Institution
Kharkiv National University of Radioelectronics
Education•Kharkiv, Ukraine•
About: Kharkiv National University of Radioelectronics is a education organization based out in Kharkiv, Ukraine. It is known for research contribution in the topics: Signal & Artificial neural network. The organization has 481 authors who have published 346 publications receiving 1565 citations. The organization is also known as: Kharkiv National University of Radio Electronics.
Topics: Signal, Artificial neural network, Encryption, Neuro-fuzzy, Fuzzy logic
Papers published on a yearly basis
Papers
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TL;DR: This review presents a general picture of the current trends and developments (2008-2013) related to electrochemiluminescence-based immunosensors, and an immuno-like electrochemILuminescent sensor (based on synthetic receptors-molecularly imprinted polymers) is highlighted.
178 citations
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TL;DR: A novel neural network approach to forecasting of financial time series based on the presentation of the series as a combination of quasiperiodic components is presented.
96 citations
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Royal Institute of Technology1, Stanford University2, Missouri University of Science and Technology3, Qualcomm4, SAS Institute5, Kharkiv National University of Radioelectronics6, University of Texas MD Anderson Cancer Center7, University of Hawaii8, Microsoft9, University of California, Berkeley10, Peking University11, Leica Microsystems12
TL;DR: The winning models far outperformed the previous effort at multi-label classification of protein localization patterns by ~20% and can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.
Abstract: Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications. The 2018 Human Protein Atlas Image Classification competition sought to improve automated classification of protein subcellular localizations from fluorescence images. The winning strategies involved innovative deep learning approaches for multi-label classification.
77 citations
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TL;DR: The Pi-Mind technology is a set of models, techniques, and tools built on principles of value-based biased decision-making and creative cognitive computing to augment the axioms of decision rationality in industry.
51 citations
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TL;DR: In this article, a method for preparing thin films of CsPbI3 and Cs4pbI6 complex compounds has been developed and their absorption spectrum is investigated in the energy range of 2-6 eV at temperatures from 90 to 500 K.
Abstract: A method for preparing thin films of CsPbI3 and Cs4PbI6 complex compounds has been developed. Their absorption spectrum is investigated in the energy range of 2–6 eV at temperatures from 90 to 500 K. It is found that the CsPbI3 compound is unstable and passes to the Cs4PbI6 phase upon heating at T ≥ 400 K.
47 citations
Authors
Showing all 485 results
Name | H-index | Papers | Citations |
---|---|---|---|
Irina Svir | 25 | 105 | 1749 |
Yevgeniy Bodyanskiy | 20 | 177 | 1330 |
Tamara Radivilova | 17 | 64 | 677 |
Tatiana E. Romanova | 16 | 62 | 826 |
Lyudmyla Kirichenko | 15 | 47 | 477 |
Vitaliy Kolodyazhniy | 14 | 37 | 577 |
Oleksii K. Tyshchenko | 14 | 38 | 434 |
Dmytro Ageyev | 14 | 47 | 456 |
Oleksandra Yeremenko | 12 | 84 | 453 |
Olena Vynokurova | 11 | 53 | 393 |
Vladimir Hahanov | 11 | 179 | 474 |
Alexander Pankratov | 9 | 25 | 271 |
Pavel Maksimchuk | 9 | 43 | 275 |
Yuri Malyukin | 9 | 17 | 196 |
Vyacheslav Lyashenko | 9 | 48 | 239 |