By Cesar F. Caiafa, Emanuele Salerno, Araceli N. Proto (auth.), Bruno Apolloni, Robert J. Howlett, Lakhmi Jain (eds.)
This ebook is a part of a three-volume set that constitutes the refereed lawsuits of the eleventh overseas convention on Knowledge-Based clever details and Engineering structures, KES 2007, held in Vietri sul Mare, Italy, September 2007. The 409 revised papers awarded have been conscientiously reviewed and chosen from approximately 1,203 submissions. The papers current a wealth of unique study effects from the sector of clever details processing within the broadest sense.
Read Online or Download Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III PDF
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Extra resources for Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007, Proceedings, Part III
Example text
As could be expected, the most sensitive to noise seems to be Γm6 . As an index of the quality of separation for each estimated source sˆl , we assumed the usual signal-to-interference ratio (SIR), defined as SIRl = 10 log10 (var(sl )/var(ˆ sl − sl )) (9) In Fig. 2, a comparison among the results of applying different measures is shown. For each measure, we averaged the results over a large set of simulations: the Mean SIR values (Fig. a) were calculated for an SNR range −20dB to 60dB over a set of 60 source estimations for each SNR value.
As for PCA, from selected factorial FCA axes the image can be partially reconstructed. The metric used in FCA is the chi-squared, which is adapted to probability laws and normalized by channels weight. FCA can be seen as a transformation going from image space to factorial space. In factorial space the coordinates of the pixels vector on each factorial axis are called pixels factors. The pixels factors can be considered as another multispectral image whose channels correspond to factorial axes: ζ : T L → T K /K < L (fλ (x) → cfα (x) = (cfα1 (x), .
Thus, the solution of the inverse problem is determined as the argument of the minimum of a proper energy function. To the aim of solving the blind problem, that is estimating the mixing matrix, we introduce another function, called target function, and we proceed as follows: given a mixing matrix, we compute the related sources by the minimization of the energy function and then we evaluate the properties of the result by the B. Apolloni et al. ): KES 2007/ WIRN 2007, Part III, LNAI 4694, pp. 25–32, 2007.
