PhotoLin: a program to identify and analyze linear structures in aerial photographs, satellite images and maps

A computer program, PhotoLin, written for an IBM-PC-compatible microcomputer is described which detects linear features in aerial photographs, satellite images and topographic maps. The program accepts images saved to PCX files as input and applies noise correction and smoothing filters and thinning routines. The output consists of a skeleton containing the median lines of linear features which can be represented on a map. The branches of the skeleton can be broken into sections of constant length for which the mean orientations are obtained for the preparation of rose diagrams. (C) 2001 Elsevier B.V. Ltd. All rights reserved.

Simulation of electron density maps for two-dimensional crystal structures using Mathematica

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

Hausdorff dimension of non-hyperbolic repellers. I: Maps with holes

This is the first paper in a two-part series devoted to studying the Hausdorff dimension of invariant sets of non-uniformly hyperbolic, non-conformal maps. Here we consider a general abstract model, that we call piecewise smooth maps with holes. We show that the Hausdorff dimension of the repeller is strictly less than the dimension of the ambient manifold. Our approach also provides information on escape rates and dynamical dimension of the repeller.

The relationship between differential equations with piecewise constant argument and the associated discrete equations, via dichotomic maps

Dichotomic maps are considered by means of the stability and asymptotic stability of the null solution of a class of differential equations with argument [t] via associated discrete equations, where [.] designates the greatest integer function.

Fitting hyperbolic universes to Cayon-Smoot spots in COBE maps

On the possibility that the universe's matter density is low (Ohm(0) < 1), cosmologies can be considered with the metric of Friedmann's open universe but with closed hyperbolic manifolds as the physical three-space. These models have nontrivial spatial topology, with the property of producing multiple images of cosmic sources. Here a fit is attempted of 10 of these models to the physical cold and hot spots found by Cayon & Smoot in the COBE/DMR maps. These spots are interpreted as early, distant images of much nearer sources of inhomogeneity. The source for one of the cold spots is seen as the seed of a known supercluster.

Non-periodic bifurcations of one-dimensional maps

We prove that a 'positive probability' subset of the boundary of '{uniformly expanding circle transformations}' consists of Kupka-Smale maps. More precisely, we construct an open class of two-parameter families of circle maps (f(alpha,theta))(alpha,theta) such that, for a positive Lebesgue measure subset of values of alpha, the family (f(alpha,theta))(theta) crosses the boundary of the uniformly expanding domain at a map for which all periodic points are hyperbolic (expanding) and no critical point is pre-periodic. Furthermore, these maps admit an absolutely continuous invariant measure. We also provide information about the geometry of the boundary of the set of hyperbolic maps.

Long term economic relationships from cointegration maps

We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the sorting points into neighborhoods (SPIN) technique, which has been previously used to analyze large data sets from DNA arrays. We exemplify the technique in three data sets: US interest rates (USIR), monthly inflation rates and gross domestic product (GDP) growth rates. (c) 2007 Elsevier B.V. All rights reserved.

On dichotomic maps for non autonomous discrete equations

A new procedure is given for the study of stability and asymptotic stability of the null solution of the non autonomous discrete equations by the method of dichotomic maps, which it includes Liapunov's Method asa special case. Examples are given to illustrate the application of the method.

DIAGNOSIS of REGIONAL CEREBRAL BLOOD FLOW ABNORMALITIES USING SPECT: AGREEMENT BETWEEN INDIVIDUALIZED STATISTICAL PARAMETRIC MAPS and VISUAL INSPECTION BY NUCLEAR MEDICINE PHYSICIANS WITH DIFFERENT LEVELS of EXPERTISE IN NUCLEAR NEUROLOGY

INTRODUCTION: Visual analysis is widely used to interpret regional cerebral blood flow (rCBF) SPECT images in clinical practice despite its limitations. Automated methods are employed to investigate between-group rCBF differences in research Studies but have rarely been explored in individual analyses.OBJECTIVES: To compare visual inspection by nuclear physicians with the automated statistical parametric mapping program using a SPECT dataset of patients with neurological disorders and normal control images.METHODS: Using statistical parametric mapping, 14 SPECT images from patients with various neurological disorders were compared individually with a databank of 32 normal images using a statistical threshold of p<0.05 (corrected for multiple comparisons at the level of individual voxels or clusters). Statistical parametric mapping results were compared with Visual analyses by a nuclear physician highly experienced in neurology (A) as well as a nuclear physician with a general background of experience (B) who independently classified images as normal or altered, and determined the location of changes and the severity.RESULTS: of the 32 images of the normal databank, 4 generated maps showing rCBF abnormalities (p<0.05, corrected). Among the 14 images from patients with neurological disorders, 13 showed rCBF alterations. Statistical parametric mapping and physician A completely agreed on 84.37% and 64.28% of cases from the normal databank and neurological disorders, respectively. The agreement between statistical parametric mapping and ratings of physician B were lower (71.18% and 35.71%, respectively).CONCLUSION: Statistical parametric mapping replicated the findings described by the more experienced nuclear physician. This finding suggests that automated methods for individually analyzing rCBF SPECT images may be a valuable resource to complement visual inspection in clinical practice.

The Meaningful Fractal Fuzzy Dimension Applied to the Design of Self Organizing Maps

This paper presents the principal results of a detailed study about the use of the Meaningful Fractal Fuzzy Dimension measure in the problem in determining adequately the topological dimension of output space of a Self-Organizing Map. This fractal measure is conceived by combining the Fractals Theory and Fuzzy Approximate Reasoning. In this work this measure was applied on the dataset in order to obtain a priori knowledge, which is used to support the decision making about the SOM output space design. Several maps were designed with this approach and their evaluations are discussed here.

Mendelian inheritance, linkage and genotypic disequilibrium in microsatellite loci isolated from Hymenaea courbaril (Leguminosae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Continuous iteration of dynamical maps

A precise meaning is given to the notion of continuous iteration of a mapping. Usual discrete iterations are extended into a dynamical flow which is a homotopy of them all. The continuous iterate reveals that a dynamic map is formed by independent component modes evolving without interference with each other. An application to turbulent flow suggests that the velocity field assumes nonseparable values. © 1998 American Institute of Physics.

Modeling and identification of fertility maps using artificial neural networks

The application of agricultural fertilizers using variable rates along the field can be made through fertility maps previously elaborated or through real-time sensors. In most of the cases applies maps previously elaborated. These maps are identified from analyzes done in soil samples collected regularly (a sample for each field cell) or irregularly along the field. At the moment, mathematical interpolation methods such as nearest neighbor, local average, weighted inverse distance, contouring and kriging are used for predicting the variables involved with elaboration of fertility maps. However, some of these methods present deficiencies that can generate different fertility maps for a same data set. Moreover, such methods can generate inprecise maps to be used in precision farming. In this paper, artificial neural networks have been applied for elaboration and identification of precise fertility maps which can reduce the production costs and environmental impacts.

A Bayesian approach for constructing genetic maps when markers are miscoded

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.

On the injectivity of C1 maps of the real plane

Let X : ℝ2 → ℝ2 be a C1 map. Denote by Spec(X) the set of (complex) eigenvalues of DXp when p varies in ℝ2. If there exists ε > 0 such that Spec(X) ∩ (-ε, ε) = ∅, then X is injective. Some applications of this result to the real Keller Jacobian conjecture are discussed.