Detection of low level gaseous releases and dose evaluation from continuous gamma dose measurements using a wavelet transformation technique

Advances in detection algorithms for radiation monitoring

This paper presents a review of up-to-date advancements in detection algorithms employed in radiation monitoring for generating radiation maps of ground contamination and tracking radioactive release into the atmosphere. Detection algorithms for true count processing, spectroscopy processing, and plume tracking are discussed in chronological order of development. Process steps of detection include height correction, solid-angle correction, background radioactivity correction, Compton continuum elimination, de-noising of gamma-radiation spectra, and recording of plume passage events.

Analysis of hydrological trend for radioactivity content in bore-hole water samples using wavelet based denoising

A wavelet transform based denoising methodology has been applied to detect the presence of any discernable trend in (137)Cs and (90)Sr activity levels in bore-hole water samples collected four times a year over a period of eight years, from 2002 to 2009, in the vicinity of typical nuclear facilities inside the restricted access zones. The conventional non-parametric methods viz., Mann-Kendall and Spearman rho, along with linear regression when applied for detecting the linear trend in the time series data do not yield results conclusive for trend detection with a confidence of 95% for most of the samples. The stationary wavelet based hard thresholding data pruning method with Haar as the analyzing wavelet was applied to remove the noise present in the same data. Results indicate that confidence interval of the established trend has significantly improved after pre-processing to more than 98% compared to the conventional non-parametric methods when applied to direct measurements.