Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Jitter measurements for Performance enhancement in the Service Sector

In a leading service economy like India, services lie at the very center of economic activity. Competitive organizations now look not only at the skills and knowledge, but also at the behavior required by an employee to be successful on the job. Emotionally competent employees can effectively deal with occupational stress and maintain psychological well-being. This study explores the scope of the first two formants and jitter to assess seven common emotional states present in the natural speech in English. The k-means method was used to classify emotional speech as neutral, happy, surprised, angry, disgusted and sad. The accuracy of classification obtained using raw jitter was more than 65 percent for happy and sad but less accurate for the others. The overall classification accuracy was 72% in the case of preprocessed jitter. The experimental study was done on 1664 English utterances of 6 females. This is a simple, interesting and more proactive method for employees from varied backgrounds to become aware of their own communication styles as well as that of their colleagues' and customers and is therefore socially beneficial. It is a cheap method also as it requires only a computer. Since knowledge of sophisticated software or signal processing is not necessary, it is easy to analyze

Percepciones del clima social escolar : diferencias tipológicas entre colegios públicos y colegios privados.

Identificar en dos muestras de población escolar urbana de Asturias, una perteneciente a colegios públicos, y otra perteneciente a colegios privados, si existen distintas tipologías de 'climas sociales en el aula', a partir de las percepciones de los alumnos, y si hay diferencias entre los centros públicos y los privados. 575 Sujetos, 200 alumnos de colegios públicos y 375 de colegios privados. Se trata de sujetos de ambos sexos, con edades entre 13 y 14 años, pertenecientes a un nivel de 8 de EGB de Avilés, Gijon y Oviedo. Variables independientes: implicación, afiliación, ayuda, tarea, competitividad, organización, claridad, control e innovación. Variables moduladoras: pertenencia por parte de los alumnos a colegios públicos o privados. Escala de clima social (ces), creada por r.H. Moos y cols.. Análisis de conglomerados cluster K-means, un tipo de análisis de cluster no jerárquico. Con este método se divide un conjunto de individuos en conglomerados, de tal forma que, al final del proceso, cada caso pertenece al cluster cuyo centro está más cercano a él. El centro del cluster viene dado por la media de los individuos que forman cada variable. Del análisis de variables que intervienen en la percepción del clima social escolar, se observan diferencias entre colegios públicos y privados, en lo que respecta a las variables de ayuda, tarea, organización e innovación. En relación a las otras cinco variables, afiliación, implicación, competitividad, claridad y control, las diferencias entre una muestra y otra son inexistentes. A la hora de estudiar cada uno de los cluster, se tiene en cuenta la reestructuración realizada tanto en la muestra de colegios públicos como privados. En la muestra de colegios privados destacan tres tipologías de climas: un clima afectivo percibido por un 50 por ciento de la población; un clima conservador y autoritario percibido por casi un 40 por ciento de los estudiantes; un clima estructurado percibido por un 10 por ciento aproximadamente. En la muestra de alumnos pertenecientes a colegios públicos, se encuentran cuatro tipos de climas: un clima afectivo percibido por un 32 por ciento de la población; un clima afectivo y no participativo, detectado por un 27 por ciento de los estudiantes; un clima autoritario percibido por un 26,5 por ciento de la muestra; un clima centrado en la organización y el esfuerzo, percibido por un 14,5 por ciento de la población. El hecho de que los estudiantes de colegios públicos o privados, perciban un determinado tipo de clima, está muy relacionado con la figura del profesor-tutor. El funcionamiento de la clase depende de las características de éste, que aunque revelen los canones de la institución, tienen una huella personal. Para evaluar la percepción del clima escolar, a las variables analizadas, habría que añadir la personalidad del profesor, lo que no descartan realizar en una posterior investigación.

Discovering similarities in the time use patterns of the Spanish Autonomous Communities by Fuzzy techniques

In 2000 the European Statistical Office published the guidelines for developing the Harmonized European Time Use Surveys system. Under such a unified framework, the first Time Use Survey of national scope was conducted in Spain during 2002– 03. The aim of these surveys is to understand human behavior and the lifestyle of people. Time allocation data are of compositional nature in origin, that is, they are subject to non-negativity and constant-sum constraints. Thus, standard multivariate techniques cannot be directly applied to analyze them. The goal of this work is to identify homogeneous Spanish Autonomous Communities with regard to the typical activity pattern of their respective populations. To this end, fuzzy clustering approach is followed. Rather than the hard partitioning of classical clustering, where objects are allocated to only a single group, fuzzy method identify overlapping groups of objects by allowing them to belong to more than one group. Concretely, the probabilistic fuzzy c-means algorithm is conveniently adapted to deal with the Spanish Time Use Survey microdata. As a result, a map distinguishing Autonomous Communities with similar activity pattern is drawn. Key words: Time use data, Fuzzy clustering; FCM; simplex space; Aitchison distance

Percepciones del clima social escolar : diferencias tipol??gicas entre varones y mujeres

Resumen tomado de la publicaci??n

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

A natureza na lineal dos funcionamentos inferenciais: um estudo empírico com base no humor gráfico

Analisa-se se os funcionamentos inferenciais têm uma estrutura própria dos sistemas dinâmicos não lineais, estudados a partir de quatro gráficas humorísticas. Os primeiros resultados com o tratamento estadístico lineal de K-medias projetam a presencia de perfis de diferentes funcionamentos inferenciais em função das diferentes piadas. Os resultados com a técnica da wavelet, proveniente dos sistemas dinâmicos não lineais, mostram patrões dos funcionamentos inferenciais que dão conta de sua natureza multifractal, sem uma sequencialidade fixa e sem uma organização aparente. Isto implica que é necessário revisar a concepção de estádios sequenciais fixos como os que dominam os estudos de desenvolvimento cognitivo.

Diferencias intergrupales en el sistema de metas personales, satisfacción y rendimiento en adolescentes.

Integrar diferentes unidades de análisis para el estudio de la personalidad y considerar estas unidades en su predicción de la satisfacción y el rendimiento en adolescentes. 296 estudiantes de ESO de entre 15 y 18 años. 162 son mujeres y 134 varones. Las aplicaciones de las pruebas se realizan en horario de tutorías dentro del Plan Acción Tutorial (PAT). Se les explica a los alumnos que participan en la investigación sobre 'metas que se proponen realizar en un futuro' y que las pruebas que se administran les pueden ayudar en el futuro para la toma de decisiones. Las aplicaciones de las pruebas se realizan en dos sesiones de evaluación. En la primera, se aplican las pruebas de personalidad y satisfacción. En la segunda se evalúan metas personales. El rendimiento académico se operativiza por la puntuación del adolescente en su curso académico. Todos los alumnos participan voluntariamente en la investigación. Escala de objetivos o metas personales, escala de satisfacción por áreas vitales (ESAV), Inventario de personalidad para adolescentes de Millón (MAPI), estilos básicos de personalidad, escalas de correlatos comportamentales. Para el análisis de los datos, se utilizan programas estadísticos SPSS, SPAD, LISREL VIII y para el cálculo del tamaño del efecto el Statistical Power Computer Analysis. Las técnicas de análisis de datos se centran en Análisis de Correspondencia Múltiple (ACM), análisis de conglomerados K means, Análisis de varianza y diferencias entre coeficientes de correlación. Los resultados indican que los adolescentes que se plantean metas relacionadas con las tareas vitales a desarrollar en un futuro próximo manifiestan mayores niveles de satisfacción. Además, las diferencias en los estilos de personalidad, permiten entender el sistema de metas personales en cuatro grupos de adolescentes. La consideración de los estilos de personalidad y las metas personales permiten entender la adaptación de los adolescentes a su entorno considerando la satisfacción autopercibida y el rendimiento académico.

La influencia del autoconcepto en el rendimiento académico en estudiantes universitarios. 'The influence of self-concept on academic achievement in university students'.

Resumen tomado de la publicación. Con el apoyo económico del departamento MIDE de la UNED. Incluye anexo con el cuestionario utilizado para la realización del estudio

Clasificaci??n de perfiles de uso de smartphones en estudiantes y docentes de la Universidad Aut??noma de Baja California, M??xico

Resumen tomado de la publicaci??n

A continuum of sudden stratospheric warmings

The k-means cluster technique is used to examine 43 yr of daily winter Northern Hemisphere (NH) polar stratospheric data from the 40-yr ECMWF Re-Analysis (ERA-40). The results show that the NH winter stratosphere exists in two natural well-separated states. In total, 10% of the analyzed days exhibit a warm disturbed state that is typical of sudden stratospheric warming events. The remaining 90% of the days are in a state typical of a colder undisturbed vortex. These states are determined objectively, with no preconceived notion of the groups. The two stratospheric states are described and compared with alternative indicators of the polar winter flow, such as the northern annular mode. It is shown that the zonally averaged zonal winds in the polar upper stratosphere at 7 hPa can best distinguish between the two states, using a threshold value of 4 m s−1, which is remarkably close to the standard WMO criterion for major warming events. The analysis also determines that there are no further divisions within the warm state, indicating that there is no well-designated threshold between major and minor warmings, nor between split and displaced vortex events. These different manifestations are simply members of a continuum of warming events.

Objective determination of the extratropical transition of tropical cyclones in the Northern Hemisphere

Extratropical transition (ET) has eluded objective identification since the realisation of its existence in the 1970s. Recent advances in numerical, computational models have provided data of higher resolution than previously available. In conjunction with this, an objective characterisation of the structure of a storm has now become widely accepted in the literature. Here we present a method of combining these two advances to provide an objective method for defining ET. The approach involves applying K-means clustering to isolate different life-cycle stages of cyclones and then analysing the progression through these stages. This methodology is then tested by applying it to five recent years from the European Centre of Medium-Range Weather Forecasting operational analyses. It is found that this method is able to determine the general characteristics for ET in the Northern Hemisphere. Between 2008 and 2012, 54% (±7, 32 of 59) of Northern Hemisphere tropical storms are estimated to undergo ET. There is great variability across basins and time of year. To fully capture all the instances of ET is necessary to introduce and characterise multiple pathways through transition. Only one of the three transition types needed has been previously well-studied. A brief description of the alternate types of transitions is given, along with illustrative storms, to assist with further study

Understanding climate change projections for precipitation over western Europe with a weather typing approach

Precipitation over western Europe (WE) is projected to increase (decrease) roughly northward (equatorward) of 50°N during the 21st century. These changes are generally attributed to alterations in the regional large-scale circulation, e.g., jet stream, cyclone activity, and blocking frequencies. A novel weather typing within the sector (30°W–10°E, 25–70°N) is used for a more comprehensive dynamical interpretation of precipitation changes. A k-means clustering on daily mean sea level pressure was undertaken for ERA-Interim reanalysis (1979–2014). Eight weather types are identified: S1, S2, S3 (summertime types), W1, W2, W3 (wintertime types), B1, and B2 (blocking-like types). Their distinctive dynamical characteristics allow identifying the main large-scale precipitation-driving mechanisms. Simulations with 22 Coupled Model Intercomparison Project 5 models for recent climate conditions show biases in reproducing the observed seasonality of weather types. In particular, an overestimation of weather type frequencies associated with zonal airflow is identified. Considering projections following the (Representative Concentration Pathways) RCP8.5 scenario over 2071–2100, the frequencies of the three driest types (S1, B2, and W3) are projected to increase (mainly S1, +4%) in detriment of the rainiest types, particularly W1 (−3%). These changes explain most of the precipitation projections over WE. However, a weather type-independent background signal is identified (increase/decrease in precipitation over northern/southern WE), suggesting modifications in precipitation-generating processes and/or model inability to accurately simulate these processes. Despite these caveats in the precipitation scenarios for WE, which must be duly taken into account, our approach permits a better understanding of the projected trends for precipitation over WE.