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2019
E. Pechenkova, Nosikova, I., Rumshiskaya, A., Litvinova, L., Rukavishnikov, I., Mershina, E., Sinitsyn, V., Van Ombergen, A., Jeurissen, B., Jillings, S., Laureys, S., Sijbers, J., Grishin, A., Chernikova, L., Naumov, I., Kornilova, L., Wuyts, F. L., Tomilovskaya, E., and Kozlovskaya, I., Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI., Front Physiol, vol. 10, no. 761, pp. 1-23, 2019.
E. Pechenkova, Nosikova, I., Rumshiskaya, A., Litvinova, L., Rukavishnikov, I., Mershina, E., Sinitsyn, V., Van Ombergen, A., Jeurissen, B., Jillings, S., Laureys, S., Sijbers, J., Grishin, A., Chernikova, L., Naumov, I., Kornilova, L., Wuyts, F. L., Tomilovskaya, E., and Kozlovskaya, I., Alterations of Functional Brain Connectivity After Long-Duration Spaceflight as Revealed by fMRI., Front Physiol, vol. 10, no. 761, pp. 1-23, 2019.
J. Fatermans, den Dekker, A. J., O'Leary, C. M., Nellist, P. D., and Van Aert, S., Atom column detection from STEM images using the maximum a posteriori probability rule, MC 2019, Berlin, Germany. 2019.
M. Nicastro and Siqueira Pinto, M., BQ-MINDED: Introducing Quantitative MRI in routine clinical practice, Marie Curie Alumni Association Annual Conference. 2019.PDF icon Download abstract (2.21 MB)
A. Van Ombergen, Jillings, S., Jeurissen, B., Tomilovskaya, E., Rumshiskaya, A., Litvinova, L., Nosikova, I., Pechenkova, E. V., Rukavishnikov, I., Manko, O., Danylichev, S., R. Rühl, M., Kozlovskaya, I. B., Sunaert, S., Parizel, P. M., Sinitsyn, V., Laureys, S. S. L., Sijbers, J., Eulenburg, Pzu, and Wuyts, F. L., Brain ventricular volume changes induced by long-duration spaceflight, Proceedings of the National Academy of Sciences, vol. 116, no. 21, pp. 10531-10536, 2019.
A. - T. Nguyen, Building forest and rice monitoring systems using artificial intelligence and multi-time series data analysis, Viettel Group, Hanoi, Vietnam, 2019.
M. Van Dael, Verboven, P., Zanella, A., Sijbers, J., and Nicolai, B., Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography, Postharvest Biology and Technology, vol. 148, pp. 218-227, 2019.
E. Ribeiro Sabidussi, Nicastro, M., Bazrafkan, S., Beirinckx, Q., Jeurissen, B., Sijbers, J., den Dekker, A. J., Klein, S., and Poot, D. H. J., A deep learning approach to T1 mapping in quantitative MRI, 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine & Biology (ESMRMB), Rotterdam, The Netherlands, vol. 32 (Suppl. 1). Magn Reson Mater Phy, 2019.
V. Nguyen, De Beenhouwer, J., Sanctorum, J., Van Wassenbergh, S., Aerts, P., Dirckx, J. J. J., and Sijbers, J., A low-cost and easy-to-use phantom for cone-beam geometry calibration of a tomographic X-ray system, in 9th Conference on Industrial Computed Tomography, Padova, Italy, 2019.PDF icon Download paper (1.93 MB)
M. Naeyaert, Roose, D., Mai, Z., Keliris, A. J., Sijbers, J., Van Der Linden, A., and Verhoye, M., Normalized Averaged Range (nAR), a Robust Quantification Method for MPIO-content, Journal of Magnetic Resonance, vol. 300, pp. 18-27, 2019.
F. L. Wuyts, Jillings, S., Van Ombergen, A., Jeurissen, B., Tomilovskaya, E., Rumshiskaya, A., Litvinova, L., Nosikova, I., Pechenkova, E., Rukavishnikov, I., and others, Novel insight on effect and recovery of long-duration spaceflight on the ventricles of the space traveller's brain, Proceedings of the International Astronautical Congress, IAC, vol. 2019. International Astronautical Federation, p. IAC–19_A1_2_4_x51230, 2019.
S. Van Aert, De wael, A., Fatermans, J., Lobato, I., De Backer, A., Jones, L., den Dekker, A. J., and Nellist, P. D., Quantifying 3D atomic structures of nanomaterials and their dynamics using model-based scanning transmission electron microscopy, Spring Meeting of the EMRS, Nice, France. 2019.
S. Van Aert, De wael, A., Fatermans, J., Lobato, I., De Backer, A., Jones, L., den Dekker, A. J., and Nellist, P. D., Strategies for quantifying 3D atomic structures of nanomaterials and their dynamics using dose-efficient ADF STEM, PICO2019, Vaalsbroek, The Netherlands. 2019.
2018
A. Van Ombergen, Jillings, S., Jeurissen, B., Tomilovskaya, E., Rühl, M., Rumshiskaya, A., Nosikova, I., Litvinova, L., Annen, J., Pechenkova, E. V., Kozlovskaya, I. B., Sunaert, S., Parizel, P. M., Sinitsyn, V., Laureys, S. S. L., Sijbers, J., Eulenburg, Pzu, and Wuyts, F. L., Brain Tissue–Volume Changes in Cosmonauts, New England Journal of Medicine, vol. 379, no. 17, pp. 1678 - 1680, 2018.
H. Q. Nguyen, Nguyen, B. T., Dong, T. Q., Ngo, D. T., and Nguyen, A. - T., Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Seoul, Korea, 2018.
H. Q. Nguyen, Nguyen, B. T., Dong, T. Q., Ngo, D. T., and Nguyen, A. - T., Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Seoul, Korea, 2018.
H. Q. Nguyen, Nguyen, B. T., Dong, T. Q., Ngo, D. T., and Nguyen, A. - T., Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Seoul, Korea, 2018.
H. Q. Nguyen, Nguyen, B. T., Dong, T. Q., Ngo, D. T., and Nguyen, A. - T., Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access, in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Seoul, Korea, 2018.
S. Van Aert, Fatermans, J., De Backer, A., van den Bos, K. H. W., O'Leary, C. M., Müller-Caspary, K., Jones, L., Lobato, I., Béché, A., den Dekker, A. J., Bals, S., and Nellist, P. D., Maximising dose efficiency in quantitative STEM to reveal the 3D atomic structure of nanomaterials, IMC19, Sydney, Australia. 2018.
E. Janssens, De Beenhouwer, J., Van Dael, M., De Schryver, T., Van Hoorebeke, L., Verboven, P., Nicolai, B., and Sijbers, J., Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection, Measurement Science and Technology, vol. 29, no. 3, 2018.PDF icon Download paper (3.4 MB)
A. - T. Nguyen, Signal compression using compressed sensing and best k-term approximation: An overview, Viettel R&D Institute, Hanoi, Vietnam, 2018.PDF icon csbkta.pdf (636.54 KB)
G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
J. Fatermans, den Dekker, A. J., Müller-Caspary, K., Lobato, I., O'Leary, C. M., Nellist, P. D., and Van Aert, S., Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images, Phys. Rev. Lett., vol. 121, p. 056101, 2018.

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