Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Fracture mechanics & Filter (video). The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Multi-Scale Gaussian Normalization for Solar Image Processing

Abstract: Extreme ultra-violet images of the corona contain information over a wide range of spatial scales, and different structures such as active regions, quiet Sun, and filament channels contain information at very different brightness regimes. Processing of these images is important to reveal information, often hidden within the data, without introducing artefacts or bias. It is also important that any process be computationally efficient, particularly given the fine spatial and temporal resolution of Atmospheric Imaging Assembly on the Solar Dynamics Observatory (AIA/SDO), and consideration of future higher resolution observations. A very efficient process is described here, which is based on localised normalising of the data at many different spatial scales. The method reveals information at the finest scales whilst maintaining enough of the larger-scale information to provide context. It also intrinsically flattens noisy regions and can reveal structure in off-limb regions out to the edge of the field of view. We also applied the method successfully to a white-light coronagraph observation.

Brightening of dark excitons in monolayers of semiconducting transition metal dichalcogenides

Abstract: We present low-temperature magneto-photoluminescence experiments which demonstrate the brightening of dark excitons by an in-plane magnetic field B applied to monolayers of different semiconducting transition metal dichalcogenides. For WSe2 and WS2 monolayers, the dark exciton emission is observed at??~50 meV below the bright exciton peak and displays a characteristic doublet structure whose intensity grows with B 2, while no magnetic field induced emission peaks appear for MoSe2 monolayer. Our experiments also show that the MoS2 monolayer has a dark exciton ground state with a dark-bright exciton splitting energy of??~100 meV.

Convolutional Neural Networks for Direct Text Deblurring.

Abstract: In this work we address the problem of blind deconvolution and denoising. We focus on restoration of text documents and we show that this type of highly structured data can be successfully restored by a convolutional neural network. The networks are trained to reconstruct high-quality images directly from blurry inputs without assuming any specific blur and noise models. We demonstrate the performance of the convolutional networks on a large set of text documents and on a combination of realistic de-focus and camera shake blur kernels. On this artificial data, the convolutional networks significantly outperform existing blind deconvolution methods, including those optimized for text, in terms of image quality and OCR accuracy. In fact, the networks outperform even state-of-the-art non-blind methods for anything but the lowest noise levels. The approach is validated on real photos taken by various devices.

Current-mode KHN filter employing current differencing transconductance amplifiers

Abstract: This study proposes a current mode (CM) Kerwin–Huelsman–Newcomb (KHN) filter employing only two current differencing transconductance amplifiers (CDTA) and two grounded capacitors. It is concluded that the circuit described here offers a simpler and more economical alternative to other CM KHN filters reported previously in literature.