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The bridge between both theories is motivated by the fact that they often arise in industrial fields such as image processing engineering, physics, computer science, economics, ladder networks, dynamic programming, control theory, stochastic filtering, statistics, telecommunications and many other applications.
In the past few years, the dynamic behaviors of coupled interacting loops neural networks have been widely studied due to their extensive applications in classification of pattern recognition, signal processing, image processing, engineering optimization and animal locomotion, and other areas, see the references therein.
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Synchronization in complex networks plays a significant role in the fields of signal generator, image processing, engineering, etc.
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Adapting image processing technology to engineering disciplines can be useful in the evaluation of the mechanical behavior of materials.
Fractional calculus is a highly useful tool in the modeling of many sorts of scientific phenomena including image processing, earthquake engineering, biomedical engineering and physics.
Areas of interest include communications, networking, signal & image processing, computer engineering and biomedical engineering.
The applications of hypergraph theory are reviewed in two areas: image processing and engineering.
Fine Arts and Psychology students were the most inclined to use object image processing while Engineering students preferred spatial processing.
The image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize.
Some examples of applications are target detection in radar environments, image processing, medical engineering, power quality analysis, features detection in satellite images, Pseudo-Noise (PN) code detectors, among others.
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