2023/01/16 · Sparse time-frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is ...

The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing.

We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data ...

This paper proposes a sparse time-frequency analysis by using an L1-norm constraint, fitting the sparse prior of a signal's spectrum.

2022/04/30 · This study proposes a method of reconstructing missing samples from multi-sensor recordings of non-stationary amplitude-modulation frequency-modulated signals ...

2020/04/30 · Abstract—Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals.

The results show that the proposed method can obtain more accurate time-frequency distributions than other algorithms. Finally, we apply the proposed method in ...

2023/04/20 · This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach.

This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the ...

In this paper we address the problem of estimating missing regions of time-frequency representations of audio signals. The problem of missing data in the time- ...