Parallel Lattice Structure for Dual Windows Computation in Multiwindow Gabor Transform

Li, Rui; Kwan, Hon Keung

doi:10.1109/access.2019.2948634

Cited by 4 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12 Frequency modulation signal Fig. 13 Gabor spectrum corresponding to M-DGT algorithm Fig. 14 Gabor spectrum corresponding to BS-DGT algorithm 10, indicating that a better time-frequency representation can be obtained when the number of windows is 4.…”

Section: Influence Of Parameters On Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Algorithm for Finding the Best Linear Combination Window Under the Sparse Solution Constraint of Discrete Gabor Transform Coefficients

Zhang

Wang

Zhou

et al. 2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

Single-window discrete Gabor transform with an adaptive window width selection algorithm (AWWS-DGT) and multi-window discrete Gabor transform (M-DGT) are the current mainstream analysis and processing methods for multi-component signals. However, the time-frequency accuracy in the time-frequency plane obtained by the two algorithms is not very high, and not all components of the signals that contain complex and very different time-frequency components can be displayed accurately. The linear combination window algorithm based on the 1 and 2 norm constraints of the transform coefficients can accurately display all the components of the signal, but the algorithm has poor anti-noise performance. In this paper, an algorithm for finding the best linear combination window under the sparse solution constraint of discrete Gabor transform coefficients is proposed. First, the optimal window number adaptive selection method proposed in this paper is used to select a set of window functions with different window widths, and the goal is to obtain the sparse solution of the transform coefficient. Through the gradient descent method, the best linear combination coefficient is obtained. Second, according to the best linear combination coefficient obtained, the selected window is combined into an analysis window function. Finally, the influence of the parameters on the combined window is further studied, and the results show that the number of windows has a great influence on the result and that the influence of the regularization parameter is negligible. Experiments on two simulated non-stationary signals and real electrocardiogram signals show that the proposed algorithm can display all components of the signals, the time-frequency accuracy is improved by 3% to 10%, and the anti-noise performance is more than 20% higher than that of the existing linear combination window algorithm. These advantages are very important for the analysis and processing of practical signals.

show abstract

Section: Influence Of Parameters On Resultsmentioning

confidence: 99%

“…The structure is: According to Eq. ( 6), given any synthesis window, the dual window can be solved quickly [8,10,11,13,21].…”

Section: Discrete Gabor Transformmentioning

confidence: 99%

Algorithm for Finding the Best Linear Combination Window Under the Sparse Solution Constraint of Discrete Gabor Transform Coefficients

Zhang

Wang

Zhou

et al. 2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

show abstract

“…To improve the time-frequency resolution of the traditional DGT, the M-DGT based on biorthogonal analysis approach has been studied in [22][23][24][25] with applications in evolutionary spectral analysis. The multiwindow discrete Gabor expansion (M-DGE) in [26][27][28][29] using the frame theory has also been applied to signal processing [30], image processing [31], macromolecular sequence processing [32], and so on. Furthermore, the multiwindow real-valued discrete Gabor transform and its fast algorithms have been proposed in [33,34] by using biorthogonal analysis approach.…”

Section: Introductionmentioning

confidence: 99%

Multiwindow discrete Gabor transform using parallel lattice structures

Tao

Kwan³

2020

IET signal process.

Self Cite

View full text Add to dashboard Cite

The multiwindow discrete Gabor transform (M-DGT) is an effective time-frequency analysis tool to analyse timevarying signals containing components with multiple frequencies. In this study, fast block time-recursive methods for computing the M-DGT coefficients of a signal and the reconstruction of the signal from the transform coefficients are presented with steps as listed, respectively, in Algorithms 1 and 2, and their implementations using unified parallel lattice structures are also given. The proposed algorithms consisting of Algorithms 1 and 2 for respective forward and inverse transforms are compared to (i) those of the existing serial algorithms in terms of computational complexity and time; and (ii) those of the existing parallel algorithms in terms of hardware complexity. The results indicate that the proposed algorithm is fast in computing M-DGT coefficients of a signal and reconstructing the signal with a reduced hardware complexity. 2 Review of M-DGT Let x(k) represent a periodic and discrete-time signal of a periodic length L, the discrete Gabor expansion with multiple windows (M-DGE) [27, 33] is given by

show abstract

Weighted multiwindow discrete Gabor transform

Kwan

2021

Digital Signal Processing

View full text Add to dashboard Cite

Parallel Lattice Structure for Dual Windows Computation in Multiwindow Gabor Transform

Cited by 4 publications

References 39 publications

Algorithm for Finding the Best Linear Combination Window Under the Sparse Solution Constraint of Discrete Gabor Transform Coefficients

Algorithm for Finding the Best Linear Combination Window Under the Sparse Solution Constraint of Discrete Gabor Transform Coefficients

Multiwindow discrete Gabor transform using parallel lattice structures

Weighted multiwindow discrete Gabor transform

Contact Info

Product

Resources

About