91 research outputs found
Satellite image classification and segmentation using non-additive entropy
Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis
entropy on the pattern recognition and segmentation of coloured images obtained
by satellites, via "Google Earth". By segmentation we mean split an image to
locate regions of interest. Here, we discriminate and define an image partition
classes according to a training basis. This training basis consists of three
pattern classes: aquatic, urban and vegetation regions. Our numerical
experiments demonstrate that the Tsallis entropy, used as a feature vector
composed of distinct entropic indexes outperforms the standard entropy.
There are several applications of our proposed methodology, once satellite
images can be used to monitor migration form rural to urban regions,
agricultural activities, oil spreading on the ocean etc.Comment: 4 pages, 5 figures, ICMSquare 201
Complex network classification using partially self-avoiding deterministic walks
Complex networks have attracted increasing interest from various fields of
science. It has been demonstrated that each complex network model presents
specific topological structures which characterize its connectivity and
dynamics. Complex network classification rely on the use of representative
measurements that model topological structures. Although there are a large
number of measurements, most of them are correlated. To overcome this
limitation, this paper presents a new measurement for complex network
classification based on partially self-avoiding walks. We validate the
measurement on a data set composed by 40.000 complex networks of four
well-known models. Our results indicate that the proposed measurement improves
correct classification of networks compared to the traditional ones
Fast, parallel and secure cryptography algorithm using Lorenz's attractor
A novel cryptography method based on the Lorenz's attractor chaotic system is
presented. The proposed algorithm is secure and fast, making it practical for
general use. We introduce the chaotic operation mode, which provides an
interaction among the password, message and a chaotic system. It ensures that
the algorithm yields a secure codification, even if the nature of the chaotic
system is known. The algorithm has been implemented in two versions: one
sequential and slow and the other, parallel and fast. Our algorithm assures the
integrity of the ciphertext (we know if it has been altered, which is not
assured by traditional algorithms) and consequently its authenticity. Numerical
experiments are presented, discussed and show the behavior of the method in
terms of security and performance. The fast version of the algorithm has a
performance comparable to AES, a popular cryptography program used commercially
nowadays, but it is more secure, which makes it immediately suitable for
general purpose cryptography applications. An internet page has been set up,
which enables the readers to test the algorithm and also to try to break into
the cipher in
An efficient algorithm to generate large random uncorrelated Euclidean distances: the random link model
A disordered medium is often constructed by points independently and
identically distributed in a -dimensional hyperspace. Characteristics
related to the statistics of this system is known as the random point problem.
As , the distances between two points become independent random
variables, leading to its mean field description: the random link model. While
the numerical treatment of large random point problems pose no major
difficulty, the same is not true for large random link systems due to Euclidean
restrictions. Exploring the deterministic nature of the congruential
pseudo-random number generators, we present techniques which allow the
consideration of models with memory consumption of order O(N), instead of
in a naive implementation but with the same time dependence .Comment: 8 pages, 2 figures and 1 tabl
Distance statistics in random media: high dimension and/or high neighborhood order cases
Consider an unlimited homogeneous medium disturbed by points generated via
Poisson process. The neighborhood of a point plays an important role in spatial
statistics problems. Here, we obtain analytically the distance statistics to
th nearest neighbor in a -dimensional media. Next, we focus our attention
in high dimensionality and high neighborhood order limits. High dimensionality
makes distance distribution behavior as a delta sequence, with mean value equal
to Cerf's conjecture. Distance statistics in high neighborhood order converges
to a Gaussian distribution. The general distance statistics can be applied to
detect departures from Poissonian point distribution hypotheses as proposed by
Thompson and generalized here.Comment: 5 pages and 2 figure
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