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Prof. Ewa Skubalska-Rafajłowicz, DSc, PhD, Eng
Email: ewa.skubalska-rafajlowicz@pwr.edu.pl
Unit: Faculty of Information and Communication Technology (N) » Department of Computer Engineering
ul. Z. Janiszewskiego 11/17, 50-372 Wrocław
building C-3, room 212
phone +48 71 320 3345
Recent papers
2017
- Skubalska-Rafajłowicz E., Spatially-organized random projections of images for dimensionality reduction and privacy-preserving classification. 10th International Workshop on Multidimensional (nD) Systems (nDS), 13-15 Sept. 2017, Zielona Góra, Poland. Ma: IEEE
2016
- Skubalska-Rafajłowicz E.,Training neural networks by optimizing random subspaces of the weight space. Źródło Artificial intelligence and soft computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016 Springer, Lecture Notes in Computer Science.
2015
- Skubalska-Rafajłowicz E., Change-point detection of the mean vector with fewer observations than the dimension using instantaneous normal random projections. W: Springer Proceedings in Mathematics & Statistics; vol. 122
2014
- Skubalska-Rafajłowicz E., Estimation of horizontal and vertical translations of large images based on columns and rows mean energy matching. Multidimensional Systems and Signal Processing. 2014, vol. 25, nr 2, s. 273-294.
2013
- Skubalska-Rafajłowicz E., Random projections and Hotelling's T2 statistics for change detection in high-dimensional data streams. International Journal of Applied Mathematics and Computer Science. 2013, vol. 23, nr 2, s. 447-461.
2012
- Skubalska-Rafajłowicz E., Rafajłowicz E., Sampling multidimensional signals by a new class of quasi-random sequences, Multidimensional Systems and Signal Processing. 2012, vol. 23, nr 1/2, s. 237-253.
2009
- Skubalska-Rafajłowicz E., Neural networks with sigmoidal activation functions-dimension reduction using normal random projection. Nonlinear Analysis, Theory, Methods & Applications. Series A, Theory and Methods. 2009, vol. 71, nr 12, s. e1255-e1263.
2008
- Skubalska-Rafajłowicz E., Local correlation and entropy maps as tools for detecting defects in industrial images, International Journal of Applied Mathematics and Computer Science. 2008, vol. 18, nr 1, s. 41-47.