Frequency Wavelet Research Articles

Dynamic Spillovers from US (Un)Conventional Monetary Policy to African Equity Markets: A Time-Varying Parameter Frequency Connectedness and Wavelet Coherence Analysis

Since the implementation of unconventional monetary policies (UMPs) by the US in response to the global financial crisis (GFC) and the COVID-19 pandemic, there have been increasing concerns that these forward guidance and quantitative easing programmes have had spillover effects on global equity markets. We specifically question whether the implementation of these UMPs have had spillovers to African equities, which have been previously speculated to be decoupled from global markets and shocks. Time-varying-parameter (TVP) frequency connectedness and wavelet coherency methods were used to examine the dynamic time-frequency spillovers between daily time series of the US shadow short rate and African equities returns/volatility between 1 January 2007 and 31 March 2023. On one hand, the TVP frequency connectedness analysis reveals robust long-run spillovers from US monetary policy to African equity markets during specific periods: 2009, 2013, 2020, and 2021. These coincide with instances when the Federal Reserve announced their transition from conventional to unconventional monetary practices and vice versa. On the other hand, the wavelet analysis provides insights into the ‘sign’ of the spillovers, indicating mixed phase dynamics during UMPs responding to the GFC. In contrast, anti-phase or negative co-movements characterize UMPs implemented during the COVID-19 pandemic, implying that these policies increased both returns and volatilities to African equities. Altogether, we conclude that US UMP has increasing deteriorated market efficiency and amplified portfolio risk in African equities whilst during ‘normalization’ periods US monetary policy has little transmission effect.

Research of the impact of noise reduction methods on the quality of audio signal recovery

The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

Asymmetric dependency among US national financial conditions and clean energy markets

This paper examines the relationship between the US National Financial Conditions Index (NFCI) and the clean energy industry using quantile and frequency connectedness, cross-quantile, and wavelet quantile correlation (WQC) techniques. Results reveal (a) a stronger dependence between the NFCI and clean energy under bullish market states. Moreover, the total connectedness between the NFCI and clean energy mostly exhibits time-varying characteristics. In particular, clean energy has a greater spillover effect than the NFCI. (b) Dynamic frequency total connectedness at extreme quantiles provided a more comprehensive view of structural shocks in financial markets, and major crises, such as COVID-19, significantly amplified this connectedness. Overall, the WilderHill Clean Energy Index and the NASDAQ OMX Renewable Energy Index demonstrate substantial potential for hedging financial conditions. (c) The cross-quantile correlation results revealed an asymmetric dependency, demonstrating a sustained significant positive relationship between the NFCI and clean energy index (CEI) across the relative higher quantiles and middle quantiles. The WQC showed that the NFCI and specific CEIs tended to exhibit the strongest positive correlations in nonextreme quantiles and lower frequencies. These results can be of considerable interest to various financial market participants.

Electrochemical noise characterization of corrosion behavior for NiCrAl-NiC coating

The electrochemical noise analysis (including frequency domain and wavelet analyses) method is used to characterize the corrosion behavior of NiCrAl-NiC Abradable Seal Coating (ASC) at different immersion times. By converting EN data to frequency domain analysis, the slope (k values) of current power spectral density (PSD) curves of NiCrAl-NiC ASC increases with prolonged immersion time, suggesting a transition from localized corrosion to pitting. Wavelet analysis results indicate that the D7-D8 detail coefficients control the wavelet energy throughout the immersion process, and with increasing immersion time, the electrochemical activity on the NiCrAl-NiC ASC surface decreases. The electrochemical noise analysis can effectively explain the corrosion behaviors of NiCrAl-NiC ASC.

Dual-Domain Fusion Network Based on Wavelet Frequency Decomposition and Fuzzy Spatial Constraint for Remote Sensing Image Segmentation

Dual-Domain Fusion Network Based on Wavelet Frequency Decomposition and Fuzzy Spatial Constraint for Remote Sensing Image Segmentation

Identification of Bolt Loosening Damage of Steel Truss Structure Based on MFCC-WPES and Optimized Random Forest

In the field of bolt loosening detection, although some progress has been made, there are still challenges such as high operational complexity, single feature extraction methods, and insufficient analysis model performance, especially in large steel truss structures, where there is a lack of efficient and accurate bolt loosening identification solutions. In response to these shortcomings, this article proposes an innovative bolt loosening damage recognition method based on sound signals. This method integrates feature extraction techniques of Mel frequency cepstral coefficients (MFCCs) and wavelet packet energy spectra (WPES), and comprehensively characterizes sound signals by constructing MFCC-WPES combined features. Subsequently, the random forest (RF) algorithm optimized by genetic algorithm was used for feature selection and model training, aiming to improve recognition accuracy and robustness. The experimental results show that this method can not only accurately identify bolt loosening signals in steel truss structure bolt loosening detection, but also has strong identification ability for environmental noise. Compared with traditional methods, the proposed solution in this article shows significant improvements in both performance and practicality, providing a new perspective and solution for the technological advancement of bolt loosening detection in steel truss structures.

Research on wavelet packet energy entropy extraction method for acoustic signal of tunnel lining cavity

Abstract Tunnel lining cavity is a common disease in high-speed railway tunnels, which seriously affects the safe operation of the line.At present, the commonly used method is manual knocking inspection, but it relies on manual judgment, subjective factors have great influence, and there are errors. This paper proposes a feature extraction method of acoustic vibration signal of tunnel lining cavity based on wavelet packet energy entropy and uses wavelet packet energy entropy to realize the identification of cavity defects.The full-scale simulation model of tunnel with a radius of 6m was constructed by COMSOL and different cavity conditions were set up. The wavelet packet energy entropy characteristics of acoustic signals were extracted and analyzed. The mapping relationship between cavity depth and cavity size and wavelet packet energy entropy was explored. The wavelet packet energy entropy and frequency band distribution characteristics were extracted to realize the identification of cavity defects.The local model which can characterize the whole tunnel is designed and the embedded defects are verified by experiments. The results show that the wavelet packet energy entropy can be used as the identification feature of tunnel lining cavity defects. When the wavelet packet energy entropy is less than 2.53, the existence of tunnel lining cavity defects is characterized.When the wavelet packet energy entropy is in the range of 2.53-2.94, it characterizes the existence of the transition boundary between tunnel lining cavity defect and no disease.This index can be used to realize the rapid and effective identification of cavity defects.

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.

A Novel Noise Reduction Approach of Acoustic Emission (AE) Signals in the SiC Lapping Process on Fixed Abrasive Pads.

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time-domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R2) for the linear fit of the RMS to the MRR.

CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been proved that exploring and embedding context knowledge in deep network can significantly improve accuracy. To emphasize these tips, we present CDT-CAD, i.e., context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images. CDT-CAD first constructs an iterative context-aware feature extractor, which not only enlarges receptive fields to encode multi-scale context information via dilated context encoding blocks, but also captures unique and scalable feature variation patterns in wavelet frequency domain via frequency pooling blocks. Afterwards, a deformable transformer detector on the extracted context features is built to accurately classify disease categories and locate regions, where a small set of key points are sampled, thus leading the detector to focus on informative feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det 10 Datasets, CDT-CAD demonstrates its effectiveness in recognizing chest abnormities and outperforms 1.4% and 6.0% than the existing methods in AP50 and AR on VinBig dateset, and 0.9% and 2.1% on Chest Det-10 dataset, respectively.

Intelligent emotion recognition in product design using multimodal physiological signals and machine learning

Identifying emotional responses in products is essential for product design and user research. Traditional methods, such as interviews and surveys, for gathering product experience data are time-consuming and resource-intensive, and often fail to capture users’ genuine emotional intentions. This article introduces an intelligent method for accurately identifying user-product emotions using multimodal physiological signals and machine learning techniques. The study involves designing experiments with 63 representative product images, and collecting various physiological signals (eye movement, electrodermal activity, and pulse). Using the K-means algorithm, we establish efficacy-arousal assessment labels to create a product emotion dataset. Preprocessing methods, including time–frequency analysis and wavelet transformation, ensure high-quality signal analysis data. By extracting temporal, frequency, and time–frequency features from multimodal physiological signals, the Relief algorithm determines the optimal feature combination. Finally, the study evaluates the performance of five machine learning classifiers, with results indicating an 81.31% recognition accuracy for the proposed product emotion recognition framework. This framework proves effective for objectively analyzing user emotions in product experiences.

Interferometric phase diagram generation method based on dot trace texture information and semantic segmentation of correlation coefficients

This paper proposes a method for generating interferometric phase diagrams, which includes interferometric image registration based on Dot Trace Texture Information (DTTI) and interferometric phase filtering based on semantic segmentation. First, the InSAR images are sharpened according to the different backscattering characteristics of various pixel values, and DTTI is extracted to generate the Dot Trace Texture Map (DTTM). Second, SIFT feature points are extracted from two InSAR coherent complex images. They are then screened by comparison with DTTI. These selected points are used to calculate the registration relationship, ensuring precise registration of the two images. Third, the interferometric phase diagram is segmented by semantics based on correlation coefficients. For high-quality interferometric phase regions, we use two-dimensional frequency domain rotational symmetric Gaussian Laplace and wavelet domain filters successively. For low-quality regions, we use an average filter to smooth out noise. The superiority of the proposed algorithm was verified through comparative experiments.

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Locating the informative frequency band of rolling bearing fault signals is of great significance for feature extraction and fault diagnosis. Benefiting from the adjustable center frequency and bandwidth as well as the similarity to impulse-like characteristics induced by bearing failures, Morlet wavelets are commonly used in resonance demodulation. However, fault impulses are highly susceptible to contamination by strong noise, which impedes the efficacy of existing wavelet parameter selection strategies and frequency band optimization methods. In this paper, an adaptive Morlet wavelet-based iterative filtering (AMIF) method is proposed for frequency band optimization under strong noise. The resonance frequency band is pinpointed based on adaptive Morlet wavelet filter banks, with off-band noise being canceled and fault features being refined during the level-by-level filtering process. Additional iterative operations are leveraged to enhance fault features of in-band signals to facilitate the optimization of the filtering parameters. Effectiveness of the proposed AMIF method and its superiority over the wavelet packet transform-based kurtogram and minimum entropy deconvolution are verified through simulation and experimental analysis. The results demonstrate that AMIF can accurately localize the informative frequency band, thereby extracting high-quality fault features, making it suitable for bearing fault diagnosis under strong noise condition with different fault types.

Characterizing B stars from Kepler /K2 Campaign 11. Optical analysis and seismic diagnostics

In this study, we analyze 122 B-type star candidates observed during Campaign 11 of the Kepler /K2 mission to investigate their variability and pulsation characteristics. A subset of 45 B star candidates was observed during the Kepler /K2 mission's Campaign 11 between September and December 2016. Our analyses aim to gain a deeper understanding of the physical characteristics of these massive stars. Our methods involve both spectroscopy and seismology. The spectroscopic analysis was performed through medium-resolution blue spectra, which also allowed us to perform a spectral classification of the objects. Our results will contribute to the ongoing effort to expand our knowledge of variable B stars and the processes that drive their variability. We used the iterative prewhitening and wavelet frequency searching algorithms to analyze the light curves to identify the different types of variability in the data. The frequencies were carefully chosen based on the signal-to-noise ratio and the magnitude of errors. We applied spectroscopic analysis techniques to enhance our understanding of the observed stars, including sme and mesa algorithms. A spectral classification was performed based on the observed spectra. The resulting astrophysical parameters were compared to Gaia mission data. Additionally, a seismology technique was applied to determine the average internal rotation frequency ($ rot $) and buoyancy travel time ($P_0$) for selected stars in the sample. We detected several types of variability among the B-type stars, including slowly pulsating B (SPB) stars, hybrid pulsators showing both beta Cep and SPB pulsations, stars with stochastic low-frequency (SLF) variability, Maia variables, and SPB/Maia hybrids. Their positions in our Gaia and classical HR diagrams are compatible with the theoretical expectations. We also found stars exhibiting variability attributed to binarity and rotation. We determined the physical characteristics for 45 of our targets and conducted a seismic analysis for 14 objects. Two SPB/Maia stars show internal velocities comparable to those of fast SPB stars. The derived average rotation frequencies, $ rot $, for these 14 stars lie between the critical $ crit Roche $ and the minimal frequency values of $ lim rot $ implied by the $ i $ measured from the spectra. Our analysis classified 41 stars as SPB stars and attributed the primary variability of 53 objects to binarity, rotation, or both. We identified five stars as Maia/fast-rotating SPB variables. Two stars were classified as hybrid SPB/beta Cep pulsators, and one as a beta Cep binary. Thirteen stars exhibited prominent, low-frequency power excess, indicating SLF variability. Additionally, we found a positive correlation between the dominant $ f g $ frequency and the internal average rotation frequency.

Stratal surfaces honoring seismic structures and interpreted geologic time surfaces

Seismic horizons play a significant role in reservoir model construction and sedimentary facies interpretation, providing crucial low-frequency constraints for seismic inversion. In basin and regional interpretations, the assumption that seismic reflections represent a stratigraphic surface with constant geologic time is significant for guiding seismic interpretation. This assumption may fail when applied to local reservoir scales due to common geologic time transgressions of a particular event in regular wavelet frequency. There will be inconsistencies between seismic events and stratigraphic surfaces. To address this issue and obtain relatively accurate stratal interpretations, we develop a hybrid horizon extraction method honoring seismic structures and time-stratigraphic frameworks, in which seismic reflection structures provide local details and interpreted geologic time surfaces offer critical constraints. First, we develop concepts and a workflow using a realistic outcrop model. We develop a new geology-guided structure tensor by fitting a gradient vector of seismic images and geologic time surfaces. We also consider existing geologic conditions, such as unconformities, and fuse them into our method to calculate accurate slopes and generate reliable relative geologic time images at a fine scale, followed by making slices. Further, we extend our method to 3D seismic data volumes. Our experiments, conducted using simulated and field data, show the superiority and accuracy of our hybrid method compared with the slope-based and stratal slicing methods. These results highlight the potential for applying our method to fine-scale subsurface modeling.

Frequency monitoring analysis of milling edge chatter in composites using WPD for frequency band selection

In the milling process of composite materials, the initial chatter frequency is not obvious and is easily swamped by the rest of the signals, making frequency monitoring difficult, so the study proposes a chatter frequency monitoring method based on frequency cancellation algorithms and wavelet packet decomposition. The results of the experiments shown that the frequency cancellation algorithm can successfully remove invalid signals, such as spindle rotation frequency and cutter tooth frequency, and only keep the necessary signals, at which point the chattering frequency may be observed at a frequency of roughly 1333 Hz. The influence of the frequency bands s5, s9, s10, s12, and s13 after de-frequency removal should be avoided because they all have a low energy share of roughly 23 %, 0.9 %, 5 %, 10 %, and 16 %, respectively, and are less sensitive to chatter. For milling edge depths of 0.5 mm, 2 mm, and 4 mm, the average chatter thresholds were around 3.27, 2.9, and 2.89, respectively. It was challenging to pinpoint the chatter of the system because the empirical modal decomposition observed an average chatter energy entropy of just 1.55 and found that its fluctuations at the milling edge depth junction were insignificant. On the other hand, the chattering could be plainly seen since the energy entropy experienced a substantial and dramatic fluctuation at the intersection of the milling edge depth when it was about 2.9, 2.6, and 2.5, respectively. The experimental findings demonstrated that the frequency cancellation technique and wavelet packet decomposition-based chattering frequency monitoring approach can precisely track the chattering state of the system.

Effects of caffeine supplementation on anaerobic power and muscle activity in youth athletes

This study aimed to investigate the effects of caffeine ingestion on anaerobic performance and muscle activity in young athletes. In this randomized, double-blind, and placebo-controlled study, ten highly trained male post-puberal futsal players aged 15.9 ± 1.2 years conducted two laboratory sessions. Athletes performed the Wingate test 60 min after ingestion of caffeine (CAF, 6 mg/kg body mass) or placebo (PL, dextrose) (blinded administration). Peak power, mean power, and the fatigue index were assessed. During the performance of the Wingate test, electromyographic (EMG) data were recorded from selected lower limbs muscles to determine the root mean square (RMS), mean power frequency (MPF), and median power frequency (MDPF) as frequency domain parameters and wavelet (WT) as time-frequency domain parameters. Caffeine ingestion increased peak (0.80 ± 0.29 W/Kg; p = 0.01; d = 0.42) and mean power (0.39 ± 0.02 W/Kg; p = 0.01; d = 0.26) but did not significantly affect the fatigue index (52.51 ± 9.48%, PL: 49.27 ± 10.39%; p = 0.34). EMG data showed that the MPF and MDPF parameters decreased and the WT increased, but caffeine did not have a significant effect on these changes (p > 0.05). Moreover, caffeine ingestion did not significantly affect RMS changes in the selected muscles (p > 0.05). Here we showed that acute caffeine ingestion improved anaerobic performance without affecting EMG parameters in young male futsal athletes.

A Daubechies wavelet analysis on coupled radiation and conduction in absorbing and scattering medium under pulsed laser

This study examines how the wavelet is affected by the wavelet function and thermal parameters by combining radiative and conductive heat transfer with Daubechies discrete wavelet decomposition for signal processing. By comparing time-frequency information under different sampling time interval, we can deduce that the large sampling time interval causes the detail coefficients to focus on the high frequency area. Regarding the wavelet coefficient, the radiation characteristics, heating power, heating time, and actual random errors will affect the wavelet frequency. The thermal conductivity and extinction coefficient have a great influence on the detail coefficient, while the albedo has an impact on the temperature and the amplitude of the wavelet detail coefficient in the range of 0.5-0.9, and the asymmetry factor has a significant effect on the time of the temperature peak. The wavelet detail coefficient of the heating side temperature changes more dramatically than that of the cooling side. The detail coefficient of the low-frequency region is more affected by thermal physical factors whereas the high-frequency portion is more affected by random errors and heating power. This law can improve the accuracy of acquiring physical attributes by filtering out random errors in practical applications.

Open circuit fault diagnosis of three‐phase inverter based on SR‐WOA‐ELM

SummaryTo ensure smooth power exchange in a three‐phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. However, the fault characteristics of power tubes are complex, the fault diagnosis results are affected by the load, and the recognition accuracy of some fault diagnosis models is low. To tackle this issue, this paper proposes an inverter open circuit fault diagnosis method named SR‐WOA‐ELM, which integrates signal reconstruction (SR), the whale optimization algorithm (WOA), and an extreme learning machine (ELM). First, the three‐phase output current is processed by the signal reconstruction method to eliminate the effect of load fluctuation, the average value of low frequency coefficients and wavelet entropy is extracted from the reconstructed three‐phase currents using the improved wavelet packet transform, and the fault feature vector is constructed by fusing the above two feature parameters. Second, the parameters of the ELM fault diagnosis model are optimized using WOA. Finally, the fault feature vector is added in the SR‐WOA‐ELM model to obtain the fault diagnosis results. Through simulation and experimental verification, the model can accurately identify the single‐tube and double‐tube faults of the inverter and is not affected by load fluctuations.

Comparing the Effects of Static Stretching Alone and in Combination with Post-Activation Performance Enhancement on Squat Jump Performance at Different Knee Starting Angles.

We aimed to investigate the impact of isolated static stretching (4 sets of 30 seconds) and its combined form with 10 repetitive drop jumps on lower limb performance during squat jumps at different knee joint starting angles (60°, 90°, and 120°). Thirteen participants completed three randomly ordered experimental visits, each including a standardized warm-up and squat jumps at three angles, apart from the intervention or control. Information was gathered through a three-dimensional movement tracking system, electromyography system, and force platform. The electromyography data underwent wavelet analysis to compute the energy values across the four wavelet frequency bands. The average power (Pavg), peak power (Ppeak), peak ground reaction force (GRFpeak), peak center of mass velocity (Vpeak), and force-velocity relationship at peak power (SFv) were extracted from the force and velocity-time data. The results revealed no significant influence of isolated static stretching, or its combined form with drop jumps, on the energy values across the frequency bands of the gastrocnemius, biceps femoris and rectus femoris, or the Pavg or Ppeak (P > 0.05). However, at 120°, static stretching reduced the GRFpeak (P = 0.001, d = 0.86) and SFv (P < 0.001, d = 1.12), and increased the Vpeak (P = 0.001, d = 0.5). The GRFpeak, Pavg, Ppeak, and SFv increased with an increase in the joint angle (P < 0.05), whereas the Vpeak decreased (P < 0.05). These findings suggest that static stretching does not diminish power output during squat jumps at the three angles; however, it alters GRFpeak, Vpeak, and the relative contributions of force and velocity to peak power at 120°, which can be eliminated by post-activation performance enhancement. Moreover, compared to 60° and 90°, 120° was more favorable for power and peak force output.