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This method has lower complexity than the one in [18].
Hence, the MLLR method has lower data requirements.
However, our method has lower outlier selection ratio than that of RANSAC.
Therefore, the conventional method has lower throughput performance with higher sensing level.
Positive values indicate that the first method has lower error than the second.
The results indicate that the proposed method has lower MSE values than those of Austere's method.
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Compared to the traditional dry blending method, the latex compounding method had lower energy consumption during mixing and better silica dispersion in rubber matrix.
The proposed method can not only achieve better bit error rate (BER) performance than the conventional BBSP method, but also preserves the advantages of the BBSP method having lower overheads of the downlink training and the CSI feedback.
In all cases, our weighted fitting method had lower RMSE than baseline method with the exception of one tie.
By contrast, the simple method had lower precisions (more FPs), but high recalls (less FNs), compared with TEMPI and PNA.
Of note, the diabetic patients who were not detected by the profiling method had lower fasting glucose levels or had well-controlled diabetes.
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