Showing papers on "Time–frequency analysis published in 2014"
TL;DR: Conventional methods of EEG feature extraction methods are discussed, comparing their performances for specific task, and recommending the most suitable method for feature extraction based on performance.
Abstract: Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.
362 citations
TL;DR: The authors introduce an iterative algorithm, called matching demodulation transform (MDT), to generate a time-frequency (TF) representation with satisfactory energy concentration, and the MDT-based synchrosqueezing algorithm is described to further enhance the concentration and reduce the diffusion of the curved IF profile in the TF representation of original syn chrosquEEzing transform.
Abstract: The authors introduce an iterative algorithm, called matching demodulation transform (MDT), to generate a time-frequency (TF) representation with satisfactory energy concentration. As opposed to conventional TF analysis methods, this algorithm does not have to devise ad-hoc parametric TF dictionary. Assuming the FM law of a signal can be well characterized by a determined mathematical model with reasonable accuracy, the MDT algorithm can adopt a partial demodulation and stepwise refinement strategy for investigating TF properties of the signal. The practical implementation of the MDT involves an iterative procedure that gradually matches the true instantaneous frequency (IF) of the signal. Theoretical analysis of the MDT's performance is provided, including quantitative analysis of the IF estimation error and the convergence condition. Moreover, the MDT-based synchrosqueezing algorithm is described to further enhance the concentration and reduce the diffusion of the curved IF profile in the TF representation of original synchrosqueezing transform. The validity and practical utility of the proposed method are demonstrated by simulated as well as real signal.
235 citations
TL;DR: Some of the most important developments in the last two decades related to the concept of the IF, performance analysis ofIF estimators, and development of IF estimators for low SNR environments are reviewed.
137 citations
TL;DR: In this paper, the adaptive optimal kernel (AOK) was applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-changing running conditions.
131 citations
TL;DR: An empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events and is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.
Abstract: This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.
124 citations
TL;DR: The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation and its potential to seismic signal processing applications is shown.
Abstract: Time-frequency analysis can provide useful information in seismic data processing and interpretation. An accurate time-frequency representation is important in highlighting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, which is often the characteristic of seismic data. The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation. We apply the SST to seismic data and show its potential to seismic signal processing applications.
117 citations
TL;DR: A novel specific emitter identification method based on transient communication signal's time-frequency-energy distribution obtained by Hilbert-Huang transform (HHT) is proposed and can represent more subtle characteristics than the RF fingerprints based on instantaneous amplitude, phase, frequency and energy envelope.
Abstract: A novel specific emitter identification method based on transient communication signal's time-frequency-energy distribution obtained by Hilbert-Huang transform (HHT) is proposed. The transient starting point is detected using the phase-based method and the transient endpoint is detected using a self-adaptive threshold based on the HHT-based energy trajectory. Thirteen features that represent both overall and subtle transient characteristics are proposed to form a radio frequency (RF) fingerprint. The principal component analysis method is used to reduce the dimension of the feature vector and a support vector machine is used for classification. A signal acquisition system is designed to capture the signals from eight mobile phones to test the performance of the proposed method. Experimental results demonstrate that the method is effective and the proposed RF fingerprint can represent more subtle characteristics than the RF fingerprints based on instantaneous amplitude, phase, frequency and energy envelope. This method can be equally applicable for any wireless emitter to enhance the security of the wireless networks.
116 citations
TL;DR: A method is described that allows analyzing both the frequency of an ERO and its evolution over time and is interpreted in the context of the so-called match-and-utilization model.
Abstract: Event-related potentials (ERPs) reflect cognitive processes and are usually analyzed in the so-called time domain. Additional information on cognitive functions can be assessed when analyzing ERPs in the frequency domain and treating them as event-related oscillations (EROs). This procedure results in frequency spectra but lacks information about the temporal dynamics of EROs. Here, we describe a method—called time–frequency analysis—that allows analyzing both the frequency of an ERO and its evolution over time. In a brief tutorial, the reader will learn how to use wavelet analysis in order to compute time–frequency transforms of ERP data. Basic steps as well as potential artifacts are described. Rather than in terms of formulas, descriptions are in textual form (written text) with numerous figures illustrating the topics. Recommendations on how to present frequency and time–frequency data in journal articles are provided. Finally, we briefly review studies that have applied time–frequency analysis to mismatch negativity paradigms. The deviant stimulus of such a paradigm evokes an ERO in the theta frequency band that is stronger than for the standard stimulus. Conversely, the standard stimulus evokes a stronger gamma-band response than does the deviant. This is interpreted in the context of the so-called match-and-utilization model.
115 citations
TL;DR: A new method, based on natural frequency changes, able to detect damages in beam-like structures and to assess their location and severity, considering the particular manner in which the natural frequencies of the weak-axis bending vibration modes change due to the occurrence of discontinuities is presented.
112 citations
TL;DR: The proposed adaptive spectral kurtosis filtering technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signaltransients in the practical application.
109 citations
TL;DR: This letter shows the equivalence of the recently proposed generalized frequency division multiplexing (GFDM) communications scheme with a finite discrete critically sampled Gabor expansion and transform with an efficient algorithm for calculation of specific GFDM receiver filters.
Abstract: This letter shows the equivalence of the recently proposed generalized frequency division multiplexing (GFDM) communications scheme with a finite discrete critically sampled Gabor expansion and transform. GFDM is described with the terminology of Gabor analysis and the Balian-Low theorem is applied to prove the non-existence of zero-forcing receivers for certain configurations, having strong impact on the system performance. An efficient algorithm for calculation of specific GFDM receiver filters is derived and numerical examples confirm the theoretical results.
TL;DR: In this paper, the problem of informative frequency band (IFB) selection in vibration signal processing for local damage detection is discussed and a method based on the local maxima approach is proposed to extend the concept of automatic and objective IFB selection.
TL;DR: In this article, a time-frequency analysis method that combines the Bark-wavelet analysis and Hilbert-Huang transform is presented for underwater noise targets classification, which is inspired by human auditory perception.
27 Jan 2014
TL;DR: A technique for computing coefficient phases in a way that makes their interpretation more natural and flexible control of the Qvalues and more regular sampling of the time-frequency plane are proposed in order to simplify signal processing in the transform domain.
Abstract: In this paper, we propose a time-frequency representation where the frequency bins are distributed uniformly in log-frequency and their Q-factors obey a linear function of the bin center frequencies. The latter allows for time-frequency representations where the bandwidths can be e.g. constant on the log-frequency scale (constant Q) or constant on the auditory critical-band scale (smoothly varying Q). The proposed techniques are published as a Matlab toolbox that extends [3]. Besides the features that stem from [3] – perfect reconstruction and computational efficiency – we propose here a technique for computing coefficient phases in a way that makes their interpretation more natural. Other extensions include flexible control of the Qvalues and more regular sampling of the time-frequency plane in order to simplify signal processing in the transform domain.
TL;DR: The efficiency of the Fourier-Bessel transform and time-frequency (TF)-based method in conjunction with the fractional Fourier transform (FrFT), for extracting micro-Doppler radar signatures from the rotating targets is reported.
Abstract: In this paper, we report the efficiency of the Fourier-Bessel transform (FBT) and time-frequency (TF)-based method in conjunction with the fractional Fourier transform (FrFT), for extracting micro-Doppler (m-D) radar signatures from the rotating targets. This approach comprises mainly of two processes, with the first being the decomposition of the radar return, in order to extract m-D features, and the second being the TF analysis to estimate motion parameters of the target. In order to extract m-D features from the radar signal returns, the time domain radar signal is decomposed into stationary and nonstationary components using the FBT in conjunction with the FrFT. The components are then reconstructed by applying the inverse Fourier-Bessel transform (IFBT). After the extraction of the m-D features from the target's original radar return, TF analysis is used to estimate the target's motion parameters. This proposed method is also an effective tool for detecting maneuvering air targets in strong sea clutter and is also applied to both simulated data and real-world experimental data.
TL;DR: A novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the Orthogonal Matching Pursuit algorithm.
Abstract: The rotation, vibration, or coning motion of a target may produce periodic Doppler modulation, which is called the micro-Doppler phenomenon and is widely used for target classification and recognition. In this paper, the signal of interest is decomposed into a family of parametric basis-signals that are generated by discretizing the micro-Doppler parameter domain and synthesizing the micro-Doppler components with over-complete time–frequency characteristics. In this manner, micro-Doppler parameter estimation is converted into the problem of sparse signal recovery with a parametric dictionary. This problem can be considered as a specific case of dictionary learning, i.e., we need to solve for both the sparse solution and the parameter inside the dictionary matrix. To solve this problem, a novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed approach is validated by simulations.
TL;DR: In this article, an accurate technique for the estimation of single-phase grid voltage fundamental amplitude and frequency is presented, consisting of a quadrature signal generator (QSG) and a discrete Fourier transform (DFT).
Abstract: This paper presents an accurate technique for the estimation of single-phase grid voltage fundamental amplitude and frequency. The technique consists of a quadrature signal generator (QSG) and a discrete Fourier transform (DFT). The frequency information required by the QSG based on a second-order generalized integrator (SOGI) is estimated using the spectral leakage property of the DFT. The presented DFT operation does not require real-time evaluation of trigonometric functions. The frequency estimation is less affected by the presence of harmonics when compared to similar techniques based on QSG-SOGI such as the phase-locked loop and the frequency-locked loop. Moreover, unlike these techniques, the DFT-based QSG-SOGI (DFT-SOGI) technique does not create any interdependent loops, thus increasing the overall stability and easing the tuning process. The effectiveness of the proposed technique has been validated on a real-time experimental setup.
TL;DR: A time-frequency analysis method, Wigner-Ville Distribution (WVD), is applied to calculate the TOF of signal based on its excellent time- frequency energy distribution property and is validated to work effectively for damage imaging of a two-dimensional structure.
TL;DR: The main focus of the present paper is to study the performance of the multiclass capability of SVM techniques, and it shows an excellent prediction performance when purely time domain data is used.
TL;DR: A new method of fault detection in rotating machinery based on a vibration time series analysis in time–frequency domain, which combines information for all sub-signals in order to validate impulsive behavior of energy.
TL;DR: In this article, the wavelet phase difference (WPD) approach is applied for the identification of power system areas with coherent generator groups, which allows observation, at different frequency bands, of movement of low frequency electromechanical oscillations (LFEO), identified at different parts of the power system and identification of inter-area components that move or do not move together.
Abstract: In this paper, the wavelet phase difference (WPD) approach is applied for the identification of power system areas with coherent generator groups. This approach allows observation, at different frequency bands, of movement of low frequency electromechanical oscillations (LFEO), identified at different parts of the power system and the identification of the inter-area components that move or do not move together. An illustration of the applied approach was performed on the New England (NE) 39-bus test system. The interesting results of WPD application are also presented in a real wide-area measurement data from European interconnected power system. By using the discrete wavelet transform (DWT) and Hilbert-Huang transform (HHT), the validation of results from WPD approach is also given.
TL;DR: A novel method is introduced that enables the parameterized TF transform to generate the well-concentrated TFR for both the monocomponent signal and a wide class of multicomponent FM signals, whose components are modulated by either the same or the different sources.
Abstract: Parameterized time-frequency (TF) transforms, with signal-dependent kernel parameters, have been proposed to analyze multicomponent frequency modulated (FM) signals. Usually, the kernel parameters are estimated through recursive approximation of TF representation (TFR) ridge when instantaneous frequency models of the components have the same parameter settings. However, it will be inapplicable if the components have the different FM sources. In this paper, we introduce a novel method that enables the parameterized TF transform to generate the well-concentrated TFR for both the monocomponent signal and a wide class of multicomponent FM signals, whose components are modulated by either the same or the different sources. The proposed method contains two aspects: 1) estimating kernel parameters based on spectrum concentration index and 2) separating components and assembling the parameterized TFRs of the separated components. An advantage of the proposed method is that it avoids the dependence of the TFR while estimating the parameters. Moreover, it is effective at low signal-to-noise rate. The validity and practical utility of the proposed method are demonstrated by both the simulated and real signals. The results show that it outperforms the traditional TF methods in providing the TFR of the improved concentration for various multicomponent FM signals.
TL;DR: The aim of this lecture note is to present and relate these two of the most important tools in the TF signal analysis, the STFT and the WD, which are introduced by two Nobel prize winners, D. Gabor and E. Wigner, respectively, and form a basis for the S-method (SM), an efficient and simpleTF signal analysis tool providing a gradual transition between these two representations.
Abstract: The analysis, processing, and parameters estimation of signals whose spectral content changes in time are of crucial interest in many applications, including radar, acoustics, biomedicine, communications, multimedia, seismic, and the car industry [1]? [11] Various signal representations have been introduced to deal with this kind of signals within the area known as time-frequency (TF) signal analysis The oldest analysis tool in this area is the short-time Fourier transform (STFT), as a direct extension of the classical Fourier analysis The other key tool is the Wigner distribution (WD), introduced in signal analysis from quantum mechanics The aim of this lecture note is to present and relate these two of the most important tools in the TF signal analysis, the STFT and the WD (introduced by two Nobel prize winners, D Gabor and E Wigner, respectively) This relation is a basis for the S-method (SM), an efficient and simple TF signal analysis tool providing a gradual transition between these two representations
TL;DR: In this article, the authors proposed a statistical approach to the identification of structural damages using guided waves, which not only provides a quantitative identification of the damages, but can also quantify the uncertainties associated with the damage identification results.
08 Sep 2014
TL;DR: In this article, the authors performed the signal analysis on vibration data of ball bearing using Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) for bearing health analysis.
Abstract: Bearing health analysis plays a significant role in industry to improve reliability and performance of critical processes by alarming the faults at early stages. Conventional techniques do no guarantee to detect the faults at early stages because the low energy bearing frequencies get suppressed by stern noise and higher vibrations. The Fast Fourier Transform fails to analyse the transient and non-stationary signals directly. This paper performs the signal analysis on vibration data of ball bearing using Variational mode decomposition (VMD). Firstly, the intrinsic mode functions are extracted using VMD followed by Fast Fourier Transform, and finally the status of bearing is analyzed to be faulty or impeccable. This paper, stress on VMD rather than on EMD, due to its qualities in the detection of close tone vibration signatures and takes less computation time.
TL;DR: The presented technique uses an adaptive wavelet signal filtering method to separate signal components and reduce the model order to reduce the demand for computing power which has a direct impact on system's costs and modal parameter's estimation time.
TL;DR: By employing the sparse signal reconstruction algorithms, ideal time-frequency representations are obtained and the presented theory is illustrated on several examples dealing with different auto-correlation functions and corresponding TFDs.
Abstract: The estimation of time-varying instantaneous frequency (IF) for monocomponent signals with an incomplete set of samples is considered. A suitable time-frequency distribution (TFD) reduces the non-stationary signal into a local sinusoid over the lag variable prior to the Fourier transform. Accordingly, the observed spectral content becomes sparse and suitable for compressive sensing reconstruction in the case of missing samples. Although the local bilinear or higher order auto-correlation functions will increase the number of the missing samples, the analysis shows that an accurate IF estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with the signals phase non-linearity. In addition, by employing the sparse signal reconstruction algorithms, ideal time-frequency representations are obtained. The presented theory is illustrated on several examples dealing with different auto-correlation functions and corresponding TFDs.
TL;DR: In this paper, a new time-frequency analysis approach based on the synchrosqueezing wavelet transform (SSWT) is proposed for detecting anomalies of high-frequency attenuation and detecting the deep-layer weak signal.
Abstract: Time-frequency (TF) decomposition is used for characterizing the non-stationary relation between time and instantaneous frequency, which is very important in the processing and interpretation of seismic data. The conventional time-frequency analysis approaches suffer from the contradiction between time resolution and frequency resolution. A new time-frequency analysis approach is proposed based on the synchrosqueezing wavelet transform (SSWT). The SSWT is an empirical-modedecomposition-like tool but uses a different approach in constructing the components. With the help of the synchrosqueezing techniques, the SSWT can obtain obvious higher time and frequency resolution. Synthetic examples show that the SSWT based TF analysis can exactly capture the variable frequency components. Field data tests show the potential of the proposed approach in detecting anomalies of high-frequency attenuation and detecting the deep-layer weak signal.
TL;DR: The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
Abstract: Objective. Spelling is one of the most important issues in brain–computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time–frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. Approach. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time–frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Main results. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min−1, with an average of 54.0 bit min−1 and 43.0 bit min−1 in the three rounds and five rounds, respectively. Significance. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
TL;DR: In this paper, the Hilbert-Huang transform (HHT) was introduced to the phase-sensitive optical time-domain reflectometer sensor system for time-frequency analysis, and the Hilbert spectral analysis made the instantaneous frequency meaningful, and acquired the high frequency resolution.
Abstract: Time-frequency analysis is a practical method to analyze the characteristics of vibration signals. We introduce the Hilbert–Huang transform (HHT) to the phase-sensitive optical time-domain reflectometer sensor system for time-frequency analysis. The Hilbert spectral analysis makes the instantaneous frequency meaningful, and acquires the high frequency resolution. Compared with other time-frequency analysis methods, such as the short-time Fourier transform and the continuous wavelet transform, HHT presents high frequency resolution for both stationary and nonstationary signals, and with much less time consuming.