Which Fractal Parameter Contributes Most to Adhesion?

2010 ◽  
Vol 24 (15-16) ◽  
pp. 2383-2396 ◽  
Author(s):  
D.-L. Liu ◽  
J. Martin ◽  
N. A. Burnham
Keyword(s):  
Fractal Parameter

CRYPTANALYSIS ON SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION

Fractals ◽  
2012 ◽  
Vol 20 (01) ◽  
pp. 41-51 ◽  
Author(s):  
CHING-HUNG YUEN ◽  
KWOK-WO WONG
Keyword(s):  
Image Coding ◽  
Standard Test ◽  
Scaling Factor ◽  
Depth Information ◽  
Selective Encryption ◽  
Fractal Parameter ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Plain Image ◽  
Domain Block

The vulnerabilities of the selective encryption scheme for fractal image coding proposed by Lian et al.1 are identified. By comparing multiple cipher-images of the same plain-image encrypted with different keys, the positions of unencrypted parameters in each encoded block are located. This allows the adversary to recover the encrypted depth of the quadtree by observing the length of each matched domain block. With this depth information and the unencrypted parameters, the adversary is able to reconstruct an intelligent image. Experimental results show that some standard test images can be successfully decoded and recognized by replacing the encrypted contrast scaling factor and brightness offset with specific values. Some remedial approaches are suggested to enhance the security of the scheme.

scholarly journals On the multi-fractal characteristics of the ULF geomagnetic field before the 1993 Guam earthquake

2013 ◽  
Vol 13 (1) ◽  
pp. 187-191 ◽  
Author(s):  
F. Masci
Keyword(s):  
Earthquake Prediction ◽  
Geomagnetic Activity ◽  
Fractal Analysis ◽  
Geomagnetic Field ◽  
Activity Level ◽  
Short Term ◽  
Fractal Parameter ◽  
Fractal Parameters ◽  
Magnetic Signatures ◽  
Fractal Characteristics

Abstract. Ida et al. (2005) document significant changes in the multi-fractal parameters of the ULF geomagnetic field H component starting about one month before the 1993 Guam earthquake. According to the authors, these multi-fractal signatures can be considered as precursory signals of the Guam earthquake. As a consequence, they conclude that the multi-fractal analysis may have an important role in the development of short-term earthquake prediction capabilities. Since this and other similar reports have motivated the idea that earthquake prediction based on electromagnetic precursory signals may one day become a routine technique, the presumed precursors need to be validated through independent datasets. In this review the seismogenic origin of the multi-fractal magnetic signatures documented by Ida et al. (2005) before the 8 August 1993 Guam earthquake is seriously put into question. By means of the geomagnetic ΣKp index, it is demonstrated that these multi-fractal parameter changes are normal signals induced by the variation of the global geomagnetic activity level.

Painlevé analysis of Fokas–Lenells equation with fractal temporal evolution

2021 ◽  
pp. 2150351
Author(s):  
Nauman Raza ◽  
Adeela Yasmeen
Keyword(s):  
Optical Fibers ◽  
Temporal Evolution ◽  
Soliton Solutions ◽  
Nonlinear Problems ◽  
Optical Solitons ◽  
Painlevé Analysis ◽  
Fractal Parameter ◽  
Painleve Analysis ◽  
Kink Soliton

This paper presents new optical solitons of a fractal Fokas–Lenells equation that models the dynamics of optical fibers. The Painlevé technique is employed to identify kink soliton solutions. The constraint conditions guarantee the existence of these solitons. The outcomes of this research give new solutions that are not achieved using some already defined algorithms. The derived method is efficient and its applications are promising for other nonlinear problems. The 3D graphical illustrations of obtained results are depicted for various values of the fractal parameter.

Fractal Characterization and Petrophysical Analysis of 3D Dynamic Digital Rocks of Sandstone

2021 ◽  
Vol 62 (5) ◽  
pp. 500-515
Author(s):  
Peiqiang Zhao ◽  
◽  
Miao Luo ◽  
Dong Li ◽  
Yuqi Wu ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Pore Structure ◽  
Fractal Geometry ◽  
Pore Space ◽  
Clay Content ◽  
Coefficient Of Determination ◽  
Formation Factor ◽  
Crucial Issue ◽  
Fractal Parameter ◽  
Fractal Parameters

It is a crucial issue to comprehensively study the relations between microstructure and seepage capacity of porous media. Several physical-based parameters of fractal geometry can analyze the pore structure of rocks, while permeability and electrical conductivity are used to study seepage capacity. In this paper, we first created 3D dynamic digital models of nine different sandstones with varying clay content, cements, and intragranular pores in feldspar. These nine models were divided into three groups. Then, fractal dimension, lacunarity, and succolarity, permeability, and electrical properties of the models were calculated, and their relationships were investigated. We used fractal parameters to interpret the correlation between fluid flow and pore structure as one of the main petrophysical properties of a rock. Results showed that the coefficient of determination for cementation exponent m and fractal dimension is 0.869, while between m and porosity, and succolarity, it is 0.784 and 0.781, respectively. This indicates that the fractal dimension and cementation exponent describe the complexity of pores. The coefficient of determination between permeability and succolarity is 0.975, which is higher than that between permeability and the fractal dimension or porosity. The coefficient of determination between formation factor and succolarity is 0.957, which is higher than that between formation factor and the fractal dimension or porosity. Overall, a stronger relationship between petrophysical parameters, permeability in particular, and succolarity allows this lesser-used fractal parameter to be a good measure for characterizing the connectivity of pore space and pore network.

Clustering Research on Fractal Parameter of GIS PD UHF Signals

2013 ◽  
Vol 380-384 ◽  
pp. 3818-3821 ◽  
Author(s):  
Yu Xin Yun
Keyword(s):  
Pattern Recognition ◽  
Noise Level ◽  
Fractal Theory ◽  
Box Dimension ◽  
Control Parameters ◽  
Wavelet Method ◽  
Filtering Algorithm ◽  
Fractal Parameter ◽  
Fractal Parameters ◽  
Signal Changes

The distribution of fractal parameters (box dimension and lacunarity) vary with noise level of GIS PD UHF signal is the main obstacle to the use of fractal theory in GIS PD pattern recognition. According to the characteristic of fractal parameters, box dimension and wavelet method have been used in control the noise level and gather the fractal parameters of each kind of GIS PD UHF signals. Simulations show that, the fuzzy control parameters filtering algorithm based on the box dimension will lead to the dispersive of lacunarity and the distribution of fractal parameters changes as the noise level of GIS PD UHF signal changes. However, the wavelet method has a good performance in gathering those two fractal parameters of different GIS PD UHF signals. And its a promising approach to expand the applicability of classifiers used in GIS PD pattern recognition.

Which Fractal Parameter Contributes Most to Adhesion?

2011 ◽  
pp. 45-58
Author(s):  
D Liu ◽  
J Martin ◽  
N Burnham
Keyword(s):  
Fractal Parameter

scholarly journals Fractal-Based Computation of Heat Source Depths and Temperatures for the Soutpansberg Basin, South Africa

2018 ◽  
Vol 10 (4) ◽  
pp. 10 ◽  
Author(s):  
Peter K. Nyabeze ◽  
Oswald Gwavava
Keyword(s):  
South Africa ◽  
Curie Point ◽  
Average Depth ◽  
Centroid Method ◽  
Fractal Parameter ◽  
Average Temperature ◽  
Rock Types ◽  
Basement Depth ◽  
Improved Technique ◽  
Parameter Values

The fractal-based approach was used for computing magnetic depths and temperatures for the Soutpansberg Basin in South Africa. The average depth to the top Zt and basement depth Zo for the Soutpansberg Basin were 4.36 ± 0.28 km, 10.43 ± 0.65 km, respectively. The average temperature at depth Zt was 184.69 ± 7.66 ºC. Magnetic source depths and basal temperatures that were in the Curie point range were determined, to be within 20.35 km to 21.68 km and 549.34 ºC to 585.24 ºC, respectively. Increasing the value of the fractal parameter β from 0 to 4, had an effect of retaining deeper depths and higher temperatures. The fractal parameter values of β > 3 retained Curie point depths and temperatures that indicated basal rock types with an igneous predisposition. The fractal-based approach proved to be an improved technique as compared to the conventional centroid method.

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
Keyword(s):  
Fractal Dimension ◽  
Leaf Surface ◽  
Folk Medicine ◽  
Fractal Parameter ◽  
Box Counting ◽  
Atomic Force ◽  
Fractal Parameters ◽  
Box Counting Method ◽  
Optimal Area ◽  
Fortran 77

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

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