The technique of stacking multiple data records to improve SNR has been applied in various fields.
When stacking multiple data records, it is important to align them according to their expected polarity from the assumed source.
When we stacked multiple data records, signal components would be enhanced in strength, whereas the noise components would cancel each other.
The uncertainty analysis is based on both non-parametric (Welch based) and parametric (VARX based) conventional stochastic identification methods and also on a CCP-VARX based method which operates on multiple data records.
Firstly, we have developed generic software for Android, EpiCollect, which allows multiple data records to be entered and stored on a mobile phone (text variables, GPS position, photo etc).
Multiple data records might be observed for each person for possible occurrence of an MI event or occurrence of censoring.
Structural response data recorded at multiple locations are combined using a multivariate time delay embedding in order to reconstruct the structure's dynamical attractor.
This is done by continuously adjusting X-band radar data based on the previous 5 30 min of rain data recorded by multiple rain gauges and propagating the rainfall estimates through a hydraulic urban drainage model.
However, the noisy training obtains multi-conditioned speech data by corrupting clean speech signals, while the multi-condition training uses real-world speech data recorded in multiple noise conditions.
