6533b86cfe1ef96bd12c815c
RESEARCH PRODUCT
Rapid parameter determination of discrete damped sinusoidal oscillations
Jim C. VisschersEmma K. WilsonAndrey MudrovLykourgos BougasThomas Conneelysubject
Signal Processing (eess.SP)Accuracy and precisionPhysics - Instrumentation and DetectorsAcousticsPolarimetryFOS: Physical sciences02 engineering and technologyApplied Physics (physics.app-ph)01 natural sciencesSignal010309 opticssymbols.namesakeOptics0103 physical sciencesFOS: Electrical engineering electronic engineering information engineeringElectrical Engineering and Systems Science - Signal ProcessingPhysicsbusiness.industrySpectral densityInstrumentation and Detectors (physics.ins-det)Physics - Applied Physics021001 nanoscience & nanotechnologyAtomic and Molecular Physics and OpticsMicrosecondFourier transformsymbols0210 nano-technologybusinessMatrix methodOptics (physics.optics)Physics - Opticsdescription
We present different computational approaches for the rapid extraction of the signal parameters of discretely sampled damped sinusoidal signals. We compare time- and frequency-domain-based computational approaches in terms of their accuracy and precision and computational time required in estimating the frequencies of such signals, and observe a general trade-off between precision and speed. Our motivation is precise and rapid analysis of damped sinusoidal signals as these become relevant in view of the recent experimental developments in cavity-enhanced polarimetry and ellipsometry, where the relevant time scales and frequencies are typically within the ∼1 − 10 µs and ∼1 − 100 MHz ranges, respectively. In such experimental efforts, single-shot analysis with high accuracy and precision becomes important when developing experiments that study dynamical effects and/or when developing portable instrumentations. Our results suggest that online, running-fashion, microsecond-resolved analysis of polarimetric/ellipsometric measurements with fractional uncertainties at the 10−6 levels, is possible, and using a proof-of-principle experimental demonstration we show that using a frequency-based analysis approach we can monitor and analyze signals at kHz rates and accurately detect signal changes at microsecond time-scales.
year | journal | country | edition | language |
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2020-10-09 |