58,488 research outputs found
A GPU-based Evolution Strategy for Optic Disk Detection in Retinal Images
La ejecución paralela de aplicaciones usando unidades de procesamiento gráfico (gpu) ha ganado gran interés en la comunidad académica en los años recientes. La computación paralela puede ser aplicada a las estrategias evolutivas para procesar individuos dentro de una población, sin embargo, las estrategias evolutivas se caracterizan por un significativo consumo de recursos computacionales al resolver problemas de gran tamaño o aquellos que se modelan mediante funciones de aptitud complejas. Este artículo describe la implementación de una estrategia evolutiva para la detección del disco óptico en imágenes de retina usando Compute Unified Device Architecture (cuda). Los resultados experimentales muestran que el tiempo de ejecución para la detección del disco óptico logra una aceleración de 5 a 7 veces, comparado con la ejecución secuencial en una cpu convencional.Parallel processing using graphic processing units (GPUs) has attracted much research interest in recent years. Parallel computation can be applied to evolution strategy (ES) for processing individuals in a population, but evolutionary strategies are time consuming to solve large computational problems or complex fitness functions. In this paper we describe the implementation of an improved ES for optic disk detection in retinal images using the Compute Unified Device Architecture (CUDA) environment. In the experimental results we show that the computational time for optic disk detection task has a speedup factor of 5x and 7x compared to an implementation on a mainstream CPU
A statistical approach to the inverse problem in magnetoencephalography
Magnetoencephalography (MEG) is an imaging technique used to measure the
magnetic field outside the human head produced by the electrical activity
inside the brain. The MEG inverse problem, identifying the location of the
electrical sources from the magnetic signal measurements, is ill-posed, that
is, there are an infinite number of mathematically correct solutions. Common
source localization methods assume the source does not vary with time and do
not provide estimates of the variability of the fitted model. Here, we
reformulate the MEG inverse problem by considering time-varying locations for
the sources and their electrical moments and we model their time evolution
using a state space model. Based on our predictive model, we investigate the
inverse problem by finding the posterior source distribution given the multiple
channels of observations at each time rather than fitting fixed source
parameters. Our new model is more realistic than common models and allows us to
estimate the variation of the strength, orientation and position. We propose
two new Monte Carlo methods based on sequential importance sampling. Unlike the
usual MCMC sampling scheme, our new methods work in this situation without
needing to tune a high-dimensional transition kernel which has a very high
cost. The dimensionality of the unknown parameters is extremely large and the
size of the data is even larger. We use Parallel Virtual Machine (PVM) to speed
up the computation.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS716 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Design and implementation of a multi-octave-band audio camera for realtime diagnosis
Noise pollution investigation takes advantage of two common methods of
diagnosis: measurement using a Sound Level Meter and acoustical imaging. The
former enables a detailed analysis of the surrounding noise spectrum whereas
the latter is rather used for source localization. Both approaches complete
each other, and merging them into a unique system, working in realtime, would
offer new possibilities of dynamic diagnosis. This paper describes the design
of a complete system for this purpose: imaging in realtime the acoustic field
at different octave bands, with a convenient device. The acoustic field is
sampled in time and space using an array of MEMS microphones. This recent
technology enables a compact and fully digital design of the system. However,
performing realtime imaging with resource-intensive algorithm on a large amount
of measured data confronts with a technical challenge. This is overcome by
executing the whole process on a Graphic Processing Unit, which has recently
become an attractive device for parallel computing
Extreme Scale De Novo Metagenome Assembly
Metagenome assembly is the process of transforming a set of short,
overlapping, and potentially erroneous DNA segments from environmental samples
into the accurate representation of the underlying microbiomes's genomes.
State-of-the-art tools require big shared memory machines and cannot handle
contemporary metagenome datasets that exceed Terabytes in size. In this paper,
we introduce the MetaHipMer pipeline, a high-quality and high-performance
metagenome assembler that employs an iterative de Bruijn graph approach.
MetaHipMer leverages a specialized scaffolding algorithm that produces long
scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is
end-to-end parallelized using the Unified Parallel C language and therefore can
run seamlessly on shared and distributed-memory systems. Experimental results
show that MetaHipMer matches or outperforms the state-of-the-art tools in terms
of accuracy. Moreover, MetaHipMer scales efficiently to large concurrencies and
is able to assemble previously intractable grand challenge metagenomes. We
demonstrate the unprecedented capability of MetaHipMer by computing the first
full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion
reads - size 2.6 TBytes.Comment: Accepted to SC1
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