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Gyroscope: Precession01:24

Gyroscope: Precession

Precession can be demonstrated effectively through a spinning top. If a spinning top is placed on a flat surface near the surface of the Earth at a vertical angle and is not spinning, it will fall over due to the force of gravity producing a torque acting on its center of mass. However, if the top is spinning on its axis, it precesses about the vertical direction, rather than topple over due to this torque. Precessional motion is a combination of a steady circular motion of the axis and the...

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Related Experiment Video

Updated: Jun 27, 2026

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

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Post-Processing of Raw Data Recorded Continuously Using a FORS-Fibre-Optic Rotational Seismograph.

Bartosz Sakowicz1, Marek Kamiński1, Michał Dudek2,3

  • 1Department of Microelectronics and Computer Science, Lodz University of Technology, 221 Wólczanska St., 93-005 Lodz, Poland.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

This study presents data enhancement techniques for fibre-optic rotational seismographs (FORS) to improve reliability in digital monitoring systems. A new signal filtering method is proposed to automatically eliminate artefacts that disrupt real-time data recording.

Keywords:
automatic data improvementdata analysingdata gatheringfibre-optic rotational seismographfilteringrotational seismology

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Area of Science:

  • Optoelectronics
  • Signal Processing
  • Geophysics

Background:

  • Digital systems in optoelectronics require reliable data streams for monitoring.
  • Limited accuracy of digital-to-analog converters (DACs) introduces artefacts in data.
  • Artefacts in real-time data recording disrupt continuous monitoring processes.

Purpose of the Study:

  • To present data enhancement strategies for fibre-optic rotational seismographs (FORS).
  • To address artefacts that interfere with real-time data acquisition.
  • To propose a novel signal filtering concept for automatic artefact elimination.

Main Methods:

  • Data enhancement using filtering techniques for noise investigation.
  • Data resampling for efficient file size reduction.
  • Development of a new signal filtering concept for artefact removal.

Main Results:

  • Demonstration of data enhancement techniques for FORS.
  • Identification of artefacts impacting real-time recording triggers.
  • Proposal of a novel filtering approach to automatically eliminate artefacts.

Conclusions:

  • Effective data enhancement is crucial for reliable optoelectronic monitoring.
  • Artefacts in data streams pose a challenge for automated systems.
  • The proposed signal filtering method offers a solution for automatic artefact elimination in FORS data.