Mucinous cystadenoma of the appendix

A cystic mass in the right iliac fossa of an asymptomatic patient

Fusing sonars and LRF data to perform SLAM in reduced visibility scenarios

Simultaneous Localization and Mapping (SLAM) approaches have evolved considerably in recent years. However, there are many situations which are not easily handled, such as the case of smoky, dusty, or foggy environments where commonly used range sensors for SLAM are highly disturbed by noise induced in the measurement process by particles of smoke, dust or steam. This work presents a sensor fusion method for range sensing in Simultaneous Localization and Mapping (SLAM) under reduced visibility conditions. The proposed method uses the complementary characteristics between a Laser Range Finder (LRF) and an array of sonars in order to ultimately map smoky environments. The method was validated through experiments in a smoky indoor scenario, and results showed that it is able to adequately cope with induced disturbances, thus decreasing the impact of smoke particles in the mapping task

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

Increased lung inflammation with oxygen supplementation in tracheotomized spontaneously breathing rabbits: an experimental prospective randomized study

BACKGROUND: Mechanical ventilation is a well-known trigger for lung inflammation. Research focuses on tidal volume reduction to prevent ventilator-induced lung injury. Mechanical ventilation is usually applied with higher than physiological oxygen fractions. The purpose of this study was to investigate the after effect of oxygen supplementation during a spontaneous ventilation set up, in order to avoid the inflammatory response linked to mechanical ventilation. METHODS: A prospective randomised study using New Zealand rabbits in a university research laboratory was carried out. Rabbits (n = 20) were randomly assigned to 4 groups (n = 5 each group). Groups 1 and 2 were submitted to 0.5 L/min oxygen supplementation, for 20 or 75 minutes, respectively; groups 3 and 4 were left at room air for 20 or 75 minutes. Ketamine/xylazine was administered for induction and maintenance of anaesthesia. Lungs were obtained for histological examination in light microscopy. RESULTS: All animals survived the complete experiment. Procedure duration did not influence the degree of inflammatory response. The hyperoxic environment was confirmed by blood gas analyses in animals that were subjected to oxygen supplementation, and was accompanied with lower mean respiratory rates. The non-oxygen supplemented group had lower mean oxygen arterial partial pressures and higher mean respiratory rates during the procedure. All animals showed some inflammatory lung response. However, rabbits submitted to oxygen supplementation showed significant more lung inflammation (Odds ratio = 16), characterized by more infiltrates and with higher cell counts; the acute inflammatory response cells was mainly constituted by eosinophils and neutrophils, with a relative proportion of 80 to 20% respectively. This cellular observation in lung tissue did not correlate with a similar increase in peripheral blood analysis. CONCLUSIONS: Oxygen supplementation in spontaneous breathing is associated with an increased inflammatory response when compared to breathing normal room air. This inflammatory response was mainly constituted with polymorphonuclear cells (eosinophils and neutrophils). As confirmed in all animals by peripheral blood analyses, the eosinophilic inflammatory response was a local organ event.The authors would like to thank Centro Hospitalar do Porto for funding regarding the purchase of animals, animal food, and other husbandry expenses. No funding was used to reimburse any of the authors, nor any of the persons who helped and are herein thanked. Funding for open access publication was supported by the Anesthesia Department at Centro Hospitalar do Port

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario