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V Koivunen

Publications and source records attributed to V Koivunen.

2 recordsLinked to original sources

Effects of heavy metal pollution on red wood ant (Formica s. str.) populations.

We studied the species composition, mound population densities, relative abundance and colony sizes of red wood ants along a well known air pollution gradient of a copper smelter in Southwest Finland. The dominant species, Formica aquilonia, was further studied for heavy metal (Al, Cu, Cd, Ni, Zn, As, Pb, Hg) levels and morphological characters (body mass, head width, labial gland disease) of workers. We found five species belonging to Formica s. str., and two of them showed changes in their relative abundance, which could not be explained by natural habitat differences. Nest mound volumes were 34% smaller in the polluted area, suggesting that smaller colonies can be maintained there. The heavy metal levels in F. aquilonia workers were higher in the polluted area for all metals, except Hg. The largest relative differences between the study areas (polluted/unpolluted) were found for As (4.1), Ni (2.4), Cu (2.1) and Pb (1.8). Morphological characters of workers were not related to the heavy metal levels. Our data showed that red wood ants can tolerate relatively high amounts of heavy metals and maintain reproducing colonies even in a heavily polluted area, but on the basis of smaller colony sizes, pollution stress may also cause trade-offs in reproduction.

Air Pollutants↗

Adaptive algorithm for blind separation from noisy time-varying mixtures.

This article addresses the problem of blind source separation from time-varying noisy mixtures using a state variable model and recursive estimation. An estimate of each source signal is produced real time at the arrival of new observed mixture vector. The goal is to perform the separation and attenuate noise simultaneously, as well as to adapt to changes that occur in the mixing system. The observed data are projected along the eigenvectors in signal subspace. The subspace is tracked real time. Source signals are modeled using low-order AR (autoregressive) models, and noise is attenuated by trading off between the model and the information provided by measurements. The type of zero-memory nonlinearity needed in separation is determined on-line. Predictor-corrector filter structures are proposed, and their performance is investigated in simulation using biomedical and communications signals at different noise levels and a time-varying mixing system. In quantitative comparison to other widely used methods, significant improvement in output signal-to-noise ratio is achieved.

Journal Article↗