First-order molecular descriptors for molecular steric similarity

En aquest treball s'analitza la contribució estèrica de les molècules a les seves propietats químiques i físiques, mitjançant l'avaluació del seu volum i de la seva mesura de semblança, a partir d'ara definits com a descriptors moleculars de primer ordre. La difeèsncia entre aquests dos conceptes ha estat aclarida: mentre que el volum és la magnitud de l'espai que ocupa la molècula com a entitat global, la mesura de semblança ens dóna una idea de com està distribuïda la densitat electrònica al llarg d'aquest volum, i reflecteix més les diferències locals existents. L'ús de diverses aproximacions per a l'obtenció d'ambdós valors ha estat analitzat sobre diferents classes d'isòmers

Relacions estructura-propietat per un conjunt d'hidrocarburs a partir de nous descriptors tridimensionals derivats de la semblança molecular quàntica

En aquest article es defineixen uns nous índexs tridimensionals per a la descripció de les molècules a partir de paràmetres derivats de la Teoria de la Semblança Molecular i de les distàncies euclidianes entre els àtoms i les càrregues atòmiques efectives. Aquests indexs, anomenats 3D, s'han aplicat a l'estudi de les relacions estructura-propietat d'una família d'hidrocarburs, i han demostrat una capacitat de descripció de tres propietats de la família (temperatura d'ebullició, temperatura de fusió i densitat) molt més acurada que quan s'utilitzen els indexs 2D clàssics

Analysis of chemical bonding and aromaticity from electronic delocalization descriptors

Interactions between electrons determine the structure and properties of matter from molecules to solids. Therefore, the understanding of the electronic structure of molecules will enable us to extract relevant chemical information. In the first part of this thesis, we focus our attention on the analysis of chemical bonding by means of the Electron Localization Function (ELF) and the Domain-Averaged Fermi Hole analysis (DAFH). In the second part, we assess the performance of some indicators of aromaticity by analyzing their advantages and drawbacks. We propose a series of tests based on well-known aromaticity trends that can be applied to evaluate the aromaticity of current and future indicators of aromaticity in both organic and inorganic species. Moreover, we investigate the nature of electron delocalization in both aromatic and antiaromatic systems in the light of Hückel’s (4n + 2) rule. Finally, we analyze the phenomenon of multiple aromaticity in all-metal clusters.

Development, implementation and application of electronic structural descriptors to the analysis of the chemical bonding, aromaticity and chemical reactivity

En la literatura sobre mecànica quàntica és freqüent trobar descriptors basats en la densitat de parells o la densitat electrònica, amb un èxit divers segons les aplicacions que atenyin. Per tal de que tingui sentit químic un descriptor ha de donar la definició d'un àtom en una molècula, o ésser capaç d'identificar regions de l'espai molecular associades amb algun concepte químic (com pot ser un parell solitari o zona d'enllaç, entre d'altres). En aquesta línia, s'han proposat diversos esquemes de partició: la teoria d'àtoms en molècules (AIM), la funció de localització electrònica (ELF), les cel·les de Voroni, els àtoms de Hirshfeld, els àtoms difusos, etc. L'objectiu d'aquesta tesi és explorar descriptors de la densitat basats en particions de l'espai molecular del tipus AIM, ELF o àtoms difusos, analitzar els descriptors existents amb diferents nivells de teoria, proposar nous descriptors d'aromaticitat, així com estudiar l'habilitat de totes aquestes eines per discernir entre diferents mecanismes de reacció.

Spatial Analysis of Cell Composition Data

A version of Matheron’s discrete Gaussian model is applied to cell composition data. The examples are for map patterns of felsic metavolcanics in two different areas. Q-Q plots of the model for cell values representing proportion of 10 km x 10 km cell area underlain by this rock type are approximately linear, and the line of best fit can be used to estimate the parameters of the model. It is also shown that felsic metavolcanics in the Abitibi area of the Canadian Shield can be modeled as a fractal

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Elaboració d'una eina d'avaluació de mòduls de màster segons els estudiants

En aquest estudi es presenta un instrument d'avaluació d'un mòdul docent de màster universitari en forma d'autoinforme a respondre pels estudiants. Aquest qüestionari consta de preguntes de format tancat i obert i cerca recollir informació sobre: correspondència dels continguts amb els descriptors del mòdul, avenç en les competències del programa, idoneïtat de la metodologia docent, funcionalitat dels aprenenentatges assolits, pertinença del tipus d'avaluació, i elements positius i a millorar del mòdul. Aquest instrument s'ha administrat per avaluar un mateix mòdul de màster en els anys acadèmics 2009-10 i 2010-11. Se'n presenten els resultats i s'analitza la seva utilitat

Descripció de propietats moleculars i activitats biològiques emprant l'energia de repulsió electró-electró

En aquest treball es descriu l'ús de les mesures de semblança molecular quàntica (MSMQ) per a caracteritzar propietats i activitats biològiques moleculars, i definir descriptors emprables per a construir models QSAR i QSPR. L'estudi que es presenta consisteix en la continuació d'un treball recent, on es descrivien relacions entre el paràmetre log P i MSMQ, donant així una alternativa a aquest parimetre hidrofòbic empíric. L'actual contribució presenta una nova mesura, capaç d'estendre l'ús de les MSMQ, que consisteix en l'energia de repulsió electró-electró (Vee). Aquest valor, disponible normalment a partir de programari de química quàntica, considera la molècula com una sola entitat, i no cal recórrer a l'ús de contribucions de fragments. La metodologia s'ha aplicat a cinc tipus diferents de compostos on diferents propietats moleculars i activitats biològiques s'han correlacionat amb Vee com a únic descriptor molecular. En tots els casos estudiats, s'han obtingut correlacions satisfactòries.