Auto-correlation based intelligent technique for complex waveform presentation and measurement

Abstract

Waveform acquisition and presentation forms the heart of many measurement systems. Particularly, data acquisition and presentation of repeating complex signals like sine sweep and frequency-modulated signals introduces the challenge of waveform time period estimation and live waveform presentation. This paper presents an intelligent technique, for waveform period estimation of both the complex and simple waveforms, based on the normalized auto-correlation method. The proposed technique is demonstrated using LabVIEW based intensive simulations on several simple and complex waveforms. Implementation of the technique is successfully demonstrated using LabVIEW based virtual instrumentation. Sine sweep vibration waveforms are successfully presented and measured for electrodynamic shaker system generated vibrations. The proposed method is also suitable for digital storage oscilloscope (DSO) triggering, for complex signals acquisition and presentation. This intelligence can be embodied into the DSO, making it an intelligent measurement system, catering wide varieties of the waveforms. The proposed technique, simulation results, robustness study and implementation results are presented in this paper.

Final results on the $$0\nu \beta \beta $$ decay half-life limit of $$^{100}$$Mo from the CUPID-Mo experiment

“…There is clearly a larger 210 Po contribution than the rest of the 238 U chain, at the level of 96 𝜇Bq/kg, possibly introduced during the purification of the enriched material [61]. There are also traces of 190 Pt, caused by the crystal growth in a platinum crucible [62] and we find 40 K and 90 Sr+ 90 Y at the level of some hundreds of 𝜇Bq/kg.…”

An approach based on rough set theory for identification of single and multiple partial discharge source

“…It is much simpler, and the required computational effort is lesser, as it involves merely the accumulation of products between corresponding sample values with time shifting [16]. In signal processing, auto-correlation is a measure of similarity of two identical waveforms as a function of a time-lag applied to one of them.…”

“…So F 21 is also D-indispensable in P. In this way the indispensable and the dispensable attributes are identified. So, according to the rules, F 11 remains D-dispensable in P. Thus the set {F 16 , F 21 } is the D-reduct of P. Therefore, the reduced form of Table 5 is given in Table 6. '-' indicates ''do not care'' (dispensable) condition.…”