Vehicle License Plate recognition has turned out to be a vital appliance of Intelligent Transportation Systems (ITS). In view of the fact that license plates can be put back, wrap or simply alter, they are not the eventual result for vehicle identification. The intention is to widen a structure where by Vehicle Identification Number (VIN) is digitally take picture and then identified by segmenting the characters from the images confined. In the proposed work a novel moves toward for segmentation of characters of license plate using Connected Component Analysis (CCA). In the beginning stage the identification number of the Vehicle was kept back in focal point and images were captured. The images were then focus to pre-processing which consists of image processing algorithms. These images were further powerfully developed by taking into account making the distance transform. The forgotten time and entropy results give a study for rising the effectiveness and highquality performance of CCA segmentation. The projected work presents a new method for License Plate Characters' Segmentation. The proposed approach is more effective than some of the presented method reported past.
The effect of solar flares on the ionospheric regions of earth, which in turn affects signals of the various Global Navigation Satellite Systems, is a very important criterion to be considered in satellite communication. In this paper, we are investigating the prediction capability of the Kriging based Model and its effect on calculating the signal delay of GPS system on 6.9.2017during which NASA have observed a solar flare which have recorded X9.3 on solar storm scale. The GPS data used in this paper for prediction of TEC is taken from the DGAR Island station. The Vertical Total Electron Content data for GPS is predicted from 3rd September 2017 to 7th September 2017 by using the previous collected 6 days of TEC data of the low latitude DGAR station from IONOLAB and also by using the input parameters like Kp index, SSN, Ap index and Dst index. The predicted results are validated by comparing them with IRI 2016 and IRI PLAS 2017 model collected during the same dates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.