Lucía Isabel Passoni

Analysis of bacterial chemotactic response using dynamic laser speckle

Chemotaxis has a meaningful role in several fields, such as microbial physiology, medicine and biotechnology. We present a new application of dynamic laser speckle (or biospeckle) to detect different degrees of bacterial motility during chemotactic response experiments. Encouraging results showed different bacterial dynamic responses due to differences in the hardness of the support in the swarming plates. We compare this method to a conventional technique that uses white light. Both methods showed to be analogous and, in some cases, complementary. The results suggest that biospeckle processed images can be used as an alternative method to evaluate bacterial chemotactic response and can supply additional information about the bacterial motility in different areas of the swarm plate assay that might be useful for biological analysis.

Evaluation of laser dynamic speckle signals applying granular computing

The laser dynamic speckle phenomenon is a grained and fluctuant interference produced when a laser light is reflected from an illuminated surface undergoing some kind of activity. This phenomenon allows developing practical applications of unlimited use in biology and technology for being a non-destructive process, enabling the detection of not easily observable activities, such as seeds viability, paints drying, bacteria activities, corrosion processes, food decomposition, fruits bruising, etc. Sequences of intensity images are obtained in order to evaluate the phenomena dynamics, and the signals generated by the intensity changes in each pixel through the sequence are processed with the finality of identifying underlying activity in each point. This paper offers a new methodology based on granular computing to characterize the signals dynamics within the time domain, reducing the time processing and proposing news evaluation parameters to characterize speckle patterns. The methodology is applicable to stationary and non-stationary cases, enabling to monitor the phenomenon in almost real time. Two dynamic processes are analyzed to assess the goodness of the proposed methodology: fast paint drying (non-stationary) and corn seed viability (stationary), being obtained results in agreement with the physical behaviour of the observed processes.

Reconfiguration of electrical networks by an Ant Colony Optimization algorithm

Electricity distribution companies constantly require improvements in service, and an appropriate reduction in costs of the system. Finding the optimal configuration of the distribution network reduces power losses, which impacts directly on costs, also leading to significant energy savings. Over recent decades, the problem of network reconfiguration for loss minimization and for other purposes has been widely studied. The algorithms improve the previously obtained minimum power loss and also reduce computational times. This paper proposes the study and application of an algorithm based on Ant Colony Optimization, method covered in the paradigm of Swarm Intelligence, sub-discipline of Computational Intelligence. We show an easy network codification and we performed a detailed study of the ranges of values for the configuration parameters of the algorithm. We achieved the optimal result for testing networks and we found appropriate configurations for more complex networks, taking low computational times.

Discrimination of motile bacteria from filamentous fungi using dynamic speckle.

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.

Automatic design of interpretable fuzzy predicate systems for clustering using self-organizing maps

Abstract In the area of pattern recognition, clustering algorithms are a family of unsupervised classifiers designed with the aim to discover unrevealed structures in the data. While this is a never ending research topic, many methods have been developed with good theoretical and practical properties. One of such methods is based on self organizing maps (SOM), which have been successfully used for data clustering, using a two levels clustering approach. Newer on the field, clustering systems based on fuzzy logic improve the performance of traditional approaches. In this paper we combine both approaches. Most of the previous works on fuzzy clustering are based on fuzzy inference systems, but we propose the design of a new clustering system in which we use predicate fuzzy logic to perform the clustering task, being automatically designed based on data. Given a datum, degrees of truth of fuzzy predicates associated with each cluster are computed using continuous membership functions defined over data features. The predicate with the maximum degree of truth determines the cluster to be assigned. Knowledge is discovered from data, obtained using the SOM generalization aptitude and taking advantage of the well-known SOM abilities to discover natural data grouping when compared with direct clustering. In addition, the proposed approach adds linguistic interpretability when membership functions are analyzed by a field expert. We also present how this approach can be used to deal with partitioned data. Results show that clustering accuracy obtained is high and it outperforms other methods in the majority of datasets tested.

Dynamic laser speckle: decision models with computational intelligence techniques

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.

Biospeckle image stack process based on artificial neural networks

This paper proposes the identification of regions of interest in biospeckle patterns using unsupervised neural networks of the type Self-Organizing Maps. Segmented images are obtained from the acquisition and processing of laser speckle sequences. The dynamic speckle is a phenomenon that occurs when a beam of coherent light illuminates a sample in which there is some type of activity, not visible, which results in a variable pattern over time. In this particular case the method is applied to the evaluation of bacterial chemotaxis. Image stacks provided by a set of experiments are processed to extract features of the intensity dynamics. A Self-Organizing Map is trained and its cells are colored according to a criterion of similarity. During the recall stage the features of patterns belonging to a new biospeckle sample impact on the map, generating a new image using the color of the map cells impacted by the sample patterns. It is considered that this method has shown better performance to identify regions of interest than those that use a single descriptor. To test the method a chemotaxis assay experiment was performed, where regions were differentiated according to the bacterial motility within the sample.

Signal Feature Extraction Using Granular Computing. Comparative Analysis with Frequency and Time Descriptors Applied to Dynamic Laser Speckle Patterns

AbstractThe laser dynamic speckle is a phenomenon caused by the fluctuant interference of the laser light reflected from an illuminated surface where some kind of activity is taking place. Signals generated by the intensity changes in each pixel through the sequence are processed with the finality of identifying underlying activity in each point. In this work we compare the performance of a Rough Fuzzy Granular Descriptor (previously published) against a set of dynamic speckle descriptors based in time and frequency processing. To perform this evaluation a numerical simulation is proposed to explore their linearity, robustness, sensitivity related to the samples quantity, as well as also by their computing time. Also the robustness to inhomogeneous spatial intensity was evaluated in an experiment performed with the illuminated surface of an actual biological object.

Using SOM as a Tool for Automated Design of Clustering Systems Based on Fuzzy Predicates

Clustering task is a never-ending research topic. New methods are permanently proposed. In particular, Fuzzy Logic and Self-organizing Maps and their mutual cooperation have demonstrated to be interesting paradigms. We propose a general approach to obtain membership functions for a ranked clustering system based on fuzzy predicates logical operations, considering Gaussian-shaped curves. We find membership functions parameters from trained Self-organizing Maps, which generalize the statistical characteristics of data. The system is self-configured and it has the advantages of other fuzzy approaches. Clustering quality is assessed by labeled data, which allow computing accuracy. The proposal must be tested with more real datasets, though the preliminary results obtained in well-known datasets suggest that it is a promising clustering scheme.

Fuzzy Tree Studio: A Tool for the Design of the Scorecard for the Management of Protected Areas

This paper presents a Scorecard, which associated with a geographic information system (GIS), will provide a management tool to assess vulnerability within a protected area. To accomplish this task a novel framework is presented, which enables the design of logical predicates evaluated with fuzzy logic. This tool may guide decisions and investment priorities in protected areas. We have taken the Valdes Peninsula Protected Natural Area as a case study, which has been declared a World Heritage Site by UNESCO. In this area we have released an intense amount of variables related to natural resources, as well as human uses of land and territory and the effectiveness of the management plan and management area. To evaluate the vulnerability values of different parcels, according to a set of field collected variables is proposed a framework that manages logic predicates using fuzzy logic. Several ecologists evaluated this framework satisfactorily due to the easy-to-use interface and that the shown results are highly understandable for those who need to make decisions on environmental care.