MESQUITA, Diego Parente Paiva. Machine Learning for incomplete data. 2017. 55 f. Dissertação (Mestrado em Ciência da Computação)-Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Jonatas Martins (jonatasmartins@lia.ufc.br) on 2017-08-29T14:42:43Z No. of bitstreams: 1 2017_dis_dppmesquita.pdf: 673221 bytes, checksum: eec550f75e2965d1120185327465a595 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2017-08-29T16:04:36Z (GMT) No. of bitstreams: 1 2017_dis_dppmesquita.pdf: 673221 bytes, checksum: eec550f75e2965d1120185327465a595 (MD5) / Made available in DSpace on 2017-08-29T16:04:36Z (GMT). No. of bitstreams: 1 2017_dis_dppmesquita.pdf: 673221 bytes, checksum: eec550f75e2965d1120185327465a595 (MD5) Previous issue date: 2017 / Methods based on basis functions (such as the sigmoid and q-Gaussian functions) and similarity measures (such as distances or kernel functions) are widely used in machine learning and related fields. These methods often take for granted that data is fully observed and are not equipped to handle incomplete data in an organic manner. This assumption is often flawed, as incomplete data is a fact in various domains such as medical diagnosis and sensor analytics. Therefore, one might find it useful to be able to estimate the value of these functions in the presence of partially observed data. We propose methodologies to estimate the Gaussian Kernel, the Euclidean Distance, the Epanechnikov kernel and arbitrary basis functions in the presence of possibly incomplete feature vectors. To obtain such estimates, the incomplete feature vectors are treated as continuous random variables and, based on that, we take the expected value of the transforms of interest. / Métodos baseados em funções de base (como as funções sigmoid e a q-Gaussian) e medidas de similaridade (como distâncias ou funções de kernel) são comuns em Aprendizado de Máquina e áreas correlatas. Comumente, no entanto, esses métodos não são equipados para utilizar dados incompletos de maneira orgânica. Isso pode ser visto como um impedimento, uma vez que dados parcialmente observados são comuns em vários domínios, como aplicações médicas e dados provenientes de sensores. Nesta dissertação, propomos metodologias para estimar o valor do kernel Gaussiano, da distância Euclidiana, do kernel Epanechnikov e de funções de base arbitrárias na presença de vetores possivelmente parcialmente observados. Para obter tais estimativas, os vetores incompletos são tratados como variáveis aleatórias contínuas e, baseado nisso, tomamos o valor esperado da transformada de interesse.

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.

Orientadores: Carlile Campos Lavor, Tibérius de Oliveira e Bonates / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-26T15:11:50Z (GMT). No. of bitstreams: 1 Lima_JorgeFerreiraAlencar_D.pdf: 1109545 bytes, checksum: 086223c23c920a9abe0d3661769a6a7d (MD5) Previous issue date: 2015 / Resumo: Geometria de Distâncias Euclidianas (GDE) é o estudo da geometria euclidiana baseado no conceito de distância. É uma teoria útil em diversas aplicações, onde os dados consistem em um conjunto de distâncias e as possíveis soluções são pontos em algum espaço euclidiano que realizam as distâncias dadas. O problema chave em GDE é conhecido como Problema de Geometria de Distâncias (PGD), em que é dado um inteiro K>0 e um grafo simples, não direcionado, ponderado G=(V,E,d), cujas arestas são ponderadas por uma função não negativa d, e queremos determinar se existe uma função (realização) que leva os vértices de V em coordenadas no espaço euclidiano K-dimensional, satisfazendo todas as restrições de distâncias dadas por d. Consideramos tanto problemas teóricos quanto aplicações da GDE. Em termos teóricos, demonstramos a quantidade exata de soluções de uma classe de PGDs muito importante para problemas de conformação molecular e, além disso, conseguimos condições necessárias e suficientes para determinar quando um grafo completo associado a um PGD é realizável e qual o espaço euclidiano com dimensão mínima para tal realização. Em termos práticos, desenvolvemos um algoritmo que calcula tal realização em dimensão mínima com resultados superiores a um algoritmo clássico da literatura. Finalmente, mostramos uma aplicação direta do PGD em problemas de escalonamento multidimensional / Abstract: Euclidean distance geometry (EDG) is the study of Euclidean geometry based on the concept of distance. This is useful in several applications, where the input data consists of an incomplete set of distances and the output is a set of points in some Euclidean space realizing the given distances. The key problem in EDG is known as the Distance Geometry Problem (DGP), where an integer K>0 is given, as well as a simple undirected weighted graph G=(V,E,d), whose edges are weighted by a non-negative function d. The problem consists in determining whether or not there is a (realization) function that associates the vertices of V with coordinates of the K-dimensional Euclidean space, in such a way that those coordinates satisfy all distances given by d. We considered both theoretical issues and applications of EDG. In theoretical terms, we proved the exact number of solutions of a subclass of DGP that is very important in the molecular conformation problems. Moreover, we described necessary and sufficient conditions for determining whether a complete graph associated to a DGP is realizable and the minimum dimension of such realization. In practical terms, we developed an algorithm that computes such realization, which outperforms a classical algorithm from the literature. Finally, we showed a direct application of DGP to multidimensional scaling / Doutorado / Matematica Aplicada / Doutor em Matemática Aplicada

Since white-nose syndrome (WNS) arrived in the northeastern U.S. in 2006, several affected bat species have exhibited marked population declines (> 90%). For areas such as Fort Drum in northern New York that are subject to regulatory mandates because of the presence of the endangered Indiana bat (Myotis sodalis), acoustic monitoring is now likely more effective than traditional capture methodologies. In the summers of 2011 and 2012, I implemented intensive acoustic sampling using Anabat detectors at Fort Drum to develop a summer acoustic monitoring protocol that is both cost efficient and effective at detecting species of high conservation or management interest, such as the Indiana bat and the little brown bat (Myotis lucifugus). Habitat analysis of radio telemetry data and occupancy models of acoustic data were congruent in confirming nocturnal spatial use of forested riparian zones by little brown bats.  Additionally, occupancy models of passive versus active sampling revealed that passive acoustic sampling is preferable to active sampling for detecting declining species in the post-WNS context. Finally, assessment of detection probabilities at various arrays of acoustic detector layouts in an expected area of use revealed that a grid of detectors covering a wide spatial extent was more effective at detecting Indiana and little brown bats than permanent stations, transects, or double transects. My findings suggest that acoustic monitoring can be affectively implemented for monitoring Indiana and little brown bats even in areas of severe decline. Future efforts should be aimed at determining effective sampling designs for additional declining species. / Master of Science

Drone vision is a surging area of research, primarily due to its surveillance and military uses.A camera-equipped drone is capable of carrying out a variety of operations like imagedetection, recognition, and classification. Image processing is an important part of theprocess; it is used in denoising and smoothing the image before recognition.We aimed to classify different images and command the drone to carry out various tasksdepending on the image shown. If shown a certain image, the drone would take off and landrespectively.We use the Euclidean distance algorithm to calculate the distance between two images. If thedistance equals zero, the images are equal. While the ideal result of 0 is impossible due tonoise, we can use digital image processing methods to reduce noise.We were able to classify basic images to some degree of accuracy; the drone was able tocarry out given tasks after a successful image classification.While Euclidean distance might be the first choice for most image-classification algorithms,it has many limitations. This might call for the use of other image processing algorithms toachieve better results.

No description available.

No description available.

The Virginia northern flying squirrel (Glaucomys sabrinus fuscus) and the Carolina northern flying squirrel (G. s. coloratus) are geographically isolated subspecies of the northern flying squirrel found in montane conifer-northern hardwood forests the Appalachian Mountains of the eastern United States. Both subspecies were listed under the Endangered Species Act in 1985 as endangered, and accordingly, the Virginia northern flying squirrel and the Carolina northern flying squirrel are considered high conservation priorities by state and federal agencies. Although the listing prompted work to determine the broad distribution and habitat associations of both subspecies, numerous data gaps remain, particularly with regard to habitat management and development of efficient monitoring techniques. Regional interest in restoration of red spruce (Picea rubens) forests in the central and southern Appalachian Mountains, considered to be the flying squirrels' primary habitat, increases the importance of understanding habitat selection and managers' ability to detect squirrels at multiple spatial and temporal scales. I compared two novel survey techniques (ultrasonic acoustics and camera trapping) to a traditional technique (live trapping) to determine which method had higher probability of detection (POD) and lower latency to detection (LTD, number of survey nights to initial detection) of northern flying squirrels in the region. Both novel techniques performed better than the traditional techniques with higher POD and lower LTD. I found that ultrasonic acoustics and camera trapping had similar POD, whereas LTD was significantly lower with ultrasonic acoustics versus camera trapping. Additionally, the ability to distinguish between northern flying squirrels and the parapatric southern flying squirrel (G. volans) also is possible with ultrasonic acoustics, but not with camera trapping. This ultimately makes ultrasonic acoustics the most effective and efficient method to obtain detection/non-detection data. To better inform management decisions and activities (i.e., red spruce restoration), this method should be used in conjunction with existing traditional monitoring techniques that provide demographic data such as nest boxes. I assessed habitat selection of radio-collared Virginia and Carolina northern flying squirrels at multiple spatial scales with use-availability techniques. I analyzed field data from paired telemetry and random points and determined Virginia northern flying squirrels microhabitat (within-stand habitat) selection showed preference for conifer-dominant stands with deep organic horizons, a factor that might be directly linked to food (hypogeal fungi) availability. Similar to previous studies on the Virginia northern flying squirrel on the landscape- and stand-level using Euclidean distance based analysis, Carolina northern flying squirrels also selectively preferred montane conifer forests in greater proportion than their availability on the landscape. Additionally, Carolina northern flying squirrels did not select for or against northern hardwood forests regardless of availability on the landscape. Habitat preference of both subspecies indicates that red spruce restoration activities may be important for the persistence of Appalachian northern flying squirrels into an uncertain future, as anthropogenic climate change may cause further reduction of the quality and extent of high-elevation montane conifer forests in the region. / Ph. D.

<p>This thesis shows the result from investigations of methods that use distance vectors when matching pictures. The distance vectors are available in a distance map made by the Euclidean Distance Transform. The investigated methods use the two characteristic features of the distance vector when matching pictures, length and direction. The length of the vector is used to calculate a value of how good a match is and the direction of the vector is used to predict a transformation to get a better match. The results shows that the number of calculation steps that are used during a search can be reduced compared to matching methods that only uses the distance during the matching.</p>

<p>The moose (Alces alces) is very important both economically and ecologically, therefore all knowledge of moose is vital for future management of the moose population. Little is known about moose habitat selection in Sweden. In coastal southwestern Sweden growing human population and new infrastructure projects continuously threaten to fragment and isolate local moose populations. The habitat selection of 22 moose, 8 males and 14 females, in southwestern Sweden was studied from February 2002 until December 2005. The moose were captured and fitted with GPS-collars and positions were collected at 2-hour intervals. The number of moose positions totaled 71103 during the study period of 46 months. Data for individual animals were divided into four seasons: spring, summer, fall and winter based on climate and moose biology. A total of 125 moose seasonal home ranges were generated and habitat use within each of the generated home ranges was studied using Euclidean distance-based analysis. A reclassified digital landcover map was divided into the land use classes agriculture, clear-cut, coniferous forest, deciduous forest, mire and mountain. The results showed that there was a difference in habitat selection between males and females. Males were significantly closer to forest and clear-cuts compared to females. Both males and females selected clear-cuts and avoided agriculture within their home ranges.</p> / <p>Älgen är en viktig art, både ekonomiskt och ekologiskt, och all kunskap är viktig för att även i framtiden kunna sköta en sund älgstam. Trots flertalet studier finns det många frågetecken om älgens habitatval i Sverige. En ökad exploateringstakt och nya infrastrukturprojekt hotar att fragmentera och isolera populationer av älg. Habitatvalet hos 22 älgar, 8 tjurar och 14 kor, i sydvästra Sverige studerades mellan februari 2002 och december 2005. Älgarna sövdes och utrustades med GPS-sändare, deras positioner registrerades varannan timma och det totala antalet positioner under den 46 månader långa studietiden var 71103 stycken. Data från varje älg delades in i 4 säsonger; vår, sommar, höst och vinter, baserat på klimat och älgens biologi. Totalt genererades 125 hemområden baserade på säsong, och valet av habitat inom varje hemområde studerades med hjälp av Euclidean distance-based analysis. En omklassificerad digital marktäckedata användes som var indelad i 6 olika klasser; odlad mark, hygge, barrskog, lövskog, myrmark och berg i dagen. Resultaten visade att det var skillnad mellan könen i hur de väljer habitat. Tjurarna var signifikant närmare barrskog och hyggen än korna, men både tjurar och kor selekterade för hyggen och undvek odlad mark inom deras hemområden.</p>