Classifiers design and implementation for material recognition on a heterogeneous computer cluster

  • Authors:

  • Plamenka Borovska;Desislava Ivanova

  • Affiliations:

  • Computer Systems Department, Technical University of Sofia, Bulgaria;Computer Systems Department, Technical University of Sofia, Bulgaria

  • Venue:
  • ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
  • Year:
  • 2010

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Abstract

The purpose of this paper is to propose a classifiers design by means of a fast parallel algorithm for training and classification of samples for material recognition. The investigated classifiers are based on spectral analysis of input signals through discrete wavelet transform (DWT). The research line is accelerating the performance of the classifiers through parallel execution of training algorithm. Classifier's training is done by fast wavelet transform on input data and extraction of characteristic coefficients. For the development of the parallel algorithm, it has been chosen «master/slave» realization: a master process that reads input data and slave worker-processes working on samples by «first-requested, first-received» principle. The chosen parallelization method is a combination of multi-threaded processing directives OpenMP and message passing interface (MPI) for communication between processes.