Ermakov Systems with Multiplicative Noise

Using the Euler-Maruyama numerical method, we present calculations of the Ermakov-Lewis invariant and the dynamic, geometric, and total phases for several cases of stochastic parametric oscillators, including the simplest case of the stochastic harmonic oscillator. The results are compared with the corresponding numerical noiseless cases to evaluate the effect of the noise. Besides, the noiseless cases are analytic and their analytic solutions are briefly presented. The Ermakov-Lewis invariant is not affected by the multiplicative noise in the three particular examples presented in this work, whereas there is a shift effect in the case of the phasesComment: 12 pages, 4 figures, 22 reference

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way. The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method

Classification of mexican paper currency denomination by extracting their discriminative colors

In this paper we describe a machine vision approach to recognize the denomination classes of the Mexican paper currency by extracting their color features. A banknote’s color is characterized by summing all the color vectors of the image’s pixels to obtain a resultant vector, the banknote’s denomination is classified by knowing the orientation of the resulting vector within the RGB space. In order to obtain a more precise characterization of paper currency, the less discriminative colors of each denomination are eliminated from the images; the color selection is applied in the RGB and HSV spaces, separately. Experimental results with the current Mexican banknotes are presented.Proyecto PROMEP 103.5/13/653

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Efecto del tratamiento con metformina y ejercicio moderado, sobre la función mitocondrial del músculo esquelético

El envejecimiento es un proceso natural e inevitable que se caracteriza por el deterioro progresivo físico y mental, presentando una acumulación de componentes disfuncionales, que finaliza con la muerte. Uno de los trastornos más importante durante el envejecimiento, es el deterioro en la movilidad y la capacidad física. Lo anterior debido a una pérdida progresiva de la masa y la fuerza del músculo esquelético, en un proceso conocido como sarcopenia. La etiología de la sarcopenia se ha relacionado con diversos factores como el estrés oxidante, la pérdida de la homeostasis de proteínas y la disfunción mitocondrial. Las mitocondrias desempeñan un papel fundamental debido a su importancia en la producción de energía, de especies reactivas de oxígeno y en la señalización para inducir la apoptosis. Se sabe que las funciones mitocondriales se deterioran con el envejecimiento, incluida la síntesis de proteínas mitocondriales, la respiración y la producción de ATP. Se ha sugerido que el ejercicio podría generar efectos benéficos en el ciclo de vida mitocondrial, ya que produce en las principales vías de señalización relacionadas con el control de calidad y cantidad de mitocondrias durante el envejecimiento. Por otro lado, se ha descrito que la metformina (MTF), una biguadina empleada en el tratamiento de diabetes tipo 2, mejora el rendimiento físico, la sensibilidad a la insulina, además de que disminuye el colesterol y las lipoproteínas de baja densidad. A nivel molecular, la MTF aumenta la actividad de la AMPK y la respuesta antioxidante. La interrogante de este trabajo se basa en esclarecer si el tratamiento conjunto de MTF + ejercicio moderado a lo largo de la vida, podrá ejercer un efecto protector contra la disfunción mitocondrial en músculo esquelético de ratas viejas. Para ello, se cuantifico el consumo de oxígeno de mitocondrias aisladas de músculo esquelético tipo cuádriceps. Además, se determinó la presencia de supercomplejos respiratorios y la actividad enzimática de los OXPHOS, así como la síntesis e hidrólisis de ATP. En nuestras condiciones experimentales observamos que los animales viejos con un tratamiento con MTF + ejercicio tuvieron una mejor respuesta durante el consumo de oxígeno, un mejor cociente P/O y actividad enzimática comparado con los animales que solo fueron tratados con metformina o sedentarios.Aging is a natural and inevitable process characterized by progressive physical and mental decline, presenting an accumulation of dysfunctional components, and ending with death. In particular, one of the health complications during aging is the failure in mobility and physical capacity, due to a progressive loss of skeletal muscle mass and strength, in a process known as sarcopenia. The etiology of sarcopenia has been related to various factors such as oxidative stress, loss of protein homeostasis and mitochondrial dysfunction. Mitochondria have a significant role due to their importance in energy production, in the reactive oxygen species generation and in apoptosis pathway signaling. It is known that mitochondrial functions deteriorate with aging, including mitochondrial protein synthesis, respiration and ATP production. It has been suggested that exercise could generate beneficial effects in the mitochondrial life cycle, since it has effects on the main signaling pathways related to quality control and quantity of mitochondria during aging. On the other hand, metformin (MTF), a biguadine which is used as a treatment for type 2 diabetes is known to improve physical performance, insulin sensitivity, and decrease cholesterol and low-density lipoproteins levels. At the molecular level, metformin increases AMPK activity and antioxidant response. So, this study aims is to clarify whether the combined treatment with MTF + moderate exercise during the rats lifespan could prevent mitochondrial dysfunction in old rats. The oxygen consumption of mitochondria isolated from quadriceps skeletal muscle was measured, ss well as the occurrence of respiratory supercomplexes and the OXPHOS enzymatic activity, ATP synthesis and hydrolysis. Under our experimental conditions the mitochondria from old animals treated with MTF + exercise had a better response during oxygen consumption, P/O ratio and enzymatic activity compared to animals that were treated only with metformin

Vitamin D deficiency among children and adolescents living in sunny South Texas

Background: Exposure to sunlight is essential to produce Vitamin D (ViD). Recent studies suggest obesity is associated with low ViD concentration. Living in South Texas with 220 sunny days a year should be enough to maintain adequate ViD levels. We aimed to analyze ViD levels and obesity in children and adolescents. Methods: We included 1239 pediatric (1.5 to 18.8 years old) participants (primary care clinic from Laredo) with registered CDC percentiles of BMI (pBMI) and serum concentrations of ViD (Atellica™). Data are described as median (p25, p75), Loess correlation between pBMI and ViD, ANCOVA to adjust by age, sex, and pBMI. We used the program Stata v16.1. The size of effects is expressed as Cohen-d and eta squared (eta2). Results: The median age was 12.5 (9.5, 15.1) years, pBMI was 94 (80, 98), 49% females (n=611). The pBMI showed small differences by sex (M 82.1±24 vs M 84.5±23, Cohen-d 0.14, p,0.001). The Loess showed an inverse relationship between pBMI with a rapid drop of ViD from p90. The ANCOVA coefficients were negative for sex (b=- 0.32 for females p=0.007, eta2=0.03) and pBMI (b=-0.001, p=0.025, eta2=0.15) on ViD concentration. Conclusion: We conclude obesity and female are related to low concentration VitD in sunny Laredo. Perhaps participants with more pBMI have less outdoor physical activity and increased sequester of ViD from adipose tissue. Future research should analyze the effect of these findings on adulthood morbidity

Color characterization comparison for machine vision-based fruit recognition

In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes

Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means

Se calcula la cantidad de grupos en que los vectores de color son agrupados usando fuzzy c-meansFuzzy C-means (FCM) is one of the most often techniques employed for color image segmentation; the drawback with this technique is the number of clusters the data, pixels’ colors, is grouped must be defined a priori. In this paper we present an approach to compute the number of clusters automatically. A competitive neural network (CNN) and a self-organizing map (SOM) are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram. The number of clusters is set by computing the number of the most activated neurons. The number of clusters is adjusted by comparing the similitude of colors. We show successful segmentation results obtained using images of the Berkeley segmentation database by training only one time the CNN and SOM, using only chromaticity data

Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works

Contrast enhacenment of RGB color images by histogram equalization of color vectors' intensities

Mejora del contraste de imagenes de color RGBThe histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces