Since the discovery of the traffic fractal properties ("scaling") in packet switched networks, there have been numerous studies and has been found this property in various data networks. This research evaluates experimentally a model based on self-similar processes in 802.11 networks, to characterize the Layer 2 traffic. Model validation is performed in two stages. In the first stage, we obtain the statistical characteristics of the actual traffic coursing on the network. In the second stage, we define a performance measure which applies to the actual data and the model tested to verify the performance of the model fit to the actual network performance through simulation and calculation of the correlation between actual performance and performance estimated by the model; generating a significant contribution to the design, planning and dimensioning of the capacity of this kind of networks, for which usually the design, planning and dimensioning takes place based only in coverage and not necessarily in the required capacity given the operation constraints.

In this paper we present a new methodology for detecting sound events in music signals using Gaussian Processes. Our method firstly takes a time-frequency representation, i.e. the spectrogram, of the input audio signal. Secondly the spectrogram dimension is reduced translating the linear Hertz frequency scale into the logarithmic Mel frequency scale using a triangular filter bank. Finally every short-time spectrum, i.e. every Mel spectrogram column, is classified as “Event” or “Not Event” by a Gaussian Processes Classifier. We compare our method with other event detection techniques widely used. To do so, we use MATLAB® to program each technique and test them using two datasets of music with different levels of complexity. Results show that the new methodology outperforms the standard approaches, getting an improvement by about 1.66 % on the dataset one and 0.45 % on the dataset two in terms of F-measure.

In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc) versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9) handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9), each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction). Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

The mainly factors studied to predict fatigue life of hot mix asphalt-HMA in flexible pavements are the loading effect, type of test, compaction methods, design parameters of HMA (e.g., particle size and size distribution curve, fine content, type of bitumen) and the variables associated with the environment (mainly moisture, temperature, aging). This study evaluated through a computer simulation, the influence of the granular layers and subgrade on the fatigue life of asphalt layers in flexible pavement structures. Mechanics parameters of granular layers of subgrade, base and subbase were obtained using the mathematical equations currently used for this purpose in the world. The emphasis of the study was the city of Bogotá, where the average annual temperature is 14°C and soils predominantly clay, generally experience CBR magnitudes between 1% and 4%. General conclusion: stiffness of the granular layers and subgrade significantly affect the fatigue resistance of HMA mixtures. Likewise, the use of different equations reported in reference literature in order to characterize granular layers may vary the fatigue life between 4.6 and 48.5 times, varying the thickness of the pavement layers in the design.

This paper presents the results obtained by developing a methodology to detect 5 types of heartbeats (Normal (N), Right bundle branch block (RBBB), Left bundle branch block (LBBB), Premature atrial contraction (APC) and Premature ventricular contraction (PVC)), using Wavelet transform packets with non-adaptative mode applied on features extraction from heartbeats. It was used the Shannon function to calculate the entropy and It was added an identification nodes stage per every type of cardiac signal in the Wavelet tree. The using of Wavelet packets transform allows the access to information which results of decomposition of low and high frecuency, giving providing a more integral analysis than achieved by the discrete Wavelet transform. Three families of mother Wavelet were evaluated on transformation: Daubechies, Symlet and Reverse Biorthogonal, which were results from a previous research in that were identified the mother Wavelet that had higher entropy with the cardiac signals. With non-adaptive mode, the computational cost is reduced when Wavelet packets are used; this cost represents the most marked disadvantage from the transform. To classify the heartbeats were used Support Vector Machines and Multilayer Perceptron. The best classification error was achieved employing Support Vector Machine and a radial basis function; it was 2.57 %.

This paper considers the edges and contrasts obtained with high-pass filters used in the estimation of daytime atmospheric visibility from digital images, and the behavior of these edges and contrasts is characterized by varying the parameters of high-pass filters such as the Ideal, Gaussian, and Homomorphic-Gaussian. A synthetic image of regions with different contrasts is used to apply different filters, then, we define an index to measure the quality of the edges obtained in the filtered image and it is used to analyze the results. The results show that both, the filter selection and the selection of its parameters: affects the characteristics and quality of the detected edges in the filtered image, also determine the amount of noise that the filter added to the image (artifacts that were not present in the original image), and also establish if achieved, or not, the edge detection. The results also show that the edge quality index reaches maximum values at certain combinations of the filters parameters, which means that some combinations of parameters reduce situations distorting the edges and distorting atmospheric visibility measures based on the Fourier transform. So these parameters which provide maximum quality edges are established as suitable for use in visibility measurement.

With the time, the number of studies associated to the martensitic transformation has been expanded; therefore, understand as this mechanism operates and as it confers different characteristics to diverse materials where it occurs, many studies has been conducted in different areas carrying out to discoveries at microstructural, kinetic and crystallographic level. This paper presents an overview of the martensitic transformation, beginning with a historical development, through a review on their morphology and kinetically characteristics, in addition to an analysis of the prospects of the studies carried out in the area, with a special interest in the phenomena of thermo-elasticity and shape memory.

The quality of pepper is mainly valued for flavor, color, and vitamin C. This study explored the effect of cooking on the physicochemical characteristics of the fruits of green pepper. Weighed 150 g, cut to size of 2.2 cm long and 1.3 cm wide and subjected to heat treatment (steam and water for 5 min, and 760 W microwave for 2 min), and quenched proceeded to determine the pH and acidity, vitamin C concentration by the indophenol method, total chlorophyll content by spectrophotometry at 652 nm and the surface color coordinates. After heat treatment the concentration of vitamin C, chlorophyll content and acidity significant differences (p <0.05), in contrast pH, L *, a *, b *, C * and h ° not statistically affected. Vitamin C ranged from 147.84 ± 4.36 and 79.31 ± 5.44 mg ascorbic acid/100 g fresh weight. The total chlorophyll ranged from 4.44 ± 0.04 and 2.61 ± 0.04 mg/ 100 of pepper. In the acidity values were between 0.10 ± 0.005 and 0.07 ± 0.001% citric acid. The microwave cooking method obtains the highest level of retention of vitamin C, and the surface color of green pepper is not affected by the different cooking methods evaluated in this study.

Anaerobic digestion systems are complex processes that unfortunately often suffer from instability causing digester failure. In order to be able to design, optimizing and operate efficiently anaerobic digestion systems, appropriate control strategies need to be designed. Such strategies require, in general, the development of mathematical models. The anaerobic digestion process comprises a complex network of sequential and parallel reactions of biochemical and physicochemical nature. Usually, such reactions contain a particular step, the so called rate-limiting step which, being the slowest, limits the reaction rate of the overall process. The first attempts for modeling anaerobic digestion led to models describing only the limiting step. However, over a wide range of operating conditions, the limiting step is not always the same. It may depend on wastewater characteristics, hydraulic loading, temperature, etc. It is apparent that the "limiting step hypothesis" leads to simple and readily usable models. Such models, however, do not describe very well the digester behavior, especially under transient operating conditions. This work reviews the current state-of-the-art in anaerobic digestion modeling. We give a brief description of the key anaerobic digestion models that have been developed so far for describing biomass growth systems, including the International Water Association’s Anaerobic Digestion Model 1 (ADM1) and we identify the areas that require further research endeavors.