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

The 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.Unidad de Investigación y Desarrollo OptimoCentro de Investigaciones Óptica

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

The 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.Unidad de Investigación y Desarrollo OptimoCentro de Investigaciones Óptica

Visualización de procesos naturales aplicando granularidad temporal difusa rugosa a imágenes de speckle

Presentamos una metodología para visualizar procesos naturales aplicando granularidad temporal a imágenes de patrones de moteado laser dinámico. Esta metodología permite identificar niveles de actividad empleando pocas imágenes y además, visualizar como la actividad evoluciona en el tiempo en casos donde otras técnicas alternativas no lo detectan. Se muestran dos ejemplos, uno relativo a la detección de daño en manzanas y otro relativo al tiempo de secado de pintura, ambos de interés comercial e industrial.We present a methodology to visualize a natural process by applying computational granularity to dynamic laser speckle pattern images. This methodology not only allows to identify activity levels with few images but also to visualise how the activity evolves over time even when alternative techniques do not detect it. Two examples are shown, one relative to detection of bruise in apples and other relative to paint drying time, which are of commercial/industrial interest.Fil: Dai Pra, Ana Lucia. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; ArgentinaFil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentin

Determination of maize hardness by biospeckle and fuzzy granularity

In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.Facultad de Ciencias Agrarias y ForestalesCentro de Investigaciones Óptica

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

The 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.Unidad de Investigación y Desarrollo OptimoCentro de Investigaciones Óptica

Speckle signal processing through FPGA

This paper introduces Field Programmable Gate Array (FPGA) technology as an alternative platform to implement algorithms for speckle patterns analysis in real time. Functions and algorithmic procedures have been expressed in pseudo languages then in Hardware Description Languages (HDL). For all cases, time performances are presented for the Xilinx Virtex-6 family. Comparisons are also made with PC platform implementations presented in the literature.Sección: Diseño de hardware FPGACentro de Técnicas Analógico-Digitale

Determination of maize hardness by biospeckle and fuzzy granularity

In recent years there has been renewed interest in the development of novel grain classification methods that could complement traditional empirical tests. A speckle pattern occurs when a laser beam illuminates an optically rough surface that flickers when the object is active and is called biospeckle. In this work, we use laser biospeckle to classify maize (Zea mays L.) kernel hardness. A series of grains of three types of maize were cut and illuminated by a laser. A series of images were then registered, stored, and processed. These were compared with results obtained by floating test. The laser speckle technique was effective in discriminating the grains based on the presence of floury or vitreous endosperm and could be considered a feasible alternative to traditional floating methods. The results indicate that this methodology can distinguish floury and vitreous grains. Moreover, the assay showed higher discrimination capability than traditional tests. It could be potentially useful for maize classification and to increase the efficiency of processing dry milling corn.Facultad de Ciencias Agrarias y ForestalesCentro de Investigaciones Óptica