Currently technologies such as EEG, EMG and EOG recordings are the established methods used in the analysis of sleep. But if these methods are to be employed to study sleep in rodents, extensive surgery and recovery is involved which can be both time consuming and costly. This thesis presents and analyzes a cost effective, non-invasive, high throughput system for detecting the sleep and wake patterns in mice using a piezoelectric sensor. This sensor was placed at the bottom of the mice cages to monitor the movements of the mice. The thesis work included the development of the instrumentation and signal acquisition system for recording the signals critical to sleep and wake classification. Classification of the mouse sleep and wake states were studied for a linear classifier and a Neural Network classifier based on 23 features extracted from the Power Spectrum (PS), Generalized Spectrum (GS), and Autocorrelation (AC) functions of short data intervals. The testing of the classifiers was done on two data sets collected from two mice, with each data set having around 5 hours of data. A scoring of the sleep and wake states was also done via human observation to aid in the training of the classifiers. The performances of these two classifiers were analyzed by looking at the misclassification error of a set of test features when run through a classifier trained by a set of training features. The best performing features were selected by first testing each of the 23 features individually in a linear classifier and ranking them according to their misclassification rate. A test was then done on the 10 best individually performing features where they were grouped in all possible combinations of 5 features to determine the feature combinations leading to the lowest error rates in a multi feature classifier. From this test 5 features were eventually chosen to do the classification. It was found that the features related to the signal energy and the spectral peaks in the 3Hz range gave the lowest errors. Error rates as low as 4% and 9% were achieved from a 5-feature linear classifier for the two data sets. The error rates from a 5-feature Neural Network classifier were found to be 6% and 12% respectively for these two data sets.

La classification des images est aujourd'hui un défi d'une grande ampleur puisque ça concerne d’un côté les millions voir des milliards d'images qui se trouvent partout sur le web et d’autre part des images pour des applications temps réel critiques. Cette classification fait appel en général à des méthodes d'apprentissage et à des classifieurs qui doivent répondre à la fois à la précision ainsi qu'à la rapidité. Ces problèmes d'apprentissage touchent aujourd'hui un grand nombre de domaines d'applications: à savoir, le web (profiling, ciblage, réseaux sociaux, moteurs de recherche), les "Big Data" et bien évidemment la vision par ordinateur tel que la reconnaissance d'objets et la classification des images. La présente thèse se situe dans cette dernière catégorie et présente des algorithmes d'apprentissage supervisé basés sur la minimisation de fonctions de perte (erreur) dites "calibrées" pour deux types de classifieurs: k-Plus Proches voisins (kNN) et classifieurs linéaires. Ces méthodes d'apprentissage ont été testées sur de grandes bases d'images et appliquées par la suite à des images biomédicales. Ainsi, cette thèse reformule dans une première étape un algorithme de Boosting des kNN et présente ensuite une deuxième méthode d'apprentissage de ces classifieurs NN mais avec une approche de descente de Newton pour une convergence plus rapide. Dans une seconde partie, cette thèse introduit un nouvel algorithme d'apprentissage par descente stochastique de Newton pour les classifieurs linéaires connus pour leur simplicité et leur rapidité de calcul. Enfin, ces trois méthodes ont été utilisées dans une application médicale qui concerne la classification de cellules en biologie et en pathologie. / Image classification becomes a big challenge since it concerns on the one hand millions or billions of images that are available on the web and on the other hand images used for critical real-time applications. This classification involves in general learning methods and classifiers that must require both precision as well as speed performance. These learning problems concern a large number of application areas: namely, web applications (profiling, targeting, social networks, search engines), "Big Data" and of course computer vision such as the object recognition and image classification. This thesis concerns the last category of applications and is about supervised learning algorithms based on the minimization of loss functions (error) called "calibrated" for two kinds of classifiers: k-Nearest Neighbours (kNN) and linear classifiers. Those learning methods have been tested on large databases of images and then applied to biomedical images. In a first step, this thesis revisited a Boosting kNN algorithm for large scale classification. Then, we introduced a new method of learning these NN classifiers using a Newton descent approach for a faster convergence. In a second part, this thesis introduces a new learning algorithm based on stochastic Newton descent for linear classifiers known for their simplicity and their speed of convergence. Finally, these three methods have been used in a medical application regarding the classification of cells in biology and pathology.

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In statistical methodology of analyzing data, two of the most commonly used techniques are classification and regression modeling. As scientific technology progresses rapidly, complex data often occurs and requires novel classification and regression modeling methodologies according to the data structure. In this dissertation, I mainly focus on developing a few approaches for analyzing the data with complex structures. Classification problems commonly occur in many areas such as biomedical, marketing, sociology and image recognition. Among various classification methods, linear classifiers have been widely used because of computational advantages, ease of implementation and interpretation compared with non-linear classifiers. Specifically, linear discriminant analysis (LDA) is one of the most important methods in the family of linear classifiers. For high dimensional data with number of variables p larger than the number of observations n occurs more frequently, it calls for advanced classification techniques. In Chapter 2, I proposed a novel sparse LDA method which generalizes LDA through a regularized approach for the two-class classification problem. The proposed method can obtain an accurate classification accuracy with attractive computation, which is suitable for high dimensional data with p>n. In Chapter 3, I deal with the classification when the data complexity lies in the non-random missing responses in the training data set. Appropriate classification method needs to be developed accordingly. Specifically, I considered the "reject inference problem'' for the application of fraud detection for online business. For online business, to prevent fraud transactions, suspicious transactions are rejected with unknown fraud status, yielding a training data with selective missing response. A two-stage modeling approach using logistic regression is proposed to enhance the efficiency and accuracy of fraud detection. Besides the classification problem, data from designed experiments in scientific areas often have complex structures. Many experiments are conducted with multiple variance sources. To increase the accuracy of the statistical modeling, the model need to be able to accommodate more than one error terms. In Chapter 4, I propose a variance component mixed model for a nano material experiment data to address the between group, within group and within subject variance components into a single model. To adjust possible systematic error introduced during the experiment, adjustment terms can be added. Specifically a group adaptive forward and backward selection (GFoBa) procedure is designed to select the significant adjustment terms. / Ph. D.

This diploma thesis deals with a problem of spores venturia inaequlis recognition. These spores are captured on a special tape which is then analyzed using a microscope. The tape can be analyzed by a laboratorian or by the program Sporedetect v3. This program provides functions for complete picture processing and object recognition. In this diploma thesis, there are also described ways to automatically control a sliding stage of a microscope utilizing motorized translation stages and linear actuators. The information about automatic control of a microscope stage was obtained from catalogues of the companies Standa and Edmundoptics.

This thesis describes the design and development of a system for detection and recognition of license plates. The work is divided into three basic parts: licence plates detection, finding of character positions and optical character recognition. To fullfill the goal of this work, a new dataset was taken. It contains 2814 license plates used for training classifiers and 2620 plates to evaluate the success rate of the system. Cascade Classifier was used to train detector of licence plates, which has success rate up to 97.8 %. After that, pozitions of individual characters were searched in detected pozitions of licence plates. If there was no character found, detected pozition was not the licence plate. Success rate of licence plates detection with all the characters found is up to 88.5 %. Character recognition is performed by SVM classifier. The system detects successfully with no errors up to 97.7 % of all licence plates.

L'application de classifieurs linéaires à l'analyse des données d'imagerie cérébrale (fMRI) a mené à plusieurs percées intéressantes au cours des dernières années. Ces classifieurs combinent linéairement les réponses des voxels pour détecter et catégoriser différents états du cerveau. Ils sont plus agnostics que les méthodes d'analyses conventionnelles qui traitent systématiquement les patterns faibles et distribués comme du bruit. Dans le présent projet, nous utilisons ces classifieurs pour valider une hypothèse portant sur l'encodage des sons dans le cerveau humain. Plus précisément, nous cherchons à localiser des neurones, dans le cortex auditif primaire, qui détecteraient les modulations spectrales et temporelles présentes dans les sons. Nous utilisons les enregistrements fMRI de sujets soumis à 49 modulations spectro-temporelles différentes. L'analyse fMRI au moyen de classifieurs linéaires n'est pas standard, jusqu'à maintenant, dans ce domaine. De plus, à long terme, nous avons aussi pour objectif le développement de nouveaux algorithmes d'apprentissage automatique spécialisés pour les données fMRI. Pour ces raisons, une bonne partie des expériences vise surtout à étudier le comportement des classifieurs. Nous nous intéressons principalement à 3 classifieurs linéaires standards, soient l'algorithme machine à vecteurs de support (linéaire), l'algorithme régression logistique (régularisée) et le modèle bayésien gaussien naïf (variances partagées). / The application of linear machine learning classifiers to the analysis of brain imaging data (fMRI) has led to several interesting breakthroughs in recent years. These classifiers combine the responses of the voxels to detect and categorize different brain states. They allow a more agnostic analysis than conventional fMRI analysis that systematically treats weak and distributed patterns as unwanted noise. In this project, we use such classifiers to validate an hypothesis concerning the encoding of sounds in the human brain. More precisely, we attempt to locate neurons tuned to spectral and temporal modulations in sound. We use fMRI recordings of brain responses of subjects listening to 49 different spectro-temporal modulations. The analysis of fMRI data through linear classifiers is not yet a standard procedure in this field. Thus, an important objective of this project, in the long term, is the development of new machine learning algorithms specialized for neuroimaging data. For these reasons, an important part of the experiments is dedicated to studying the behaviour of the classifiers. We are mainly interested in 3 standard linear classifiers, namely the support vectors machine algorithm (linear), the logistic regression algorithm (regularized) and the naïve bayesian gaussian model (shared variances).

No description available.

The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query on the human body and allows the user to interact with it to explore the results. Through usage cases the function and utility of the approach is shown.