Signal and data processing
Section 1
Signal and data processing
Post-graduate level knowledge of processes and principles required to uncover actionable insights from complex signal data through filtering, appropriate visualisations and finding patterns in the time- and frequency-domains. Key knowledge components include:
- Understanding sources of noise and artifacts within empirical data and determine steps to reduce or eliminate noise at both the data collection and post-processing stages.
- Understanding the impact of noise within data on results and calculated derivatives.
- Selection of appropriate signal conditioning solutions based on the activity, data and critical variables of interest.
- Application of appropriate visualisation methodology to present information