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Rough-Fuzzy Granularity in the Study of Optical Phenomena., and . FLinAI@IJCAI, volume 1424 of CEUR Workshop Proceedings, CEUR-WS.org, (2015)Signal Feature Extraction Using Granular Computing. Comparative Analysis with Frequency and Time Descriptors Applied to Dynamic Laser Speckle Patterns., , , , and . Int. J. Comput. Intell. Syst., 8 (sup2): 28-40 (2015)Automatic design of interpretable fuzzy predicate systems for clustering using self-organizing maps., , , , and . Neurocomputing, (2015)Comparing Methods of Assessing R&D Efficiency in Latin-American Countries., , , and . Soft Computing for Business Intelligence, volume 537 of Studies in Computational Intelligence, Springer, (2014)Improvements in the Visualization of Segmented Areas of Patterns of Dynamic Laser Speckle., , , , , , , and . WSOM, volume 198 of Advances in Intelligent Systems and Computing, page 163-171. Springer, (2012)Real-time speckle image processing., , , , , , and . J. Real Time Image Process., 11 (3): 535-545 (2016)Evaluation of laser dynamic speckle signals applying granular computing., , and . Signal Process., 89 (3): 266-274 (2009)Fuzzy Tree Studio: A Tool for the Design of the Scorecard for the Management of Protected Areas., , , , and . Soft Computing for Business Intelligence, volume 537 of Studies in Computational Intelligence, Springer, (2014)Using SOM as a Tool for Automated Design of Clustering Systems Based on Fuzzy Predicates., , , , and . WSOM, volume 198 of Advances in Intelligent Systems and Computing, page 85-94. Springer, (2012)Unsupervised learning segmentation for dynamic speckle activity images., , , , , , and . CoRR, (2014)