Sentence examples for oss algorithm from inspiring English sources

The OSS algorithm is better than the RAAR-l 0 algorithm, but it suffers from much artifacts and loses much details (see Fig. 7b).

Under the noisy data case, we incorporate the oversampling smoothness (OSS) algorithm [20], which produces consistently better reconstruction than the HIO algorithm under the noisy scenario, instead of the HIO algorithm for comparison.

In this paper, a general OSS design algorithm for the accumulative measurement is proposed.

Due to these aspects, only the OSS-NLMS-id algorithm will be considered as a benchmark in the following experiments.

Evolution of the optimal control parameters: a α opt(n) of the OSS-NLMS-id algorithm and b δ opt(n) of the OR-NLMS-id algorithm.

Other conditions are the same as in Fig. 5. Nevertheless, the OSS-NLMS-id algorithm still requires a constant regularization parameter, especially in case of non-stationary inputs like speech.

Misalignment of the NLMS (for different values of α), NPVSS-NLMS, JO-NLMS, and OSS-NLMS-id algorithms.

Misalignment of the NLMS (with α=1), NPVSS-NLMS, and OSS-NLMS-id algorithms for different values of δ.

However, in all the following experiments, we will consider a constant regularization (delta = 20{sigma _{x}^{2}}) for the NLMS, NPVSS-NLMS, and OSS-NLMS-id algorithms.

The NLMS, NPVSS-NLMS, and OSS-NLMS-id algorithms are compared when using two different values of the regularization constant, i.e., (delta = {sigma _{x}^{2}}) and (delta = 20{sigma _{x}^{2}}).

To conclude this experiment, the influence of the regularization parameter can be also noticed in Fig. 8, where the control parameters of the NPVSS-NLMS and OSS-NLMS-id algorithms are depicted, i.e., α NPVSS(n) and α opt(n), respectively.