scholarly journals Estudio fractal de la superficie de la hoja de la especie vegetal Copaifera sp. haciendo uso del Microscopio de Fuerza Atómica-AFM

2018 ◽  
pp. 10-16

Estudio fractal de la superficie de la hoja de la especie vegetal Copaifera sp. haciendo uso del Microscopio de Fuerza Atómica-AFM   Study fractal leaf surface of the plant species Copaifera sp. using the Microscope Atomic-Force-AFM Mario Omar Calla Salcedo, Robert Ronald Maguiña Zamora, y José Carlos Tavares Carvalho Universidade Federal de Amapá, Rodovia Juscelino Kubitschek de Oliveira, Km 02 - s/n, Bairro Jardim Marco Zero - Macapá -AP, CEP 68.902-280  DOI: https://doi.org/10.33017/RevECIPeru2016.0002/ Resumen Las especies de copaifera sp, que también son denominadas de copaíba y que son ampliamente utilizadas en la medicina popular debido a sus propiedades etnofarmacológicas. Este trabajo fue realizado con el objetivo de padronizar las hojas, mediante el estudio de la textura superficial da la hoja, para eso se necesita la obtención de los parámetros fractales como la dimensión Fractal, Lagunaridad y succolaridad, haciendo uso de los datos que proporciona el Microscopio de Fuerza Atómica, más conocido como AFM (por las siglas en inglés) se trabajó con la área óptima (25x25 mm2), con el procesamiento de datos y aplicando la geometría fractal, se desarrollaron los algoritmos haciendo uso del programa computacional Fortran 77, el estudio fue realizado a partir de la dificultad que se tiene al diferenciar una especie de otra de la Copaifera sp, ya que para hacer tal identificación se necesita la flor y hoja, esto es porque la planta solo florece una vez al año, y por eso se está proponiendo una manera más fácil, y efectiva da tal identificación solo haciendo uso de la hoja de la Copaifera sp, para el cálculo de la dimensión fractal se hizo uso del método de conteo de cajas (Box-Counting), se usó este método por su simplicidad y exactitud, la dimensión fractal va a servir para calcular la rugosidad y porosidad de la superficie de la hoja de la Copaifera sp., donde el valor de la rugosidad obtenido por medio de la dimensión fractal es más exacto que el cálculo de la rugosidad por medio de la geometría Euclidiana. La lagunaridad, es otro parámetro fractal, que sirve para medir el grado de uniformidad de los huecos en la superficie de la hoja de la Copaifera sp, para el cálculo de la lagunaridad se hizo uso de método conteo de Caja Diferencial (Differential Box Counting) que es un método basado en el conteo de cajá (Box-Counting), si la lagunaridad es mucho mayor que 1, existe mayor desorden de los huecos, si la lagunaridad es más próximo a 1, existe menor desorden, ahora si la lagunaridad es igual 1, la superficie es completamente uniforme, seria invariante a la rotación. La succolaridad es el último parámetro fractal que se aplicó al estudio de la superficie de la hoja, que mide la capacidad de un flujo de agua de atravesar toda la superfície en una determinada dirección, a este proceso se le llama percolación, se midió la succolaridad en las cuatro direcciones es decir de arriba hacia abajo, de abajo hacia arriba, de izquierda a la derecha, y por ultimo de derecha a la izquierda. Teniendo calculado los tres parámetros fractales: dimensión fractal, lagunaridad, y succolaridad, se tiene caracterizado completamente la superficie foliar. Descriptores: Copaifera, Dimensión Fractal, Lagunaridad, Succolaridad, Textura. Abstract The species of Copaifera sp. which are also called copal are widely used in folk medicine due to its ethnopharmacological properties. This work was accomplished with the purpose of the possibility of standardization of the leaves, on the study of the surface texture of the leaf, for this you need to obtain the fractal parameters as fractal dimension (roughness, porosity), lacunarity (rotational invariance of the holes ) and succolarity (percolation), making use of the data of the Atomic Force Microscopy (AFM) worked with the optimal area (25x25 mm2), with the data process and applying fractal mathematics, algorithms were developed with the computer program Fortran 77. The study was conducted from difficulty that one has to distinguish one species from another of Copaifera sp., and to make such identification is needed flower and leaf Copaifera sp., this is because the plant blooms only once a year. That's why it is proposing an easier and effective way to such identification, only making use of leaf Copaifera sp. for the calculation of the fractal dimension. It will make use of Box Counting method for its simplicity and exactitude, which will serve to calculate the roughness and porosity of the surface of the sheet Copaifera sp. It is expected that the value of roughness obtained by the Fractal geometry is more accurate, the calculation of roughness with Euclidean mathematics. The Lacunarity is another fractal parameter used to determine readily the uniformity of the holes for the calculation of lacunarity be made using the method of the counting boxes (Differential Box Counting) which is a method based on the counting boxes (Box-Counting), but the lacunarity is much greater than one, there is greater disorder of the holes.The lacunarity is closer to 1, there is less clutter, now the lacunarity is equal to 1, the surface is completely uniform, is down is invariant rotation, it is expected that lacunarity of Copaifera sp leaf is close to an a succolarity is the last fractal parameter that is doing applied to the study of surfaces, which measures the ability of a flow through the entire surface that serves to measure the percolation surface level. It is measured succolarity in the four directions is down from above down, bottom-up, from left to right, and finally from right to left. When it has calculated the three fractal parameters: fractal dimension, lacunarity and succolarity, it is possible to have fully characterized the leaf surface. Keywords: Copaifera. Fractal Dimension. Lacunarity. Succolarity. Texture

2012 ◽  
Vol 588-589 ◽  
pp. 1930-1933
Author(s):  
Guo Song Han ◽  
Hai Yan Yang ◽  
Xin Pei Jiang

Based on industrial CT technique, Meso-mechanical experiment was conducted on construction waste recycled brick to get the real-time CT image and stress-strain curve of brick during the loading process. Box counting method was used to calculate the fractal dimension of the inner pore transfixion and crack evolution. The results showed that lots of pore in the interfacial transition zone mainly resulted in the damage of the brick. With the increase of stress, the opening through-pore appeared and crack expanded, and the fractal dimension increased.


2021 ◽  
Author(s):  
Nicholas Dudu ◽  
Arturo Rodriguez ◽  
Gael Moran ◽  
Jose Terrazas ◽  
Richard Adansi ◽  
...  

Abstract Atmospheric turbulence studies indicate the presence of self-similar scaling structures over a range of scales from the inertial outer scale to the dissipative inner scale. A measure of this self-similar structure has been obtained by computing the fractal dimension of images visualizing the turbulence using the widely used box-counting method. If applied blindly, the box-counting method can lead to misleading results in which the edges of the scaling range, corresponding to the upper and lower length scales referred to above are incorporated in an incorrect way. Furthermore, certain structures arising in turbulent flows that are not self-similar can deliver spurious contributions to the box-counting dimension. An appropriately trained Convolutional Neural Network can take account of both the above features in an appropriate way, using as inputs more detailed information than just the number of boxes covering the putative fractal set. To give a particular example, how the shape of clusters of covering boxes covering the object changes with box size could be analyzed. We will create a data set of decaying isotropic turbulence scenarios for atmospheric turbulence using Large-Eddy Simulations (LES) and analyze characteristic structures arising from these. These could include contours of velocity magnitude, as well as of levels of a passive scalar introduced into the simulated flows. We will then identify features of the structures that can be used to train the networks to obtain the most appropriate fractal dimension describing the scaling range, even when this range is of limited extent, down to a minimum of one order of magnitude.


2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Youping Fan ◽  
Dai Zhang ◽  
Jingjiao Li

The paper aims to understand how the fractal dimension and growth time of electrical trees change with temperature and moisture. The fractal dimension of final electrical trees was estimated using 2-D box-counting method. Four groups of electrical trees were grown at variable moisture and temperature. The relation between growth time and fractal dimension of electrical trees were summarized. The results indicate the final electrical trees can have similar fractal dimensions via similar tree growth time at different combinations of moisture level and temperature conditions.


2011 ◽  
Vol 19 (1) ◽  
pp. 45 ◽  
Author(s):  
Ian Parkinson ◽  
Nick Fazzalari

A standardised methodology for the fractal analysis of histological sections of trabecular bone has been established. A modified box counting method has been developed for use on a PC based image analyser (Quantimet 500MC, Leica Cambridge). The effect of image analyser settings, magnification, image orientation and threshold levels, was determined. Also, the range of scale over which trabecular bone is effectively fractal was determined and a method formulated to objectively calculate more than one fractal dimension from the modified Richardson plot. The results show that magnification, image orientation and threshold settings have little effect on the estimate of fractal dimension. Trabecular bone has a lower limit below which it is not fractal (λ<25 μm) and the upper limit is 4250 μm. There are three distinct fractal dimensions for trabecular bone (sectional fractals), with magnitudes greater than 1.0 and less than 2.0. It has been shown that trabecular bone is effectively fractal over a defined range of scale. Also, within this range, there is more than 1 fractal dimension, describing spatial structural entities. Fractal analysis is a model independent method for describing a complex multifaceted structure, which can be adapted for the study of other biological systems. This may be at the cell, tissue or organ level and compliments conventional histomorphometric and stereological techniques.


Author(s):  
M. van Leeuwen ◽  
J. A. N. van Aardt ◽  
T. Kampe ◽  
K. Krause

Monitoring forest productivity and health is key to sustainable ecosystem management and informed decision making. A key parameter used in monitoring forest resources is the leaf area index (LAI), which is defined as the one-sided leaf area per unit ground area and is used to describe the canopy radiation regime, among other forest biophysical dynamics. Traditional optics-based methods to estimate LAI rely on the measurement of canopy transmission and foliage clumping. Extending optical methods to LiDAR data has been challenging and studies have reported effective LAI assessments, with no further quantification of foliage clumping. This study investigates the use of the box-counting method to assess the fractal dimension of point cloud data for contrasting forest types and along a gradient of foliage dispersal. We demonstrate the box-counting method on simulated ‘range-to-hit’, as well as acquired airborne discrete LiDAR data. Coherent results obtained from the different test cases hint at the potential of the box-counting fractal dimension to characterize foliage clumping and bode well for the use of clumping assessments in support of airborne, wall-to-wall estimates of LAI.


2019 ◽  
Vol 1 ◽  
pp. 281-287
Author(s):  
N N Abdulsalam ◽  
O Ologe

Fractal characterization of Earthquake occurrences in Nigeria was carried out in order to know the b-value of tremor occurrences in the country. This will help in hazard analysis and research in the geological and geophysical structures of Nigeria. The method used in determining the b-value is the box counting method, but for simplicity, we used circle. The areas that are tremor prone were posted on a digitized Nigeria map using Google earth and Surfer 7.0 software. The computation with the box counting method was performed with picked radius of the circle from 50km - 350km and the average number of points that falls within each circle were recorded. The graph of log r (the logarithms of radius of circle or scale) against log <N> (logarithms of average number of points) was plotted using grapher and excels Microsoft word and the slope of the graph was determined. The determined slope gave the fractal dimension and the b-value was thus calculated. In this work, a b-value of 0.6 was obtained indicating that Nigeria falls within seismically less active zone.


2020 ◽  
pp. 30-42
Author(s):  
Anna Zhurba ◽  
Michail Gasik

An essential element of fractal analysis of functional coatings is the fractal dimension, which is an important quantitative characteristic. Typically, coating images are represented as colored or halftone, and most fractal dimension algorithms are for binary images. Therefore, an important step in fractal analysis is binarization, which is a threshold separation operation and the result of which is a binary image.The purpose of the study is to study and program the methods of image binarization and to study the influence of these methods on the value of fractal dimension of functional coatings.As a result of the binarization threshold, the image is split into two regions, one containing all pixels with values below a certain threshold and the other containing all pixels with values above that threshold. Of great importance is the determination of the binarization threshold.The study analyzed a number of functional coating images, determined the fractal dimension of the image by the Box Counting method at different binarization thresholds and when applying different binarization methods (binarization with lower and upper threshold, with double restriction, and the average method for determining the optimal binarization threshold) images. The Box Counting method is used to depict any structure on a plane. This method allows us to determine the fractal dimension of not strictly self-similar objects. Each image binarization method is used for different types of images and for solving different problems.As a result, the methods of image binarization were developed and implemented, the fractal dimension of binary images was calculated, and the influence of these methods on the value of fractal dimension of functional coatings was investigated.The surfaces of composite steel structure, metallic porous materials, and natural cave structures are analyzed.


2017 ◽  
Vol 4 (1) ◽  
pp. 16
Author(s):  
Musibau A. Ibrahim ◽  
Oladotun A. Ojo ◽  
Peter A. Oluwafisoye

Fractal dimension (FD) is a very useful metric for the analysis of image structures with statistically self-similar properties. It has applications in areas such as texture segmentation, shape classification and analysis of medical images. Several approaches can be used for calculating the fractal dimension of digital images; the most popular method is the box-counting method. It is also very challenging and difficult to classify patterns in high resolution computed tomography images (HRCT) using this important descriptor. This paper applied the Holder exponent computation of the local intensity values for detecting the emphysema patterns in HRCT images. The absolute differences between the normal and the abnormal regions in the images are the key for a successful classification of emphysema patterns using the statistical analysis. The results obtained in this paper demonstrated the effectiveness of the predictive power of the features extracted from the Holder exponent in the analysis and classification of HRCT images. The overall classification accuracy achieved in lung tissue layers is greater than 90%, which is an evidence to prove the effectiveness of the methods investigated in this paper.


2019 ◽  
Author(s):  
Bahary Setyawan ◽  
Benyamin Sapiie

Abstract. This study discusses the correlation between the fractal of spatial epicentre distribution of aftershock (D2) and active fault (D0) in the Sumatra region. We identified 15 earthquakes in this region that were followed by aftershock cluster and related to the Sumatra Fault Zone or Southern Andaman West Fault. The spatial epicentre distribution of the aftershock was estimated by using two-point correlation integral and the D2 values found were varying from 1.03 ± 0.03 to 1.68 ± 0.08. We estimated the fractal dimension of the active fault by using Box–Counting Method and found that the variation of D0 values in the range of 0.95 ± 0.03 to 1.16 ± 0.01. Positive correlation was found in this study and two patterns were identified that had similar slope with different intercept. However, there was also a correlation that had steeper slope. The steeper slope was related to earthquake doublet mechanism that could generate more random spatial distribution of the aftershock in the fault system.


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