2,357 research outputs found
Two Dimensional Random Patterns
A new approach to the generation of random sequences and two dimensional
random patterns is proposed in this paper in which random sequences are
generated by making use of either Delaunay triangulation or Voronoi diagrams
drawn from random points taken in a two dimensional plane. Both the random
sequences and two dimensional random patterns generated in this manner are
shown to be more random when compared to pseudo-random sequences and patterns.Comment: 13 pages, 7 page
Vesicle computers: Approximating Voronoi diagram on Voronoi automata
Irregular arrangements of vesicles filled with excitable and precipitating
chemical systems are imitated by Voronoi automata --- finite-state machines
defined on a planar Voronoi diagram. Every Voronoi cell takes four states:
resting, excited, refractory and precipitate. A resting cell excites if it has
at least one excited neighbour; the cell precipitates if a ratio of excited
cells in its neighbourhood to its number of neighbours exceed certain
threshold. To approximate a Voronoi diagram on Voronoi automata we project a
planar set onto automaton lattice, thus cells corresponding to data-points are
excited. Excitation waves propagate across the Voronoi automaton, interact with
each other and form precipitate in result of the interaction. Configuration of
precipitate represents edges of approximated Voronoi diagram. We discover
relation between quality of Voronoi diagram approximation and precipitation
threshold, and demonstrate feasibility of our model in approximation Voronoi
diagram of arbitrary-shaped objects and a skeleton of a planar shape.Comment: Chaos, Solitons & Fractals (2011), in pres
Coordinating views for data visualisation and algorithmic profiling
A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system
Evolution of Voronoi based Fuzzy Recurrent Controllers
A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the -completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics
Computers from plants we never made. Speculations
We discuss possible designs and prototypes of computing systems that could be
based on morphological development of roots, interaction of roots, and analog
electrical computation with plants, and plant-derived electronic components. In
morphological plant processors data are represented by initial configuration of
roots and configurations of sources of attractants and repellents; results of
computation are represented by topology of the roots' network. Computation is
implemented by the roots following gradients of attractants and repellents, as
well as interacting with each other. Problems solvable by plant roots, in
principle, include shortest-path, minimum spanning tree, Voronoi diagram,
-shapes, convex subdivision of concave polygons. Electrical properties
of plants can be modified by loading the plants with functional nanoparticles
or coating parts of plants of conductive polymers. Thus, we are in position to
make living variable resistors, capacitors, operational amplifiers,
multipliers, potentiometers and fixed-function generators. The electrically
modified plants can implement summation, integration with respect to time,
inversion, multiplication, exponentiation, logarithm, division. Mathematical
and engineering problems to be solved can be represented in plant root networks
of resistive or reaction elements. Developments in plant-based computing
architectures will trigger emergence of a unique community of biologists,
electronic engineering and computer scientists working together to produce
living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing
inspired by physics, chemistry and biology. Essays presented to Julian Miller
on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew
Adamatzky (Springer, 2017
Nature of the learning algorithms for feedforward neural networks
The neural network model (NN) comprised of relatively simple computing elements, operating in parallel, offers an attractive and versatile framework for exploring a variety of learning
structures and processes for intelligent systems. Due to the amount of research developed in
the area many types of networks have been defined. The one of interest here is the multi-layer
perceptron as it is one of the simplest and it is considered a powerful representation tool whose
complete potential has not been adequately exploited and whose limitations need yet to be
specified in a formal and coherent framework. This dissertation addresses the theory of generalisation performance and architecture selection for the multi-layer perceptron; a subsidiary
aim is to compare and integrate this model with existing data analysis techniques and exploit
its potential by combining it with certain constructs from computational geometry creating a
reliable, coherent network design process which conforms to the characteristics of a generative
learning algorithm, ie. one including mechanisms for manipulating the connections and/or
units that comprise the architecture in addition to the procedure for updating the weights of
the connections. This means that it is unnecessary to provide an initial network as input to
the complete training process.After discussing in general terms the motivation for this study, the multi-layer perceptron
model is introduced and reviewed, along with the relevant supervised training algorithm, ie.
backpropagation. More particularly, it is argued that a network developed employing this model
can in general be trained and designed in a much better way by extracting more information
about the domains of interest through the application of certain geometric constructs in a preprocessing stage, specifically by generating the Voronoi Diagram and Delaunav Triangulation
[Okabe et al. 92] of the set of points comprising the training set and once a final architecture which performs appropriately on it has been obtained, Principal Component Analysis
[Jolliffe 86] is applied to the outputs produced by the units in the network's hidden layer to
eliminate the redundant dimensions of this space
- …