DEFB1 (defensin, beta 1)

Review on DEFB1 (defensin, beta 1), with data on DNA, on the protein encoded, and where the gene is implicated

Developing Frameworks to Assess Impacts of Multiple Drivers of Change on Grassland System

Grassland systems face many simultaneous pressures including market and policy compliance that operate from local to global scale. The ability to adapt to these pressures against a background of constrained natural resources and inputs is vital to the continued success of the grassland livestock industry and all those dependent on its outputs. New Zealand and Uruguay collaborators have been developing a suite of tools and processes embedded in an “innovation platform” to enable farmers, agribusiness and policy planners to engage and collectively learn about the impact of their interacting individual decisions and strategies. We describe the generic framework and demonstrate examples of the tools and processes used and their applicability across scale in both New Zealand and Uruguay

An application of ARX stochastic models to iris recognition

We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

A statistical sampling strategy for iris recognition

We present a new approach for iris recognition based on a random sampling strategy. Iris recognition is a method to identify individuals, based on the analysis of the eye iris. This technique has received a great deal of attention lately, mainly due to iris unique characterics: highly randomized appearance and impossibility to alter its features. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. Our work uses standard integrodifferential operators to locate the iris. Then, we process iris image with histogram equalization to compensate for illumination variations.The characterization of iris features is performed by using accumulated histograms. These histograms are built from randomly selected subimages of iris. After that, a comparison is made between accumulated histograms of couples of iris samples, and a decision is taken based on their differences and on a threshold calculated experimentally. We ran experiments with a database of 210 iris, extracted from 70 individuals, and found a rate of succesful identifications in the order of 97 %.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

An application of ARX stochastic models to iris recognition

We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI