Forecasting Gamma radiation levels using Digital image processing
Abou-Bakr M.Ramadan 1, Ahmed M. El-Garhy 2, Fathy Z.Amer 2, and Mazhar M. Hefnawi 1 *
1 Department of National Network for Monitoring Radioactivity, Atomic Energy Authority of Egypt, Cairo, Egypt; 2Department of Electronics, Communications and Computers, Faculty of Engineering, Helwan University, Cairo, Egypt.firstname.lastname@example.org
Abstract: This work introduces a new way for data visualization. Its name is " Digital 'Application name' Image". Normal digital image is created by digital camera or digital scanner but digital application name image is created by measurements of monitoring data. This work uses the data which is measured by some radiation monitoring stations and classifies it using fuzzy logic rules to create some digital radiation images. The main unique advantage of digital radiation image is that it expresses thousands of measurements in a very clear form through only one picture while the maximum number of measurements does not exceed 100 for other conventional visualization methods. This feature gives a facility to view one year of all recorded measurements in only one photo. This picture helps the user to observe the behavior of thousands of measurements in few minutes instead of spending few hours in reviewing hundreds of charts for the same measurements. This work also introduces a new way for forecasting Gamma radiation levels. This way uses image restoration technique to predict the gamma levels. Of course, this technique is used after creating digital radiation image. The quality for the output result from this model is at least accepted for forecasting and covering lost data. The main feature from this model is that it needs only one kind of data while other prediction models need at least three kinds of data. Therefore this model covers the common limitation in famous prediction models and saves money, time and effort.
[Abou-Bakr M.Ramadan, Ahmed M. El-Garhy, Fathy Z.Amer and Mazhar M. Hefnawi. Forecasting Gamma radiation levels using Digital image processing. Life Sci J 2012;9(1):701-710]. (ISSN: 1097-8135). http://www.lifesciencesite.com. 101
Keywords: Data visualization; Digital image processing; Digital Radiation Image; Environmental Forecast Full Text 101