Time-frequency Analysis Techniques Research Articles

Adaptive time-frequency analysis of signals in AFM

The probe-sample interactions during the AFM experiments represent a case of a multi-frequency behaviour, since they are characterized by the simultaneous excitation of cantilevers eigenmodes and/or their harmonics. Analysis of the data obtained as a result of the interaction between the probe and the sample is of a particular interest not only from a practical point of view, i.e. obtaining information about the surface of the sample, but also this is an interesting methodological task, since the instantaneous processes resulting from the interaction of the probe with the sample surface carry a huge amount of information. To obtain this information, it is necessary to provide an analysis method that is capable of characterizing all of the excited cantilevers frequencies simultaneously and without averaging. In other words, it is necessary to obtain the complete spectral response of the cantilever interacting with the sample surface, i.e. information about the dynamics of cantilever behaviour in time. The aim of the present work is to show the possibilities of time-frequency analysis techniques in analysing the signals of AFM experiments.

Brain robot interface using artificial neural network

Recent researches in Brain Computer Interface (BCI) that can decode brain EEG signals has aided in an effective robot control which has led to the raise of Brain Robot Interface (BRI). This project focuses on the accurate classification of the user’s Action/Cognitive thoughts, where successful decoding of EEG signals can provide a higher degree of freedom control in BRI applications. The EEG signals from the user’s scalp are recorded through a non-invasive electrode and prepossessed to produce a noise free EEG signals. Time-Frequency Analysis techniques are used to extract featured from the EEG signal. In this work an Artificial Neural Network (ANN) machine learning algorithm is used as classifier to learn the EEG signal features for effective output classification. This work presents a performance analysis on the accuracy of the system for the proposed combination of Time-Frequency analysis and ANN algorithm for the EEG feature extraction and classifier respectively.

A Novel Approach for Seismic Time-Frequency Analysis Based on High-Order Synchrosqueezing Transform

Time-frequency analysis always plays a central role in the field of seismic processing due to the advantage in characterizing nonstationary signals. In this letter, we present a novel technique for seismic time-frequency analysis based on the high-order synchrosqueezing transform, which obtains more accurate instantaneous frequencies by using the higher order approximations for both amplitude and phase in order to achieve a highly energy-concentrated time-frequency representation. A synthetic example is employed to demonstrate the validity of the proposed method in sharpening time-frequency representation. Application on field data example further proves its potential in enhancing time-frequency resolution and delineating stratigraphic characteristics with higher precision and renders that this technique is promising for seismic data analysis.

Effect of sparsity‐aware time–frequency analysis on dynamic hand gesture classification with radar micro‐Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.

Deformation Forecasting of Surrounding Rock Mass Based on Correlation between Frequency and Fracture Scale of Microseismicity

The macroscopic deformation and failure of engineering rock mass may occur as a result of evolution and breakdown of its internal microfracture. Therefore, the macroscopic state of rock mass can be obtained from fracture scale of microfracture in real time. To assess instability and predict macroscopic deformation and failure of engineering rock mass, a time-frequency analysis technique based on S transform was proposed to investigate microseismic waveform and reveal the correlation between macroscopic deformation failure and microseismic frequency characteristics of engineering rock mass in combination with fracture scale. To minimize the influence of external factors on parameters calculated, a significant amount of microseismic data from three large-scale hydropower projects in southwestern China was collected as the statistical sample. The analysis of correlation between fracture scale and frequency characteristics of microseismic events was carried out based on the statistical sample. Combining with microseismic data and multipoint extensometers in the underground powerhouse of the Houziyan hydropower station, engineering verification was conducted. The result shows that the high-frequency components decrease and microseismic signals display low-frequency characteristic as the fracture scale increases; the microseismic high-frequency components decreased at first and then increased during the deformation process of surrounding rock mass, and the frequency of microseismic events shifts from high band to a lower one before deformation.

Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time

The linear part of transient evoked otoacoustic emission (TEOAE) is thought to be generated via coherent reflection near the characteristic place of constituent wave components. Because of the tonotopic organization of the cochlea, high frequency emissions return earlier than low frequencies; however, due to the random nature of coherent reflection, the instantaneous frequency (IF) and amplitude envelope of TEOAEs both fluctuate. Multiple reflection components and synchronized spontaneous emissions can further make it difficult to extract the IF by linear transforms. This paper proposes to model TEOAEs as a sum of intrinsic mode-type functions and analyze it by a nonlinear-type time-frequency (T-F) analysis technique called concentration of frequency and time (ConceFT). When tested with synthetic otoacoustic emission signals with possibly multiple oscillatory components, the present method is able to produce clearly visualized traces of individual components on the T-F plane. Further, when the signal is noisy, the proposed method is compared with existing linear and bilinear methods in its accuracy for estimating the fluctuating IF. Results suggest that ConceFT outperforms the best of these methods in terms of optimal transport distance, reducing the error by 10% to 21% when the signal to noise ratio is 10 dB or below.

Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Study

Abstract The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions

On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements

Corona-originated discharge of DC transmission lines is the main reason for the radiated electromagnetic interference (EMI) field in the vicinity of transmission lines. A joint time-frequency analysis technique was proposed to extract the radiated EMI current (excitation current) of DC corona based on corona current statistical measurements. A reduced-scale experimental platform was setup to measure the statistical distributions of current waveform parameters of aluminum conductor steel reinforced. Based on the measured results, the peak value, root-mean-square value and average value with 9 kHz and 200 Hz band-with of 0.5 MHz radiated EMI current were calculated by the technique proposed and validated with conventional excitation function method. Radio interference (RI) was calculated based on the radiated EMI current and a wire-to-plate platform was built for the validity of the RI computation results. The reason for the certain deviation between the computations and measurements was detailed analyzed.

On dissipative nonlinear evolutional pseudo-differential equations

Using the local time-frequency analysis techniques, we obtain an equivalent norm on modulation spaces. Secondly, applying this equivalent norm, we consider the Cauchy problem for the dissipative evolutionary pseudo-differential equation∂tu+A(x,D)u=F((∂xαu)|α|⩽κ),u(0,x)=u0(x), where A(x,D) is a dissipative pseudo-differential operator and F(z) is a multi-polynomial. We will develop the uniform decomposition techniques in both physical and frequency spaces to study its local well posedness in modulation spaces Mp,qs and in Sobolev spaces Hs. Moreover, the local solution can be extended to a global one in L2 and in Hs (s>κ+d/2) for certain nonlinearities.

Multiband imaging and linear unmixing of optical fiber intrusion signal

Feature extraction of the optical fiber intrusion signal is one of the most basic problems in the optical fiber pre-warning system (OFPS). This paper proposes a spectrum analysis method in the OFPS that adopts the time-frequency analysis technique to realize multiband imaging of the intrusion signal at first. Subsequently, the characteristic spectrum of the intrusion signal can be obtained as the intrusion feature from the imaging results. Since the practical intrusion spectrum is usually a mixture of multiple intrusion types, in order to extract the spectrum corresponding to each intrusion type, the mixed spectrum is characterized by a linear mixing mode. Then, we exploit the nonnegative matrix factorization (NMF) in the mixture analysis. Moreover, we add physical constraints to the NMF and solve it by gradient descent algorithm. Finally, we extract the characteristic spectrums from the mixed spectrum resulting to the spectrum unmixing. The real data experiments verify the effectiveness of this method.

FEATURE EXTRACTION ENHANCEMENT BASED ON PARAMETERLESS EMPIRICAL WAVELET TRANSFORM: APPLICATION TO BEARING FAULT DIAGNOSIS

ABSTRACTRolling-element bearings are usually subject to faults that need promptdetection in order to prevent sudden failures. Many time-frequency analysistechniques have been used for the purpose of bearing fault detection anddiagnosis. From these techniques, wavelets and empirical modedecomposition (EMD) stand out as the most widely applied methods inbearing fault diagnosis. Recently, a novel method named the parameterlessempirical wavelet transform (PEWT) has been proposed to combine thewavelet formulation with the adaptability of the empirical modedecomposition. In this paper, the parameterless empirical wavelet transform(PEWT) is combined with envelope detection (ED) to present a new schemenamed PEWT-ED for non-stationary signal analysis. The capabilities andlimitations of the new method in bearing fault diagnosis are investigatedusing simulation and experiment. The results show that the new approachcan effectively extract the bearing fault characteristics. The PEWT-ED isfound to be a powerful tool in signal de-noising and enhancement for faultdiagnosis purposes.

WiStep

Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.

Discrete Time-Frequency Signal Analysis and Processing Techniques for Non-Stationary Signals

This paper presents the methodology, properties and processing of the time-frequency techniques for non-stationary signals, which are frequently used in biomedical, communication and image processing fields. Two classes of time-frequency analysis techniques are chosen for this study. One is short-time Fourier Transform (STFT) technique from linear time-frequency analysis and the other is the Wigner-Ville Distribution (WVD) from Quadratic time-frequency analysis technique. Algorithms for both these techniques are developed and implemented on non-stationary signals for spectrum analysis. The results of this study revealed that the WVD and its classes are most suitable for time-frequency analysis.

Hilbert Huang transform and pattern recognition to detect defects in induction motor

In this paper, a new time-frequency analysis technique called Hilbert Huang transform is applied to detect bars and ring defects in an induction motor. A pattern recognition method called support vector machine is used to classify the different operating modes of the motor (healthy and/or defective). The aim of this study is to improve the classification results obtained using the Fourier transform (as frequency analysis technique) and temporal signals.

Hilbert Huang transform and pattern recognition to detect defects in induction motor

In this paper, a new time-frequency analysis technique called Hilbert Huang transform is applied to detect bars and ring defects in an induction motor. A pattern recognition method called support v...

Multiband imaging and characteristic spectrum extraction of optical fiber intrusion signal

Abstract This paper proposes a multiband imaging method for optical fiber intrusion signals to realize the multi-dimensional analysis and the characteristic spectrum extraction. Firstly, the time-frequency analysis technique is performed on the initial collected intrusion signals to obtain the time-frequency distribution at each spatial intrusion position, on which energy band-wise integration is carried out along the frequency direction to generate spatial-temporal two-dimensional images in each frequency band. Then, these images are further stacked up as layers resulting in a spatial-temporal-frequency three-dimensional (3-D) cubic data. Based on this data, we extract the characteristic spectrums as the features of the intrusion signals. The real data experiments verify the effectiveness of this method.

Midterm Periodicity Analysis of the Mount Wilson Magnetic Indices Using the Synchrosqueezing Transform

Abstract A novel time–frequency technique, called the synchrosqueezing transform (SST), is used to investigate the midterm periodic variations of magnetic fields on the solar surface. The Magnetic Plage Strength Index (MPSI) and the Mount Wilson Sunspot Index (MWSI), measured daily by the Mount Wilson Observatory between 1970 January 19 and 2012 January 22, are selected. Short-, mid, and longer-term periodicities are represented and decomposed by the SST with hardly any mode mixing. This demonstrates that the SST is a useful time–frequency analysis technique to characterize the periodic modes of helioseismic data. Apart from the fundamental modes of the annual periodicity, ∼27 day rotational cycle and ∼11 year solar cycle, the SST reveals several midterm periodicities in the two magnetic activity indices, specifically, ∼157 days (i.e., Rieger-type periodicity), and ∼1.3 and 1.7 years. The periodic modes, with 116.4 and 276.2 day periodicity in the MPSI, 108.5 and 251.6 day periodicity in the MWSI, and 157.7 day periodicity in the two indices, are in better accord with those significant periodicities derived from the Rossby waves theoretical model. This study suggests that the modes are caused by Rossby waves. For the 1.30 and 1.71 year periodicity of the MPSI, and the 1.33 and 1.67 year periodicity of the MWSI, our analysis infers that they are related to those periodicities with the same timescale in the interior of the Sun and in the high atmospheric layers.

Low Probability of Intercept Triangular Modulated Frequency Modulated Continuous Wave Signal Characterization Comparison using the Spectrogram and the Scalogram

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing low probability of intercept radar signals. This paper presents the novel approach of characterizing low probability of intercept frequency modulated continuous wave radar signals through utilization and direct comparison of the Spectrogram versus the Scalogram. Two different triangular modulated frequency modulated continuous wave signals were analyzed. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Spectrogram produced more accurate characterization metrics than the Scalogram. An improvement in performance may well translate into saved equipment and lives.

Monitoring Electrical and Thermal Characteristics of HTS Cable Systems via Time–Frequency Domain Reflectometry

A high-temperature superconducting (HTS) cable system with the 22.9 kV, 50 MVA, and 410 m length is installed and operated at 154 kV Icheon substation of Korea Electric Power Corporation (KEPCO). Unfortunately, it is a difficult task to diagnose and monitor electrical and thermal characteristics of the HTS cable system in a real-time manner. In order to protect operational failures of grid-connected HTS cable systems, this paper proposes time-frequency domain reflectometry (TFDR) and analysis techniques, i.e., time-frequency cross correlation and instantaneous frequency estimation. To verify the performance of the proposed method, the temperature is changed via the cryogenic refrigeration system and the status of the grid-connected HTS cable is monitored via TFDR in a real-time manner.

Wind Turbine Diagnosis under Variable Speed Conditions Using a Single Sensor Based on the Synchrosqueezing Transform Method.

The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.