Frequency Content Of Signal Research Articles

Detection of infrasound sources using three-years array data in 2019-2021 deployed at the Lützow-Holm Bay region, Antarctica

Time-space variations of infrasound source locations for three years, 2019-2021, were studied byusing a combination of two local arrays in the Lützow-Holm Bay region (LHB), Antarctica. The local arrays deployed at two coastal outcrops clearly detected temporal variations in signal frequency content as well as propagating directions during the three years. A large number of infrasound sources were detected with many located to the north and north-west directions from the arrays. These events were generated within the Southern Indian Ocean and the northern part of LHB with frequency-content of a few seconds; these “microbaroms” are believed to originate from oceanic swells. From austral summer to fall additional infrasound sources are determined to be located to the north-east. These sources might be related to the effects of katabatic winds across the continental coastal area. Furthermore, several impulsive infrasound events during the winter had higher predominant frequencies of a few Hz, higher than the microbaroms. On the basis of a comparison of source locations with sea-ice and glacier distribution form MODIS satellite images, these high-frequency sporadic sources may be cryo-seismic signals associated with cryosphere dynamics near the local arrays. These results suggest that infrasound waves can be used to monitor surface environments in the coastal area of Antarctica.

Finite Element Simulation of Acoustic Emissions from Different Failure Mechanisms in Composite Materials

Damage in composite laminates evolves through complex interactions of different failure modes, influenced by load type, environment, and initial damage, such as from transverse impact. This paper investigates damage growth in cross-ply polymeric matrix laminates under tensile load, focusing on three primary failure modes: transverse matrix cracks, delaminations, and fiber breaks in the primary loadbearing 0-degree laminae. Acoustic emission (AE) techniques can monitor and quantify damage in real time, provided the signals from these failure modes can be distinguished. However, directly observing crack growth and related AE signals is challenging, making numerical simulations a useful alternative. AE signals generated by the three failure modes were simulated using modified step impulses of appropriate durations based on incremental crack growth. Linear elastic finite element analysis (FEA) was applied to model the AE signal propagating as Lamb waves. Experimental attenuation data were used to modify the simulated AE waveforms by designing arbitrary magnitude response filters. The propagating waves can be detected as surface displacements or surface strains depending upon the type of sensor employed. This paper presents the signals corresponding to surface strains measured by surface-bonded piezoelectric sensors. Fiber break events showed higher-order Lamb wave modes with frequencies over 2 MHz, while matrix cracks primarily exhibited the fundamental S0 and A0 modes with frequencies ranging up to 650 kHz, with delaminations having a dominant A0 mode and frequency content less than 250 kHz. The amplitude and frequency content of signals from these failure modes are seen to change significantly with source–sensor distance, hence requiring an array of dense sensors to acquire the signals effectively. Furthermore, the reasonable correlation between the simulated waveforms and experimental acoustic emission signals obtained during quasi-static tensile test highlights the effectiveness of FEA in accurately modeling these failure modes in composite materials.

Spectral graph model for fMRI: A biophysical, connectivity-based generative model for the analysis of frequency-resolved resting-state fMRI

Abstract Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain’s functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure–function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal’s spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure–function relationships in a variety of brain disorders.

Linear prediction on Cent scale for fundamental frequency analysis

Understanding the fundamental frequency and harmonic content of audio signals is crucial for many applications in music analysis, including music transcription, audio synthesis, and genre identification. This study formulates a signal processing approach combining Linear Prediction (LP) analysis and the Cent scale to accurately characterize the pitch and harmonic structure of the audio signals. Pitch tracking on the LP spectrum in the Cent scale provides more accurate and reliable pitch estimation, especially in the presence of noise or overlapping harmonics. The Cent scale aligns the harmonics of different notes more closely, making it easier to discern the correct pitch.

Deep Learning Model Size Performance Evaluation for Lightning Whistler Detection on Arase Satellite Dataset

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their propagation through the plasmasphere essential for predicting space weather impacts on various technologies. In this study, we evaluate the performance of different deep learning model sizes for lightning whistler detection using the YOLO (You Only Look Once) architecture. To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. Our approach involves exposing the models to spectrogram diagrams—visual representations of the frequency content of signals—derived from the Arase Satellite’s WFC (WaveForm Capture) subsystem, with a focus on analyzing whistler-mode plasma waves. We experimented with various model sizes, adjusting epochs, and conducted performance analysis using a partial set of labeled data. The testing phase confirmed the effectiveness of the models, with YOLOv5n emerging as the optimal choice due to its compact size (3.7 MB) and impressive detection speed, making it suitable for resource-constrained applications. Despite challenges such as image quality and the detection of smaller whistlers, YOLOv5n demonstrated commendable accuracy in identifying scenarios with simple shapes, thereby contributing to a deeper understanding of whistlers’ impact on Earth’s magnetosphere and fulfilling the core objectives of this study.

Experimental validation of the amplitude ratio as a metric for milling stability identification

This paper presents the experimental validation of the amplitude ratio, a metric for milling stability identification. The amplitude ratio quantifies the severity of chatter by comparing the amplitude of the expected frequency content of a milling signal (i.e., tooth passing frequency, runout frequency, and harmonics) to the amplitude of the chatter frequency, if present. Through multiple iterations of a milling time domain simulation, the amplitude ratio diagram, which characterizes stable and unstable milling behavior over a range of spindle speeds and axial depths of cut, may be generated. In this paper, a comparison of the simulated and measured amplitude ratios for a series of milling test cuts is presented. It is shown that the amplitude ratio is suitable for identifying milling stability in both simulations and experiments. Additionally, it is shown that through judicious selection of low-cost sensors, implementation of the amplitude ratio is cost efficient. Direct comparison of the simulated and measured amplitude ratios demonstrates the effectiveness of the approach.

Joint Time-Frequency Signal Analysis of Li-Ion Batteries

Measurements acquired from batteries, such as voltage-time and capacity-time signals, offer valuable insights into their cyclability performance. While analyzing these signals in the time domain provides an understanding of global characteristics, scrutinizing local behavior is crucial for detecting issues like battery fading. In this context, we delve into the application of short-time Fourier transform for time-frequency deconstruction of galvanostatic charge/discharge signals, using lithium-sulfur batteries as an illustrative example. Spectrograms resulting from this analysis unveil the evolving frequency content of signals throughout the battery's lifespan, facilitating the identification of critical changes in the response and early detection of issues leading to fading and potential default. Simultaneously, the stability analysis of the dynamic response of red phosphorus and sulfide polyacrylonitrile (RP-SPAN) composite, a promising anode material for lithium batteries, can also be studied using Fourier transform analysis. Our study employs transfer function stability analysis, Kramers-Kronig integral relations, and differential capacity analysis to assess the cell's behavior in both frequency and time domains. Parameters such as stationarity, stability, linearity, dissipation, and degradation are scrutinized over extended charge/discharge cycles. Results reveal a highly nonlinear and time-variant system at low frequencies, aligning with the observed 0.21% average capacity loss per cycle. Our results indicate that short-time Fourier transform could provide insightful information for analyzing battery health.

Resolution limits for radiation-induced acoustic imaging for in vivo radiation dosimetry

Objective. Radiation-induced acoustic (RA) computed tomographic (RACT) imaging is being thoroughly explored for radiation dosimetry. It is essential to understand how key machine parameters like beam pulse, size, and energy deposition affect image quality in RACT. We investigate the intricate interplay of these parameters and how these factors influence dose map resolution in RACT. Approach. We first conduct an analytical assessment of time-domain RA signals and their corresponding frequency spectra for certain testcases, and computationally validate these analyses. Subsequently, we simulated a series of x-ray-based RACT (XACT) experiments and compared the simulations with experimental measurements. In-silico reconstruction studies have also been conducted to demonstrate the resolution limits imposed by the temporal pulse profiles on RACT. XACT experiments were performed using clinical machines and the reconstructions were analyzed for resolution capabilities. Main results. Our paper establishes the theory for predicting the time- and frequency-domain behavior of RA signals. We illustrate that the frequency content of RA signal is not solely dependent on the spatial energy deposition characteristics but also on the temporal features of radiation. The same spatial energy deposition through a Gaussian pulse and a rectangular pulse of equal pulsewidths results in different frequency spectra of the RA signals. RA signals corresponding to the rectangular pulse exhibit more high-frequency content than their Gaussian pulse counterparts and hence provide better resolution in the reconstructions. XACT experiments with ∼3.2 us and ∼4 us rectangular radiation pulses were performed, and the reconstruction results were found to correlate well with the in-silico results. Significance. Here, we discuss the inherent resolution limits for RACT-based radiation dosimetric systems. While our study is relevant to the broader community engaged in research on photoacoustics, x-ray-acoustics, and proto/ionoacoustics, it holds particular significance for medical physics researchers aiming to set up RACT for dosimetry and radiography using clinical radiation machines.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Dynamics of a 3-D inlet/isolator measured with fast pressure-sensitive paint

Fast pressure-sensitive paint (PSP) was applied to an inlet/isolator designed using the Osculating Internal Waverider Inlet with Parallel Streamlines (OIWPS) method. The dorsal isolator surface pressure was measured using anodized-aluminum PSP through transparent cast acrylic that makes up the ventral portion of the isolator. Temperature-sensitive paint was utilized to correct for the PSP’s temperature sensitivity. The model was tested under Mach 5.7 flow at Re =\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$=$$\\end{document} 8.5 ×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 10^6$$\\end{document} /m and 10.2 ×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 10^6$$\\end{document} /m in the AFOSR–Notre Dame Large Mach-6 Quiet Tunnel (ANDLM6QT) under conventional noise conditions. Flow phenomena, such as shocks originating in the inlet and flow separation at the throat, were visualized with high spatial resolution. The dynamics measured by the PSP and pressure transducers matched well where the spectral signal-to-noise ratio was above unity. Power spectral densities showed significant frequency content at ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document}1 kHz in the shock-wave/boundary-layer interaction (SWBLI) regions. Coherence analysis showed a linear relationship between the unsteady pressures at locations underneath different SWBLI in the isolator, with the exception of the Busemann throat shock. Temporal correlation of shock positions indicated that disturbances propagated downstream at 114% of the core-flow velocity; however, improved calculations of the core-flow velocity are needed to refine this assessment. The surface pressure fields at Re = 8.5 ×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 10^6$$\\end{document} /m and 10.2 ×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes 10^6$$\\end{document} /m were quantitatively very similar, and the results in the ANDLM6QT were qualitatively similar to previous studies in the Boeing/AFOSR Mach-6 Quiet Tunnel under noisy flow.

Geometry effects in Impact Echo Signals - why traditional time-frequency filtering fails to remove them

Impact Echo signals can sometimes contain a lot of noise, even though the wavelength of the investigated modes is in the order of the specimen thickness. A special type of noise signals are the so-called geometry effects. These effects occur when the lateral dimensions of the investigated structure are not much larger than its thickness. This study compares signals with and without geometry effects using various types of signal processing methods such as classical fast Fourier transform, short-time Fourier transform, and the continuous wavelet transform. Results show that signals without geometry effects have a much more stable resonance frequency, whereas the main frequency content in Impact Echo signals containing geometry effects typically oscillates between two or more discrete frequencies. As this oscillation lasts for the entirety of the signals, traditional time-frequency filters will likely not be capable to remove geometry effects from Impact Echo signals. Resonance frequencies in Impact Echo signals unaffected by geometry effects are much more stable, with no fluctuation or oscillation over time. By comparing time-frequency representations of signals obtained at the same spot with different impactors, it is observed that the peak frequency at the beginning of each signal is similar for all three measurements. This result suggests that, for signals heavily affected by geometry effects, the beginning of the signal, which is usually neglected during Impact Echo inspection, could provide useful information regarding the thickness of the inspected structure. Two ways of obtaining thickness information from the signal beginning are presented and results are compared using a dataset from a reference specimen: one method uses classical Fourier transform of the signal beginning and the other approach uses the maximum frequency determined from the continuous wavelet transform. The results for both approaches show that the Impact Echo signal beginning contains depth information, which could prove valuable for evaluations of noisy datasets.

Partial discharge localization in power transformer tanks using machine learning methods

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

Ictal fast activity chirps as markers of the epileptogenic zone.

The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.

Underwater Object Prediction Using Sonar Waves

The detection and differentiation of potentially hazardous objects, such as mines and rocks, in underwater environments are crucial for the safe navigation of submarines during warfare scenarios. This study compares and contrasts different machine learning algorithms to determine whether an object detected by a submarine's sonar system is a mine or a rock. From the comparative analysis, we have proposed the Logistic Regression Model (LRM), which is used to train and test the models. To extract pertinent features like signal intensity, frequency content, and time-domain characteristics, sonar signals are essential to the labeled dataset. This dataset is made up of sonar signals and corresponding object classifications (mine or rock) based on the frequency range. These features served as inputs to the machine learning algorithms and enabled the features to learn the underlying patterns and make accurate predictions. In this paper, we have contributed a novel approach to underwater object detection using LRM, which is applied to sonar data. In summary, this research presents an innovative solution to the challenge of underwater object detection using sonar signals and machine learning algorithms. The developed model exhibits promising results, opening up new possibilities for advancing our understanding of underwater environments and enhancing underwater exploration and security capabilities.

Head Exposure to Acceleration Database in Sport (HEADSport): a kinematic signal processing method to enable instrumented mouthguard (iMG) field-based inter-study comparisons

ObjectiveInstrumented mouthguard (iMG) systems use different signal processing approaches limiting field-based inter-study comparisons, especially when artefacts are present in the signal. The objective of this study was to assess the...

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers.

Biometric authentication is a widely used method for verifying individuals' identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model's performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.

Full-waveform simulation of DAS records, response and cable-ground coupling

SUMMARY Over the past several years, the use of optical fibre cables as ground motion sensors has become a central topic for seismologists, with successful applications of distributed acoustic sensing (DAS) in various key fields such as seismic monitoring, structural imaging and source characterization. DAS response is a combination of both instrument response and cable-ground coupling, with the latter having a strong, spatially variable, but yet largely unquantified effect. This limits the application of a large number of staple seismological techniques (e.g. earthquake magnitude estimation, waveform tomography) that can require accurate knowledge of a signal's amplitude and frequency content. Here we present a method for accurately simulating a DAS cable and its ground coupling. The scheme is based on molecular dynamic-like particle-based numerical modelling, allowing the investigation of the effect of varying DAS-ground coupling scenarios. We start by computing the full strain field directly, for each pair of neighbouring particles in the model. We then define a virtual DAS cable, embedded within the model and formed by a single string of interconnected particles. This allows us to control all aspects of the cable-ground coupling and their properties at an effective granular level through changing the bond stiffness and bond types (e.g. non-linearity) for both the cable and the surrounding medium. Arbitrary cable geometries and heterogeneous materials can be accommodated at the desired scale of investigation. We observe that at the meter scale, the cable-ground coupling and local site effects can substantially alter the recorded signal. We find that the stiffness of the thin layer of material to which the cable is coupled has the strongest effects, selectively amplifying portions of the wave train and contributing to substantial phase delays. These differences show that cable coupling and local site effects should be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes and/or frequencies are considered. The codes developed herein for calculating full waveform DAS responses and coupling are made publicly available.

Short-Time Fourier Transform Analysis of Current Charge/Discharge Response of Lithium-Sulfur Batteries

Measurements acquired on batteries in the form of time signals such as voltage-time and capacity-time to assess their cyclability performance can be supplemented by examining their frequency-domain response. This allows one to determine the global characteristics of the signals and the battery, but not the local behavior, which is very important for determining for example battery fading. In this study we examine the short-time Fourier transform for time-frequency deconstruction of galvanostatic charge/discharge signals of lithium-sulfur batteries, taken as an example. The results displayed in terms of spectrograms show how the frequency content of such signals (e.g. charge and voltage time series) evolve with the lifetime of the batteries allowing the detection of critical changes in the response that may lead to fading and eventually default.

Metaplectic Gabor frames and symplectic analysis of time-frequency spaces

We introduce new frames, called metaplectic Gabor frames, as natural generalizations of Gabor frames in the framework of metaplectic Wigner distributions, cf. [7,8,5,17,27,28]. Namely, we develop the theory of metaplectic atoms in a full-general setting and prove an inversion formula for metaplectic Wigner distributions on Rd. Its discretization provides metaplectic Gabor frames.Next, we deepen the understanding of the so-called shift-invertible metaplectic Wigner distributions, showing that they can be represented, up to chirps, as rescaled short-time Fourier transforms. As an application, we derive a new characterization of modulation and Wiener amalgam spaces. Thus, these metaplectic distributions (and related frames) provide meaningful definitions of local frequencies and can be used to measure effectively the local frequency content of signals.

Gear mesh stiffness and dynamics: Influence of tooth pair structural stiffness asymmetry

There are certainly a lot of different gear mesh stiffness models in the literature, nonetheless, it is not possible to find comprehensive and in-depth gear parametric studies that sweep the gear geometrical domain. To do so, it is required to have a fast and accurate gear mesh stiffness model, which is precisely one of the main developments of this work. By taking advantage of this new model, a parametric study on the influence of tooth pair structural stiffness asymmetry is conducted. This investigation evaluates how the referred stiffness asymmetry affects the gear mesh stiffness, dynamic displacements and load distribution by performing an extensive number of simulations that cover the entire selected gear geometry spectrum — two randomly generated populations of gears, one for spur and another for helical, are tested with and without the tooth pair structural stiffness asymmetry. It is concluded that tooth pair structural stiffness asymmetry cannot be neglected since it can lead to significant modifications on the frequency content of the gear mesh stiffness signal as well as the dynamic behavior of the geared system.