Wyniki wyszukiwania - Biblioteka Nauki

1

Comparative analysis of variants of Gabor-Wigner Transform for crossterm reduction

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Ajab M. , Taj I. A. , Khan N. A.

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2012

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tom Vol. 19, nr 3

499-508

EN

Gabor Wigner Transform (GWT) is a composition of two time-frequency planes (Gabor Transform (GT) and Wigner Distribution (WD)), and hence GWT takes the advantages of both transforms (high resolution of WD and cross-terms free GT). In multi-component signal analysis where GWT fails to extract auto-components, the marriage of signal processing and image processing techniques proved their potential to extract autocomponents. The proposed algorithm maintained the resolution of auto-components. This work also shows that the Fractional Fourier Transform (FRFT) domain is a powerful tool for signal analysis. Performance analysis of modified fractional GWT reveals that it provides a solution of cross-terms of WD and blurring of GT.

2

A Comprehensive Survey on Fractional Fourier Transform

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Zhang Y. , Wang S. , Yang J.-F. , Zhang Z. , Phillips P. , Sun P. , Yan J.

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tom Vol. 151, nr 1/4

1--48

EN

The Fractional Fourier transform (FRFT) is a relatively novel linear transforms that is a generalization of conventional Fourier transform (FT). FRFT can transform a particular signal to a unified time-frequency domain. In this survey, we try to present a comprehensive investigation of FRFT. Firstly, we provided definition of FRFT and its three discrete versions (weighted-type, sampling-type, and eigendecomposition-type). Secondly, we offered a comprehensive theoretical research and technological studies that consisted of hardware implementation, software implementation, and optimal order selection. Thirdly, we presented a survey on applications of FRFT to following fields: communication, encryption, optimal engineering, radiology, remote sensing, fractional calculus, fractional wavelet transform, pseudo-differential operator, pattern recognition, and image processing. It is hoped that this survey would be beneficial for the researchers studying on FRFT.

3

Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm

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Wang S. , Rao R. V. , Chen P. , Zhang Y. , Liu A. , Wei L.

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tom Vol. 151, nr 1/4

191--211

EN

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26% ± 3.44%, specificity of 92.28% ± 3.58%, and accuracy of 92.27% ± 3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO.

4

The fractional Fourier transform as a biomedical signal and image processing tool: A review

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Gómez-Echavarría A. , Ugarte J. P. , Tobón C.

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tom Vol. 40, no. 3

1081--1093

EN

This work presents a literature review of the fractional Fourier transform (FrFT) investiga-tions and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has been used for signal and image processing due to its capability in capturing the nonstationary characteristics of real signals. Most biomedical signals are an example of such non-stationarity. Thus, the FrFT-based solutions can be formulated, aiming to enhance the health technology. As the literature review indicates, common applications of the FrFT involves signal detection, filtering and features extraction. Establishing adequate solutions for these tasks requires a proper fractional order estimation and implementing the suitable numeric approach for the discrete FrFT calculation. Since most of the reports barely describe the methodology on this regard, it is important that future works include detailed information about the implementation criteria of the FrFT. Although the applications in biomedical sciences are not yet among the most frequent FrFT fields of action, the growing interest of the scientific community in the FrFT, supports its practical usefulness for developing new biomedical tools.

5

Fetal heart rate estimation using fractional Fourier transform and wavelet analysis

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Jaba Deva Krupa A. , Dhanalakshmi S. , Sanjana N. L. , Manivannan N. , Kumar R. , Tripathy S.

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2021

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tom Vol. 41, no. 4

1533--1547

EN

Objective: Monitoring fetal cardiac activity during pregnancy is a critical part of assessing the fetus’s health. Non-invasive fetal electrocardiogram (NIFECG) is a safe emerging fetal cardiac monitoring approach receiving considerable interest. This paper proposes an effective way to separate the fetal ECG signal from the single-channel abdominal ECG signals. Methods: The paper proposes a novel algorithm based on time-frequency analysis combining fractional Fourier transform (FrFT) and wavelet analysis to extract fetal ECG from abdominal signals at higher accuracy. The abdominal signals acquired from pregnant women are preprocessed and subjected to suppressing maternal ECG using fractional Fourier transform and maximum likelihood estimate. The estimated maternal signal is removed from the abdominal ECG. The residue is processed using wavelet decomposition to obtain a clean fetal ECG and calculate fetal heart rate. Results: The proposed algorithm’s performance is validated using signals from the Daisy database and Physionet challenge 2013 set-a dataset. Real-time signals acquired using Powerlab data acquisition hardware are also included for validation. The obtained results show that the proposed algorithm can effectively extract the fetal ECG and accurately estimate the fetal heart rate. Conclusion: The proposed method is a promising and straightforward algorithm for FECG extraction. Fractional Fourier transform maps the time domain abdominal signal into the fractional frequency domain, distinguishing the fetal and maternal ECG. The Wavelet transform can efficiently denoise the residue abdominal signal and provides a clean fetal ECG. The proposed approach achieves 98.12% of accuracy, 98.85% of sensitivity, 99.16% of positive predictive value, and 99.42% of F1 measure.