Changes In Instantaneous Frequency Research Articles

Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions

The implementation of prognostics and health management strategies is essential for enhancing the safety and maintenance of rotating equipment in chemical plants. The examination of vibration signal behaviours under variable-speed conditions and the development of signal decomposition methods in such contexts are of substantial theoretical and practical relevance. This paper proposes a novel multicomponent collaborative time-frequency state-space method for the decomposition of vibration signals. The method employs a multi-component proportional model to accurately describe the synchronisation of high-frequency components with the rotational frequency component. Based on this framework, the instantaneous frequency change curves of multiple components are input into the Vold–Kalman filter algorithm for precise decomposition of multicomponent vibration signals under variable-speed conditions. Experimental results demonstrate the effectiveness of the proposed method in achieving accurate instantaneous frequency tracking and signal decomposition, showing clear advantages over traditional methods.

Neural entrainment underpins sensorimotor synchronization to dynamic rhythmic stimuli

Neural entrainment, defined as unidirectional synchronization of neural oscillations to an external rhythmic stimulus, is a topic of major interest in the field of neuroscience. Despite broad scientific consensus on its existence, on its pivotal role in sensory and motor processes, and on its fundamental definition, empirical research struggles in quantifying it with non-invasive electrophysiology. To this date, broadly adopted state-of-the-art methods still fail to capture the dynamic underlying the phenomenon.Here, we present event-related frequency adjustment (ERFA) as a methodological framework to induce and to measure neural entrainment in human participants, optimized for multivariate EEG datasets. By applying dynamic phase and tempo perturbations to isochronous auditory metronomes during a finger-tapping task, we analyzed adaptive changes in instantaneous frequency of entrained oscillatory components during error correction. Spatial filter design allowed us to untangle, from the multivariate EEG signal, perceptual and sensorimotor oscillatory components attuned to the stimulation frequency.Both components dynamically adjusted their frequency in response to perturbations, tracking the stimulus dynamics by slowing down and speeding up the oscillation over time. Source separation revealed that sensorimotor processing enhanced the entrained response, supporting the notion that the active engagement of the motor system plays a critical role in processing rhythmic stimuli. In the case of phase shift, motor engagement was a necessary condition to observe any response, whereas sustained tempo changes induced frequency adjustment even in the perceptual oscillatory component. Although the magnitude of the perturbations was controlled across positive and negative direction, we observed a general bias in the frequency adjustments towards positive changes, which points at the effect of intrinsic dynamics constraining neural entrainment.We conclude that our findings provide compelling evidence for neural entrainment as mechanism underlying overt sensorimotor synchronization, and highlight that our methodology offers a paradigm and a measure for quantifying its oscillatory dynamics by means of non-invasive electrophysiology, rigorously informed by the fundamental definition of entrainment.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Adaptive synchroextracting transform and its application in bearing fault diagnosis

Time–frequency analysis methods can be used to characterize the time-varying characteristics of a signal. The postprocessing algorithm further enhances this ability. The synchroextracting transform is a typical postprocessing algorithm that has the advantage of energy aggregation. However, based on a short-time Fourier transform, shortcomings such as a fixed window length and amplitude distortion when processing frequency modulation signals are unavoidable. This paper proposes a time–frequency postprocessing algorithm with high adaptability, which is called the adaptive synchroextracting transform (ASET). The filter window width for the ASET is adaptive and is determined by the instantaneous frequency change rate for the signal. On this basis, the improved extraction operator can be used to achieve a high-resolution time–frequency​ representation. This algorithm can be used to better deal with strong frequency modulation signals and has better noise robustness while allowing for signal reconstruction. The effectiveness and practicability of the proposed algorithm are demonstrated by simulation signals and faulty bearing signals.

Molecular-resolution micro-resonant biosensor with adjustable natural frequency

Modern sensors are becoming increasingly small in size while their sensitivity requirements remain relatively strict, to the point that the processing technology and test technology are highly difficult and costly. This paper proposes a micro-resonant biosensor with adjustable natural frequency. A positive feedback signal with a phase difference of 180 degrees is used to reduce the equivalent mass of the resonator, yielding an ultrahigh resonant frequency. The biosensor is formed by a coating bovine blood solution on the surface of the resonator of a cantilever sensor. The instantaneous frequency equation of the biosensor is established using a coupling dynamic calculation. The changes in instantaneous frequency during hemoglobin oxygen absorption and deoxygenation, are then measured. The proposed millimeter micro-resonant biosensor system measures the molecular weight of a single oxygen molecule quality at 5.7619×10−23 g, only deviating by 8.306 % from the theoretical value. Finally, the potential of the micron scale sensor is deeply taped.

A Method Against Interrupted Sampling Repeater Jamming Based on Genetic Algorithm and Compressed Sensing Reconstruction

By analyzing the discontinuous character in time domain, a jamming suppression method is propose to counter Interrupted sampling reperter jamming(ISRJ, which is an effective coherent jamming to wide-band linear frequency modulation signal. The discontinuous character of ISRJ and the instantaneous frequency change rule after stretch is firstly analyzed. Secondly, by controlling the adjustment parameters, the generalized S transform is utilized to obtain the time-frequency grayscale image of received signal. Then, the grayscale image can be segmented by using genetic algorithm to construct a time-domain filter, and the time-domain filter can be used to extract the echo band without jamming signal. Finally, the extracted signal is used as compressed data to construct a compressed sensing minimization model, and the compressed sampling matching pursuit algorithm is used to reconstruct the target echo signal. Simulation shows that this method has good jamming suppression performance.

ОЦІНЮВАННЯ ТОЧНОСТІ ВИМІРЮВАННЯ МИТТЄВИХ ЧАСТОТ ТА ФАЗ КОМПОНЕНТ БАГАТОКОМПОНЕНТНОГО НЕСТАЦІОНАРНОГО СИГНАЛУ

Measurements of instantaneous frequencies and phases of non-stationary components of the multicomponent signals are actual in processing medical, radar, sonar, seismic, vibration, and speech signals. When we measure the parameters of multi-component non-stationary signals, it is a need to measure the instantaneous frequency and current phase of several non-stationary frequency-modulated signal components. In this article, estimates of the measurement accuracy of the specified parameters of the non-stationary signal components were obtained using the method of simulation modeling. Measurement methods based on the short-time Fourier transform and the proposed method with local adaptive matching filtering of signal components in the frequency domain in the time-sliding observation window were studied. Adjustment of matched filters is performed separately for each component of the signal, returning to the time domain and taking into account the filter settings in the previous time window. The simulation method is used for the signal model consisting of three components. The first two components are the frequency modulated harmonic ones and the last is harmonic with constant frequency. The amplitudes of components and additive Gaussian noise variance were variated to study the influence of signal-to-noise ratio on the accuracy of measurements. Estimates of the experimental standard deviation of instantaneous frequencies and current phase measurements were obtained by comparing them with their known dependencies. The frequency laws of non-stationary signal components were chosen to be harmonic, which also allowed us to evaluate the influence of the nonlinearity of the instantaneous frequency change on the measurement accuracy. The results of differential phase measurements between two measurement channels are obtained to illustrate the quality of such ones for multi-component non-stationary signals situations. The results of this study may be useful for the development of imaging methods in inverse synthetic aperture radars (ISAR) and interferometric ISAR.

Determination of Glycaemic Index (GI) through Detecting Insulin Secretion in Pancreas Using GMR Sensor

Diabetes is a metabolic disease that affects the ability of the body to process blood glucose, otherwise known as blood sugar. Diabetes occurs when the body produces minimal or no insulin. The diabetes patients check their glycaemic index after each meal and intake medicine to control glycaemic index. Traditionally, glycaemic index estimates the glucometer by acquiring blood sample. In this paper, we propose a noninvasive method to estimate glycaemic index from the pancreas. The magnetic signal from the pancreas acquires with Giant Magneto Resistance (GMR) sensor for glycaemic index estimation. The GMR acquired pancreatic magnetic signal process with Multi Synchro Squeezing Transform (MSST) for feature extraction. The MSST analysis shows significant changes in instantaneous frequency of the pancreas biomagnetic signal before and after meal consumption. The signal statistical parameters help to predict glycaemic index via regression modelling. The proposed method estimates glycaemic index with 88% accuracy.

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.

Neural Responses to Physical Characteristics of a High-velocity, Low-amplitude Spinal Manipulation: Effect of Thrust Direction.

Electrophysiological recordings were obtained from proprioceptors in deep lumbar paraspinal muscles of anesthetized cats during high-velocity low-amplitude spinal manipulation (HVLA-SM). To determine how thrust direction of an HVLA-SM affects neural input from back musculature. A clinician's ability to apply the thrust of an HVLA-SM in a specified direction is considered an important component of its optimal delivery. However, previous biomechanical studies indicate that the shear force component of the thrust vector is not actually transmitted to paraspinal tissues deep to the thoracolumbar fascia because the skin-fascia interface is frictionless. Neural activity from muscle spindles in the multifidus and longissimus muscles was recorded from L6 dorsal rootlets in 18 anesthetized cats. After preload to the spinal tissues, HVLA-SMs (100-ms thrust duration) were applied through the intact skin overlying the L6 lamina. Thrusts were applied at angles oriented perpendicularly to the back and obliquely at 15° and 30° medialward or cranialward using a 6 × 6 Latin square design with three replicates. The normal force component was kept constant at 21.3 N. HVLA-SMs were preceded and followed by simulated spinal movement applied to the L6 vertebra. Changes in mean instantaneous discharge frequency (ΔMIF) of muscle spindles were determined both during the thrust and spinal movement. ΔMIFs during the HVLA-SM thrust were significantly greater in response to all thrust directions compared with the preload alone, but there was no difference in ΔMIF for any of the thrust directions during the HVLA-SM. HVLA-SM decreased some of the responses to simulated spinal movement but thrust direction had no effect on these changes. The shear force component of an HVLA-SM's thrust vector is not transmitted to the underlying vertebra sufficient to activate muscle spindles of the attached muscles. Implications for clinical practice and clinical research are discussed. N/A.

Fluctuations in instantaneous frequency predict alpha amplitude during visual perception

Rhythmic neural activity in the alpha band (8–13 Hz) is thought to have an important role in the selective processing of visual information. Typically, modulations in alpha amplitude and instantaneous frequency are thought to reflect independent mechanisms impacting dissociable aspects of visual information processing. However, in complex systems with interacting oscillators such as the brain, amplitude and frequency are mathematically dependent. Here, we record electroencephalography in human subjects and show that both alpha amplitude and instantaneous frequency predict behavioral performance in the same visual discrimination task. Consistent with a model of coupled oscillators, we show that fluctuations in instantaneous frequency predict alpha amplitude on a single trial basis, empirically demonstrating that these metrics are not independent. This interdependence suggests that changes in amplitude and instantaneous frequency reflect a common change in the excitatory and inhibitory neural activity that regulates alpha oscillations and visual information processing.

Applicability of LED-Based Excitation Source for Defect Depth Resolved Frequency Modulated Thermal Wave Imaging

This paper proposes an energy efficient instrumentation set-up for pulse-compression thermal-wave imaging with a low-power LED excitation source. The set-up consists of three different subsystems that are synchronised in time. The individual systems consist of a LED modulation circuitry, reference signal measurement circuitry, and an IR camera trigger signal generation circuitry for frame capture. A separate reference acquisition circuitry is useful in quantifying defect depth resolution. This paper also proposes a nonuniform frame capture technique to reduce the memory allocation space of the recorded video. The technique is based on varying the sampling rate with a change in instantaneous frequency and is specifically useful for frequency modulated excitation signal. The proposed technique is implemented on a carbon fiber reinforced polymer test-piece. The variation of pulse compression parameters with different defect dimension is studied, and the results are verified with an electro-thermal simulator. Further, an objective comparison of pulse compression experiment for different experiment duration is presented.

Changes in echo-pulse instantaneous frequency when scanning the transducer

A theoretical model has been proposed that makes it possible to explain peculiarities in the frequency characteristics of echo signals at the output of a receiving transducer depending on the geometrical parameters of a reflecting spherical surface when the transducer axis is displaced with respect to the surface’s center of curvature, that is, during scanning. It has been demonstrated that both with and without displacement of the transducer’s geometrical axis with respect to the center of curvature of the reflecting surface, the type of changes in the temporal dependences of the instantaneous frequency of signals at the output of the receiving transducer essentially depends on the geometrical parameters of a wavefront reflected from the surface: the smaller the radius of wavefront curvature at the aperture of the receiving transducer, the stronger the changes of the instantaneous frequency in the echo pulse. It has also been shown that the larger the curvature of this surface, the stronger the changes in the frequency characteristics of the echo pulse as the transducer axis is displaced with respect to the center of curvature of the reflecting spherical surface.

The use of pseudorandom sweeps for vibroseis acquisition

The use of pseudorandom sweeps (rather than the more conventional sweeps whose instantaneous frequency changes linearly) has received periodic attention over the past 45 years. In a previous article (Dean, 2014) I summarised the work published in this area including the different motivations for their use as well as different methods for generating a variety of different sweeps. At the time, I attributed their lack of use to the difficulty in transmitting them and their relatively low energy levels when compared with conventional, linear, sweeps. In this article I summarise recent work (i.e. not covered in Dean (2014)) involving pseudorandom sweeps before describing some of our own test results. I then discuss the implications of this recent work on the future of the method.

Chirped Waveform Generation With Envelope Reconfigurability for Pulse Compression Radar

Chirped pulses generation is evaluated and experimentally demonstrated by means of the processing of incoherent optical signals using a nonlinear dispersive element. The capability of generating chirped electrical pulses, in which the instantaneous frequency changes with time, has been demonstrated for a Gaussian waveform envelope similar to photonic coherent techniques. For pulse compression radar systems, the control of the pulse envelope permits to increase the coverage distance of the transmitted signals and the spatial resolution of the system. In this sense, the envelope control of the generated waveform has been also experimentally demonstrated for different profiles, such as gradual, hyperbolic tangent, and uniform by means of the optical signal power spectral density. Indeed, we have experimentally probed how the resolution and the coverage distance of the signals can be increased for a uniform waveform in a pulse compression radar system.

Neural responses to the mechanical characteristics of high velocity, low amplitude spinal manipulation: Effect of specific contact site

BackgroundSystematic investigations are needed identifying how variability in the biomechanical characteristics of spinal manipulation affects physiological responses. Such knowledge may inform future clinical practice and research study design. ObjectiveTo determine how contact site for high velocity, low amplitude spinal manipulation (HVLA-SM) affects sensory input to the central nervous system. DesignHVLA-SM was applied to 4 specific anatomic locations using a no-HVLA-SM control at each location randomized in an 8×8 Latin square design in an animal model. MethodsNeural activity from muscle spindles in the multifidus and longissimus muscles were recorded from L6 dorsal rootlets in 16 anesthetized cats. A posterior to anterior HVLA-SM was applied through the intact skin overlying the L6 spinous process, lamina, inferior articular process and L7 spinous process. HVLA-SMs were preceded and followed by simulated spinal movement applied to the L6 vertebra. Change in mean instantaneous discharge frequency (ΔMIF) was determined during the thrust and the simulated spinal movement. ResultsAll contact sites increased L6 muscle spindle discharge during the thrust. Contact at all L6 sites significantly increased spindle discharge more than at the L7 site when recording at L6. There were no differences between L6 contact sites. For simulated movement, the L6 contact sites but not the L7 contact site significantly decreased L6 spindle responses to a change in vertebral position but not to movement to that position. ConclusionsThis animal study showed that contact site for an HVLA-SM can have a significant effect on the magnitude of sensory input arising from muscle spindles in the back.

Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

Smart tuned mass dampers: recent developments

This special issue focuses on Smart Tuned Mass Dampers (STMD) that are either active or smart or semi-active in nature. Active tuned mass dampers or active mass dampers have found wide acceptance and have been implemented in many tall buildings and long span bridges. Recently researchers have developed a new class of smart tuned mass dampers using either variable stiffness and/or variable damping to effect the change in instantaneous frequency and damping. Since tuning plays a central role in STMDs it is of great current interest thus the topic of this special issue. Discussions of recent active and smart TMD implementations in tall buildings and bridges are also included.

Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time–frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG.To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts′ agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification.The uniqueness of this study arises from newly proposed features of the time–frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.

The auriculo-vagal afferent pathway and its role in seizure suppression in rats

BackgroundThe afferent projections from the auricular branch of the vagus nerve (ABVN) to the nucleus tractus solitaries (NTS) have been proposed as the anatomical basis for the increased parasympathetic tone seen in auriculo-vagal reflexes. As the afferent center of the vagus nerve, the NTS has been considered to play roles in the anticonvulsant effect of cervical vagus nerve stimulation (VNS). Here we proposed an “auriculo-vagal afferent pathway” (AVAP), by which transcutaneous auricular vagus nerve stimulation (ta-VNS) suppresses pentylenetetrazol (PTZ)-induced epileptic seizures by activating the NTS neurons in rats.ResultsThe afferent projections from the ABVN to the NTS were firstly observed in rats. ta-VNS increased the first grand mal latency of the epileptic seizure and decreased the seizure scores in awake rats. Furthermore, when the firing rates of the NTS neurons decreased, epileptiform activity manifested as electroencephalogram (EEG) synchronization increased with 0.37±0.12 s delay in anaesthetized rats. The change of instantaneous frequency, mean frequency of the NTS neurons was negative correlated with the amplitude of the epileptic activity in EEG traces. ta-VNS significantly suppressed epileptiform activity in EEG traces via increasing the firing rates of the neurons of the NTS. In comparison with tan-VNS, the anticonvulsant durations of VNS and ta-VNS were significantly longer (P<0.01). There was no significant difference between the anticonvulsant durations of VNS and ta-VNS (P>0.05). The anticonvulsant effect of ta-VNS was weakened by reversible cold block of the NTS.ConclusionsThere existed an anatomical relationship between the ABVN and the NTS, which strongly supports the concept that ta-VNS has the potential for suppressing epileptiform activity via the AVAP in rats. ta-VNS will provide alternative treatments for neurological disorders, which can avoid the disadvantage of VNS.