Variation Of Instantaneous Frequency Research Articles

Complexity of the instantaneous frequency variation in auditory steady-state response: A high sensitivity, high anti-interference index of mental fatigue

The quantification of mental fatigue (MF) in aviation remains a long-lasting problem which has a bearing on flight safety. In this study, we have developed a complexity-based MF index based on multiscale entropy (MSE) analysis of instantaneous frequency variation (IFV) in auditory steady-state response (ASSR). Subjects were asked to undertake a flight simulation task, before and after which, electroencephalograms (EEGs) were recorded with no auditory stimulus, and with Don chirp sound to elicit ASSR. In the anti-interference tests, our proposed MF index showed higher performance in terms of anti-interference from electrooculogram (EOG) and environment. In the flight simulation task, fatigue evaluations based on ASSR have achieved a better fatigue detection effect comparing with the conventional methods: 95% in the fatigue detection rate and −13.885% in the MF index. In conclusion, the proposed MF index has high sensitivity and high anti-interference capacities, therefore has potential applications in a variety of fields that require robust monitoring of MF outside a shielded chamber.

Improvements in Frequency Control of an AC Microgrid by Means of Micro-Hydropower Combined Flow-Reduced Dump Load Control Method

Load Frequency Control (LFC) is an issue of top importance to ensure microgrids (MGs) safe and reliable operation in AC MGs. Primary frequency control of each energy source can be guaranteed in AC MGs in order to integrate other energy sources. This paper proposes a micro-hydro frequency control scheme, combining the control of a reduced dump load and the nozzle flow control of Pelton turbines operating in isolated from electricity grid. Some researchs have reported the integration of dump load and flow control methods, but they did not reduce the dump load value and adjust the nozzle flow linearly to the power value demanded by users, causing the inefficient use of water. Simulation results were obtained in Matlab®/Simulink® using models obtained from previous research and proven by means of experimental studies. The simulation of the proposed scheme shows that instantaneous frequency variation is about 2 % even for perturbations magnitude up to 12 %. The validation result shows a 60 % reduction in overshoot and settling time of frequency temporal behaviour operating the micro-hydro autonomously.

Three-Phase Unbalance Analysis Method Based on Three-Phase Motor Current Instantaneous Information

Motor current signal analysis is a common method that can be applied to the monitoring and diagnosis of electromechanical equipment. Three-phase unbalance will increase the line power loss and affect the safe operation of power-using equipment. To quickly analyze motor current three-phase unbalance, we propose a method to analyze three-phase unbalance by calculating the coefficient of variation of the three-phase current’s instantaneous frequency and instantaneous amplitude. Simulations were conducted to analyze the effects of amplitude deviation and phase deviation on the coefficient of variation of instantaneous frequency and instantaneous amplitude. The three-phase unbalance with continuous amplitude change was simulated and its instantaneous performance was analyzed. Experiments were conducted to compare and analyze the instantaneous information characteristics of the motor with the continuous changing of three-phase unbalance under different speed conditions. Simulations and experiments show that the proposed method in this paper can analyze three-phase unbalance.

A Data-Driven Hybrid Methodology Using Randomized Low-Rank DMD Approximation and Flat-Top FIR Filter for Voltage Fluctuations Monitoring in Grid-Connected Distributed Generation Systems

The voltage fluctuations are caused by variations in renewable energy source outputs, increased usage of nonlinear loads, high reactive power consumption of loads, etc. This results in damage to electric components and enormous economic losses. It is necessary to measure the parameters of voltage fluctuations and flicker levels to achieve a secure supply of power. However, higher-order harmonics and noises in voltage waveform make such assessment a burdensome task. The traditional techniques fail to provide exact monitoring due to the uncharacteristic variations in the input voltage signal. Nowadays, data-driven methods have become a prominent choice for non-stationary, nonlinear signal analysis as it efficiently tackles atypical changes by its capability to identify spatio-temporal information from given data. The present paper proposes a data-driven hybrid methodology for monitoring voltage fluctuations using the low-rank approximation of dynamic mode decomposition (DMD) and flat-top finite impulse response (FIR) filter. The low-rank approximation of DMD enables to identify the spatio-temporal coherent structures from the data by mapping the data into a lower-dimensional space. Adaptive flat-top FIR filter tracks the fundamental dynamic phasor by identifying the instantaneous frequency, magnitude and phase variations. The performance of the proposed hybrid methodology is assessed using different test cases, and is compared with various existing approaches. The promising results suggest that the proposed methodology can be used for accurate parameter identification and monitoring voltage fluctuations in smart grid scenarios. It can also be used as an accurate tool in distribution grids to detect higher-order harmonics.

Multiple squeezing based on velocity synchronous chirplet transform with application for bearing fault diagnosis

Traditional constant basis time–frequency analysis (TFA) methods are limited by the frequency smearing effect when dealing with time-varying synchronous multi-component signals, which makes it difficult to obtain a clear time–frequency representation (TFR) for signals with closely-spaced frequencies. Therefore, it is still a challenge to segregate the synchronous components and to concentrate the energy of frequency components in the TFR. As such, a new nonlinear basis TFA method is proposed in this paper, named multiple squeezing based on velocity synchronous chirplet transform (MSVSCT). In the MSVSCT, the second-order velocity-synchronous chirplet transform is used to generate a nonlinear basis that has a consistent trend complying the instantaneous frequency (IF) variations, so that synchronous components can be separated even in a closely spaced situation. The multi-squeezing technique is adopted to further improve the energy concentration and the accuracy of the TFR. Simulated and experimental signals are employed for validations. The analysis results confirm that MSVSCT has higher accuracy, more robustness to noise, better observability, and higher fidelity of signal reconstruction over other popular TFA methods when processing synchronous multi-component signals.

Frequency modulation of cortical rhythmicity governs behavioral variability, excitability and synchrony of neurons in the visual cortex

Research in cognitive neuroscience has renewed the idea that brain oscillations are a core organization implicated in fundamental brain functions. Growing evidence reveals that the characteristic features of these oscillations, including power, phase and frequency, are highly non-stationary, fluctuating alongside alternations in sensation, cognition and behavior. However, there is little consensus on the functional implications of the instantaneous frequency variation in cortical excitability and concomitant behavior. Here, we capitalized on intracortical electrophysiology in the macaque monkey’s visual area MT performing a visuospatial discrimination task with visual cues. We observed that the instantaneous frequency of the theta–alpha oscillations (4–13 Hz) is modulated among specific neurons whose RFs overlap with the cued stimulus location. Interestingly, we found that such frequency modulation is causally correlated with MT excitability at both scales of individual and ensemble of neurons. Moreover, studying the functional relevance of frequency variations indicated that the average theta–alpha frequencies foreshadow the monkey’s reaction time. Our results also revealed that the neural synchronization strength alters with the average frequency shift in theta–alpha oscillations, suggesting frequency modulation is critical for mutually adjusting MTs’ rhythms. Overall, our findings propose that theta–alpha frequency variations modulate MT’s excitability, regulate mutual neurons’ rhythmicity and indicate variability in behavior.

Mental fatigue decreases complexity: Evidence from multiscale entropy analysis of instantaneous frequency variation in alpha rhythm.

Mental fatigue (MF) jeopardizes performance and safety through a variety of cognitive impairments and according to the complexity loss theory, should represent "complexity loss" in electroencephalogram (EEG). However, the studies are few and inconsistent concerning the relationship between MF and loss of complexity, probably because of the susceptibility of brain waves to noise. In this study, MF was induced in thirteen male college students by a simulated flight task. Before and at the end of the task, spontaneous EEG and auditory steady-state response (ASSR) were recorded and instantaneous frequency variation (IFV) in alpha rhythm was extracted and analyzed by multiscale entropy (MSE) analysis. The results show that there were significant differences in IFV in alpha rhythm either from spontaneous EEG or from ASSR for all subjects. Therefore, the proposed method can be effective in revealing the complexity loss caused by MF in spontaneous EEG and ASSR, which may serve as a promising analyzing method to mark mild mental impairments.

Railway bridge damage detection based on extraction of instantaneous frequency by Wavelet Synchrosqueezed Transform

In bridge structural health monitoring, typically the dynamic response of the system is used to assess the health condition of the bridge. However, the dynamic interaction between a bridge and a passing vehicle imposes non-stationarity on the system response, whereby the bridge modal parameters become time-dependent and detecting damage, for example, based on the bridge modal parameters, becomes challenging. Dynamic vehicle-bridge interaction (VBI) responses have mainly been investigated for damage detection through identifying signal singularities and abrupt changes. The singularities are usually associated with high-frequency components (relative to the bridge natural frequencies), and it is demanding to isolate the damage-induced singularities from those caused by either an operational condition, i.e., track irregularities, or noise. Unlike the high-frequency range, the influence of damage on the resonance frequency of the coupled system has not been fully explored. The present study proposes the shape of the bridge instantaneous frequency as a damage sensitive feature in which the influence of the vehicle dynamics can be excluded. This study demonstrates the feasibility of a damage detection approach based on the bridge instantaneous frequency by applying Wavelet Synchrosqueezed Transform (WSST). In this approach the bridge instantaneous frequency variation induced by damage is distinguished from the bridge instantaneous frequency variation induced by the vehicle. Several damage scenarios that are implemented numerically are analyzed to verify the method’s performance. The results demonstrate that a high resolution instantaneous frequency extracted from the VBI dynamic response outperforms the resonance frequency in determining the local disruption, leading to detecting the damage. A Damage Index (DI) is also proposed as an attempt to quantify the damage severity.

Highly Integrated Dual‐Modality Microwave Frequency Identification System

AbstractPhotonic‐assisted microwave frequency identification systems have been extensively explored and widely applied for civil and defense applications. Full integration of this system is a promising candidate in attempts to meet the requirements of reduction in size, weight, and power. Although many integrated photonic chips have been reported in different technologies, none has monolithically integrated all the main active and passive optoelectronic components. Furthermore, previous approaches could identify either a single frequency signal instantaneously or multiple frequency signals statistically, but not both, hindering the practical applications. Here, a highly integrated dual‐modality microwave frequency identification system on a single chip is reported. All critical components including a modulator, a sweeping‐frequency filter, a frequency‐selected filter, an amplitude comparison function, and parallel photodetectors, are monolithically integrated on the silicon‐on‐insulator platform. Thanks to the dual‐modality scheme, the chip is able to identify different types of microwave signals, including single‐frequency, multiple‐frequency, chirped, and frequency‐hopping microwave signals, as well as discriminate instantaneous frequency variation among the frequency‐modulated signals. This demonstration exhibits a highly integrated solution and fully functional microwave frequency identification, which can meet the requirements in reduction of size, weight, and power for future advanced microwave photonic processor.

Wave breaking probabilities under wind forcing in open sea and laboratory

Water wave breaking represents one of the most arduous problems in fluid mechanics. Understanding the process of wave breaking and developing an ability to quantify the associated energy losses and redistribution are critical across a wide range of coastal oceanic applications, maritime navigation, and climate and hydrodynamic research. Naturally, waves become steeper toward the inception of breaking; however, there is still a lack of unanimity regarding the relationship between breaking probability statistics and wave steepness. Here, we present a detailed investigation of breaking vs non-breaking statistics estimated using a recently developed method for accurate detection of breaking waves, based on the phase-time approach to identify breaking-associated patterns in the instantaneous frequency variations of surface elevation fluctuations. The findings are based on data collected both in the open sea and in a laboratory wind wave flume. An in-depth examination of celerities and steepnesses of breaking and non-breaking waves is presented. The analysis, which involved wave-by-wave examination, produced skewed Gaussian-like steepness histograms, revealing that non-breaking waves and breaking waves can reach steeper profiles, above the Stokes limit. All extreme steepness values were investigated and are presented here.

Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG

Generally, for healthy adults, the entropy of electroencephalogram (EEG) signals gradually decreases from wake to sleep stages N1, N2, to N3, and increases during REM. However, some researchers found that multiscale entropy curves of sleep and wakefulness intercept, a cross-over phenomenon whose origin remains unexplored. The objective of the present work is to trace the origin of the cross-over phenomenon and to propose a workaround strategy. We simulated EEG by generating 1/f broadband signal and chirp signals with continuously varying frequencies. We then retrieved the rhythmic component from simulated EEG and real-world EEG and conducted MSE analysis of the instantaneous frequency variation (IFV) of the rhythmic component. The simulation revealed that this interception was ubiquitous in the MSE analysis of simulated EEG with rhythmic components of different frequencies. The cross-over point moved toward larger scale factors with the increasing sampling rate. We found that the MSE curve of IFV from real-world EEG for the wakefulness group was higher than that for sleep, showing no interception. These results suggest that (1) for a rhythmic signal like EEG, MSE analysis of the raw signal is highly affected by the rhythmic component, presenting artificial cross-over curves in sleep EEG study, (2) frequency variation of rhythmic components are complex signal which differs between wakefulness and sleep, in accordance with the complexity loss theory.

Tracking the natural evolution of bearing spall size using cyclic natural frequency perturbations in vibration signals

Spalling caused by fatigue is the most common reason for rolling element bearing failure, and spall size can be a good indicator to predict the remaining useful life of the bearing. Previous studies reported that, in the acceleration signal, a low-frequency step response is caused by de-stressing or re-stressing of the rolling element during its entry into and exit from the spall zone, and a high-frequency impact response is often induced when the roller hits the trailing edge of the spall. The conventional spall size estimation methods thus attempt to capture these events (entry, impact, and exit) and use their time difference to estimate the spall size. But the limitation of these methods is that they were mostly built on artificial spalls and have proved scarcely effective in dealing with natural ones. A recent study investigated the effect of these events on bearing stiffness and reported that the stiffness of the bearing structure decreases when the rolling element traverses the spall zone. Inspired by the study, a novel approach is proposed in this paper, which utilises the duration of natural frequency perturbations (induced by stiffness variation) of the bearing structure to perform spall-size estimation. Compared to the existing methods, this approach investigates the instantaneous resonance frequency variation during the spall span instead of the signal events at the entry and exit points. This novel approach along with three other representative existing methods are tested on three experimental datasets: one obtained by an artificially induced spall and the other two by naturally extended spalls. The performance analysis results reveal that all the methods are effective for artificial spalls, but only the proposed approach is effective for naturally extended spalls.

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Time-frequency analysis of structural vibration responses provides essential information for structural system identification, modal updating, and condition assessment. However, spurious peaks introduced by the strong noise will significantly increase the false positive rate as well as compromise the sparse time-frequency signal representation. This paper proposes a high-resolution time-frequency representation approach for nonstationary signals polluted with strong noise. With a high sensitivity to detect slight frequency shift and an immunity to noise effect, the intermittent chaotic of Duffing oscillator system is introduced to accurately identify the time-varying instantaneous frequencies. Furthermore, an adaptive Duffing oscillator array is adopted to improve the instantaneous frequency identification efficiency. The feasibility and effectiveness of the proposed method are verified with numerical and experimental studies. Numerical studies are conducted on a multicomponent synthetic nonstationary signal as well as on a two-story shear building with time-varying stiffness under seismic loads. In experimental validations, the acceleration response of a laboratory bridge model under moving vehicle load is also analyzed by using the proposed approach to obtain the instantaneous frequency variations induced by the bridge–vehicle interaction. These results are compared with those obtained from a method based on empirical wavelet transform and Hilbert transform (EWT-HT), to highlight the superiority of the proposed approach in obtaining high-resolution time-frequency analysis results for nonstationary signals with strong noise.

Time Frequency Signal Classification Using Continuous Wavelet Transformation

Time-frequency analysis can provide useful information in digital signal seismic data processing and interpretation. The energy concentration of the spectrum depends on the consistency of function of the time-frequency analysis and instantaneous frequency variation digital signal. In this case, we used the digital signal seismic from selected seismometer broadband which deployed in Sumatera Island. The aim of this study to classify the waveform based on the time-frequency analysis using continuous wavelet transform (CWT). The sample data used the earthquake of 20 February 2018 in North Sumatera. The result indicated the classification between the horizontal and vertical components from the seismometer broadband is different. The classification of vertical is affected by seismic source and horizontal component affected the site effect.

A Micro Resonant Gas Sensor With Adjustable Natural Frequency

In order to improve the sensitivity of micro-resonant gas sensors, the size of the resonator has to be reduced but this complicates the fabrication process. Therefore, a micro-resonant gas sensor with adjustable natural frequency is proposed in this article. The equivalent mass of the resonator is adjusted by a feedback signal, and its natural frequency is increased without reducing the resonator size. The mechanical-electro-chemical coupled dynamics equation of the sensor is established, its instantaneous frequency equation is obtained, and the instantaneous frequency variation during gas adsorption is analyzed. The results demonstrate an improvement in the sensor's sensitivity. The resonant sensor is fabricated, and the excitation and detection circuit is developed. The instantaneous frequency of the sensor is measured in ethanol vapor. The results show that the sensitivity of the sensor is increased by about 90 times when 180° feedback is added.

Seismic time–frequency spectrum analysis based on local polynomial Fourier transform

Time–frequency analysis technology is widely used in non-stationary seismic data analysis. The energy concentration of the spectrum depends on the consistency of the kernel function of the time–frequency analysis method and the instantaneous frequency variation of the signals. The conventional time–frequency analysis methods usually require that the local instantaneous frequency of the signals remains unchanged or linearly changed. So it is difficult to accurately characterize the instantaneous frequency nonlinear variation of the non-stationary signal. The local polynomial Fourier transform (LPFT) method can effectively describe the instantaneous frequency variation by local high-order polynomial fitting and obtain the results with high spectral and energy concentration. The numerical simulations and field seismic data applications show that the time–frequency spectrum results obtained by LPFT can reflect the instantaneous frequency variation characteristics of the seismic data, while ensuring the concentration of time–frequency energy.

Time Window Selection Algorithm for ISAR Ship Imaging Based on Instantaneous Doppler Frequency Estimations of Multiple Scatterers

Selecting a suitable time window is an important aspect of the ship imaging process for inverse synthetic aperture radar (ISAR) systems. One type of existing algorithm involves finding the time period during which a ship's rotation is relatively stable by extracting the instantaneous Doppler frequency variation caused by the ship's rotation. However, existing algorithms extract either the instantaneous Doppler frequency of only a single scatterer or the average Doppler frequency of the entire ship. A novel time window selection algorithm for ISAR ship imaging is proposed through extraction of the instantaneous Doppler frequencies of multiple scatterers. The instantaneous Doppler frequency of a scatterer caused by a ship's rotation is verified to behave approximately as a linear frequency modulation signal over a short period. The formula for calculating the length of such a short period is derived. A parameter estimation method based on compressed sensing after data extrapolation is presented to extract the centroid and frequency modulation rate for short-time chirp signals. The instantaneous Doppler frequency variations in the multiple scatterers over the entire acquisition period are obtained by associating the data from adjacent short time periods. The suitable time window during which the instantaneous Doppler frequency changes are all relatively stable in the multiple scatterers is then selected. The results of the performed simulation and real data processing verify the effectiveness of the proposed algorithm. The proposed algorithm achieves better image quality than the image-contrast-based algorithm and algorithms based on instantaneous Doppler frequency estimation of a single scatterer.

Detection of Breaking Waves in Single Wave Gauge Records of Surface Elevation Fluctuations

We report the development of a new method for accurate detection of breaking water waves that addresses the need for an accurate and cost-effective method that is independent of human decisions. The new detection method, which enables the detection of breakers using only surface elevation fluctuation measurements from a single wave gauge, supports the development of a new method for research relating to water waves and wind–wave interactions. According to the proposed method, detection is based on the use of the phase-time method to identify breaking-associated patterns in the instantaneous frequency variations of surface elevation fluctuations. A wavelet-based pattern recognition algorithm is devised to detect such patterns and provide accurate detection of breakers in the examined records. Validation and performance tests, conducted using both laboratory and open-sea data, including mechanically generated and wind-forced waves, are reported as well. These tests allow us to derive a set of parameters that assure high detection accuracy rates. The method is shown to be capable to achieve a positive detection rate exceeding 90%.

Mutually Coupled Time-to-Digital Converters (TDCs) for Direct Time-of-Flight (dTOF) Image Sensors ‡

Direct time-of-flight (dTOF) image sensors require accurate and robust timing references for precise depth calculation. On-chip timing references are well-known and understood, but for imaging systems where several thousands of pixels require seamless references, area and power consumption limit the use of more traditional synthesizers, such as phase/delay-locked loops (PLLs/DLLs). Other methods, such as relative timing measurement (start/stop), require constant foreground calibration, which is not feasible for outdoor applications, where conditions of temperature, background illumination, etc. can change drastically and frequently. In this paper, a scalable reference generation and synchronization is provided, using minimum resources of area and power, while being robust to mismatches. The suitability of this approach is demonstrated through the design of an time-to-digital converter (TDC) array, distributed over 1.69 mm2, fabricated using TSMC 65 nm technology (1.2 V core voltage and 4 metal layers—3 thin + 1 thick). Each TDC is based on a ring oscillator (RO) coupled to a ripple counter, occupying a very small area of 550 m2, while consuming 500 W of power, and has 2 s range, 125 ps least significant bit (LSB), and 14-bit resolution. Phase and frequency locking among the ROs is achieved, while providing 18 dB phase noise improvement over an equivalent individual oscillator. The integrated root mean square (RMS) jitter is less than 9 ps, the instantaneous frequency variation is less than 0.11%, differential nonlinearity (DNL) is less than 2 LSB, and integral nonlinearity (INL) is less than 3 LSB.

Adaptive time‐frequency representation for weak chirp signals based on Duffing oscillator stopping oscillation system

SummaryTo investigate high‐resolution time‐frequency representations for any type of weak chirp signals with a very low signal‐noise ratio, we revisit the inherent deficiencies of conventional Duffing oscillator detection methods and propose a novel Duffing oscillator stopping oscillation detection system. As a result, the detection of chirp signals can be successfully realized, and the influence of nondetection zones and critical thresholds on the detection accuracy is successfully eliminated. Furthermore, we propose an adaptive Duffing oscillator stopping oscillation detection method to measure the instantaneous frequency variation of a highly dynamic chirp signal within a large frequency range. The simulation results indicate that, compared with the conventional Duffing oscillator detection methods and the Choi‐Williams distribution, the proposed method greatly expands the frequency detection range of a single Duffing oscillator and has a lower computing cost and effective real‐time performance in detecting a high‐precision weak chirp signal, which provides a new solution for the time‐frequency representation of weak chirp signals at a lower signal‐noise ration and reveals broad prospects for applications in engineering.