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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)

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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)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)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)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)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)Real-time speckle image processing., , , , , , and . J. Real Time Image Process., 11 (3): 535-545 (2016)Unsupervised learning segmentation for dynamic speckle activity images., , , , , , and . CoRR, (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)