Causes of the apparent scale independence of fractal indices associated with forest fragmentation in Bolivia

In a recent investigation, Landsat TM and ETM+ data were used to simulate different resolutions of remotely-sensed images (from 30 to 1100 m) and to analyze the effect of resolution on a range of landscape metrics associated with spatial patterns of forest fragmentation in Chapare, Bolivia since the mid-1980s. Whereas most metrics were found to be highly dependent on pixel size, several fractal metrics (DLFD, MPFD, and AWMPFD) were apparently independent of image resolution, in contradiction with a sizeable body of literature indicating that fractal dimensions of natural objects depend strongly on image characteristics. The present re-analysis of the Chapare images, using two alternative algorithms routinely used for the evaluation of fractal dimensions, shows that the values of the box-counting and information fractal dimensions are systematically larger, sometimes by as much as 85%, than the "fractal" indices DLFD, MPFD, and AWMFD for the same images. In addition, the geometrical fractal features of the forest and non-forest patches in the Chapare region strongly depend on the resolution of images used in the analysis. The largest dependency on resolution occurs for the box-counting fractal dimension in the case of the non-forest patches in 1993, where the difference between the 30 and I 100 m-resolution images corresponds to 24% of the full theoretical range (1.0 to 2.0) of the mass fractal dimension. The observation that the indices DLFD, MPFD, and AWMPFD, unlike the classical fractal dimensions, appear relatively unaffected by resolution in the case of the Chapare images seems due essentially to the fact that these indices are based on a heuristic, "non-geometric" approach to fractals. Because of their lack of a foundation in fractal geometry, nothing guarantees that these indices will be resolution-independent in general. (C) 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Fractal geometry of aggregate snowflakes revealed by triple wavelength radar measurements

Radar reflectivity measurements from three different wavelengths are used to retrieve information about the shape of aggregate snowflakes in deep stratiform ice clouds. Dual-wavelength ratios are calculated for different shape models and compared to observations at 3, 35 and 94 GHz. It is demonstrated that many scattering models, including spherical and spheroidal models, do not adequately describe the aggregate snowflakes that are observed. The observations are consistent with fractal aggregate geometries generated by a physically-based aggregation model. It is demonstrated that the fractal dimension of large aggregates can be inferred directly from the radar data. Fractal dimensions close to 2 are retrieved, consistent with previous theoretical models and in-situ observations.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

Unsupervised learning to cluster the disease stages in parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

O global e o contextual no aprendizado gerencial de multinacionais: uma perspectiva brasileira

The major purpose of this thesis is to verify, from a Brazilian perspective, how global and contextual issues influence the management learning in Multinationals. The management learning derived from the interaction of holding and sidiaries/colligates of Multinational corporation is supposed to be subject to convergent and divergent forces, the former related to global and standardized organizational practices, and the latter, is seen as a social practice subject to cultural and organizational singularities. A model was constructed to relate the dichotomy between the universality of the management practices and technologies and the particularity of the contexts where they operate, to the dichotomy between the singularities in organization and national level. This model is composed of the international, global, managerial and inter-organizational dimensions related, respectively, to the cultural and political diversity; to the universal forces of practices and values; to the managerial capabilities and resources in the organization, consolidated as best practices and to the interaction between holding and subsidiaries and the resulted learning. The combined result of these dimensions influences the knowledge flow and the learning derived from it. The field research was constituted of five cases of internationalized Brazilian firms, with a solid experience in their management systems. The main subjects of this study were executives and ofessionals/managers who respond to the management development. The data were first collected in the headquarters and complemented with visits to subsidiaries/joint ventures in other countries, in loco or with expatriated people who return to Brazil. The central supposition was validated. So, the management learning ¿ is driven by the global capitalism practices and by the global culture where they are immersed, reproducing a hegemonic vision and a common language (global dimension); ¿ incorporates the more propagated and dominant managerial values, although there are some variations when they are applied in the subsidiaries/joint ventures; is the product of the assimilation of international recognized and planned managerial practices, with the acculturation power, although not completely; is the result mainly of the managerial practice in work; is impacted not only by cross-cultural and managerial factors, but also by the business environment of the firm; is given according to the capabilities and resources in the organization, guiding the form of assimilation of practices and technologies, with global application or not (managerial dimension); ¿ is affected by the cross-cultural diversity involving the countries of the holding and the subsidiaries/joint ventures where the firm is and is given as a reproduction of the political context of the holding and subsidiaries countries (international dimension); ¿ faces aligned concurrent institutional pressures between corporate or global systems, practices of other subsidiaries/joint ventures and local practices; is more difficult to reach when there is not permeability between organizational cultures and identities of a Multinational firm; is affected by how much the relationship process across these unities is self-referenced; is facilitated by the construction and improvement of the knowledge network (interorganizational dimension). Finally some contributions of this study are exposed, including extensions of the proposed model and suggestions, recommendations for future research.

Fractal analysis along stretch zone for an aluminum alloy

The topography of fracture surface along stretch zone front for Al7050 is analyzed about its fractal behavior and compared against local distributions of microstructural parameters and stretch zone height, considered here as a toughness parameter. Major influence on microscale was presented by precipitation density. Larger grains should be significant on topographic behavior at macroscale, besides the local toughness measured along stretch zone. The large scattering of fractal measurements along specimen width should limit the validity of models relating fractal values and fracture toughness. It is proposed that models based on mixed fractals must also consider some dispersion parameter instead of mean fractal measurements due to the overall complexity of fracture relief formation. It is suggested that sampling for fractal measurement must be restricted to plane strain region along fracture surface, due to smaller scattering in this region. (C) 2004 Elsevier B.V. All rights reserved.

Fractal characterization of brittle fracture in ceramics under mode I stress loading

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fractal behavior throughout stretch zone of 15-5PH steel under elastic-plastic loading conditions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

3-D reconstruction by extended depth-of-field in failure analysis - Case study II: Fractal analysis of interlaminar fracture in carbon/epoxy composites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bioglass Associated With Leukocyte-Poor Platelet-Rich Plasma in the Rabbit Maxillary Sinus: Histomorphometric, Densitometric, and Fractal Analysis

The objective of the present study was to evaluate the outcomes of autogenous bone graft (AB) and bioglass (BG) associated or not with leukocyte-poor platelet-rich plasma (LP-PRP) in the rabbit maxillary sinus (MS) by histomorphometric and radiographic analysis. Twenty rabbits divided into 2 groups (G1, G2) were submitted to sinus lift surgery. In G1, 10 MS were grafted with AB and 10 MS were grafted with BG. In G2, 10 MS were grafted with AB + LP-PRP and 10 MS were grafted with BG + LP-PRP. After 90 days, the animals were killed and specimens were obtained, x-rayed, and submitted to histomorphometric, radiographic bone density (RD) and fractal dimension analysis. Radiographic bone density mean values (SD), expressed as aluminum equivalent in mm, of AB, BG, AB + LP-PRP, and BG + LP-PRP groups were 1.79 (0.31), 2.04 (0.39), 1.61 (0.28), and 1.53 (0.30), respectively. Significant differences (P < 0.05) were observed between BG and AB, and BG + PRP and BG. Fractal dimension mean values were 1.48 (0.04), 1.35 (0.08), 1.44 (0.04), and 1.44 (0.06), respectively. Significant differences were observed between BG and AB, and AB + LP-PRP and BG. Mean values for the percentage of bone inside MS were 63.30 (8.60), 52.65 (10.41), 55.25 (7.01), and 51.07 (10.25), respectively. No differences were found. No correlations were observed among percentage of bone, RD and FD. Histological analysis showed that MS treated with AB presented mature and new bone formation. The other groups showed minor bone formation. Within the limitations of this study, the results indicated that at a 90-day time end point, AB yielded better results than AB + LP-PRP, BG, and BG + LP-PRP and should be considered the primary material for MS augmentation.

Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

Computer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.

A new approach to integrable theories in any dimension

The zero curvature representation for two-dimensional integrable models is generalized to spacetimes of dimension d + 1 by the introduction of a d-form connection. The new generalized zero curvature conditions can be used to represent the equations of motion of some relativistic invariant field theories of physical interest in 2 + 1 dimensions (BF theories, Chern-Simons, 2 + 1 gravity and the CP1 model) and 3 + 1 dimensions (self-dual Yang-Mills theory and the Bogomolny equations). Our approach leads to new methods of constructing conserved currents and solutions. In a submodel of the 2 + 1-dimensional CP1 model, we explicitly construct an infinite number of previously unknown non-trivial conserved currents. For each positive integer spin representation of sl(2) we construct 2j + 1 conserved currents leading to 2j + 1 Lorentz scalar charges. (C) 1998 Elsevier B.V. B.V.

Persistence Length, Mass Fractal, and Branching in the Aggregating of Vinyltriethoxysilane-Derived Organic/Silica Hybrids

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dynamical scaling in fractal structures in the aggregation of tetraethoxysilane-derived sonogels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)