1,411 research outputs found
Programs as Polypeptides
We describe a visual programming language for defining behaviors manifested
by reified actors in a 2D virtual world that can be compiled into programs
comprised of sequences of combinators that are themselves reified as actors.
This makes it possible to build programs that build programs from components of
a few fixed types delivered by diffusion using processes that resemble
chemistry as much as computation.Comment: in European Conference on Artificial Life (ECAL '15), York, UK, 201
Color image processing in a cellular neural-network environment
When low-level hardware simulations of cellular neural networks (CNNs) are very costly for exploring new applications, the use of a behavioral simulator becomes indispensable. This paper presents a software prototype capable of performing image processing applications using CNNs. The software is based on a CNN multilayer structure in which each primary color is assigned to a unique layer. This allows an added flexibility as different processing applications can be performed in parallel. To be able to handle a full range of color tones, two novel color mapping schemes were derived. In the proposed schemes the color information is obtained from the cell's state rather than from its output. This modification is necessary because for many templates CNN has only binary stable outputs from which only either a fully saturated or a black color can be obtained. Additionally, a postprocessor capable of performing pixelwise logical operations among color layers was developed to enhance the results obtained from CNN. Examples in the areas of medical image processing, image restoration, and weather forecasting are provided to demonstrate the robustness of the software and the vast potential of CN
Completeness Results for Parameterized Space Classes
The parameterized complexity of a problem is considered "settled" once it has
been shown to lie in FPT or to be complete for a class in the W-hierarchy or a
similar parameterized hierarchy. Several natural parameterized problems have,
however, resisted such a classification. At least in some cases, the reason is
that upper and lower bounds for their parameterized space complexity have
recently been obtained that rule out completeness results for parameterized
time classes. In this paper, we make progress in this direction by proving that
the associative generability problem and the longest common subsequence problem
are complete for parameterized space classes. These classes are defined in
terms of different forms of bounded nondeterminism and in terms of simultaneous
time--space bounds. As a technical tool we introduce a "union operation" that
translates between problems complete for classical complexity classes and for
W-classes.Comment: IPEC 201
Cellular Automata
Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented
Fractals from genomes: exact solutions of a biology-inspired problem
This is a review of a set of recent papers with some new data added. After a
brief biological introduction a visualization scheme of the string composition
of long DNA sequences, in particular, of bacterial complete genomes, will be
described. This scheme leads to a class of self-similar and self-overlapping
fractals in the limit of infinitely long constotuent strings. The calculation
of their exact dimensions and the counting of true and redundant avoided
strings at different string lengths turn out to be one and the same problem. We
give exact solution of the problem using two independent methods: the
Goulden-Jackson cluster method in combinatorics and the method of formal
language theory.Comment: 24 pages, LaTeX, 5 PostScript figures (two in color), psfi
Ultra Low Energy Analog Image Processing Using Spin Neurons
In this work we present an ultra low energy, 'on-sensor' image processing
architecture, based on cellular array of spin based neurons. The 'neuron'
constitutes of a lateral spin valve (LSV) with multiple input magnets,
connected to an output magnet, using metal channels. The low resistance,
magneto-metallic neurons operate at a small terminal voltage of ~20mV, while
performing analog computation upon photo sensor inputs. The static current-flow
across the device terminals is limited to small periods, corresponding to
magnet switching time, and, is determined by a low duty-cycle system-clock.
Thus, the energy-cost of analog-mode processing, inevitable in most image
sensing applications, is reduced and made comparable to that of dynamic and
leakage power consumption in peripheral CMOS units. Performance of the proposed
architecture for some common image sensing and processing applications like,
feature extraction, halftone compression and digitization, have been obtained
through physics based device simulation framework, coupled with SPICE. Results
indicate that the proposed design scheme can achieve more than two orders of
magnitude reduction in computation energy, as compared to the state of art CMOS
designs, that are based on conventional mixed-signal image acquisition and
processing schemes. To the best of authors' knowledge, this is the first work
where application of nano magnets (in LSV's) in analog signal processing has
been proposed
SIRENA: A CAD environment for behavioural modelling and simulation of VLSI cellular neural network chips
This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI parallel processing chips based on cellular neural networks. SIRENA includes capabilities for: (a) the description of nominal and non-ideal operation of CNN analogue circuitry at the behavioural level; (b) performing realistic simulations of the transient evolution of physical CNNs including deviations due to second-order effects of the hardware; and, (c) evaluating sensitivity figures, and realize noise and Monte Carlo simulations in the time domain. These capabilities portray SIRENA as better suited for CNN chip development than algorithmic simulation packages (such as OpenSimulator, Sesame) or conventional neural networks simulators (RCS, GENESIS, SFINX), which are not oriented to the evaluation of hardware non-idealities. As compared to conventional electrical simulators (such as HSPICE or ELDO-FAS), SIRENA provides easier modelling of the hardware parasitics, a significant reduction in computation time, and similar accuracy levels. Consequently, iteration during the design procedure becomes possible, supporting decision making regarding design strategies and dimensioning. SIRENA has been developed using object-oriented programming techniques in C, and currently runs under the UNIX operating system and X-Windows framework. It employs a dedicated high-level hardware description language: DECEL, fitted to the description of non-idealities arising in CNN hardware. This language has been developed aiming generality, in the sense of making no restrictions on the network models that can be implemented. SIRENA is highly modular and composed of independent tools. This simplifies future expansions and improvements.Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C02-0
Cellular neural networks for NP-hard optimization problems
Nowadays, Cellular Neural Networks (CNN) are practically implemented in
parallel, analog computers, showing a fast developing trend. Physicist must be
aware that such computers are appropriate for solving in an elegant manner
practically important problems, which are extremely slow on the classical
digital architecture. Here, CNN is used for solving NP-hard optimization
problems on lattices. It is proved, that a CNN in which the parameters of all
cells can be separately controlled, is the analog correspondent of a
two-dimensional Ising type (Edwards-Anderson) spin-glass system. Using the
properties of CNN computers a fast optimization method can be built for such
problems. Estimating the simulation time needed for solving such NP-hard
optimization problems on CNN based computers, and comparing it with the time
needed on normal digital computers using the simulated annealing algorithm, the
results are astonishing: CNN computers would be faster than digital computers
already at 10*10 lattice sizes. Hardwares realized nowadays are of 176*144
size. Also, there seems to be no technical difficulties adapting CNN chips for
such problems and the needed local control is expected to be fully developed in
the near future
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