Overlap
Consider the following example: If there is a need to collect and analyze a frequency range of 1 kHz, the data collection time (also known as the time window) for collecting 1024 samples could be exactly 40 ms. The FFT processor (Figure 4.7) can calculate and display a spectrum in 10 ms, after which it encounters an idle duration of 30 ms until the acquisition of the next block is completed.
Once the first block is collected, rather than waiting for the next block to be fully collected, it is possible to proceed and calculate a new spectrum by using part of the data from the new block and part of the data from the old block. If the process under consideration is stationary (not varying with time), the data from the two blocks can be averaged.
Considering the example mentioned above, we could initiate a new FFT calculation by using 75% of the previous block and 25% of the new one. We would then be performing a 75% overlap processing and our apparent processing time (after the first block) would be 10 ms per spectrum, rather than 40 ms. The method of overlapping becomes even more significant when we are operating at very low frequencies, or when we want to calculate many spectral averages.
For example, let us assume we are collecting data in a 100-Hz frequency range and wish to calculate 16 averages. The data collection time is 4 s, and without overlap processing we will need 64 s. With 75% overlap, we need 4 s for the first block and 1 s for each successive one, or 4 × 1 + 1 × 15 = 19 s to perform the same task. A considerable amount of time can be saved during data collection by the use of overlapping. Themethod enables more efficient use of the collected data.
Consider the following example: If there is a need to collect and analyze a frequency range of 1 kHz, the data collection time (also known as the time window) for collecting 1024 samples could be exactly 40 ms. The FFT processor (Figure 4.7) can calculate and display a spectrum in 10 ms, after which it encounters an idle duration of 30 ms until the acquisition of the next block is completed.
Once the first block is collected, rather than waiting for the next block to be fully collected, it is possible to proceed and calculate a new spectrum by using part of the data from the new block and part of the data from the old block. If the process under consideration is stationary (not varying with time), the data from the two blocks can be averaged.
Considering the example mentioned above, we could initiate a new FFT calculation by using 75% of the previous block and 25% of the new one. We would then be performing a 75% overlap processing and our apparent processing time (after the first block) would be 10 ms per spectrum, rather than 40 ms. The method of overlapping becomes even more significant when we are operating at very low frequencies, or when we want to calculate many spectral averages.
For example, let us assume we are collecting data in a 100-Hz frequency range and wish to calculate 16 averages. The data collection time is 4 s, and without overlap processing we will need 64 s. With 75% overlap, we need 4 s for the first block and 1 s for each successive one, or 4 × 1 + 1 × 15 = 19 s to perform the same task. A considerable amount of time can be saved during data collection by the use of overlapping. Themethod enables more efficient use of the collected data.
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