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This article is from Frontiers in Neuroengineering , volume 6 . Abstract Electrocortical stimulation remains the standard for functional brain mapping of eloquent areas to prevent postoperative functional deficits. The aim of this study was to investigate whether the short-train technique (monopolar stimulation) and Penfield's technique (bipolar stimulation) would induce different effects on brain oscillatory activity in awake patients, as quantified by electrocorticography (ECoG). The study population was seven patients undergoing brain tumor surgery. Intraoperative bipolar and monopolar electrical stimulation for cortical mapping was performed during awake surgery. ECoG was recorded using 1 × 8 electrode strip. Spectral estimation was calculated using a parametric approach based on an autoregressive model. Wavelet-based time-frequency analysis was then applied to evaluate the temporal evolution of brain oscillatory activity. Both monopolar and bipolar stimulation produced an increment in delta and a decrease in beta powers for the motor and the sensory channels. These phenomena lasted about 4 s. Comparison between monopolar and bipolar stimulation showed no significant difference in brain activity. Given the importance of quantitative signal analysis for evaluating response accuracy, ECoG recording during electrical stimulation is necessary to characterize the dynamic processes underlying changes in cortical responses in vivo. This study is a preliminary approach to the quantitative analysis of post-stimulation ECoG signals.

Accurate knowledge of the velocity dispersion of Lamb modes is important for ultrasonic nondestructive evaluation methods used in detecting and locating flaws in thin plates and in determining their elastic stiffness coefficients. Lamb mode dispersion is also important in the acoustic emission technique for accurately triangulating the location of emissions in thin plates. In this research, the ability to characterize Lamb mode dispersion through a time-frequency analysis (the pseudo Wigner-Ville distribution) was demonstrated. A major advantage of time-frequency methods is the ability to analyze acoustic signals containing multiple propagation modes, which overlap and superimpose in the time domain signal. By combining time-frequency analysis with a broadband acoustic excitation source, the dispersion of multiple Lamb modes over a wide frequency range can be determined from as little as a single measurement. In addition, the technique provides a direct measurement of the group velocity dispersion. The technique was first demonstrated in the analysis of a simulated waveform in an aluminum plate in which the Lamb mode dispersion was well known. Portions of the dispersion curves of the A(sub 0), A(sub 1), S(sub 0), and S(sub 2)Lamb modes were obtained from this one waveform. The technique was also applied for the analysis of experimental waveforms from a unidirectional graphite/epoxy composite plate. Measurements were made both along, and perpendicular to the fiber direction. In this case, the signals contained only the lowest order symmetric and antisymmetric modes. A least squares fit of the results from several source to detector distances was used. Theoretical dispersion curves were calculated and are shown to be in good agreement with experimental results.

Nonstationary ultrasound Doppler signals, those are changing with time and frequency simultaneously, are widely observed in biological and speech signals. A Cohen's class time-frequency (TF) analysis can analyze nonstationary signals with high resolution in time and frequency at a same time. A time-frequency distribution (TFD) is largely affected by a kernel function. Thus, there is sometimes a case where auto-terms (those are signal components) are covered by cross-terms (those are spurious components). In order to apply TFDs to a nonstationary and nonlinear Doppler ultrasound signals, experimental data were obtained by moving a steel ball to and fro by continuously irradiating it with ultrasound in olive oil. The movement of the steel ball was controlled by various functions. To analyze these signals, four kernels were used: (1) a Wigner kernel, (2) a Choi-Williams kernel, (3) a figure eight kernel, and (4) a 2nd figure eight kernel. Using our 2nd figure eight kernel, the demodulation accuracy was improved even with noise.

Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application 作者： Wang, Hongshuang 1,2 Yang, Xing 1,2 Leng, Yongbin 1,3 Zhou, Yimei 4 Wang, Jigang 3 作者单位： 1. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, China 4. National Synchrotron Radiation Laboratory, University of Science and Technology of China 通讯作者： Wang, Hongshuang Email:[email protected] 提交时间： 2024-03-12 16:55:50 摘要: This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings. This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters, such as bunch length and synchronous phase. The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz. The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s, and the single-shot sampling data buffer covered thousands of turns. The bunch length and synchronous phase information were extracted via offline calculations using Python scripts. The calibration coefficient of the system was determined using a commercial streak camera. Moreover, this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility (SSRF), and longitudinal instabilities were observed during the single bunch accumulation process at Hefei Light Source (HLS). For Gaussian-distribution bunches, the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps, and the bunch length uncertainty was better than 1 ps. The dynamic range exceeded 10 ms. This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings. bunch-by-bunch diagnostic bunch-length measurement synchronous phase measurement joint time–frequency domain analysis longitudinal instability 来自： 汪宏双 分类： 核科学技术 >> 核科学与技术 投稿状态： 已被期刊接收 引用： ChinaXiv:202403.00213 (或此版本 ChinaXiv:202403.00213V1 ) DOI:10.12074/202403.00213V1 CSTR:32003.36.ChinaXiv.202403.00213.V1 推荐引用方式： Wang, Hongshuang,Yang, Xing,Leng, Yongbin,Zhou, Yimei,Wang, Jigang.(2024).Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application.中国科学院科技论文预发布平台.doi:10.12074/202403.00213V1 版本历史 [V1] 2024-03-12 16:55:50 ChinaXiv:202403.00213V1 下载全文

The area of Fourier analysis connected to signal processing theory has undergone a rapid development in the last two decades. The aspect of this development that has received the most publicity is the theory of wavelets and their relatives, which involves expansions in terms of sets of functions generated from a single function by translations and dilations. However, there has also been much progress in the related area known as \emph{time-frequency analysis} or \emph{Gabor analysis}, which involves expansions in terms of sets of functions generated from a single function by translations and modulations. In this area there are some questions of a concrete and practical nature whose study reveals connections with aspects of harmonic and functional analysis that were previously considered quite pure and perhaps rather exotic. In this expository paper, I give a survey of some of these interactions between the abstruse and the applicable. It is based on the thematic lectures which I gave at the Ninth Discussion Meeting on Harmonic Analysis at the Harish-Chandra Research Institute in Allahabad in October 2005.

Grafakos and Sansing \cite{GS} have shown how to obtain directionally sensitive time-frequency decompositions in $L^2(\mr^n)$ based on Gabor systems in $\ltr;$ the key tool is the "ridge idea," which lifts a function of one variable to a function of several variables. We generalize their result by showing that similar results hold starting with general frames for $L^2(\mr),$ both in the setting of discrete frames and continuous frames. This allows to apply the theory for several other classes of frames, e.g., wavelet frames and shift-invariant systems. We will consider applications to the Meyer wavelet and complex B-splines. In the special case of wavelet systems we show how to discretize the representations using $\epsilon$-nets.

We show that the time frequency analysis of the autocorrelation function is, in many ways, a more appropriate tool to resolve fractional revivals of a wave packet than the usual time domain analysis. This advantage is crucial in reconstructing the initial state of the wave packet when its coherent structure is short-lived and decays before it is fully revived. Our calculations are based on the model example of fractional revivals in a Rydberg wave packet of circular states. We end by providing an analytical investigation which fully agrees with our numerical observations on the utility of time-frequency analysis in the study of wave packet fractional revivals.

Electron energy loss spectroscopy (EELS) and Cathodoluminescence (CL) play a pivotal role in many of the cutting edge experiments in plasmonics. EELS and CL experiments are usually supported by numerical simulations, which, whilst accurate, may not provide as much physical insight as analytical calculations do. Fully analytical solutions to EELS and CL systems in plasmonics are rare and difficult to obtain. This paper aims to narrow this gap by introducing a new method based on Transformation optics that allows to calculate the quasi-static frequency and time-domain response of plasmonic particles under electron beam excitation.

We present our results of pulse broadening time estimates and the study of the frequency scaling of this quantity for 60 pulsars based on actual multi-frequency scattering estimates. This research was based on our own measurements, performed on the observational data and the profiles from various pulsar profile databases, as well as the scatter time measurements that were found in the literature. We were able to construct a database of over 60 pulsars with true multi-frequency $\alpha$ measurements, which allowed us to revise the previously proposed relations between the scatter time spectral slope and the dispersion measure (DM). We found that the deviations from theoretical predictions of the value of $\alpha$ appear for pulsars regardless of their DM, however the DM-averaged value of the scaling index is almost constant except for pulsars with very high DMs. Based on the obtained slopes we were also able to estimate the amount of scattering at the standard frequency of 1 GHz. We found that while the estimated standardized pulse broadening time increases with DM the relation seems to be much flatter than it was previously proposed, which suggests higher values of the scatter time for mid-DM pulsars, and lower values of expected pulse broadening for highly dispersed sources.

Accurate assessment of shipboard machinery condition is essential in increasing the operational capability of naval vessels. Current shipboard machinery vibration monitoring and diagnostic procedures use frequency domain spectra to identify possible faults and problem areas. This method has proven to be inadequate for reciprocating machinery analysis. In reciprocating machinery the vibration signal is no longer a stationary, ergodic process, but is time-dependent and in some cases transient. This study proposes the use of Pseudo Wigner-Ville Distribution and introduces Wavelet analysis as two advanced methods for condition monitoring of non-stationary and transient shipboard machinery. These methods employ a time-frequency domain distribution for the detection of fault location and severity level. To demonstrate the benefits of a time-frequency representation, vibration data from two types of reciprocating air compressors will be processed for analysis. To simulate faulty conditions, constructed artificial fault signals will be introduced into the vibration data and analyzed. It is proposed Wavelet Analysis will play a complementary role to the Pseudo Wigner-Ville Distribution technique.

Surveys of the fluctuating wall pressure were conducted on a sub-scale, thrust- optimized parabolic nozzle in order to develop a physical intuition for its Fourier-azimuthal mode behavior during fixed and transient start-up conditions. These unsteady signatures are driven by shock wave turbulent boundary layer interactions which depend on the nozzle pressure ratio and nozzle geometry. The focus however, is on the degree of similarity between the spectral footprints of these modes obtained from transient start-ups as opposed to a sequence of fixed nozzle pressure ratio conditions. For the latter, statistically converged spectra are computed using conventional Fourier analyses techniques, whereas the former are investigated by way of time-frequency analysis. The findings suggest that at low nozzle pressure ratios -- where the flow resides in a Free Shock Separation state -- strong spectral similarities occur between fixed and transient conditions. Conversely, at higher nozzle pressure ratios -- where the flow resides in Restricted Shock Separation -- stark differences are observed between the fixed and transient conditions and depends greatly on the ramping rate of the transient period. And so, it appears that an understanding of the dynamics during transient start-up conditions cannot be furnished by a way of fixed flow analysis.

There have been considerable efforts expended in analog fault analysis. Most of them employ frequency-domain approach, i.e., diagnosing faulty components from measured transfer function, while few use time-domain approach to isolate faults by means of Markov parameters. Theoretically, both approaches are still under development and all seem feasible. It is the purpose of this report to compare these two approaches numerically by simulation on RC ladders. In this example, it is shown that the time-domain approach is far better than the frequency-domain approach. (Author)

A meta-analysis of randomized controlled trials of individual psychotherapy for generalized anxiety disorder to investigate the relation between total length of treatment (in weeks), total therapist contact time across treatment (in hours), total number of therapy sessions, and treatment frequency (in sessions per week) to outcome effect size.

In this paper, we address the problem of constructing a feature extractor which combines Mallat's scattering transform framework with time-frequency (Gabor) representations. To do this, we introduce a class of frames, called uniform covering frames, which includes a variety of semi-discrete Gabor systems. Incorporating a uniform covering frame with a neural network structure yields the Fourier scattering transform $\mathcal{S}_{\mathcal F}$. We prove that $\mathcal{S}_{\mathcal F}$ propagates energy along frequency decreasing paths and its energy decays exponentially as a function of the depth. These quantitative estimates are fundamental in showing that $\mathcal{S}_{\mathcal F}$ satisfies the typical scattering transform properties, and in controlling the information loss due to width and depth truncation. We introduce the truncated Fourier scattering transform and the fast Fourier scattering transform algorithm, and illustrate the algorithm's performance. The time-frequency covering techniques developed in this paper are flexible and give insight into the analysis of scattering transforms.

Accurate knowledge of the velocity dispersion of Lamb modes is important for ultrasonic nondestructive evaluation methods used in detecting and locating flaws in thin plates and in determining their elastic stiffness coefficients. Lamb mode dispersion is also important in the acoustic emission technique for accurately triangulating the location of emissions in thin plates. In this research, the ability to characterize Lamb mode dispersion through a time-frequency analysis (the pseudo Wigner-Ville distribution) was demonstrated. A major advantage of time-frequency methods is the ability to analyze acoustic signals containing multiple propagation modes, which overlap and superimpose in the time domain signal. By combining time-frequency analysis with a broadband acoustic excitation source, the dispersion of multiple Lamb modes over a wide frequency range can be determined from as little as a single measurement. In addition, the technique provides a direct measurement of the group velocity dispersion. The technique was first demonstrated in the analysis of a simulated waveform in an aluminum plate in which the Lamb mode dispersion was well known. Portions of the dispersion curves of the A(sub 0), A(sub 1), S(sub 0), and S(sub 2)Lamb modes were obtained from this one waveform. The technique was also applied for the analysis of experimental waveforms from a unidirectional graphite/epoxy composite plate. Measurements were made both along, and perpendicular to the fiber direction. In this case, the signals contained only the lowest order symmetric and antisymmetric modes. A least squares fit of the results from several source to detector distances was used. Theoretical dispersion curves were calculated and are shown to be in good agreement with experimental results.

Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.

We present in this paper a general model for determining the quantum properties of the light generated by a synchronously pumped optical parametric oscillator (SPOPO) operating below threshold. This model considers time and frequency on an equal footing, which allows us to find new quantum properties, related for example to the carrier envelope offset (CEO) phase, and to consider situations that are close to real experiments. We show that, in addition to multimode squeezing in the so-called 'supermodes', the system exhibits quadrature entanglement between frequency combs of opposite CEO phases. We have also determined the quantum properties of the individual pulses and their quantum correlations with the neighboring pulses. Finally, we determine the quantum Cramer-Rao limit for an ultra-short time delay measurement using a given number of pulses generated by the SPOPO.

Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexible methodology that provides accurate estimation across a wide range of processes is a subject of ongoing interest. We propose a novel approach to model-based time-frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through three modeling applications; namely, insect communication signals, environmental data (wind components), and macroeconomic data (US gross domestic product (GDP) and consumption).

A systematic review and meta-analysis that unpicks the relationship between different frequencies, intensities, timings and types of exercise interventions on breast cancer patient survival and secondary recurrence.

Locally stationary Hawkes processes have been introduced in order to generalise classical Hawkes processes away from stationarity by allowing for a time-varying second-order structure. This class of self-exciting point processes has recently attracted a lot of interest in applications in the life sciences (seismology, genomics, neuro-science,...), but also in the modelling of high-frequency financial data. In this contribution we provide a fully developed nonparametric estimation theory of both local mean density and local Bartlett spectra of a locally stationary Hawkes process. In particular we apply our kernel estimation of the spectrum localised both in time and frequency to two data sets of transaction times revealing pertinent features in the data that had not been made visible by classical non-localised approaches based on models with constant fertility functions over time.

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Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of two time-frequency analytical methods - the Hilbert Huang transform (HHT) and the Wavelet Transform (WT) are compared and discussed. As a result, a more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established using time-dependent intrinsic correlation (TDIC) approaches.

Digital communication signals are inherently nonstationary (time varying spectral content) signals due to their information content. This suggests the use of time frequency analysis methods for detecting these signals as well as obtaining the desired signal parameters. For signals employing phase- shift-keying (PSK) to convey information, the important signal parameters are the carrier (sinusoid) center frequency and information (bit) rate.

Digital communication signals are inherently nonstationary (time varying spectral content) signals due to their information content. This suggests the use of time frequency analysis methods for detecting these signals as well as obtaining the desired signal parameters. For signals employing phase- shift-keying (PSK) to convey information, the important signal parameters are the carrier (sinusoid) center frequency and information (bit) rate.

This article is from Journal of Human Kinetics , volume 32 . Abstract The swimming involves accelerations and decelerations in the swimmer’s body. Thus, the main objective of this study is to make a temporal and frequency analysis of the acceleration in front crawl swimming, regarding the gender and the performance. The sample was composed by 31 male swimmers (15 of high-level and 16 of low-level) and 20 female swimmers (11 of high-level and 9 of low-level). The acceleration was registered from the third complete cycle during eight seconds in a 25 meters maximum velocity test. A position transducer (200Hz) was used to collect the data, and it was synchronized to an aquatic camera (25Hz). The acceleration in the temporal (root mean square, minimum and maximum of the acceleration) and frequency (power peak, power peak frequency and spectral area) domains was calculated with Fourier analysis, as well as the velocity and the spectrums distribution in function to present one or more main peaks (type 1 and type 2). A one-way ANOVA was used to establish differences between gender and performance. Results show differences between genders in all the temporal domain variables (p

Correlated scattering occurs naturally in frequency-selective fading channels and its impact on the performance needs to be understood. In particular, we answer the question whether the uncorrelated scattering model leads to an optimistic or pessimistic estimation of the actual average capacity. In the paper, we use majorization for functions to show that the average rate with perfectly informed receiver is largest for uncorrelated scattering if the transmitter is uninformed. If the transmitter knows the channel statistics, it can exploit this knowledge. We show that for small SNR, the behavior is opposite, uncorrelated scattering leads to a lower bound on the average capacity. Finally, we provide an example of the theoretical results for an attenuated Ornstein-Uhlenbeck process including illustrations.

The population around the world is ageing rapidly. Ageing is accompanied by cognitive decline, of which older adults consider word-finding difficulties most prominent (Burke & Shafto, 2004; Salthouse, 2009). One of the lexical factors that influences word processing is whether the word is a noun or verb where nouns are processed faster than verbs (e.g., Cordier et al., 2013; Kauschke & Stenneken, 2008). One explanation of the noun-verb distinction is that words or sentences that describe physical actions activate sensorimotor areas in the brain (e.g., Hauk et al., 2004). This is in line with the idea of embodied cognition, which assumes that our cognitive processes are shaped by the interactions between our bodies and the world around us (Jirak et al., 2010). Although the literature is sparse, recent studies showed a link between age and action language processing (Bidet-Ildei et al., 2020; Reifegerste et al., 2021). According to Bidet-lldei and colleagues (2020), the link between action and language fades in older age. They presented verbs auditorily as primes and the participant had to judge a subsequent picture as to whether a person was depicted or not. Whilst younger adults were faster at responding when the prime and picture matched (i.e., the verb was depicted by the picture) than not, older adults did not show such a difference. In contrast, Reifegerste and colleagues (2021) concluded that motor-relatedness remains relatively preserved with age, suggesting that the neural networks underlying action language remain relatively unaffected by age. They conducted a lexical decision task, which included words that described greater and lesser physical action (i.e., high and low motor relatedness, respectively). The study revealed age-related effects for verbs with low motor relatedness but the effect for verbs with high motor relatedness was minimal. However, the literature presents contrasting evidence and it is currently unclear whether age affects action-word processing or whether the action features of words actually support word-finding in older adults. Differences between action and non-action words are also found at a neural level. Reading action-related words elicits activity in the frontocentral brain regions, whilst object words elicit activity in the temporo-occipital brain regions (Moseley et al., 2013). Moreover, an event-related potential (ERP) study showed different polarity for motor-related verbs compared to sound-related verbs (Popp et al., 2016). Verbs elicit more positive potentials, such as the P200, in frontal and central brain regions than nouns during a lexical decision task (Zhao et al., 2017). In addition, studies have shown that action words elicit more positive N400 amplitudes than non-action words or nouns in frontal brain areas, even after controlling for grammatical class (Barber et al., 2010; Cervetto et al., 2021; Khader et al., 2003). With age, the N400 decreases in magnitude and the latency becomes longer, although semantic processing seems to remain relatively stable with increasing age (Friedman, 2011). However, it is currently unclear whether age-related changes in and action-word relatedness of N400 amplitude are linked as well. Researchers have argued that action and non-action words are processed differently, with topologically different groups of neurons involved in the access of verbs compared to nouns (Khader et al., 2003; Preissl et al., 1996). Time-frequency-based analyses enable the analysis of features within an ERP component, such as power or phase synchronisation. Inter-Trial Phase Coherence (ITPC) is a measure of phase synchronisation and has been suggested to reflect neural population­ level activity (Cohen, 2014). A recent exploratory study demonstrated that verbs and nouns could be classified using alpha and beta band ITPC of temporal and frontal brain areas (Jensen et al., 2019). However, few studies have investigated the relationship between age and ITPC. Theta band ITPC seems to increase from childhood into adulthood and decreases again in older adults, possibly reflecting increases and decreases in cognitive control (Papenberg et al., 2013). Ho et al. (2012) also showed higher theta band ITPC in younger compared to older adults during an auditory oddball paradigm, which might reflect early working memory-related attention processes. Younger adults show increased ITPC in both the theta and alpha-1 (7-10 Hz) frequency bands compared to children, which may reflect increased brain plasticity within the information processing network (Yordanova & Kolev, 2009). Indeed, increased ITPC in stroke has also been argued to reflect brain reorganisation (Gyulai et al., 2021). Hence, investigating age-related changes in ITPC can provide us with insights into the brain mechanisms underlying age-related word-finding abilities. Taken together, it is currently unclear whether action word processing declines with age or whether action language is relatively spared. Moreover, action-related words seem to involve different electrophysiological processes than non-action-related words (e.g., low-action nouns), as reflected in P200 and N400 amplitude differences. It has been proposed that such differences emerge because different neuronal populations are involved in action compared to non-action words. The aim of this study is two-fold: i) to reproduce the amplitude difference between high- and low-action words, and ii) to investigate age-related differences in ITPC underlying word finding of action words. To our knowledge, this is the first study investigating age-related differences in ITPC underlying action word processing. If indeed topologically different neural populations are involved in the lexical access of action compared to non-action words, this should be reflected in the ITPC value. Specifically, ITPC for the N400 in frontocentral regions would be higher for verbs compared to nouns. The findings of this study will help us to better understand the neural processes underlying action word comprehension, which could be utilised to indicate healthy neural processing and to identify the development of pathological neural activity.

Vibration analysis is playing an increasingly important role in the early detection of helicopter transmission faults. Current vibration analysis techniques used in helicopter transmission fault detection require selective filtering or manipulation of the signal, based on assumptions about the nature of the signal. In some cases these techniques can give misleading results. It is shown that the application of time-frequency domain representations, based on the Wigner-Ville distribution, is capable of detecting a variety of vibration features which can be used to classify faults.

Time-frequency analysis techniques are used to produce a plot of a signal's power spectrum as a function of time. The most well-known time-frequency representation is the spectrogram. Although relatively simple to compute, it suffers from having a significant limitation in that it cannot offer good time or frequency resolution simultaneously. To overcome this weakness, many other representations have been developed that provide combined high resolution over time and frequency. The Wigner-Ville distribution, the scalogram, and the discrete Gabor transform are among the most well-known of these methods. Due to specific shortcomings with regard to these distributions for multi-component signals, and for certain mathematical concerns such as shift invariance and time and frequency marginal conditions, several classes of representations have been developed which effectively address specific signal types. Examples of these categories are Cohen's class, the affine class, and the signal adaptive expansions based upon the Matching Pursuit method. The goals of any of these specific methods are to minimize cross-term interference, provide good time and frequency resolution, and provide a good model for the signal of interest.

Time-frequency analysis techniques are used to produce a plot of a signal's power spectrum as a function of time. The most well-known time-frequency representation is the spectrogram. Although relatively simple to compute, it suffers from having a significant limitation in that it cannot offer good time or frequency resolution simultaneously. To overcome this weakness, many other representations have been developed that provide combined high resolution over time and frequency. The Wigner-Ville distribution, the scalogram, and the discrete Gabor transform are among the most well-known of these methods. Due to specific shortcomings with regard to these distributions for multi-component signals, and for certain mathematical concerns such as shift invariance and time and frequency marginal conditions, several classes of representations have been developed which effectively address specific signal types. Examples of these categories are Cohen's class, the affine class, and the signal adaptive expansions based upon the Matching Pursuit method. The goals of any of these specific methods are to minimize cross-term interference, provide good time and frequency resolution, and provide a good model for the signal of interest.

Time-frequency analysis techniques are used to produce a plot of a signal's power spectrum as a function of time. The most well-known time-frequency representation is the spectrogram. Although relatively simple to compute, it suffers from having a significant limitation in that it cannot offer good time or frequency resolution simultaneously. To overcome this weakness, many other representations have been developed that provide combined high resolution over time and frequency. The Wigner-Ville distribution, the scalogram, and the discrete Gabor transform are among the most well-known of these methods. Due to specific shortcomings with regard to these distributions for multi-component signals, and for certain mathematical concerns such as shift invariance and time and frequency marginal conditions, several classes of representations have been developed which effectively address specific signal types. Examples of these categories are Cohen's class, the affine class, and the signal adaptive expansions based upon the Matching Pursuit method. The goals of any of these specific methods are to minimize cross-term interference, provide good time and frequency resolution, and provide a good model for the signal of interest.

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT and S-transform method are analyzed the switch fault of VSI. To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.

In this paper we construct frames of Gabor type for the space $L^2_{rad}(\R^d)$ of radial $L^2$-functions, and more generally, for subspaces of modulation spaces consisting of radial distributions. Hereby, each frame element itself is a radial function. This construction is based on a generalization of the so called Feichtinger-Gr\"ochenig theory -- sometimes also called coorbit space theory -- which was developed in an earlier article. We show that this new type of Gabor frames behaves better in linear and non-linear approximation in a certain sense than usual Gabor frames when approximating a radial function. Moreover, we derive new embedding theorems for coorbit spaces restricted to invariant vectors (functions) and apply them to modulation spaces of radial distributions. As a special case this result implies that the Feichtinger algebra $(S_0)_{rad}(\R^d) = M^1_{rad}(\R^d)$ restricted to radial functions is embedded into the Sobolev space $H^{(d-1)/2}_{rad}(\R^d)$. Moreover, for $d\geq 2$ the embedding $(S_0)_{rad}(\R^d) \hookrightarrow L^2_{rad}(\R^d)$ is compact.

In this paper we construct frames of Gabor type for the space $L^2_{rad}(\R^d)$ of radial $L^2$-functions, and more generally, for subspaces of modulation spaces consisting of radial distributions. Hereby, each frame element itself is a radial function. This construction is based on a generalization of the so called Feichtinger-Gr\"ochenig theory -- sometimes also called coorbit space theory -- which was developed in an earlier article. We show that this new type of Gabor frames behaves better in linear and non-linear approximation in a certain sense than usual Gabor frames when approximating a radial function. Moreover, we derive new embedding theorems for coorbit spaces restricted to invariant vectors (functions) and apply them to modulation spaces of radial distributions. As a special case this result implies that the Feichtinger algebra $(S_0)_{rad}(\R^d) = M^1_{rad}(\R^d)$ restricted to radial functions is embedded into the Sobolev space $H^{(d-1)/2}_{rad}(\R^d)$. Moreover, for $d\geq 2$ the embedding $(S_0)_{rad}(\R^d) \hookrightarrow L^2_{rad}(\R^d)$ is compact.

This research was concerned with stochastic and statistical characterizations of quasi-periodic random processes, such as those associated with rotating machinery. The value of such characterizations includes enhanced abilities in condition monitoring for purposes of safety and performance. Major achievements of this research effort include: (1) characterization of the influence of period uncertainty on estimation of a periodic time/frequency spectrum associated with a nominally wide sense cyclostationary (wsc) process, (2) large sample distribution descriptions for the AR(p) and MV(p) spectral estimators for processes with mixed spectrum, (3) a time-to-angle transformation method to better accommodate period variability, combined with an improved method for tracking real sinusoids with slowly varying frequency, (4) greater insight into issues related to application of advanced spectral analysis methods for characterizing random processes associated with engines, compressors, and helicopter drivetrains, (5) a Matlab-based virtual signal analyzer which incorporates a number of our results in a user-friendly fashion, and (6) development of research collaborations and workshops which serve to bring signal processing problems associated with rotating machinery to a broader base of researchers in industry, defense and academic institutions.

Determining the exact timing of cardiac events, represented by the first (S1) and second (S2) sounds, from the PCC signals (phonocardiogram), represents a great challenge, specially in pathological cases. A system that allows this kind of detection could be used to synchronize several important biomedical devices and diagnosis techniques, such as intraaortic balloons (IABP) and synchronous images of Ultrasound, Magnetic Resonance and Computerized Tomography. This work presents a wavelet-based technique for S1 and S2 detection in PCC signals, that is able to perform a good detection in both normal and abnormal cases. It will be shown the criterion of choosing the most suitable wavelet from a set of classical ones, as well as the principles of the final detection method. The results in the analysis of 756 cardiac cycles, from 19 subjects, including normal and abnormal ones present an error ratio of 0.8%, point out to the efficiency of the proposed method.

NAS 1.60:1818 Digitized from IA1177308-07-0046 .

Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth System under the external and internal forcings give out very complex signals.The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is a very useful technique for detecting, understanding and monitoring climate changes.

We present an algorithm for sound analysis and resynthesis with local automatic adaptation of time-frequency resolution. There exists several algorithms allowing to adapt the analysis window depending on its time or frequency location; in what follows we propose a method which select the optimal resolution depending on both time and frequency. We consider an approach that we denote as analysis-weighting, from the point of view of Gabor frame theory. We analyze in particular the case of different adaptive time-varying resolutions within two complementary frequency bands; this is a typical case where perfect signal reconstruction cannot in general be achieved with fast algorithms, causing a certain error to be minimized. We provide examples of adaptive analyses of a music sound, and outline several possibilities that this work opens.

Let $v$ be a submultiplicative weight. Then we prove that $v$ satisfies GRS-condition, if and only if $v\cdot e^{-\ep |\cdo |}$ is bounded for every positive $\ep$. We use this equivalence to establish identification properties between weighted Lebesgue spaces, and between certain modulation spaces and Gelfand-Shilov spaces.

We give a brief survey of recent results concerning almost diagonalization of pseudodifferential operators via Gabor frames. Moreover, we show new connections between symbols with Gevrey, analytic or ultra-analityc regularity and time-frequency analysis of the corresponding pseudodifferential operators.

This paper will apply various joint time-frequency (JTF) and wavelet techniques to extract features from shockwave and muzzle blast signatures for the purpose of classification. The techniques used will include short-time Fourier transform (STFT), Smoothed Pseudo Wigner-Ville distribution (SPWVD) and wavelet multi-scale analysis. A projectile's trajectory can be estimated by measuring the arrival times of the acoustic energy at several locations in space. In the case of a supersonic projectile fired from a gun, both the acoustic shockwave and muzzle blast may be observed. For acoustic sensor systems attempting to determine a projectile's trajectory, the challenge is to first, correctly classify the transient signal as either a shockwave or a muzzle blast and then, calculate the direction-of-arrival via appropriate arrival times across a sensor array. This can be extremely challenging when the shockwave has lost substantial high frequency content. The change in spectral characteristics can stem from propagation over a long distance, propagation over snow covered terrain or arriving from a non-perfect reflector. An incorrect classification will result in large estimation errors of the projectile's trajectory. Experimental results are presented for proper classification over various miss distances from the sensors for all of the above techniques mentioned.

The present paper describes the effect of magnetic pick-up coil transfer functions on mode number analysis in magnetically confined fusion plasmas. Magnetic probes mounted inside the vacuum chamber are widely used to characterize the mode structure of magnetohydrodynamic modes, as, due to their relative simplicity and compact nature, several coils can be distributed over the vessel. Phase differences between the transfer functions of different magnetic pick-up coils lead to systematic errors in time- and frequency resolved mode number analysis. This paper presents the first in-situ, end-to-end calibration of a magnetic pick-up coil system which was carried out by using an in-vessel driving coil on ASDEX Upgrade. The effect of the phase differences in the pick-up coil transfer functions is most significant in the 50-250 kHz frequency range, where the relative phase shift between the different probes can be up to 1 radian (~60{\deg}). By applying a correction based on the transfer functions we found smaller residuals of mode number fitting in the considered discharges. In most cases an order of magnitude improvement was observed in the residuals of the mode number fits, which could open the way to investigate weaker electromagnetic oscillations with even high mode numbers.

Many phenomena are described by bivariate signals or bidimensional vectors in applications ranging from radar to EEG, optics and oceanography. The time-frequency analysis of bivariate signals is usually carried out by analyzing two separate quantities, e.g. rotary components. We show that an adequate quaternion Fourier transform permits to build relevant time-frequency representations of bivariate signals that naturally identify geometrical or polarization properties. First, the quaternion embedding of bivariate signals is introduced, similar to the usual analytic signal of real signals. Then two fundamental theorems ensure that a quaternion short term Fourier transform and a quaternion continuous wavelet transform are well defined and obey desirable properties such as conservation laws and reconstruction formulas. The resulting spectrograms and scalograms provide meaningful representations of both the time-frequency and geometrical/polarization content of the signal. Moreover the numerical implementation remains simply based on the use of FFT. A toolbox is available for reproducibility. Synthetic and real-world examples illustrate the relevance and efficiency of the proposed approach.

We study the real-time dynamics retrieval from a time series via the time-frequency (TF) analysis with the minimal latency guarantee. While different from the well-known intrinsic latency definition in the filter design, a rigorous definition of intrinsic latency for different time-frequency representations (TFR) is provided, including the short time Fourier transform (STFT), synchrosqeezing transform (SST) and reassignment method (RM). To achieve the minimal latency, a systematic method is proposed to construct an asymmetric window from a well-designed symmetric one based on the concept of minimum-phase, if the window satisfies some weak conditions. We theoretically show that the TFR determined by SST with the constructed asymmetric window does have a smaller intrinsic latency. Finally, the music onset detection problem is studied to show the strength of the proposed algorithm.

A class of random non-stationary signals termed timbre x dynamics is introduced and studied. These signals are obtained by non-linear transformations of sta-tionary random gaussian signals, in such a way that the transformation can be approximated by translations in an appropriate representation domain. In such situations, approximate maximum likelihood estimation techniques can be de-rived, which yield simultaneous estimation of the transformation and the power spectrum of the underlying stationary signal. This paper focuses on the case of modulation and time warping of station-ary signals, and proposes and studies estimation algorithms (based on time-frequency and time-scale representations respectively) for these quantities of interest. The proposed approach is validated on numerical simulations on synthetic signals, and examples on real life car engine sounds.

Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth System under the external and internal forcings give out very complex signals.The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is a very useful technique for detecting, understanding and monitoring climate changes.

An algorithm was developed which can be used to obtain the equations. In a more general context, the algorithm computes a real nonsingular similarity transformation matrix which reduces a real nonsymmetric matrix to block diagonal form, each block of which is a real quasi upper triangular matrix. The algorithm works with both defective and derogatory matrices and when and if it fails, the resultant output can be used as a guide for the reformulation of the mathematical equations that lead up to the ill conditioned matrix which could not be block diagonalized.

We discuss the most common types of weight functions in harmonic analysis and how they occur in \tfa . As a general rule, submultiplicative weights characterize algebra properties, moderate weights characterize module properties, Gelfand-Raikov-Shilov weights determine spectral invariance, and Beurling-Domar weights guarantee the existence of compactly supported test functions.

The aim of this article is to present a time-frequency theory for orthogonal polynomials on the interval [-1,1] that runs parallel to the time-frequency analysis of bandlimited functions developed by Landau, Pollak and Slepian. For this purpose, the spectral decomposition of a particular compact time-frequency-operator is studied. This decomposition and its eigenvalues are closely related to the theory of orthogonal polynomials. Results from both theories, the theory of orthogonal polynomials and the Landau-Pollak-Slepian theory, can be used to prove localization and approximation properties of the corresponding eigenfunctions. Finally, an uncertainty principle is proven that reflects the limitation of coupled time and frequency locatability.