Use of Image Processing Techniques for the Analysis of Echocardiographic Images

Echocardiography is a medical imaging modality that uses ultrasound in order to obtain cross sectional views of the heart. The basic problem in the use of echocardiography is the ability to obtain a reliable set of physical parameters related to cardiac status, so that assessment of heart disease can be performed automatically. This work overviews different image processing techniques used in the analysis of two dimensional echocardiographic images. After reviewing how the echocardiographic image formation process works, an outline of the general processing steps from image acquisition to automatic detection of important features is presented. Special emphasis on cardiac image segmentation is presented. In particular, a relaxation algorithm for image segmentation is discussed. Also, echocardiographic image segmentation using temporal analysis and a new algorithm for boundary detection is described. Measurements of left ventricular area, wall thickness, and ejection fraction is also presented. Shape analysis is introduced as a tool for echocardiographic image analysis. A high level description of the left ventricular boundaries using curvature is proposed. Curvature analysis attempts to identify stable landmarks during the beating process, muscles. Tracking these landmarks aids in the detection of abnormal heart contractions. Finally the use of expert systems is proposed in the analysis of echocardiographic images

Competitive exclusion and Hebbian couplings in random generalised Lotka-Volterra systems

We study communities emerging from generalised random Lotka--Volterra dynamics with a large number of species with interactions determined by the degree of niche overlap. Each species is endowed with a number of traits, and competition between pairs of species increases with their similarity in trait space. This leads to a model with random Hopfield-like interactions. We use tools from the theory of disordered systems, notably dynamic mean field theory, to characterise the statistics of the resulting communities at stable fixed points and determine analytically when stability breaks down. Two distinct types of transition are identified in this way, both marked by diverging abundances, but differing in the behaviour of the integrated response function. At fixed points only a fraction of the initial pool of species survives. We numerically study the eigenvalue spectra of the interaction matrix between extant species. We find evidence that the two types of dynamical transition are, respectively, associated with the bulk spectrum or an outlier eigenvalue crossing into the right half of the complex plane.Comment: 14 pages, 9 figures + Supplemen

Properties of pattern formation and selection processes in nonequilibrium systems with external fluctuations

We extend the phase field crystal method for nonequilibrium patterning to stochastic systems with external source where transient dynamics is essential. It was shown that at short time scales the system manifests pattern selection processes. These processes are studied by means of the structure function dynamics analysis. Nonequilibrium pattern-forming transitions are analyzed by means of numerical simulations.Comment: 15 poages, 8 figure

Proporciones aúreas de la mezquita del Cristo de la Luz

Con el objeto de favorecer la formación de los alumnos y reducir al mínimo el trauma que supone cada examen, en la cátedra de Construcción que D. Román Ferreras imparte en la E.I.T.O.P., se solicita de aquellos la presentación de un estudio, que al suponer trabajo personal tiene gran repercusión en la calificación final. De entre los realizados últimamente hemos seleccionado el presente, que contiene aportaciones de indudable interés al conocimiento de nuestro acervo arquitectónico y refleja las posibilidades del análisis armónico como herramientas de diseño

The Probability Distribution Function of Column Density in Molecular Clouds

(Abridged) We discuss the probability distribution function (PDF) of column density resulting from density fields with lognormal PDFs, applicable to isothermal gas (e.g., probably molecular clouds). We suggest that a ``decorrelation length'' can be defined as the distance over which the density auto-correlation function has decayed to, for example, 10% of its zero-lag value, so that the density ``events'' along a line of sight can be assumed to be independent over distances larger than this, and the Central Limit Theorem should be applicable. However, using random realizations of lognormal fields, we show that the convergence to a Gaussian is extremely slow in the high- density tail. Thus, the column density PDF is not expected to exhibit a unique functional shape, but to transit instead from a lognormal to a Gaussian form as the ratio $\eta$ of the column length to the decorrelation length increases. Simultaneously, the PDF's variance decreases. For intermediate values of $\eta$, the column density PDF assumes a nearly exponential decay. We then discuss the density power spectrum and the expected value of $\eta$ in actual molecular clouds. Observationally, our results suggest that $\eta$ may be inferred from the shape and width of the column density PDF in optically-thin-line or extinction studies. Our results should also hold for gas with finite-extent power-law underlying density PDFs, which should be characteristic of the diffuse, non-isothermal neutral medium (temperatures ranging from a few hundred to a few thousand degrees). Finally, we note that for $\eta \gtrsim 100$, the dynamic range in column density is small ($\lesssim$ a factor of 10), but this is only an averaging effect, with no implication on the dynamic range of the underlying density distribution.Comment: 13 pages, 7 figures (10 postscript files). Accepted in ApJ. Eliminated implication that ratio of column length to correlation length necessarily increases with resolution, and thus that 3D simulations are unresolved. Added discussion of dependence of autocorrelation function with parameters of the turbulenc

Use of Pleurotus pulmonarius to change the nutritional quality of wheat straw. I. effect on chemical composition

The effect of Pleurotus pulmonarius on the chemical composition of wheat straw was evaluated. Wheat straw, treated and untreated with P. pulmonarius, was obtained from a commercial facility. Ten samples plastic bags of wheat straw used previously as substrate to culture edible fungus were collected at random. The negative control group consisted of the pasteurized wheat straw untreated with P. pulmonarius. All samples were analyzed to determine dry matter, organic matter, crude protein, neutral detergent fiber, acid detergent fiber, cellulose and hemicellulose of each wheat straw. Data were analyzed by mean comparison using a t-Student test. No differences (P>0.05) between treatments were found for dry matter, crude protein and hemicellulose; however, straw treated with P. pulmonarius showed higher percentages (P<0.05) of organic matter, neutral and acid detergent fiber. It is concluded that growing P. pulmonarius in wheat straw improves the chemical composition of the straw by increasing its organic matter content and modifies the fiber structure, which increases the soluble carbohydrates content

Adaptation of Urban High-density Neighbourhoods in Nodes of Sustainable Intelligent Mobility Condensers

In Europe and since the early twentieth century, municipal mobility policies have provided underground parking and public transport to many of the rapidly built social neighbourhoods between 1960 and 1980, which were planned lacking them. However, the climatic emergency requires new approaches that reduce CO2 emissions. This paper sets out the steps for the implementation of an Intelligent Mobility Condenser (IMC) in an existing neighbourhood. IMCs combine connectivity to public transport, together with the creation of a transport cooperative that meets the mobility needs of its neighbours without the need to own a private car. Similar to car-sharing, the IMC offers hybrid, electric, solar cars, along with motorcycles and electric bicycles. This together with a digital platform that facilitates the management of their needs. On the other hand, IMCs are automatic surface parkings, with solar collection and urban gardens, which, being high-access nodes in the neighbourhood, allow the incorporation of community, social and commercial spaces. The paper discusses the results based on the economic and environmental benefits of the model, and the threats of its implementation due to the difficulties of giving up the private car.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Application of mathematical and machine learning techniques to analyse eye tracking data enabling better understanding of children’s visual cognitive behaviours

In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of machine-learning were used (Bayesian and K-Means) to obtain separate model sequences or average scanpaths for those children who responded either correctly or incorrectly to the graph task. Application of these machine-learning approaches indicated distinct differences in the resulting scanpaths for children who completed the graph task correctly or incorrectly: children who responded correctly accessed information that was mostly categorised as critical, whereas children responding incorrectly did not. There was also evidence that the children who were correct accessed the graph information in a different, more logical order, compared to the children who were incorrect. The visual behaviours aligned with different aspects of graph comprehension, such as initial understanding and orienting to the graph, and later interpretation and use of relevant information on the graph. The findings are discussed in terms of the implications for early mathematics teaching and learning, particularly in the development of graph comprehension, as well as the application of machine learning techniques to investigations of other visual-cognitive behaviours.Peer reviewe