Digital Shoreline Analysis System improvement for uncertain data detection in measurements.

Digital Shoreline Analysis System (DSAS) is the most frequently used coastal engineering system for shoreline change quantification. Factors like human and system errors, wrong perception of the shoreline changes, and non-exact data sources may cause errors in the measured data. Detection and modification of such data can increase the accuracy of results. At present, the DSAS tool lacks this capability, so this research aimed to present a new module for DSAS to detect uncertain data in shoreline change rate measurements. The module's basis for detecting uncertain data is to use statistical methods: adjusted boxplot, Grubbs' test, standard deviation tests, median test, modified Z-score test, and voting method. The module's performance was evaluated based on a data set obtained through Qeshm Island shoreline change quantification in Iran. The details of these methods, the prepared module, the case study, and the shoreline change measurement statistical methods were discussed in this study. The results showed the acceptable output of this module in detecting uncertain data.

Rapid and sensitive magnetic field sensor based on photonic crystal fiber with magnetic fluid infiltrated nanoholes.

A fast response time (0.1 s) magnetic field sensor has been demonstrated utilizing a photonic crystal fiber with nano-size air holes infiltrated with polyethylene glycol based magnetic fluid. The effect of magnetic nanoparticles concentration in the fluid on the magneto-optical sensor performance and its dependence under varying magnetic-field loads was investigated in detail. In particular, the sensor response was analytically modelled with a Langevin function with a good fit (R[Formula: see text]0.996). A threshold sensing point as low as 20 gauss was recorded and a detection range of 0-350 gauss was demonstrated by means of optical transmission measurements. The experimental results were validated by theory using a waveguide light transmission model fed by finite-element method simulations of the principal guided modes in the infiltrated fiber sensor. The simple interrogation scheme, high sensitivity and quick response time makes the proposed hybrid fiber-optic magneto-fluidic probe a promising platform for novel biochemical sensing applications.