Biodiversity can be described as the total variation of life forms, where diversity ranges from gene level up to the ecosystem level. The diversity can be calculated in a number of ways, and this study use one of these methods. In this study empirical food webs have been used and analyzed, where eleven characters are defined and used as parameters to calculate the Euclidean distances between food webs that describe the variation that may exist within classes of terrestrial, marine and freshwater habitats. The class who stood out and showed the greatest diversity at the ecosystem level was the marine food webs, which showed a high value of the average euclidean distance. The other networks were not as distinctive and the average of the euclidean distance in these classes was comparatively low.

The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. / The main objective of this study is to find a wavelet-based, feature extracting algorithm for push-to-talk transmitter identification. A distance-measure algorithm is introduced to classify signals belonging to one of four transmitters. The signals are first preprocessed to put them into a form suitable for wavelet analysis. The preprocessing scheme includes taking the envelopes and differentials. Median filtering is also applied to the outputs of the wavelet transform. The distance algorithm uses local extrema of the wavelet coefficients, and computes the distance between the local extrema of a template and the processed signals. A small distance implies high similarity . A signal from each transmitter is selected as a template. A small distance measure indicates that the signal belongs to the transmitter from which the template originated. The distance algorithm can classify correctly the four different signal sets provided for the research. Even at lower signal-to-noise levels, good identification is achieved.

This thesis presents the research work regarding the solution of a localization problem in indoor WLANs by introducing a distributive time division multiplexed localization technique based on the convex semidefinite programming. Convex optimizations have proven to give promising results but have limitations of computational complexity for a larger problem size. In the case of localization problem the size is determined depending on the number of nodes to be localized. Thus a convex localization technique could not be applied to real time tracking of mobile nodes within the WLANs that are already providing computationally intensive real time multimedia services. Here we have developed a distributive technique to circumvent this problem such that we divide a larger network into computationally manageable smaller subnets. The division of a larger network is based on the mobility levels of the nodes. There are two types of nodes in a network; mobile, and stationery. We have placed the mobile nodes into separate subnets which are tagged as mobile whereas the stationary nodes are placed into subnets tagged as stationary. The purpose of this classification of networks into subnets is to achieve a priority-based localization with a higher priority given to mobile subnets. Then the classified subnets are localized by scheduling them in a time division multiplexed way. For this purpose a time-frame is defined consisting of finite number of fixed duration time-slots such that within the slot duration a subnet could be localized. The subnets are scheduled within the frames with a 1:n ratio pattern that is within n number of frames each mobile subnet is localized n times while each stationary subnet consisting of stationary nodes is localized once. By using this priority-based scheduling we have achieved a real time tracking of mobile node positions by using the computationally intensive convex optimization technique. In addition, we present that the resultant distributive technique can be applied to a network having diverse node density that is a network with its nodes varying from very few to large numbers can be localized by increasing frame duration. This results in a scalable technique. In addition to computational complexity, another problem that arises while formulating the distance based localization as a convex optimization problem is the high-rank solution. We have also developed the solution based on virtual nodes to circumvent this problem. Virtual nodes are not real nodes but these are nodes that are only added within the network to achieve low rank realization. Finally, we developed a distributive 3D real-time localization technique that exploited the mobile user behaviour within the multi-storey indoor environments. The estimates of heights by using this technique were found to be coarse. Therefore, it can only be used to identify floors in which a node is located.

We study the complexity of finding so called spanner paths between arbitrary nodes in Euclidean graphs. We study both general Euclidean graphs and a special type of graphs called Integer Graphs. The problem is proven NP-complete for general Euclidean graphs with non-constant stretches (e.g. (2n)^(3/2) where n denotes the number of nodes in the graph). An algorithm solving the problem in O(2^(0.822n)) is presented. Integer graphs are simpler and for these special cases a better algorithm is presented. By using a partial order of so called Images the algorithm solves the spanner path problem using O(2^(c(\log n)^2)) time, where c is a constant depending only on the stretch.

Biodiversity is a fundamental part of the functioning of ecosystems and their ability to provide ecosystem services. It has been shown that a high biodiversity increases the robustness of an ecosystem according to the insurance hypothesis. I propose that a similar effect can be seen on a higher scale, where a high diversity of ecosystem types might stabilize the ecological functionality of a region. By comparing eleven network characters in 70 tropical and temperate ecosystems, their diversity was measured as Euclidean distance between the systems in the 11-dimensional room defined by these characters.  The diversity of ecosystems was shown to be significantly higher in tropical latitudes than in temperate. A possible explanation to this result could be that the higher species diversity in the tropics allows for more types of ecosystems. A higher diversity of ecosystems in a region might indicate a larger amount and variation of possible ecosystem goods and services as well as provide the region with an increased robustness. The measurement of ecosystem diversity between regions might also be of importance in a conservation perspective, where unique and vulnerable ecosystems can be discovered and protected.

Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzir o tempo de execução da TDE. Dois algoritmos de TDE são paralelizados. O primeiro, um algoritmo de TDE por varredura independente, é paralelizado em um SMP e em um agregado. O segundo, um algoritmo de TDE por propagação ordenada, é paralelizado no agregado. / The Euclidean distance transform is the operation that converts a binary image made of object and background pixels into another image, the Euclidean distance map, where each pixel has a value corresponding to the Euclidean distance from this pixel to the background. The Euclidean distance transform has important uses in computer vision, image analysis and robotics, but it is time-consuming, mainly when processing 3-D images. In this work two types of parallel computers are used to speed up the Euclidean distance transform, (i) symmetric multiprocessors (SMPs) and (ii) clusters of workstations. Two algorithms are parallelized. The first one, an independent line-column Euclidean distance transform algorithm, is parallelized on a SMP, and on a cluster. The second one, an ordered propagation Euclidean distance transform algorithm, is paralellized on a cluster.

Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzir o tempo de execução da TDE. Dois algoritmos de TDE são paralelizados. O primeiro, um algoritmo de TDE por varredura independente, é paralelizado em um SMP e em um agregado. O segundo, um algoritmo de TDE por propagação ordenada, é paralelizado no agregado. / The Euclidean distance transform is the operation that converts a binary image made of object and background pixels into another image, the Euclidean distance map, where each pixel has a value corresponding to the Euclidean distance from this pixel to the background. The Euclidean distance transform has important uses in computer vision, image analysis and robotics, but it is time-consuming, mainly when processing 3-D images. In this work two types of parallel computers are used to speed up the Euclidean distance transform, (i) symmetric multiprocessors (SMPs) and (ii) clusters of workstations. Two algorithms are parallelized. The first one, an independent line-column Euclidean distance transform algorithm, is parallelized on a SMP, and on a cluster. The second one, an ordered propagation Euclidean distance transform algorithm, is paralellized on a cluster.

This study looks at the difference in accessibility to green spaces within the four largest Scottish cities. Having access to green spaces provides several physical and mental health benefits while also providing important ecosystem services. Previous studies show that the frequency of use of a green space declines once the distance surpasses 300 m to an access point. Geographic Information Systems (GIS) were used to analyse the service area of an access point to a green space, from which the rate of accessibility is established. The study also analyses the difference in accessibility between Euclidean and network distance. It is found that the Euclidean difference underestimates the distance needed to reach an access point and that 300 m recommendation by Euclidean distance is more closely resembles 500 m network distance. This study recommends that a distinction be made between which measurement metric is used when stating distances regarding accessibility, in order to create a more consistent approach.

Com a crescente necessidade de segurança, o processo de identificação pessoal é cada vez mais exigido. A extração de características faciais é um passo importante quando se lida com interpretação visual automatizada no reconhecimento de faces humanas. Dentre as características faciais, os olhos são partes importantes no processo de reconhecimento, pois determinam o início da busca por outras características relevantes. Neste trabalho é apresentado um método de localização de olhos em imagens frontais de faces humanas. Este método é subdividido em duas partes. Primeiro, são identificados os possíveis candidatos a olhos usando a Transformada de Hough para círculos; depois é aplicada a Distância Euclidiana confirmando-se a localização pro biometria facial. / Personal identification process is an exigency for security systems. Facial feature extraction is a crucial step for automated visual interpretation in human face recognition. Withim all the facial features, the eyes are significantly parts for the recognition process, therefore they set up the start for another relevant feature search. In this work, we present a method for eyes locating in digital images of frontal human faces. This method is subdivided into two parts. First, we identify the possible eyes\'s candidates by Hough Transfor for circules, them we apply the Euclidian distance and calculate the eyes\'s position by facial biometric measurement.

Florida is expanding its urban borders into areas of the native habitat. Increased expansion is predicted through the next several decades. Several sections of the state are home to large carnivores, such as Florida panther and black bear, which are important to ecosystem function. Expansion of roads and urban centers will greatly reduce the quality and quantity of carnivore habitat. In this study, I used Euclidean distance analyses and very high frequency (VHF) telemetry points to produce distance categories in which carnivores either have a negative/neutral/positive association with roads and urban centers. The seven black bear populations followed four different trends: 1) Slight avoidance of roads and urban centers, 2) strong avoidance of roads and urban centers, 3) neutrality toward roads and urban centers, and 3) one population with a positive association of roads. Florida panther showed strong avoidance to roads and urban centers. Finally I modeled Florida panther and black bear habitat using Maximum Entropy Species Distribution software and placed future urban expansion and sea level incursions associated with climate change over the habitat to find high priority conservation areas.