Dinâmica do uso e cobertura da terra no sudeste de Roraima utilizando técnicas de detecção de mudanças

A ocupação e consolidação do território na Amazônia apresentam diferentes características relacionadas à dinâmica das conversões de uso e cobertura da terra, que podem ser analisadas utilizando imagens orbitais de sensoriamento remoto. O objetivo do presente trabalho foi avaliar os produtos de detecção de mudanças gerados por análise de vetor de mudança (AVM) e subtração de imagens, a partir de imagens-fração derivadas das imagens ópticas TM/Landsat, para o estudo das conversões de uso e cobertura da terra presentes em área de colonização agrícola na região sudeste de Roraima. Analisaram-se as imagens de mudança provenientes da aplicação do AVM (magnitude, alfa e beta) e da subtração das imagens-fração (solo, sombra e vegetação) quanto à sua capacidade de identificar e discriminar as conversões existentes, de acordo com levantamento de campo. Foram testados dois algoritmos de classificação de imagens do tipo supervisionado, Bhattacharyya e Support Vector Machine. Foram feitos agrupamentos para otimizar a identificação das conversões nas classificações testadas. Houve melhor desempenho do classificador por regiões Bhattacharyya na discriminação das conversões. A utilização das imagens-diferença das frações como informação de entrada para o classificador apresentou qualidade de classificação muito boa ou excelente, sendo superior às classificações utilizando os produtos AVM, isoladamente ou em conjunto com as imagens-diferença.

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector Machine

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

How early can we predict Alzheimer's disease using computational anatomy?

Computational anatomy with magnetic resonance imaging (MRI) is well established as a noninvasive biomarker of Alzheimer's disease (AD); however, there is less certainty about its dependency on the staging of AD. We use classical group analyses and automated machine learning classification of standard structural MRI scans to investigate AD diagnostic accuracy from the preclinical phase to clinical dementia. Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative were stratified into 4 groups according to the clinical status-(1) AD patients; (2) mild cognitive impairment (MCI) converters; (3) MCI nonconverters; and (4) healthy controls-and submitted to a support vector machine. The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI. Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients. We also identified a subgroup of control subjects with brain structure and cognitive changes highly similar to those observed in AD. Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models. The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.

Visual measurement of laser-arc hybrid welding

Tässä työssä raportoidaan hybridihitsauksesta otettujen suurnopeuskuvasarjojen automaattisen analyysijärjestelmän kehittäminen.Järjestelmän tarkoitus oli tuottaa tietoa, joka avustaisi analysoijaa arvioimaan kuvatun hitsausprosessin laatua. Tutkimus keskittyi valokaaren taajuuden säännöllisyyden ja lisäainepisaroiden lentosuuntien mittaamiseen. Valokaaria havaittiin kuvasarjoista sumean c-means-klusterointimenetelmän avullaja perättäisten valokaarien välistä aikaväliä käytettiin valokaaren taajuuden säännöllisyyden mittarina. Pisaroita paikannettiin menetelmällä, jossa yhdistyi pääkomponenttianalyysi ja tukivektoriluokitin. Kalman-suodinta käytettiin tuottamaan arvioita pisaroiden lentosuunnista ja nopeuksista. Lentosuunnanmääritysmenetelmä luokitteli pisarat niiden arvioitujen lentosuuntien perusteella. Järjestelmän kehittämiseen käytettävissä olleet kuvasarjat poikkesivat merkittävästi toisistaan kuvanlaadun ja pisaroiden ulkomuodon osalta, johtuen eroista kuvaus- ja hitsausprosesseissa. Analyysijärjestelmä kehitettiin toimimaan pienellä osajoukolla kuvasarjoja, joissa oli tietynlainen kuvaus- ja hitsausprosessi ja joiden kuvanlaatu ja pisaroiden ulkomuoto olivat samankaltaisia, mutta järjestelmää testattiin myös osajoukon ulkopuolisilla kuvasarjoilla. Testitulokset osoittivat, että lentosuunnanmääritystarkkuus oli kohtuullisen suuri osajoukonsisällä ja pieni muissa kuvasarjoissa. Valokaaren taajuuden säännöllisyyden määritys oli tarkka useammassa kuvasarjassa.

Quantifying the time for accurate EEG decoding of single value-based decisions.

BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for 'easy' vs. ∼700ms for 'hard' decisions, well before subjects' response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S): We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Security Authentication using Phase-Encoded Nanoparticle Structures and Polarized Light

Phase encoded nano structures such as Quick Response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase encoded QR codes. The system is illuminated using polarized light and the QR code is encoded using a phase-only random mask. Using classification algorithms it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase encoded QR codes using polarimetric signatures.

Prediction of market switching and delisting events from OMX First North Nordic multilateral stock exchange

This thesis studies the predictability of market switching and delisting events from OMX First North Nordic multilateral stock exchange by using financial statement information and market information from 2007 to 2012. This study was conducted by using a three stage process. In first stage relevant theoretical framework and initial variable pool were constructed. Then, explanatory analysis of the initial variable pool was done in order to further limit and identify relevant variables. The explanatory analysis was conducted by using self-organizing map methodology. In the third stage, the predictive modeling was carried out with random forests and support vector machine methodologies. It was found that the explanatory analysis was able to identify relevant variables. The results indicate that the market switching and delisting events can be predicted in some extent. The empirical results also support the usability of financial statement and market information in the prediction of market switching and delisting events.

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

salud financiera y riesgo en los estados financieros de la empresa avianca del sector aeronautico

La globalización y la competitividad como realidad de las empresas, implica que los gerentes preparen a sus empresas de la mejor manera para sobrevivir en este mundo tan inestable y cambiante. El primer paso consta de investigar y medir como se encuentra la empresa en cada uno de sus componentes, tales como recurso humano, mercadeo, logística, operación y por último y más importante las finanzas. El conocimiento de salud financiera y de los riesgos asociados a la actividad de las empresas, les permitirá a los gerentes tomar las decisiones correctas para ser rentables y perdurables en el mundo de los negocios inmerso en la globalización y competitividad. Esta apreciación es pertinente en Avianca S.A. esto teniendo en cuenta su progreso y evolución desde su primer vuelo el 5 de diciembre de 1919 comercial, hasta hoy cuando cotiza en la bolsa de Nueva York. Se realizó un análisis de tipo descriptivo, acompañado de la aplicación de ratios y nomenclaturas, dando lugar a establecer la salud financiera y los riesgos, no solo de Avianca sino también del sector aeronáutico. Como resultado se obtuvo que el sector aeronáutico sea financieramente saludable en el corto plazo, pero en el largo plazo su salud financiera se ve comprometida por los riegos asociados al sector y a la actividad desarrollada.