The proposed method achieves results comparable to that of wavelet compression methods, achieving low reconstruction errors while preserving the morphologiies of the signals.
Most of the annotation methods achieve low recall due to the large number of available fine labels, i.e., vocabulary words.
On average, unsupervised methods achieve low prediction accuracies, with the notable exception of the Z-SCORE method, and are considerably outperformed by supervised and semi-supervised methods.
In this study, to improve the speed of the lifting-based discrete wavelet transform (DWT) for large-scale data, we propose a parallel method that achieves low memory usage and highly efficient memory access on a graphics processing unit (GPU).
This low-cost method achieves a low retear rate and few bone tunnel problems, the mean postoperative UCLA score being comparable to that obtained by using an arthroscopic anchor suture technique.
Additionally, a comparison with an existing path planning method shows that the proposed method achieves a low computational complexity (that is approximately 150 times faster) and a certain level of shortness in the settling time.
The method achieves extremely low limits of detection (<0.02 μg/L), in both surface and drinking waters and it is used for identification and verification purposes as well as for determinations at the ppt level.
Furthermore our method achieves a low false positive rate.
The MH method achieves the lowest transmission counts of all the methods; however, it is not optimal in time, therefore it delivers packets later than the other methods.
From the results, we can see that the proposed method achieves the lowest error rates in most of the image pairs.
