Rafael Guzman-Cabrera

A Highly Sensitive Fiber Optic Sensor Based on Two-Core Fiber for Refractive Index Measurement

A simple and compact fiber optic sensor based on a two-core fiber is demonstrated for high-performance measurements of refractive indices (RI) of liquids. In order to demonstrate the suitability of the proposed sensor to perform high-sensitivity sensing in a variety of applications, the sensor has been used to measure the RI of binary liquid mixtures. Such measurements can accurately determine the salinity of salt water solutions, and detect the water content of adulterated alcoholic beverages. The largest sensitivity of the RI sensor that has been experimentally demonstrated is 3,119 nm per Refractive Index Units (RIU) for the RI range from 1.3160 to 1.3943. On the other hand, our results suggest that the sensitivity can be enhanced up to 3485.67 nm/RIU approximately for the same RI range.

Using the Web as corpus for self-training text categorization

Most current methods for automatic text categorization are based on supervised learning techniques and, therefore, they face the problem of requiring a great number of training instances to construct an accurate classifier. In order to tackle this problem, this paper proposes a new semi-supervised method for text categorization, which considers the automatic extraction of unlabeled examples from the Web and the application of an enriched self-training approach for the construction of the classifier. This method, even though language independent, is more pertinent for scenarios where large sets of labeled resources do not exist. That, for instance, could be the case of several application domains in different non-English languages such as Spanish. The experimental evaluation of the method was carried out in three different tasks and in two different languages. The achieved results demonstrate the applicability and usefulness of the proposed method.

A Web-Based Self-training Approach for Authorship Attribution

As any other text categorization task, authorship attribution requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using Web-based text mining methods for the identification of the author of a given poem. In particular, we propose a semi-supervised method that is specially suited to work with justfew training examples in order to tackle the problem of the lack of data with the same writing style. The method considers the automatic extraction of the unlabeled examples from the Web and its iterative integration into the training data set. To the knowledge of the authors, a semi-supervised method which makes use of the Web as support lexical resource has not been previously employed in this task. The results obtained on poem categorization show that this method may improve the classification accuracy and it is appropriate to handle the attribution of short documents.

Semi-supervised Word Sense Disambiguation Using the Web as Corpus

As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using a Web-based approach for determining the correct sense of an ambiguous word based only in its surrounding context. In particular, we propose a semi-supervised method that is specially suited to work with just a few training examples. The method considers the automatic extraction of unlabeled examples from the Web and their iterative integration into the training data set. The experimental results, obtained over a subset of ten nouns from the SemEval lexical sample task, are encouraging. They showed that it is possible to improve the baseline accuracy of classifiers such as Naive Bayes and SVM using some unlabeled examples extracted from the Web.

Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

Taking advantage of the web for text classification with imbalanced classes

A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real-world applications the training sets are extremely small and present imbalanced class distributions. In order to confront these problems, this paper proposes a novel approach for text classification that combines under-sampling with a semi-supervised learning method. In particular, the proposed semi-supervised method is specially suited to work with very few training examples and considers the automatic extraction of untagged data from the Web. Experimental results on a subset of Reuters-21578 text collection indicate that the proposed approach can be a practical solution for dealing with the class-imbalance problem, since it allows achieving very good results using very small training sets.

Tunable field depth: hyperbolic optical masks

For controlling the depth of field, in an optical system working at full pupil apertures, we unveil the use of a pair of hyperbolic phase masks. For suitably framing our proposal, we link the Strehl ratio versus defocus with the area under the modulation transfer function (MTF). We show that by using hyperbolic phase masks, one can simultaneously reduce the impact of focus errors as well as increase the area under the MTF. We show that hyperbolic amplitude masks, with moderate absorption, can reduce the artifact noise caused by the use of phase masks. Finally, by exploiting the Lohmann–Alvarez technique, we describe the use of pairs of hyperbolic masks for governing field depth at fixed pupil apertures.

Biosensing using long-range surface plasmon structures

We report a parametric study of a long-range plasmon waveguide for the modal profiles, effective index and propagation losses as a function of the metal layer thickness and the variations in the refraction index of the upper cladding. Such device can be used as an optical biosensor. All calculations are performed using COMSOL Multiphysics, and the amplitude- and phase- responses of the device are obtained from the changes in the real and imaginary part of the effective index of the plasmon mode, respectively.

Parkinson's disease: Improved diagnosis using image processing

An intensity-based texture segmentation approach for the detection of regions with abnormal texture characteristics in magnetic resonance imaging is presented. Our algorithm is tested over several images taken from The Parkinsons Progression Markers Initiative (PPMI-database), and the results suggest that this approach is suitable for the successful identification and extraction of regions of interest whose properties can be potentially related to signature features of Parkinson disease.

Hadamard circular masks: high focal depth with high throughput

We present a class of binary masks that encode, in polar coordinates, the values of a Hadamard matrix of order N. For order N ≥ 2, the binary masks increase the Strehl ratio vs. focus error by the factor N, with the highest possible light throughput. Since a Strehl ratio with high tolerance to defocus does not guarantee a modulation transfer function (MTF) with low sensitivity to focus errors, then, we show that for N = 16 the binary mask reduces also the impact of focus error on the MTF. Equivalently, the discrete binary mask has Fisher information with low variations to defocus.