Galactic R Coronae Borealis Stars: The C-2 Swan Bands, The Carbon Problem, And The C-12/C-13 Ratio

Observed spectra of R Coronae Borealis (RCB) and hydrogen-deficient carbon (HdC) stars are analyzed by synthesizing the C-2 Swan bands (1, 0), (0, 0), and (0, 1) using our detailed line list and the Uppsala model atmospheres. The (0, 1) and (0, 0) C-2 bands are used to derive the C-12 abundance, and the (1, 0) (CC)-C-12-C-13 band to determine the C-12/C-13 ratios. The carbon abundance derived from the C-2 Swan bands is about the same for the adopted models constructed with different carbon abundances over the range 8.5 (C/He = 0.1%) to 10.5 (C/He = 10%). Carbon abundances derived from C I lines are about a factor of four lower than the carbon abundance of the adopted model atmosphere over the same C/He interval, as reported by Asplund et al., who dubbed the mismatch between adopted and derived C abundance as the "carbon problem." In principle, the carbon abundances obtained from C-2 Swan bands and that assumed for the model atmosphere can be equated for a particular choice of C/He that varies from star to star. Then, the carbon problem for C-2 bands is eliminated. However, such C/He ratios are in general less than those of the extreme helium stars, the seemingly natural relatives to the RCB and HdC stars. A more likely solution to the C-2 carbon problem may lie in a modification of the model atmosphere's temperature structure. The derived carbon abundances and the C-12/C-13 ratios are discussed in light of the double degenerate and the final flash scenarios.Robert A. Welch Foundation of Houston, TX F-634McDonald Observator

Measuring Hydrogen-to-Helium Ratio in Cool Stars

Conventionally, the helium-to-hydrogen ratio for the stars are adopted to be 0.1, as standard, unless, the stars are severely deficient in hydrogen like in RCB-class, or the stars' helium abundance is accurately measured using He I transitions in warm/hotter stars. In our study, the small change in helium-to-hydrogen ratio (from standard value, 0.1) in normal giants were detected from the large difference (> 0.3 dex) in the Mg-abundance measured from Mg I lines and the subordinate lines of (0,0) MgH band. These are the stars that are mildly hydrogen-deficient/He-enhanced. Such stars were spectroscopically discovered for the first time among giants of the globular cluster Omega Centauri. The sample selection, observations, methodology and results are discussed in detail.Comment: Accepted for publication in Bulletin de la Soci\'et\'e Royale des Sciences de Li\`ege (BSRSL), In press 10 Pages, 3 Figures and 1 Table. arXiv admin note: text overlap with arXiv:1408.120

Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN

Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages.  Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision

Spontaneous Assessment Packet Origination

Nowadays systems aren't getting any slighter, they are expanding in size and it's transforming into a monotonous employment for system chairmen to adjust the system since they put trust in conventional instruments like ping and follow course for this work. Our paper tosses a development chronic and precise way to deal with test and right a system alluded to as Automatic check Packet Generation (ATPG). ATPG produces a model that isn't dependent on strategy once perusing course of action from switches. The model is utilized to get least mixed bag of check parcels to shroud every connection amid a system and each statute net. ATPG is skillful of work. Each down to earth and execution issues check parcels are sent at standard interims and separate strategy is utilized to limit issues. The working of few disconnected from the net instruments that mechanically produce check parcels too are given. On the other hand, ATPG goes on the far side and sooner the work in static (Checking aliveness and shortcoming limitation). Systems is created numerous mind boggling heads devices in the season of troubleshoot issues We propose the computerized and delivers 0rinted testing and investigating systems called "Unmanned Quality Container Prompting peruses switch setups and produces little devises show This model is utilized to produce a base arrangement of test compartment to practice each connection in the system changes each principle in the system model Test holder are sent to information and identified mistakes a different models to limit the issue ATPG can distinguish both utilitarian and execution issues supplements yet goes past prior work in static confirm shortcoming environment We take our convention usage and results it distinctive applications Stanford University's spine system and Internet2 We observe that a little number of test holder everyone to test all guidelines in diverse systems model