Distances, Radial Distribution and Total Number of Galactic Supernova Remnants

We present a table of 215 SNRs with distances. New distances are found to SNR G$51.26+0.11$ of $6.6 \pm 1.7$ kpc using HI absorption spectra, and to 5 other SNRs using maser/molecular cloud associations. We recalculate the distances and errors to all SNRs using a consistent rotation curve and provide errors where they were not previously estimated. This results in a significant distance revisions for 20 SNRs. Because of observational constraints and selection effects, there to be is an apparent deficit of observed number of Galactic supernova remnants (SNRs). To investigate this, we employ two methods. The first method applies correction factors for the selection effects to derive the radial density distribution. The second method compares functional forms for the SNR surface density and selection function against the data to find which functions are consistent with the data. The total number of SNRs in the Galaxy is $\sim3500$ (Method 1) or in the range $\sim2400$ to $\sim5600$ (Method 2). We conclude that the current observed number of SNRs is not yet complete enough to give a well-determined total SNR number or radial density function.Comment: 24 pages, 8 figure

A Statistical Analysis of Galactic Radio Supernova Remnants

We present an revised table of 390 Galactic radio supernova remnants (SNRs) and their basic parameters. Statistical analyses are performed on SNR diameters, ages, spectral indices, Galactic heights and spherical symmetries. Furthermore, the accuracy of distances estimated using the $\Sigma$-D relation is examined. The arithmetic mean of the Galactic SNR diameters is $30.5$ pc with standard error $1.7$ pc and standard deviation $25.4$ pc. The geometric mean and geometric standard deviation factor of Galactic SNR diameters is $21.9$ pc and $2.4$, respectively. We estimate ages of 97 SNRs and find that the supernova (SN) birth rate to be lower than, but within $2\sigma$ of currently accepted values for SN birth rate. The mean spectral index of shell-type SNRs is $-0.51 \pm 0.01$ and no correlations are found between spectral indices and the SNR parameters of molecular cloud (MC) association, SN type, diameter, Galactic height and surface brightness. The Galactic height distribution of SNRs is best described by an exponential distribution with a scale height of $48 \pm 4$ pc. The spherical symmetry measured by the ovality of radio SNRs is not correlated to any other SNR parameters considered here or to explosion type.Comment: 12 pages, 9 figure

Spatial prediction in mobile robotic wireless sensor networks with network constraints

© 2016 IEEE. In recent years mobile robotic wireless sensor networks have been a popular choice for modelling spatial phenomena. This research is highly demanding and non-trivial due to challenges from both network and robotic aspects. In this paper, we address the spatial modelling of a physical phenomena with the network connectivity constraints while the mobile robots are striving to achieve the minimum modelling mismatch in terms of root mean square error (RMSE). We have resolved it through Gauss markov random field based approach which is a computationally efficient implementation of Gaussian processes. In this strategy, the Mobile Robotic Wireless Sensor Node (MRWSN) are centrally controlled to maintain the connectivity while minimizing the RMSE. Once the number of MRWSNs reach their maximum coverage, a new MRWSN is requested at the most informative location. The experimental results are convincing and they show the effectiveness of the algorithm

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