Distances to Supernova Remnants G20.4+0.120.4+0.1, G24.7−0.624.7-0.6 and G28.6−0.128.6-0.1 and New Molecular Cloud Associations

Accurate distances to supernova remnants (SNRs) are crucial in determining their size, age, luminosity and evolutionary state. To determine distances, we chose three SNRs from the VLA Galactic Plane Survey (VGPS) for extraction of HI absorption spectra. Analysing HI absorption spectra, $^{13}$CO emission spectra, and HI and $^{13}$CO channel maps, kinematic velocities (or their limits) to the three SNRs were calculated. The three SNRs are probably associated with molecular clouds and the new distance to G$20.4+0.1$, G$24.7-0.6$ and G$28.6-0.1$ are $7.8 \pm0.5$ kpc, $3.8\pm 0.2$ kpc and $9.6 \pm 0.3$ kpc, respectively.Comment: 10 pages, 14 figure

Fast indoor scene classification using 3D point clouds

A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinect™, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect™ data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data

Road terrain type classification based on laser measurement system data

For road vehicles, knowledge of terrain types is useful in improving passenger safety and comfort. The conventional methods are susceptible to vehicle speed variations and in this paper we present a method of using Laser Measurement System (LMS) data for speed independent road type classification. Experiments were carried out with an instrumented road vehicle (CRUISE), by manually driving on a variety of road terrain types namely Asphalt, Concrete, Grass, and Gravel roads at different speeds. A looking down LMS is used for capturing the terrain data. The range data is capable of capturing the structural differences while the remission values are used to observe anomalies in surface reflectance properties. Both measurements are combined and used in a Support Vector Machines Classifier to achieve an average accuracy of 95% on different road types

Emotional intelligence, perceived stress and academic performance of Sri Lankan medical undergraduates

Abstract Background Previous research has shown that higher Emotional Intelligence (EI) is associated with better academic and work performance. The present study intended to explore the relationship between EI, perceived stress and academic performance and associated factors among medical undergraduates. Methods This descriptive cross-sectional research study was conducted among 471 medical undergraduates of 2nd, 4th and final years of University of Colombo, Sri Lanka. Students were rated on self administered Perceived Stress Scale (PSS) and Schutte Self-Report Emotional Intelligence Test (SEIT). Examination results were used as the dichotomous outcome variable in a logistic regression analysis. Results Females had higher mean EI scores (p = 0.014). A positive correlation was found between the EI score and the number of extracurricular activities (r = 0.121, p = 0.008). Those who were satisfied regarding their choice to study medicine, and who were planning to do postgraduate studies had significantly higher EI scores and lower PSS scores (p <0.001). Among final year undergraduates, those who passed the Clinical Sciences examination in the first attempt had a higher EI score (p <0.001) and a lower PSS score (p <0.05). Results of the binary logistic-regression analysis in the entire study population indicated that female gender (OR:1.98) and being satisfied regarding their choice of the medical undergraduate programme (OR:3.69) were significantly associated with passing the examinations. However, PSS Score and engagement in extracurricular activities were not associated with ‘Examination Results’. Conclusions Higher EI was associated with better academic performance amongst final year medical students. In addition a higher EI was observed in those who had a higher level of self satisfaction. Self-perceived stress was lower in those with a higher EI. Enhancing EI might help to improve academic performance among final year medical student and also help to reduce the stress levels and cultivate better coping during professional life in the future