reservoir intervals have not been as extensively studied. A total of 87 wells in Bindley Field and the surrounding 50 square mile area where applied to the ANN model. The model predicted that the productive facies thickens gradually to the northwest of Bindley Field. Cross sections as well as an isopach map were created using the prediction data from the ANN. Finally, an analysis for the accuracy of the ANN and the predicted facies was created. The productive facies yielded an accuracy value of 77%.

Reliable routing using alternate paths is investigated in this dissertation. We propose precalculated alternate paths as a method for fast recovery from link and node failures in IP networks. We demonstrate that path switching time, and thus failure recovery time are, as expected, considerably faster than the standard method of recalculating a new path on the fly. However, to be effective, the alternate paths should share a minimal set of links and nodes - preferably none - with the failed path. As shared links are considered in this work, we give a reliability model for this situation (non-disjoint alternate paths) and develop estimates of reliability as a function of the number of shared links. Alternate path finding algorithms to calculate suitable alternate paths subject to predefined constraints are also developed. Implementation of these techniques for improving routing reliability is shown to be straightforward for explicit routing protocols such as Multi-Protocol Label Switching (MPLS) with Explicit Routing mode. This mode is expected to be the protocol of choice for applications requiring guaranteed Quality of Service (QoS) carried on the coming generation of wavelength-switched networks (Internet II, CA Net III, etc.) We propose a Reliable MPLS (R-MPLS) protocol by applying alternate path routing to MPLS, using our new algorithms to precalculate appropriate alternate paths. Simulation results show that R-MPLS can achieve fast recovery from failures. We also address reliability issues for the problem of optimal Service Level Agreement (SLA) admission control. To achieve reliable admission control, we apply alternate path routing to an existing SLA-based admission controller called SLAOpt. In the existing Utility Model, SLA admission control is mapped to the Multiple-Choice Multi-Dimension Knapsack Problem (MMKP), where the aim is to maximize system utility (i.e., revenue). However, SLAOpt is static in terms of network topology and does not consider reliability. Motivated by this, we propose a Reliable SLAOpt (R-SLAOpt), in which utility optimization is subject to the additional constraint of reliability. A new algorithm was also developed to calculate multiple groups of alternate paths that meet the desired QoS demands and reliability requirement. After QoS adaptation, R-SLAOpt selects an appropriate path group containing two or three paths for each admitted session and performs resource reservation on all paths in the group. In the event of node or link failure, a session can be quickly switched to one of the alternate paths, maintaining the guaranteed QoS without having to run the full admission algorithm again. In this way, we have obtained a unified treatment of routing reliability and optimal SLA admission control. Finally, simulations are presented which investigate R-SLAOpt's impact on system performance and the gains made in reliability. / Graduate

Historically, artificial neural networks have been loosely defined as biologically inspired computational models. When deciding what sort of network to use for a given task there are two things that need to be considered. The first is the representational power of the given network, that is what class of problems can be solved by this network? Given a set of problems to be solved by neural networks, a network that can solve any of these problems is called a universal approximator. The second problem is the ability to find a desired network given an initial network via a learning rule. Here we are interested in the question of universal approximation. A general definition of artificial neural networks is provided along with definitions for different kinds of universal approximation. We then prove that the recurrent temporal restricted Boltzmann machine (RTRBM) satisfies a general type of universal approximation for stochastic processes, an extention of previous results for the simple RBM. We conclude by examining the potential use of such temporal artificial neural networks in the biological process of perception. / Graduate

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