Time-frequency Approach Research Articles

Spillover effects among cryptocurrencies in a pandemic: a time frequency approach

Spillover effects among cryptocurrencies in a pandemic: a time frequency approach

Dynamic correlation among renewable energy, technology, and carbon markets: Evidence from a novel nonparametric time-frequency approach

In addressing the challenges of energy security and climate change, ongoing efforts involve the development of innovative technologies to support the shift from conventional to clean energy sources. Concurrently, the establishment of carbon markets aims to facilitate a decrease in carbon emissions by enabling the trade of carbon credits. This study adds to the energy-climate discussion by examining the evolving dynamics of returns on investments in renewable energy, technology, and carbon markets. Specifically, the time-varying correlation among renewable energy, technology, and carbon markets in the European Union is examined from September 18, 2017 to December 14, 2022. The techniques employed for empirical analyses are the nonparametric time-frequency, Benjamini-Hochberg, and Benjamini-Yekutieli correlation techniques, as well as the time-varying Granger causality. Highlights from the results are as follows. Renewable energy, technology, and carbon markets exhibit time and frequency variations in their relationships, with significant correlations notably present during periods marked by major shifts in energy policy. Secondly, renewable energy and carbon markets exhibit very weak or even absent correlation, indicating their suitability for portfolio diversification. Furthermore, investors could benefit significantly by including both technology and carbon markets in their portfolios to reduce overall risk. Several additional recommendations are also provided in this study.

Digital technologies for signal radio vision and radio monitoring

Objectives. Radiophysical processes involving the electrodynamic formation of signal radio images diffusely scattered by the signature of small-sized objects or induced by the near field of radio devices are relevant for identifying radiogenomic (cumulant) features of objects in the microwave range in the development of neuroimaging ultra-short pulse (USP) signal radio vision systems, telemonitoring, and near-radio detection. The paper sets out to develop methods and algorithms for vector analysis of radio wave deformation of nonstationary fields forming a signal radio image based on radiophysical and topological characteristics of small-sized objects; to develop software and hardware for registration and neural network recognition of signal radio images, including methods for the synthesis and extraction of signal radiogenomes using digital twins of objects obtained through vector electrodynamic modeling; and to analyze signal radio images induced by elements of printed topology of electronic devices.Methods. The study is based on statistical radiophysics methods, time-frequency approaches for wavelet transformation of USP radio images, numerical electrodynamic methods for creating digital twins of small-sized objects, as well as neural network authentication algorithms based on the cumulant theory of pole-genetic and resonant physically unclonable functions used in identifying signal radio images.Results. The results of fundamental research on electrodynamic effects of vector-wave deformation of nonstationary fields of sub-nanosecond configuration are presented as a means of identifying and authenticating signal radio images. Neural network techniques for cumulant formation of radio genomes of signal radio images are proposed on the basis of pole-genetic and resonant functions.Conclusions. A radiogenome, representing the unique authenticator of a radio image, is shown to be formed on the basis of physically unclonable functions determined by the structure and set of radiophysical parameters of the image. Cumulant features of signal radio images identified on the basis of pole-genetic and physically unclonable resonant functions of small-sized objects are revealed.

Dynamic network analysis of electrophysiological task data

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Dynamic Interrelation between Aggregated Revenues and Expenditures of Regional-Level Budgets of Federal States: A Time-Frequency Approach to Analysis

Objective: The article analyzes the dynamic (time-frequency) relationship between aggregate budget expenditures and revenues of the federal constituent entities, considering four competing theoretical hypotheses: “expenditures-revenues”; “revenues-expenditures”; “fiscal synchronization”; and “fiscal neutrality”, over time (2013:12÷2023:03). Method: The study was conducted using wavelet coherence analysis, which made it possible to separate short-term, medium-term and long-term effects in the dynamic dependence of the analyzed variables, and identify the direction and intensity of this relationship. Finding: The research results indicate the existence of all four relationships between aggregated budget revenues and expenditures of the federal constituent entities for various time periods, on different time scales, throughout the entire observation time interval (2013:12÷2023:03). These findings demonstrate that, to a greater extent, the budgets of the federal constituent entities are characterized by the presence of a dynamic relationship between aggregated revenues and expenditures in the following modes: “revenues-expenditures” (when revenues are ahead of expenditures) and “expenditures-revenues” (when expenditures are ahead of revenues). In addition, the time scale and time range of the emergence of the “fiscal synchronization” and “fiscal neutrality” modes in the relationship between aggregated revenues and expenditures of the federal constituent entities are determined. Implication: The results obtained provide a possible explanation for the contradictory results of numerous studies based on traditional methods and empirical observations over different historical periods, since each of these methods only partially explains a particular mode of the dynamic relationship between revenues and expenditures. The theoretical significance of the study lies in the substantiation that continuous wavelet analysis is a powerful tool for studying the nonlinear relationship between revenues and expenditures, since understanding the causalities between revenues and expenditures on various time scales is significant for choosing measures aimed at maintaining the budget sustainability of the federal constituent entities. The practical significance of the research results is determined by the fact that they can be an essential starting point for determining the necessary fiscal policy measures aimed at increasing the budget sustainability of the federal constituent entities.

Advancing non-invasive fetal health monitoring: A time–frequency approach to extracting fetal electrocardiogram signals

The fetal electrocardiogram (FECG) provides an important way for clinicians to assess fetal health and to monitor pregnancy conditions. Although FECG can be captured through abdominal electrocardiogram (AECG) recordings from pregnant women, it often receives interference from maternal cardiac activity and other external sources. Particularly, as the high-amplitude maternal electrocardiogram (MECG) typically obscures the waveform of the FECG, isolating FECG from the AECG signals is challenging. Nevertheless, the differences in frequency patterns between MECG and FECG suggest a potential way for reconstructing FECG by combining the frequency and amplitude information. To this end, we proposed a novel method for separating FECG signals from the AECG via the time–frequency representation. We designed an encoder–decoder architecture based hierarchical multiscale-aware network to capture features embedded in both the time and frequency domains. Equipped with a holistic attention-aware module (HAA), the model extracts and consolidates fetal heart activity-related features in the time–frequency domain. Further introducingan attention gate module (AG) to emphasize key features during encoder-to-decoder transmission, the model enhances the quality and efficiency of FECG extraction without any redundancy in high-quality feature mapping. The model was trained using a hybrid time-domain and frequency-domain loss function and validated on two public databases. Excellent performances, such as an average Pearson correlation coefficient of 0.85 for estimating the quality of FECG signals, were observed demonstrating its excellent performance and providing a promising non-invasive method for monitoring fetal health. These findings indicate that the proposed method effectively captures the amplitude differences between fetal and maternal heartbeats across various frequency components within the AECG, leading to the extraction of high-quality FECG signals.

Does the source of oil price shock matter for Indian sectoral stock returns? A time-frequency approach to analyse dynamic connectedness and spillovers

ABSTRACT This paper examines the connectedness and spillovers between decomposed oil shocks and Indian sectoral equities in a time-frequency domain using the most recent Barunik and Krehlik (2018) approach. Our empirical results show that the oil demand shock is the major spillover transmitter across all time horizons followed by the risk shocks; while oil supply shock appears to be a net receiver in all the frequency bands indicating the differential impact of oil shocks. Among the sectors, Basic Materials and Finance receive the highest spillovers from oil shocks in the short-term; while Consumer Discretionary Goods & Services and Industrials join the list in the medium- and long-term. FMCG, Health, Telecom and IT sectors receive the least spillovers in all the bands, making them apt for investments during periods of high volatility. Our empirical results are robust to the application of the alternative TVP-VAR time-frequency parameter framework. The portfolio analysis shows that inclusion of stocks in Oil and sectors like Metal and Telecom significantly reduces the portfolio risk. Dynamic connectedness analysis reveals that the spillovers dramatically increase during times of extreme turmoil, especially during the Global Financial Crisis (2008–2009) and the COVID-19 pandemic. Policy implications of our empirical results are also discussed.

Time–Frequency Approach for Cutting Tool Power Signal Separation in Face Milling Operations

Face milling is among the processes that can produce a high-precision surface finish. Tool condition monitoring and signal processing algorithms are under extensive research to improve production quality and productivity in machining processes. In the present research, the time–frequency analysis technique was applied to the signal obtained from a sensor integrated into the primary AC power circuitry during the milling of steel bars to evaluate its applicability in detecting the current variations associated with the cutting force. The signal acquired from the sensor was processed in the time–frequency domain using wavelet analysis, and the results were compared with the traditional time and frequency analyses. The results showed that the signal variations produced by the cutting force were well localized in the frequency spectra with both approaches. However, the wavelet processing method yielded a poorly defined cutting force signal shape due to the limited resolution inherent in the sub-bands containing the frequencies of interest.

On the linkage of oil prices and oil uncertainty with US equities: a combination analysis based on the wavelet approach and quantile-on-quantile regression

This paper aims to investigate the dynamic and asymmetric linkage between crude oil, oil uncertainty, and the United States (US) equity markets across various horizons and tails using a combination of a time-frequency approach, Granger causality, and quantile-on-quantile regression from January 2020 to December 2022. The empirical results indicate that causal relationships and the dynamic co-movement between crude oil, oil implied volatility, and the Dow Jones industrial and transportation indices are confirmed across various frequencies through wavelet-based Granger causality and wavelet coherence. Then, the wavelet-based quantile-on-quantile regression shows that the relationship between oil, oil implied volatility, and both US equity markets is heterogeneous and asymmetric across short- and long-run horizons, in particular. The findings provide new insights into the sensitivity of US stock markets to oil shocks across various time frequencies and tails, offering several portfolio implications useful for heterogeneous investors and portfolio managers.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI.

Magnetic resonance imaging (MRI) is a standard procedure in medical imaging, on a par with echography and tomodensitometry. In contrast to radiological procedures, no harmful radiation is produced. The constant development of magnetic resonance imaging (MRI) techniques has enabled the production of higher resolution images. The switching of magnetic field gradients for MRI imaging generates induced voltages that strongly interfere with the electrophysiological signals (EPs) collected simultaneously. When the bandwidth of the collection amplifiers is higher than 150 Hz, these induced voltages are difficult to eliminate. Understanding the behavior of these artefacts contributes to the development of new digital processing tools for better quality EPs. In this paper, we present a study of induced voltages collected in vitro using a device (350 Hz bandwidth). The experiments were conducted on a 1.5T MRI machine with two MRI sequences (fast spin echo (FSE) and cine gradient echo (CINE)) and three slice orientations. The recorded induced voltages were then segmented into extract patterns called "artefact puffs". Two analysis series, "global" and "local", were then performed. The study found that the temporal and frequency characteristics were specific to the sequences and orientations of the slice and that, despite the pseudo-periodic character of the artefacts, the variabilities within the same recording were significant. These evolutions were confirmed by two stationarity tests: the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and the time-frequency approach. The induced potentials, all stationary at the global scale, are no longer stationary at the local scale, which is an important issue in the design of optimal filters adapted to reduce MRI artifacts contaminating a large bandwidth, which varies between 0 and 500 Hz.

A Novel Time–Frequency Approach Based on the Noise Characterization for Structural Health Monitoring (SHM) Using GNSS Observations

A Novel Time–Frequency Approach Based on the Noise Characterization for Structural Health Monitoring (SHM) Using GNSS Observations

Analysis of the dynamic relationship between income and expenditure of the Moscow budget (2013:01÷2023:03): Time-frequency approach

The article conducts a study of the sustainability of the Moscow city budget, based on an analysis of the time-frequency relationship between its monthly expenses and income, for the period from December 2012 to March 2023, using the calculated values of wavelet coherence and the phase difference between the wavelets of expenses and income. Four main concepts concerning the correlation between government expenditures and income, including tax revenues, are considered — these are the hypotheses: “income-expenses”; “expenses-income”; fiscal synchronization and the institutional separation hypothesis. The results of the study show that in the case of the Moscow city budget, all four dynamic relationships between its income and expenses are observed, in different time periods, on different time scales, over the observation time interval (2013:01?2023:03), proposed by the theory. Assessing the dynamic relationship between income and expenditure of the budgets of the constituent entities of the Russian Federation is important for the development of fiscal policy aimed at increasing the sustainability of the budgets of the constituent entities of the Russian Federation.

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT.The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.

Post-Earthquake Damage Identification of Buildings with LMSST

The structure is said to be damaged if there is a permanent shift in the post-event natural frequency of a structure as compared with the pre-event frequency. To assess the damage to the structure, a time-frequency approach that can capture the pre-event and post-event frequency of the structure is required. In this study, to determine these frequencies, a local maximum synchrosqueezing transform (LMSST) method is employed. Through the simulation results, we have shown that the traditional methods such as the Wigner distribution, Wigner–Ville distributions, pseudo-Wigner–Ville distributions, smoothed pseudo-Wigner–Ville distribution, and synchrosqueezing transforms are not capable of capturing the pre-event and post-event frequency of the structure. The amplitude of the signal captured by sensors during those events is very small compared with the signal captured during the seismic event. Thus, traditional methods cannot capture the frequency of pre-event and post-event, whereas LMSST employed in this work can easily identify these frequencies. This attribute of LMSST makes it a very attractive method for post-earthquake damage detection. In this study, these claims are qualitatively and quantitatively substantiated by comprehensive numerical analysis.

Dynamic relations between housing Markets, stock Markets, and uncertainty in global Cities: A Time-Frequency approach

This paper considers the dynamic features of housing prices in metropolises that are characterised by a high degree of internationalisation. Using the wavelet coherency procedure, the degree of co-movement and causality between housing, stock markets, and macroeconomic uncertainty are investigated. In addition, the existence of volatility spillover across housing markets is assessed in the time–frequency domain using a novel procedure that involves combining the wavelet decomposition with a time varying parameter vector autoregression model. The results highlight that the clustering of global business in a limited number of metropolises that act as “global hubs” leaves the local housing markets exposed to international shocks and volatility spillover. The empirical analysis suggests that the correlations between real estate and stock markets from one side, and real estates and uncertainty on the other side, intensify during the turmoil periods. Causality and co-movement relationships appear predominately in the medium and long run periods. The evidence presented in this paper suggests that policymakers cannot ignore the possibility that international shocks to housing markets may affect the domestic markets. In this respect, macroprudential policy tools may target tapering off the unintended effects of housing market globalisation such as house price shock synchronisation, especially when these shocks take place in cities that are also major financial centres.

Higher moment connectedness of cryptocurrencies: a time-frequency approach

The purpose of the study is to examine higher moment connectedness among 12 cryptocurrencies using data sampled at the 1-minute high-frequency interval. We use methods that demonstrate the heterogeneity of agents from their distinct investing horizons. This includes wavelet multiple cross-correlations, CEEMDAN-based Diebold-Yilmaz (DY) connectedness index and the Barunik-Krehlik (BK) frequency connectedness index. First, our results show that higher moment multiple correlations among the sampled cryptocurrencies are higher at all time scales and the relationship strengthens at lower frequencies. Second, the wavelet cross-correlations show different cryptocurrencies with the potential to lead and lag in the transmission of higher moment shocks to the whole system at different frequencies. Again, the multiple wavelet cross-correlations increase with increasing time scales. The results from the CEEMDAN-based DY connectedness index as well as the BK framework also reveal cyclical connectedness and differences in connectedness across different frequencies. The results show more connectedness of higher moments than the connectedness empirically reported for returns and volatility. Cryptocurrency connectedness has mostly been examined using the first two moments. We extend this line of literature by examining the third and fourth moments, which might be more useful for risk management purposes.

Building occupancy estimation using microwave Doppler radar and wavelet transform

Room-level occupancy estimation is a critical input for real estate management, including building utilization optimization, energy-efficient building control, building security, and occupant health and well-being. Knowing how many people are using a room at a time can enable businesses to use their real estate, energy and human capital more efficiently, reducing operating expenses as well as carbon footprint. Radar-based occupancy estimation is attractive because it is unobtrusive and does not introduce the privacy issues brought with video imaging-based sensors. In this paper, we present an occupant estimation approach based on continuous wave Doppler radar and wavelet-based signal processing techniques. Theoretical background provides a rational for using a wavelet-based time-frequency approach, and comprehensive simulation and experimental results demonstrate the effectiveness of the proposed approaches on a data set that includes 1–10 occupants. These results indicate that Doppler radar with wavelet-based signal processing may be an effective tool for occupant count in smart building applications.

Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques

This paper presents a current- and voltage-driven protection scheme for transmission lines based on a hybrid mix of Stockwell transform (ST) and Hilbert transform (HT). Use of both current and voltage waveforms to detect and categorize faults, improves the reliability of this protection scheme and avoids false tripping. Current and voltage waveforms captured during a period of fault are analyzed using ST to compute a median intermediate fault index (MIFI), a maximum value intermediate fault index (MVFI), and a summation intermediate fault index (SIFI). Current and voltage signals are analyzed via applying HT to compute a Hilbert fault index (HFI). The proposed hybrid current and voltage fault index (HCVFI) is obtained from the MIFI, MVFI, SIFI, and HFI. A threshold magnitude for this hybrid current and voltage fault index (HCVFITH) is set to 500 to identify the faulty phase. The HCVFIT is selected after testing the method for various conditions of different fault locations, different fault impedances, different fault occurrence angles, and reverse flows of power. Fault classification is performed using the number of faulty phases and an index for ground detection (IGD). The ground involved in a fault is detected by comparison of peak IGD magnitude with a threshold for ground detection (THGD). THGD is considered equal to 1000 in this study. The study is carried out using a two-terminal transmission line modeled in MATLAB software. The performance of the proposed technique is better compared to a discrete wavelet transform (DWT)-based technique, a time–frequency approach, and an alienation method. Our algorithm effectively detected an AG fault, observed on a practical transmission line.

Forced Oscillation Identification and Filtering From Multi-Channel Time-Frequency Representation

Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially under resonance condition with natural system modes. In this case, the magnitudes of the oscillations could be greater in places distant from the source and the oscillation spreads over a large region of the power system. Detection, frequency identification and filtering of FO oscillatory components constitutes an initial and critical step for the application of oscillation source location (OSL) methods. Specifically, this step has a major impact on the performance of the OSL method, such as the Dissipating Energy Flow (DEF) method. In this paper we develop a systematic methodology for detection, identification and filtering of non-stationary FO based on multi-channel time-frequency (TF) representation (TFR). We compare three TF approaches applied together with the DEF method: short-time Fourier transform (STFT), STFT-based synchrosqueezing transform (FSST) and second order FSST (FSST2). We have used simulated signals and real world PMU data to show that the proposed method provides a systematic framework for the identification and filtering of power systems non-stationary forced oscillations.