a b s t r a c tHigh temporal resolution solar observations in the decimetric range (1-3 GHz) can provide additional information on solar active regions dynamics and thus contribute to better understanding of solar geoeffective events as flares and coronal mass ejections. The June 6, 2000 flares are a set of remarkable geoeffective eruptive phenomena observed as solar radio bursts (SRB) by means of the 3 GHz Ondrejov Observatory radiometer. We have selected and analyzed, applying detrended fluctuation analysis (DFA), three decimetric bursts associated to X1.1, X1.2 and X2.3 flare-classes, respectively. The association with geomagnetic activity is also reported. DFA method is performed in the framework of a radio burst automatic monitoring system. Our results may characterize the SRB evolution, computing the DFA scaling exponent, scanning the SRB time series by a short windowing before the extreme event. For the first time, the importance of DFA in the context of SRB monitoring analysis is presented.

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Analysis of information from multiple data sources obtained through high resolution instrumental measurements has become a fundamental task in all scientific areas. The development of expert methods able to treat such multi-source data systems, with both large variability and measurement extension, is a key for studying complex scientific phenomena, especially those related to systemic analysis in space and environmental sciences. In this paper, we propose a time series generalization introducing the concept of generalized numerical lattice, which represents a discrete sequence of temporal measures for a given variable. In this novel representation approach each generalized numerical lattice brings post-analytical data information. We define a generalized numerical lattice £ as a set of three coefficients (κ, λ , μ p ), representing the following data properties: dimensionality, size and post-analytical parameters, respectively. From this generalization, any multi-source database can be reduced to a closed set of classified time series in spatio-temporal generalized dimensions. As a case study, we show a preliminary application in space science data, highlighting the possibility of a real time analysis expert system to be developed in a future work.

In contrast to the perceptual capability of artificial systems, the biological perception of spatial patterns is a continuous cognitive process. In particular, the visual system of primates has a space-variant nature where the resolution is high on the fovea and decreases continuously to the periphery of the visual field. Moreover, the pattern perception and recognition may change, also continuously, when orientation and depth changes. An interesting aspect is that the perceptual performance needs to increase when the structure in recognition gets more complex in terms of irregular spatial contents (asymmetries). Based on these properties, we introduce a computational measurement procedure where the asymmetries are "continuously" quantified using intersections among partially fuzzy images. The asymmetries are quantified using the first gradient moment from the Gradient Pattern Analysis methodology. In this application, the first gradient moment is a fuzzy parameter whose fuzzy deviation is set in the same level of biological perceptual uncertainty. The performance of our approach is tested over texture variation perception in SAR (Synthetic Aperture Radar) images and the results show that this measure can be useful for real-time machine navigation and, in a more general sense, for biologically motivated morphology research.

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