Institution
Tallinn University of Technology
Education•Tallinn, Estonia•
About: Tallinn University of Technology is a education organization based out in Tallinn, Estonia. It is known for research contribution in the topics: European union & Oil shale. The organization has 3688 authors who have published 10313 publications receiving 145058 citations. The organization is also known as: Tallinn Technical University & Tallinna Tehnikaülikool.
Topics: European union, Oil shale, Thin film, Nonlinear system, Microstructure
Papers published on a yearly basis
Papers
More filters
•
TL;DR: In this paper, the role of fundamentals, global factors and policies related to renminbi internationalisation in driving the pricing differential between the onshore and offshore exchange rates is explored.
Abstract: Renminbi internationalisation has brought about an active offshore market where the exchange rate frequently diverges from the onshore market Using extended GARCH models, we explore the role of fundamentals, global factors and policies related to renminbi internationalisation in driving the pricing differential between the onshore and offshore exchange rates Differences in the liquidity of the two markets play an important role in explaining the level of the differential, while rises in global risk aversion tend to increase the differential's volatility On the policy front, measures permitting cross-border renminbi outflows have a particularly discernible impact in reducing the volatility of the pricing gap between the two markets
75 citations
••
TL;DR: This work analyzes the Richardson-Lucy iterative algorithm that is derived for Poisson noise and combined with total variation (TV) regularization, and proposes a practical method to deconvolve confocal microscope images that uses estimated regularization parameter depending on the input image.
Abstract: Although confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced mathematically if the optical and data acquisition effects are accounted for. For that, several deconvolution algorithms have been proposed. As a practical solution, maximum-likelihood algorithms with regularization have been used. However, the choice of regularization parameters is often unknown although it has considerable effect on the result of deconvolution process. The aims of this work were: to find good estimates of deconvolution parameters; and to develop an open source software package that would allow testing different deconvolution algorithms and that would be easy to use in practice. Here, Richardson–Lucy algorithm has been implemented together with the total variation regularization in an open source software package IOCBio Microscope. The influence of total variation regularization on deconvolution process is determined by one parameter. We derived a formula to estimate this regularization parameter automatically from the images as the algorithm progresses. To assess the effectiveness of this algorithm, synthetic images were composed on the basis of confocal images of rat cardiomyocytes. From the analysis of deconvolved results, we have determined under which conditions our estimation of total variation regularization parameter gives good results. The estimated total variation regularization parameter can be monitored during deconvolution process and used as a stopping criterion. An inverse relation between the optimal regularization parameter and the peak signal-to-noise ratio of an image is shown. Finally, we demonstrate the use of the developed software by deconvolving images of rat cardiomyocytes with stained mitochondria and sarcolemma obtained by confocal and widefield microscopes.
75 citations
••
TL;DR: Insight is provided into such promising ways to harness biotechnology as ecofriendly methods for remediation and restoration and how to design plants with specific microbial partners.
75 citations
••
TL;DR: Tertiary cyclopropanols react rapidly with Togni reagent in methanol at room temperature in the presence of catalytic amounts of CuCl affording β-trifluoromethyl ketones in 65-73% isolated yields.
75 citations
••
TL;DR: An overview of the widely used optimization techniques in electrical machinery is given and the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies are summarized.
Abstract: The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.
75 citations
Authors
Showing all 3757 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Chapman | 82 | 483 | 36468 |
Alexandre Alexakis | 67 | 540 | 17247 |
Bernard Waeber | 56 | 370 | 35335 |
Peter A. Andrekson | 54 | 573 | 12042 |
Charles S. Peirce | 51 | 167 | 11998 |
Lars M. Blank | 49 | 301 | 8011 |
Fushuan Wen | 49 | 465 | 9189 |
Mati Karelson | 48 | 207 | 10210 |
Ago Samoson | 46 | 119 | 8807 |
Zebo Peng | 45 | 359 | 7312 |
Petru Eles | 44 | 300 | 6749 |
Vijai Kumar Gupta | 43 | 301 | 6901 |
Eero Vasar | 43 | 263 | 6930 |
Rik Ossenkoppele | 42 | 192 | 6839 |
Tõnis Timmusk | 41 | 105 | 11056 |