The tunable negative refractive index in granular composite

The effective refractive index of a kind of granular composite, which consists of granular metallic and magnetic inclusions with different radius embedded in a host medium, is theoretically investigated. Results show that for certain volume fractions of these two inclusions, the negative permittivity peak shifts to low frequency and the peak value increases with increasing radius ratio of the radius of magnetic granulae to that of metallic granulae. Simultaneously, peak value of permeability decreases with the radius ratio, and value peak shifts to high frequency with increasing volume fraction of magnetic inclusion. Therefore, the radius ratio can affect the effective refractive index considerably, and it is found that by adjusting the radius ratio, the refractive index may change between negative and positive values for certain volume fractions of the two inclusions. (c) 2005 Elsevier B.V. All rights reserved.

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.