Some lattice computing approaches towards computational intelligence, image processing and analysis

• Grant number: 18/13657-1
• Support type: Regular Research Grants
• Duration: September 01, 2018 - August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Peter Sussner
• Grantee: Peter Sussner
• Home Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The technical term "lattice computing" was recently coined to refer to an evolving collection of tools and mathematical models for processing lattice ordered data such as numbers, intervals, possibility and probability distributions, (fuzzy) sets, extensions of fuzzy sets as well as other types of information granules. Note that many classes of information granules such as the classes of the extended integers, the extended reals, intervals, as well as classes of fuzzy sets and several of their extensions represent complete lattices that have played important roles in mathematical morphology and fuzzy set theory since many years. Since the 1990's, some researchers including myself are transferring operators, ideas, and concepts of lattice algebra into the area of computational intelligence. The goal of this research project is to develop new lattice computing approaches towards computational intelligence as well as image processing and analysis, in particular approaches towards L-fuzzy mathematical morphology and L-fuzzy systems, where L denotes an arbitrary complete lattice or semilattice. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

WASQUES, VINICIUS F.; ESMI, ESTEVAO; BARROS, LAECIO C.; SUSSNER, PETER. The generalized fuzzy derivative is interactive. INFORMATION SCIENCES, v. 519, p. 93-109, MAY 2020. Web of Science Citations: 0.

SUSSNER, PETER; CAMPIOTTI, ISRAEL. Extreme learning machine for a new hybrid morphological/linear perceptron. NEURAL NETWORKS, v. 123, p. 288-298, MAR 2020. Web of Science Citations: 1.