Tampere University of Technology

About: Tampere University of Technology is a based out in . It is known for research contribution in the topics: Laser & Context (language use). The organization has 6802 authors who have published 19787 publications receiving 431793 citations. The organization is also known as: Tampereen teknillinen yliopisto.

Structural hierarchy in molecular films of two class II hydrophobins

Abstract: Hydrophobins are highly surface-active proteins that are specific to filamentous fungi. They function as coatings on various fungal structures, enable aerial growth of hyphae, and facilitate attachment to surfaces. Little is known about their structures and structure-function relationships. In this work we show highly organized surface layers of hydrophobins, representing the most detailed structural study of hydrophobin films so far. Langmuir-Blodgett films of class II hydrophobins HFBI and HFBII from Trichoderma reesei were prepared and analyzed by atomic force microscopy. The films showed highly ordered two-dimensional crystalline structures. By combining our recent results on small-angle X-ray scattering of hydrophobin solutions, we found that the unit cells in the films have dimensions similar to those of tetrameric aggregates found in solutions. Further analysis leads to a model in which the building blocks of the two-dimensional crystals are shape-persistent supramolecules consisting of four hydrophobin molecules. The results also indicate functional and structural differences between HFBI and HFBII that help to explain differences in their properties. The possibility that the highly organized surface assemblies of hydrophobins could allow a route for manufacturing functional surfaces is suggested.

A novel video coding scheme based on temporal prediction using digital image warping

Abstract: A method for temporal prediction of image sequences is proposed. The motion vectors of conventional block-based motion compensation schemes are used to convey a mapping of a selected set of image points, instead of blocks, between the previous and the current image. The prediction is made by geometrically transforming, or warping, the previous image using the point pairs defined by the mapping as fixed points in the transformation. This method produces a prediction image without block artifacts and can compensate for many motion types where conventional block matching fails, such as scaling and rotation. It can also be incorporated in existing hybrid video compression systems with little additional complexity and few or no changes in the bit stream syntax. It is shown that a significant subjective improvement in the prediction as well as a consistent reduction in the objectively measured prediction error is obtained. >

Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule

Abstract: We describe a novel approach to solve a problem of window size (bandwidth) selection for filtering an image signal given with a noise. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and nearly optimal in the point-wise mean squared error risk. The local polynomial approximation (LPA) is used in order to derive the 2D transforms (filters) and demonstrate the efficiency of the approach. The ICI rule gives the adaptive varying window size and enables the algorithm to be spatially adaptive in the sense that its quality is close to that which one could achieve if the smoothness of the estimated signal was known in advance. Optimization of the threshold (design parameter of the ICI) is studied. It is shown that the cross-validation adjustment of the threshold significantly improves the algorithm accuracy. In particular, simulation demonstrates that the adaptive transforms with the adjusted threshold parameter perform better than the adaptive wavelet estimators.