1,316 research outputs found
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 of 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 (Method 1) or in the range to (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 -D relation
is examined. The arithmetic mean of the Galactic SNR diameters is pc
with standard error pc and standard deviation pc. The geometric
mean and geometric standard deviation factor of Galactic SNR diameters is
pc and , respectively. We estimate ages of 97 SNRs and find that
the supernova (SN) birth rate to be lower than, but within of
currently accepted values for SN birth rate. The mean spectral index of
shell-type SNRs is 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 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
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