As an alternative, a moving window technique is applied.
Moreover, sliding window technique [34, 35] is used.
The de-noised electroencephalography (EEG) signals are partitioned into segments using a window technique.
Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique.
The analysis was performed in time domain using moving time window technique.
Here we focus on three window technique named as parks McClellan, Rectangular and Kaiser window technique as well as some prototype filter deign and showing some result with respect to their filter characteristics.
The whole time sequence is divided into several smaller sub-sequence by means of the sliding time window technique.
The most common method in statistical approaches is the moving window technique.
Furthermore, the focus window technique is implemented to show the details without distortion.
A window technique was applied for surgical removal of the impacted canine.
Therefore, in order to use the machine learning prediction algorithms sliding window technique is used.
