1,478 research outputs found
Improved texture image classification through the use of a corrosion-inspired cellular automaton
In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.Comment: 13 pages, 14 figure
A split-and-perturb decomposition of number-conserving cellular automata
This paper concerns -dimensional cellular automata with the von Neumann
neighborhood that conserve the sum of the states of all their cells. These
automata, called number-conserving or density-conserving cellular automata, are
of particular interest to mathematicians, computer scientists and physicists,
as they can serve as models of physical phenomena obeying some conservation
law. We propose a new approach to study such cellular automata that works in
any dimension and for any set of states . Essentially, the local rule of
a cellular automaton is decomposed into two parts: a split function and a
perturbation. This decomposition is unique and, moreover, the set of all
possible split functions has a very simple structure, while the set of all
perturbations forms a linear space and is therefore very easy to describe in
terms of its basis. We show how this approach allows to find all
number-conserving cellular automata in many cases of and . In
particular, we find all three-dimensional number-conserving CAs with three
states, which until now was beyond the capabilities of computers
Neighborhood detection and rule selection from cellular automata patterns
Using genetic algorithms (GAs) to search for cellular automation (CA) rules from spatio-temporal patterns produced in CA evolution is usually complicated and time-consuming when both, the neighborhood structure and the local rule are searched simultaneously. The complexity of this problem motivates the development of a new search which separates the neighborhood detection from the GA search. In the paper, the neighborhood is determined by independently selecting terms from a large term set on the basis of the contribution each term makes to the next state of the cell to be updated. The GA search is then started with a considerably smaller set of candidate rules pre-defined by the detected neighhorhood. This approach is tested over a large set of one-dimensional (1-D) and two-dimensional (2-D) CA rules. Simulation results illustrate the efficiency of the new algorith
Extracting Boolean rules from CA patterns
A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois
When--and how--can a cellular automaton be rewritten as a lattice gas?
Both cellular automata (CA) and lattice-gas automata (LG) provide finite
algorithmic presentations for certain classes of infinite dynamical systems
studied by symbolic dynamics; it is customary to use the term `cellular
automaton' or `lattice gas' for the dynamic system itself as well as for its
presentation. The two kinds of presentation share many traits but also display
profound differences on issues ranging from decidability to modeling
convenience and physical implementability.
Following a conjecture by Toffoli and Margolus, it had been proved by Kari
(and by Durand--Lose for more than two dimensions) that any invertible CA can
be rewritten as an LG (with a possibly much more complex ``unit cell''). But
until now it was not known whether this is possible in general for
noninvertible CA--which comprise ``almost all'' CA and represent the bulk of
examples in theory and applications. Even circumstantial evidence--whether in
favor or against--was lacking.
Here, for noninvertible CA, (a) we prove that an LG presentation is out of
the question for the vanishingly small class of surjective ones. We then turn
our attention to all the rest--noninvertible and nonsurjective--which comprise
all the typical ones, including Conway's `Game of Life'. For these (b) we prove
by explicit construction that all the one-dimensional ones are representable as
LG, and (c) we present and motivate the conjecture that this result extends to
any number of dimensions.
The tradeoff between dissipation rate and structural complexity implied by
the above results have compelling implications for the thermodynamics of
computation at a microscopic scale.Comment: 16 page
Simulating city growth by using the cellular automata algorithm
The objective of this thesis is to develop and implement a Cellular Automata
(CA) algorithm to simulate urban growth process. It attempts to satisfy the
need to predict the future shape of a city, the way land uses sprawl in the
surroundings of that city and its population. Salonica city in Greece is
selected as a case study to simulate its urban growth. Cellular automaton
(CA) based models are increasingly used to investigate cities and urban
systems. Sprawling cities may be considered as complex adaptive systems,
and this warrants use of methodology that can accommodate the space-time
dynamics of many interacting entities. Automata tools are well-suited for
representation of such systems. By means of illustrating this point, the
development of a model for simulating the sprawl of land uses such as
commercial and residential and calculating the population who will reside in
the city is discussed
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