Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation

Evaluación del impacto antrópico, sobre la calidad de las aguas del río Lurín, a partir de indicadores físico-químicos, microbiológicos y macroinvertebrados

Evalúa el impacto antrópico sobre la calidad de las aguas del río Lurín en los sectores medio y bajo de su cuenca, a partir de indicadores físico-químicos, microbiológicos y macroinvertebrados. Realizó un muestreo de las aguas del río Lurín, considerado el menos contaminado de los tres ríos que cruzan por Lima Metropolitana, a partir del cual se calculó el Índice de Calidad Ambiental para aguas superficiales (ICA_sp) que involucra parámetros físico-químicos, microbiológicos en combinación con el Índice Biological Monitoring Working Party (BMWP) modificado y el Average Score Per Taxa (ASPT) que hacen uso de los macrobentos. Los puntos de muestreo fueron seis, durante el mes de noviembre del año 2017 en época de estiaje; se evaluó de esta manera la cuenca media y parte de la baja del río Lurín. Con la aplicación del Índice ICA_sp se obtuvo que los puntos P2 y P3 tienen aguas de “excelente calidad”, el punto P5 de “aceptable calidad”, los puntos P1 y P4 “medianamente contaminadas”, el P6 “altamente contaminada”; mientras que con la aplicación del Índice BMWP/nPe-mod se obtuvo que el punto P1 tienen “aguas con signos de estrés”, los puntos P2, P3 y P5 son “aguas contaminadas” y los puntos P4 y P6 son “aguas muy contaminadas” y con la aplicación del índice ASPT se obtuvo como resultado que todos los seis puntos presentan aguas con “probable contaminación moderada” . El uso combinado de los tres índices muestra una buena coincidencia y un alto grado de complementariedad.Tesi

Evolutionary segmentation of yeast genome

Segmentation algorithms differ from clustering algorithms with regard to how to deal with the physical location of genes throughout the sequence. Therefore, segments have to keep the original positions of consecutive genes, which is not a constraint for clustering algorithms. It has been proven that exist functional relations among neighbour-genes, so the localization of the boundaries between these functionally similar groups of genes has turned out an important challenge. In this paper, we present an evolutionary algorithm to segment the yeast genome

Modelling laser milling of microcavities for the manufacturing of DES with ensembles

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.This work was partially funded through Grants fromthe IREBID Project (FP7-PEOPLE-2009-IRSES- 247476) of the European Commission and Projects TIN2011- 24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and Competitivenes

Searching for rules to detect defective modules: A subgroup discovery approach

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only fault-prone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases.Ministerio de Educación y Ciencia TIN2007-68084-C02-00Ministerio de Educación y Ciencia TIN2010-21715-C02-0

Segmentation of RV in 4D Cardiac MR Volumes using region-merging graph cuts

Non-invasive quantitative assessment of the right ventricular anatomical and functional parameters is a challenging task. We present a semi-automatic approach for right ventricle (RV) segmentation from 4D MR images in two variants, which differ in the amount of user interaction. The method consists of three main phases: First, foreground and background markers are generated from the user input. Next, an over-segmented region image is obtained applying a watershed transform. Finally, these regions are merged using 4D graph-cuts with an intensity based boundary term. For the first variant the user outlines the inside of the RV wall in a few end-diastole slices, for the second two marker pixels serve as starting point for a statistical atlas application. Results were obtained by blind evaluation on 16 testing 4D MR volumes. They prove our method to be robust against markers location and place it favourably in the ranks of existing approaches

A General Tracking and Auditing Architecture for the OpenACS framework

The paper describes the Tracking and Auditing Engines (TAE) in process of development for the OpenACS framework through the implementation of a tracking subsystem and an auditing API built upon it. The main theoretical considerations that must fulfill such system are discussed in the paper, specially the differences between the responsibilities and functions for the tracking and auditing processes. The data required and where to get it from the framework, the architecture designed, and the technology to be used in the implementation are also presented. As a practical use of the TAE, the paper presents on-going authors’ research that is based on analyzing dotLRN users’ interactions. These research works will benefit from the audit trails provided by the TAE

3D Frangi-based lung vessel enhancement filter penalizing airways

This paper describes a fully automatic simultaneous lung vessel and airway enhancement filter. The approach consists of a Frangi-based multiscale vessel enhancement filtering specifically designed for lung vessel and airway detection, where arteries and veins have high contrast with respect to the lung parenchyma, and airway walls are hollow tubular structures with a non negative response using the classical Frangi's filter. The features extracted from the Hessian matrix are used to detect centerlines and approximate walls of airways, decreasing the filter response in those areas by applying a penalty function to the vesselness measure. We validate the segmentation method in 20 CT scans with different pathological states within the VESSEL12 challenge framework. Results indicate that our approach obtains good results, decreasing the number of false positives in airway walls

Sensors and systems for environmental monitoring and control

Editorial titulado Sensors and systems for environmental monitoring and control, publicado en la Journal of sensors, en 2017. Se analiza el valor de controlar con monitores especializados los contaminantes de origen químico orgánico e inorgánico.Editorial entitled Sensors and systems for environmental monitoring and control, published in the Journal of sensors, in 2017. The value of controlling pollutants of organic and inorganic chemical origin with specialized monitors is analyzed.peerReviewe