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The proposed approach has been successfully tested on real data sets collected by two different microphone settings.
We present extensive experimental results, using two large urban data sets collected by our research platform.
For the calibration of the proposed model, data sets collected from Copais Greecee) were used.
All models were independently validated with test sets collected in both accessible and poorly-accessible areas.
Data from several heterogeneous data sets collected around the years 1990 and 2000 are used.
Both neural network models were trained using 498 data sets collected from the literature and unpublished sources.
The proposed algorithm is tested on one sample every three seconds of data sets collected from the CPMN.
This is explained by the absence of iron in most of the data sets collected in other buildings.
Furthermore, the accuracy and reliability of associated velocity distribution equations have not been tested thoroughly using data sets collected using advanced techniques.
Results are based on long-term data sets collected by federal and academic research and monitoring programs that place recent changes into a historic context.
Two data sets collected from the Chang Ying highway located in Jilin province, China, were used to validate the proposed method.
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