Antonio Benitez

Automatic control for laboratory sterilization process based on arduino hardware

In this paper It is introduced an automated control system for the sterilization process of biological material. The proposed system is composed of four modules: flame detection, pressure sensor, Gas Control Module and CPU Module. This System is based on Arduino Board. Arduino is an Open Hardware platform that allows a fast prototype development, Arduino microcontroller is programmed on C++. making development and tests not a difficult part of overall work.

Designing Ontological Agents: an Alternative to Improve Information Retrieval in Federated Digital Libraries

Search engines base their operation just in keywords management; some of them are ambiguous, consequently, a lot of irrelevant results are produced. To take into account the meaning of keywords, we describe the architecture of a multi-agent system in charged of information retrieval tasks in digital libraries. The main components are termed ontological agents. They are software entities that make use of ontologies to expand queries and to avoid as much ambiguity as possible of keywords. Ontologies are also used to identify relevant sources of information. The design of these agents is based on Gaia methodology.

Organizing open archives via lightweight ontologies to facilitate the use of heterogeneous collections

Purpose – This paper seeks to focus on the problems of integrating information from open, distributed scholarly collections, and on the opportunities these collections represent for research communities in developing countries. The paper aims to introduce OntOAIr, a semi‐automatic method for constructing lightweight ontologies of documents in repositories such as those provided by the Open Archives Initiative (OAI).Design/methodology/approach – OntOAIr uses simplified document representations, a clustering algorithm, and ontological engineering techniques.Findings – The paper presents experimental results of the potential positive impact of ontologies and specifically of OntOAIr on the use of collections provided by OAI.Research limitations/implications – By applying OntOAIr, scholars who frequently spend many hours organizing OAI information spaces will obtain support that will allow them to speed up the entire research cycle and, expectedly, participate more fully in global research communities.Original...

Image-based classification of diabetic retinopathy using machine learning

In this paper we present experimental results of an automated method for image-based classification of diabetic retinopathy. The method is divided into three stages: image processing, feature extraction and image classification. In the first stage we have used two image processing techniques in order to enhance their features. Then, the second stage reduces the dimensionality of the images and finds features using the statistical method of principal component analysis. Finally, in the third stage the images are classified using machine learning algorithms, particularly, the naive Bayes classifier, neural networks, k-nearest neighbors and support vector machines. In our experimental study we classify two types of retinopathy: non-proliferative and proliferative. Preliminary results show that k-nearest neighbors obtained the best result with 68.7% using f-measure as metric, for a data set of 151 images with different resolutions.

Interactive platform for kinematic control on virtual humans

Virtual humans are being increasingly used in different domains. Virtual human modeling requires to consider aspects belonging to different levels of abstractions. This work presents the results obtained after building two stages on the virtual humans hierarchy. Initially, we have modeled a virtual human, next, forward kinematic to animate the virtual human has been applied. The aim of this developed is to have an interactive platform that allows to give movement to every of 53 articulations involved on human model proposed. Besides, this kinematic control allow us to go on working until we can reach the behavioral and cognitive stages. Simulation using a windows platform where the virtual human is moving articulations belong the trunk and extremity are presented.

Machine learning from imbalanced data sets for astronomical object classification

In this paper we present an experimental study of machine learning from imbalanced data sets applied to the difficult problem of astronomical object classification in multi-spectral wide-field images. The imbalanced data set problem is very common in several domains, and occurs when there are many more examples of some classes than others; therefore, classifiers perform poorly on these data sets. In order to improve the performance of machine learning algorithms over minority class examples, we propose to create new instances using a modification of the well-known SMOTE technique, but only of those misclassified examples given by an ensemble of classifiers. Our preliminary experimental results show that the proposed approach obtain above. 700 using recall, precision and f-measure as metrics for evaluation; using small data sets.

A 3D simulation environment for kinematic task of the PUMA 560 robot

Robots with articulated morphology has acquired major importance because of representing la basic application to scale to highly articulated chains. Hence, this paper is focus to present how manipulate kinematic chains with six degrees of freedom, in particular way the model associated to PUMA 560 robot. Techniques to compute forward and inverse kinematic are described. Besides, a platform to simulate on three dimension space is presented as a important tool to verify the movements programmed on the robot. Interesting results from inverse kinematic applications are shown.

The imbalanced problem in morphological galaxy classification

In this paper we present an experimental study of the performance of six machine learning algorithms applied to morphological galaxy classification. We also address the learning approach from imbalanced data sets, inherent to many real-world applications, such as astronomical data analysis problems. We used two over-sampling techniques: SMOTE and Resampling, and we vary the amount of generated instances for classification. Our experimental results show that the learning method Random Forest with Resampling obtain the best results for three, five and seven galaxy types, with a F-measure about. 99 for all cases.

Key Elements for Motion Planning Algorithms

Planning a collision-free path for a rigid or articulated robot to move from an initial to a final configuration in a static environment is a central problem in robotics and has been extensively addressed over the last. The complexity of the problem is NP-hard (Latombe, 1991). There exist several family sets of variations of the basic problem, that consider flexible robots, and where robots can modify the environment. The problem is well known in other domains, such as planning for graphics and simulation (Koga et al., 1994), planning for virtual prototyping (Chang & Li, 1995), and planning for medical (Tombropoulos et al., 1999) and pharmaceutical (Finn & Kavraki, 1999) applications.

New Technique to Improve Probabilistic Roadmap Methods

A probabilistic roadmap is a network of simple paths connecting collision-free configurations obtained by sampling a robot’s configuration space at random. Several probabilistic roadmap planners have solved unusually difficult path planning problems, but their efficiency remains disappointing when the free space contains narrow passages. This paper provides a new technique to find free configurations into narrow corridors, sampling the configuration space using geometric features into the workspace and computing configurations close to the obstacles. An initial roadmap is built using spheres in low cost, next an improving connectivity phase based on “straightness”, “volume” and “normal vectors” features on the workspace is computed, and the roadmap is improved capturing a better connectivity of configuration space. Experiments show that the new approach is able to solve different benchmarks in motion planning problems containing difficult narrow corridors.