Search results

Filters

  • Journals
  • Keywords

Search results

Number of results: 3
items per page: 25 50 75
Sort by:

Abstract

This paper presents application of optical microscope for evaluation of microtexture changes of coarse aggregate during simulated polishing in laboratory. Observations of the apparent changes on surfaces of seven different aggregates are presented. Simulation polishing of aggregate was performed in accordance with PN-EN 1097-8:2009. lmages of the aggregate surface were taken with the optical microscope in the reflection mode in particular stages of polishing. Digital images were analyzed. Standard deviation was determined on the basis of the histogram of intensities from digital images of the surfaces of aggregate grains which was assurned as the measure of changes in microtexture during simulated polishing (namely the σh parameter). Statistical analysis has shown that the changes of the σh parameter between the particular stages of polishing confirm certain trends related to the petrographic characteristic of the rocks. Aggregates which included minerals of similar hardness (granodiorite, dolomile, basalt) were more prone to polishing than gabbro and postglacial. Regeneration of the microtexture, the recovery to its original asperity, occurred in the case of quartz sandstone and steelmaking slag.
Go to article

Abstract

The Histogram Test method is a popular technique in analog-to-digital converter (ADC) testing. The presence of additive noise in the test setup or in the ADC itself can potentially affect the accuracy of the test results. In this study, we demonstrate that additive noise causes a bias in the terminal based estimation of the gain but not in the estimation of the offset. The estimation error is determined analytically as a function of the sinusoidal stimulus signal amplitude and the noise standard deviation. We derive an exact but computationally difficult expression as well as a simpler closed form approximation that provides an upper bound of the bias of the terminal based gain. The estimators are validated numerically using a Monte Carlo procedure with simulated and experimental data.
Go to article

This page uses 'cookies'. Learn more