Instantaneous Frequency Research Articles

Research on online monitoring method for time-varying tension in transmission lines based on operational modal response

The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.

Seismic mitigation analysis of base-isolated structure with sliding magnetic bearings considering local-site conditions

Base-isolation systems applied in building structures and infrastructures often suffer from damage even failure during strong earthquakes. The adaptability of isolators to local sites is a main concern for the robust design of base-isolation systems. In this study, seismic mitigation analysis of base-isolated structure with newly-developed sliding hydro-magnetic bearing (HMB) and sliding implant-magnetic bearing (IMB) considering local-site conditions is carried out. Extension of modeling of sliding magnetic bearings is first conducted based on the data of shaking-table tests of a reduced-scale base-isolated structure. To assess the instantaneous frequency for quantifying sliding magnetic bearings’ resisting force, the Hilbert–Huang transform (HHT) is applied. The influences of local-site conditions upon mitigation performance of the sliding magnetic bearings are then addressed, including far-field and near-field, ground motions with and without velocity pulses, and hard-soil and soft-soil. For comparison purposes, the seismic mitigation analysis of the base-isolated structure attached with lead rubber bearing (LRB) and curved surface slider (CSS) is carried out as well. Numerical results show that the sliding magnetic bearings outperform the state-of-the-art seismic isolators in both seismic mitigation and deformation constraint, and the IMB has excellent applicability for engineering since its perfect adaptability to local sites. However, the LRB cannot achieve the desired seismic mitigation under near-field pulse-like ground motions.

Phase tracking using a Kalman filter based on probability density distribution in frequency-scanning interferometry

Frequency-scanning interferometry (FSI) utilizing external cavity diode lasers (ECDL) stands out as a potent technique for absolute distance measurement. Nevertheless, the inherent scanning nonlinearity of ECDL and phase noise pose a challenge, as it can compromise the accuracy of phase extraction from interference signals, thereby reducing the measurement accuracy of FSI. In this study, we propose a composite algorithm aimed at mitigating non-orthogonal errors by integrating the least-squares and Heydemann correction technique. Furthermore, we employ Kalman filtering for precise phase tracking. We introduce a parameter selection strategy based on the statistical distribution of instantaneous frequency to achieve the fusion estimation of phase observation values and theoretical models, which starts a new perspective for the application of multi-dimensional data fusion in FSI measurement. Through simulation and experimental validation, the efficacy of this approach is confirmed. The experimental results show promising outcomes: with an average phase error of 0.12%, a standard deviation of less than 1.7 µm in absolute distance measurement, and an average positioning accuracy error of 0.29 µm.

Multiple enhanced synchrosqueezing in the time–frequency–chirprate space

It is challenging to analyze multi-component signals with crossover instantaneous frequencies (IFs), both in producing high-resolution time–frequency representations and disentangling intrinsic modes. One of the most promising attempts is lifting the two-dimensional TFRs to three-dimensional time–frequency–chirprate (TFC) representations. However, the strong “blurring” effect in the chirp rate (CR) axis makes it rather challenging to estimate accurate IF and CR trajectories. Driven by this need, a method to provide highly energy-concentrated TFC representation, called multiple enhanced synchrosqueezing chirplet transform (MESCT), is proposed in this paper. MESCT is an enhanced iterative framework that combines multi-synchrosqueezing and multi-taper techniques. MESCT estimates IF and CR using phase information from different chirplet transform results with different windows, which provides more accurate estimates for reassignment. Then, three-dimensional multi-synchrosqueezing operations subsequently concentrate the ambiguous energy. As a result, MESCT accurately sharpens the TFC representations, even around the crossing points. Thus, accurate IF and CR trajectories can be jointly estimated by the three-dimensional ridge detection algorithm. Finally, crossover modes can be retrieved with high precision based on accurate trajectory estimation. A series of numerical experiments, both simulations and real-world cases, are considered to demonstrate the effectiveness and advantages of the proposed method.

A Novel Method Based on Probability Density Function Used in Instantaneous Frequency Measurement System for Wider Range and Less Error

A Novel Method Based on Probability Density Function Used in Instantaneous Frequency Measurement System for Wider Range and Less Error

Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals

Surface electromyography techniques are widely used in field of motion analysis and ergonomics combining precise muscular activation assessment with low-invasiveness and wearability. The aim of this investigation is to identify the myoelectrical manifestations of fatigue and to compare the effectiveness of sEMG-based quantitative indices for fatigue assessment. The investigated indexes are the ARV and RMS signal amplitudes, the mean frequency, the median frequency, the Dimitrov index, the instantaneous mean frequency and Wavelet distribution-based WIRE51 index. Two different protocols were developed, and the activity of the lateral deltoid and middle trapezius muscles was recorded. The WIRE51 index is found to have the highest sensitivity in the detection of the difference between the repetitions of each exercise for both protocols. Due to the lack of a unified standard for the performance comparison of fatigue indices, a correlation analysis was carried out between the result provided by the indices and the subjective fatigue perception employing the RPE scale.

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task.

The S-transform is a fundamental time-frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0-3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Weight extracting transform for instantaneous frequency estimation and signal reconstruction

This paper introduces a novel time–frequency analysis method called the Weight Extracting Transform (WET). The primary objective of WET is to enhance the energy concentration in linear time–frequency representations for time-varying signals. By aggregating the time–frequency blur produced by the short-time Fourier transform onto the actual instantaneous frequency, WET improves the readability and accuracy of the time–frequency representation. Additionally, the algorithm is extended to a more general linear time–frequency transform known as the chirplet transform, which effectively handles fast-varying signals. The WET method excels at distinguishing components with closely spaced instantaneous frequencies and can reconstruct the original signal from its time–frequency representation. Experimental results using simulated and real-world signals demonstrate that WET achieves superior energy concentration and noise robustness compared to existing methods.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Tunnel indirect damage identification algorithm based on service train dynamic response time series

In this study, a tunnel indirect damage identification algorithm based on a service train dynamic response time series was proposed. First, the principles and processes of the proposed algorithm are introduced. The instantaneous frequency and spectral entropy of the trend and seasonal terms of the train dynamic response decomposed by Seasonal and Trend decomposition using Loess are calculated, respectively, to further extract the time-frequency features of the signal. The calculated sequence is input into the Bi-directional Long Short-Term Memory network, and the type of damage case is the output. Second, the feasibility of the algorithm is verified using a model test. Finally, the key links in the algorithm are discussed. The results indicate that the proposed algorithm can identify damage cases with a small number of training and test sets, and the accuracy of the test set can reach 83.33%. Through time-frequency feature extraction and normalization processing, the length of the network input sequence is reduced, the training speed is increased, and damage identification can be realized without a deep network.

The Cross-Correlation Between Spikes and Local Field Potentials at Different Running Speeds During Entorhinal Cortex Inactivation

Understanding neural spike dynamics and their interaction with local field potentials is crucial for deciphering neural connectivity. This study utilizes data generously shared by Zutshi et al. in 2022 (PMID: 34890566) to investigate spike characteristics and their cross-correlation with local field potentials. Our findings reveal significant shifts in the interspike interval (ISI) distributions during the medial entorhinal cortex (mEC) inactivation, with the instantaneous burst frequency exhibiting an overall decrease and preference for lower frequencies. These differences in the spike distributions were observed in individual cells, accompanied by changes in mean firing rates. Further exploration of the cross-correlation between spike multiunit activity (MUA) and the theta rhythm uncovers distinct coupling patterns. In the control cases, spikes closely align with theta peaks, whereas this alignment is disrupted during the mEC inactivation. Notably, we find that the strength of this cross-correlation is amplified at higher running speeds. This research enhances our understanding of neural dynamics, shedding light on the intricate relationship between spikes, local field potentials, and running speed, with potential implications for neural connectivity studies. R01MH126236 1R01MH109548-01A1. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Research on Frequency Discrimination Method Using Multiplicative-Integral and Linear Transformation Network

In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions.

Structural and Stratigraphic Interpretations for Mishrif Formation in Kumait Oil Field, Using Seismic Reflection Data, Southern-Eastern Iraq

The Kumait oil field is located in the Maysan Governorate southeastern Iraq, which was studied and interpreted by using 2D and 3D seismic data provided by the Oil Exploration Company using Petrel software. The study concerned the Mishrif Formation belongs to the Cretaceous Age. The reflector was detected based on synthetic seismograms and well logs for Kt-1 well. Structural maps (two-way time, velocity, and depth) were derived from seismic reflection interpretations to show several structural features, such as four isolated irregular enclosures representing the Kumait structure plunging NW-SE and demonstrating that the Kumait oil field is influenced by the Najd fault system trending NW-SE. The stratigraphic interpretation used the variance attribute to delineate the edge of the fault, which extended from the Mishrif Formation with a depth of 3090m and used the Direct Hydrocarbon Indicators (DHI) to identify the stratigraphic features in the research region and the extraction of seismic attributes. The applied seismic attributes, such as the instantaneous phase, show dim spots, flat spots, and mounds and is followed by down-lap; the instantaneous frequency, which offers low-frequency areas indicating a high probability of hydrocarbon accumulation; and the RMS amplitude, which shows high amplitude in the seismic data that conforms to the structure and confirms the presence of hydrocarbon.

Photonic architecture for remote multi-parameter measurement and transmission of microwave signals

A photonic architecture for remote multi-parameter measurement and transmission of microwave signals is proposed and demonstrated, which utilizes a dual-parallel dual-drive Mach-Zehnder modulator (DP-DDMZM) in the antenna unit and a dual-drive Mach-Zehnder modulator (DDMZM) in the processing unit. Doppler frequency shift (DFS) and angle of arrival (AOA) can be determined by analyzing the down-converted intermediate frequency signals. Introducing a reference signal in the processing unit ensures DFS measurement without directional ambiguity. The proposed architecture can also be applied for instantaneous frequency measurement based on down-conversion. Due to the use of optical single sideband modulation, long-distance transmission of radio frequency (RF) signals without dispersion-induced power fading can be achieved. Experiments for accurate and stable DFS and AOA measurement as well as long-distance RF signal transmission with dispersion-induced power fading are presented. The approach avoids the use of optical filters and polarization-related devices, facilitating wideband and stable operation, which is highly desirable. The proposed architecture is a potential solution for microwave photonic antenna remoting, offering support for both remote transmission and multi-parameter measurement.

Speech emotion recognition using a combination of variational mode decomposition and Hilbert transform

The objective of automated speech emotion recognition (SER) is to recognize each emotion of the speech signal uniquely and efficiently using machines like computers, mobile devices, etc. The popularity of SER has been widely acknowledged among researchers due to its extensive applicability in practical contexts. The use of SER has shown to be advantageous in several domains, including medical treatment, enhancement of security systems, surveillance operations, online marketing strategies, online educational platforms, online search engines, personal communication, customer relationship management, reinforcement of machine and human connection, and numerous other areas. Numerous authors have used various techniques, including the combination of multiple features (acoustic, non-acoustic, or both) and classifiers (machine learning, deep learning, or both), in order to enhance the efficacy of emotion categorization. In the present work, our objective is to enhance the performance of emotion classification (PECL) by using a combined approach of the variational mode decomposition (VMD) and Hilbert transform (HT) techniques. Using the VMD method, we decomposed the speech signal frame into many sub-signals, or intrinsic mode functions (IMFs). Then HT is applied to each VMD-based IMF signal to find the mode instantaneous amplitude (MIA) and mode instantaneous frequency (MIF) signal vectors. We extracted proposed features such as HT-based approximate entropy (HTAE), HT-based permutation entropy (HTPE), HT-based increment entropy (HTIE), and HT-based sample entropy (HTSE) using each MIA and MIF signal vector. The combination of the proposed HT-based features is called HT-based entropy (HTE) features. Then, we accessed the PECL using the HTE and MFCC features alone and in conjunction with a deep neural network (DNN) classifier. The experiment's results showed that the combinations of the proposed feature (MFCC + HTE) using a DNN classifier outperformed the individual features and obtained a SER accuracy of 86.92% for the EMOVO dataset and 91.63% for the EMO-DB dataset.

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.

Responsive regularity of neurons in the hindlimb region of primary somatosensory cortex to different temperature thermal needle stimulation of "Zusanli"(ST36) in mice.

To study the regularity of central response to thermal needle stimulation of "Zusanli" (ST36) at different temperature, and to analyze the temperature difference of central responses. Six male C57BL/6j adult mice were used in the present study. For observing activities of neurons in the hindlimb region of left primary somatosensory cortex (S1HL, A/P=0.46 mm, M/L=1.32 mm, D/V=-0.14 mm) by using a fast high-resolution miniature two-photon microscopy (FHIRM-TPM), the mice were anesthetized with 3% isoflurane (inhalation), with its head fixed in a stereotaxic apparatus, then, adeno-associated virus (AAV-hSyn-GCaMP6f-WPRE-hGHpA, for showing intracellular calcium transients in neurons transfected) was injected into the left S1HL region using a micro-syringe after scalp surgical operation. The mice's right ST36 were stimulated using internal thermal needles with the temperature being 43 ℃, or 45 ℃, or 47 ℃, separately. Image J software and MATLAB 2020b software were used to process the image data of neuronal calcium activity (Ca2+ signaling) in the left S1HL region, including the instant maximum calcium peak value (ΔF/F) in 2 s, instant calcium spike frequency in 2 s, short-term calcium peak value (ΔF/F) in 3.5 min, short-term calcium spike frequency in 3.5 min, calcium peak duration in 3.5 min, maximum calcium peak value (ΔF/F) at the 1st , 2nd and 3rd min, and calcium spike frequency at the 1st, 2nd and 3rd min after thermal needle stimulation. In comparison with the normal temperature needle stimulation, the instant intracellular maximum calcium peak value, instant calcium spike frequency, short-term maximum calcium peak value, short-term calcium spike frequency, and calcium peak duration of S1HL neurons in response to 43 ℃, 45 ℃ and 47 ℃ internal thermal needle stimulation of ST36 were significantly increased (P<0.001, P<0.01). Comparison among the 43 ℃, 45 ℃ and 47 ℃ thermal needle stimulation showed that the 45 ℃ thermal needle stimulation was obviously superior to 43 ℃ and 47 ℃ thermal needle stimulation in increasing instant calcium spike frequency, short-term calcium spike frequency and calcium peak duration of S1HL neurons (P<0.001, P<0.01). The 47 ℃ thermal needle stimulation was stronger than 43 ℃ and 45 ℃ thermal needle stimulation in increasing the instant maximum calcium peak value (P<0.001). The maximum calcium peak value was apparently higher (P<0.001) at the 2nd min than that at the 1st and 3rd min after 43 ℃, 45 ℃ and 47 ℃ thermal needle stimulation. No significant differences were found in the short-term maximum calcium peak value among the 3 thermal needle stimulation and in the calcium spike frequency among the 3 time points after 43 ℃, 45 ℃ and 47 ℃ thermal needle stimulation. S1HL neurons respond to all 43 ℃, 45 ℃ and 47 ℃ thermal needle stimulation of ST36 in mice, while more actively to 45 ℃ thermal needle stimulation.

1300-m-High gas plume from pockmarks in the north Nansha waters, South China sea

The northeastern region of Nansha Waters, located in the southern margin of the South China Sea, is abundant in gas resources. However, it remains unclear if the central and northern areas of Nansha Waters possess gas resources and if there are any associated leaks. The Jiuzhang Basin is located in the northern Nansha Waters. Pre-stack depth migration, analysis of seismic attributes and multibeam data processing were conducted to image the seabed topography and backscatter intensity, as well as the seismic structure of the water and sedimentary layers in the Jiuzhang Basin. The result reveals a presence of large plumes, approximately 1500 m in width and up to 1300 m in height, from a pockmark. The plume is characterized by a strong seismic amplitude featuring chaotic reflections, a high instantaneous frequency (100–150 Hz), and a strong instantaneous amplitude. These seismic features commonly appear in hydrocarbon-bearing seawater, indicating that the plume is composed of gas. The relationship between the volume fraction of bubbles in the gas plumes and the amplitude of multi-channel seismic waves suggests that the volume fraction of bubbles within the gas plume can reach up to 5.0 × 10−3. Meanwhile, a new pockmark with a length of 248 m, a width of 90 m, and a depth of 20.4 m, and an abrupt 22 m drop in water depth of an existing pockmark are observed in the gas plume area. These observations, combined with the gas plume revealed by the multi-channel seismic collected in 2013 and multibeam collected in 2020, suggest a very strong and recently active gas leaks in the pockmark area of the northern Jiuzhang Basin. Furthermore, in the pockmark area, there are gas-bearing formations with weak reflections, and mud diapirs charactered by low seismic velocity, low amplitude chaotic reflections, and pulled up reflections on both sides, and faults extend to the seabed. These observations suggest that gas plume originate from deep strata of the northern Jiuzhang Basin. Large gas plumes and mud diapirs are often indicative of the existence of deep gas reservoirs. Therefore, the presence of such active gas plumes and mud diapirs in the northern region of the Jiuzhang Basin signifies the presence of abundant gas resources.

Phase demodulation method of high line density grating interferometric signal based on wavelet transform

The increasing line density of the reference grating and the accelerating miniaturization of ultra-precision displacement measurement technology necessitate more stable interferometric signal processing methods for high line density gratings, particularly in low signal-to-noise ratio scenarios. This paper presents a phase demodulation method for dynamic interferometric signals for high line density gratings. The Morlet wavelet transform is utilized to obtain the instantaneous frequency of the interferometric signal, integration of which yields the relative displacement, while adding adjacent relative displacements without gaps provides the absolute displacement during dynamic motion of the grating. In simulations with a signal-to-noise ratio ranging from 40 to 70 dB, the proposed method demonstrates greater robustness compared to the traditional method. By establishing a platform for repeated experiments and comparing it with traditional methods, it was found that the maximum deviation between calculation results obtained using this method and traditional methods is 0.8 nm, further confirming its potential application.

High-order iterative rearrangement transform for time–frequency characterization of bearing fault impact

Time–frequency analysis (TFA) can effectively characterize features of non-stationary signals. Traditional TFA algorithms construct signal models in the time domain and make the assumption that the instantaneous characteristics of each component are continuous. However, the instantaneous frequency (IF) of the transient signal is discontinuous in the time domain and exhibits a multifaceted relationship with time, such as shock, vibration wave, damped sound wave, etc. Additionally, in most existing TFA methods, low-order group delay (GD) is used to describe transient signals, which leads to unsatisfactory energy concentration and calculation accuracy. To address about issues, a novel TFA technique, termed high-order iterative rearrangement transform (HOIRT), is developed in this research. First, the signal model is defined within the frequency domain, and the frequency ridge of the transient signal is described by a high-order GD (HOGD), which is similar to the IF. Second, a HOGD-based iterative synchrosqueezing operator is defined to reassign time–frequency coefficients into the GD trajectories along the time direction. Finally, the HOGD-based frequency extraction operator is constructed to only retain the target time–frequency information of the transient signal from the rearranged results, such that the noise interference is eliminated and the energy-concentrated TFR is obtained. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HOIRT. Compared with the other seven typical TFA algorithms, the developed technique has the smallest calculation error and Rényi entropy, showing that the HOIRT has the highest accuracy and energy concentration. Analysis result of the bearing fault impact signal shows that the proposed HOIRT can display the time when pulses occur while ensuring high time–frequency resolution, making it suitable for detecting bearing faults.