We present a dynamic laser speckle method to easily discriminate filamentous fungi from motile bacteria in soft surfaces, such as agar plate. The method allows the detection and discrimination between fungi and bacteria faster than with conventional techniques. The new procedure could be straightforwardly extended to different micro-organisms, as well as applied to biological and biomedical research, infected tissues analysis, and hospital water and wastewaters studies.
This paper proposes the design of decision models with Computational Intelligence techniques using image sequences of
dynamic laser speckle. These models aim to characterize the dynamic of the process evaluated through Temporal History
Speckle Patterns (THSP) using a set of available descriptors. The models use those sets selected to improve its
effectiveness, depending on the specific application. The techniques of computational intelligence field include using
Artificial Neural Networks, Fuzzy Granular Computation, Evolutionary Computation elements such as Genetic
Algorithms, among others. The results obtained in experiments such as the evaluation of bacterial chemotaxis, and the
estimation of the drying time of coatings are encouraging and significantly improve those obtained using a single
descriptor.
In this work we present to methods to evaluate activity in low dynamic speckle patterns. The first one is based on the
behavior analysis of the vortices associated to the pattern. The other one consists in binarizing the speckle image. The
speckle grain areas, also called islands, experiment displacements and deformations. The variations of the island features
were analyzed with the aim of finding a correlation with the activity of the speckle pattern. Both methods were evaluated
in numerical simulations and controlled experiments. From the obtained results, it was possible to conclude that the
developed methods can be very useful for the analysis of low activity speckle patterns with some advantages with other
methods.
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