Cross-terms reduction in the Wigner–Ville distribution using tunable-Q wavelet transform

Pachori, Ram Bilas; Nishad, Anurag

doi:10.1016/j.sigpro.2015.07.026

Cited by 115 publications

(45 citation statements)

References 32 publications

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“…The first key step is to represent complex time series with features images by the appropriate strategy. The commonly used time-frequency representations of non-stationary signals are Wigner-Ville Distribution (WVD) [35], short-time Fourier transform (STFT) [36], and Gabor transform [37]. However, considering the characteristics of the transient abruptness of the response signal of the e-tongue system, STFT was adopted to extract the time-frequency characteristics.…”

Section: Features Extraction and Classification Methodsmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are "hand-crafted", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.

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Section: Features Extraction and Classification Methodsmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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“…The sub-band signals of the individual channels have been obtained using TQWT. The TQWT is a special type of DWT and has found a wide range of applicability in biomedical signal analysis [37][38][39][40][41], bearing fault detection [42,43], and cross-terms reduction in time-frequency distribution [44]. The TQWT is useful to analyse oscillatory signals by adjusting input parameters-namely, Q, redundancy or over-sampling rate denoted by R [36], and number of decomposition levels denoted by J.…”

Section: Tqwt Based Multivariate Sub-band Fuzzy Entropymentioning

confidence: 99%

“…The lower value of Q gives good frequency resolution in the low frequency region; on the other hand, higher value of Q is useful to get good frequency resolution in the high frequency region of the spectrum. This property of TQWT has been used to design a filter-bank that provides nearly uniform resolution for all frequency components in [44]. The designed filter-bank has been studied for the reduction of cross-terms in Wigner-Ville distribution based time-frequency analysis.…”

Section: Tqwt Based Multivariate Sub-band Fuzzy Entropymentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

Bhattacharyya

Pachori

Acharya

2017

Entropy

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This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals.

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“…As a bilinear time-frequency distribution, the Wigner-Ville Distribution (WVD) has excellent characteristics, such as high resolution, energy concentration, and time-frequency edge characteristics [4]. However, Pachori points out that WVD is prone to cross-term interference when dealing with multi-component signals [5]. In [6], Lunden combines WVD, Choi-Williams distribution (CWD), and a classification system to achieve accurate pulse compression waveform recognition.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Accurate Recognition System for Radar Emitter Signals

et al. 2019

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Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1–P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals.

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Cross-terms reduction in the Wigner–Ville distribution using tunable-Q wavelet transform

Cited by 115 publications

References 32 publications

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

A Rapid Accurate Recognition System for Radar Emitter Signals

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