RESUMEN
One-dimensional magnetic nanostructures have magnetic properties superior to non-organized materials due to strong uniaxial shape anisotropy. Magnetosome chains in magnetotactic bacteria represent a biological paradigm of such magnet, where magnetite crystals synthesized in organelles called magnetosomes are arranged into linear chains. Two-dimensional synchrotron X-ray diffraction (XRD) is applied to cells of magnetotactic bacteria that are pre-aligned with a magnetic field to determine the crystallographic orientation of magnetosomes relative to the chain axis. The obtained pole figure patterns reveal a [111] fiber texture along the chain direction for magnetospirilla strains MSR-1 and AMB-1, whereas a [100] fiber texture is measured for Desulfovibrio magneticus strain RS-1. The [100] axis appears energetically unfavorable because it represents a magnetic hard axis in magnetite, but can be turned into an effective easy axis by particle elongation along [100] for aspect ratios higher than 1.25, consistent with aspect ratios in RS-1 magnetosomes determined earlier. The pronounced fiber textures can be explained either by a strain-specific biological control on crystal orientation at the chain level or by physical alignment effects due to intra-chain magnetic interactions. In this case, biological control of the axis of elongation would be sufficient to influence the crystallographic texture of the magnetosome chain.
RESUMEN
With a huge amount of printed documents nowadays, identifying their source is useful for criminal investigations and also to authenticate digital copies of a document. In this paper, we propose novel techniques for laser printer attribution. Our solutions do not need very high resolution scanning of the investigated document and explore the multidirectional, multiscale and low-level gradient texture patterns yielded by printing devices. The main contributions of this work are: (1) the description of printed areas using multidirectional and multiscale co-occurring texture patterns; (2) description of texture on low-level gradient areas by a convolution texture gradient filter that emphasizes textures in specific transition areas and (3) the analysis of printer patterns in segments of interest, which we call frames, instead of whole documents or only printed letters. We show by experiments in a well documented dataset that the proposed methods outperform techniques described in the literature and present near-perfect classification accuracy being very promising for deployment in real-world forensic investigations.