Post-exercise heart rate variability recovery: a time-frequency analysis.

PubMed

Peçanha, Tiago; de Paula-Ribeiro, Marcelle; Nasario-Junior, Olivassé; de Lima, Jorge Roberto Perrout

2013-12-01

Most studies investigating the effects of non-pharmacological interventions, such as physical training (PT), on cardiac autonomic control, assessed the HRV only in resting conditions. Recently, a new time-frequency mathematical approach based on the short-time Fourier transform (STFT) method has been validated for the assessment of HRV in non-stationary conditions such as the immediate post-exercise period. The aim of this study was to evaluate the effects of the PT on post-exercise cardiac autonomic control using the time-frequency STFT analysis of the HRV. Twenty-one healthy male volunteers participated in this study. The subjects were initially evaluated for their physical exercise/sport practice and allocated to groups of low physical training ((Low)PT, n = 13) or high physical training (H(igh)PT, n = 8). The post-exercise HRV was assessed by the STFT method, which provides the analysis of dynamic changes in the power of the low- and high-frequency spectral components (LF and HF, respectively) of the HRV during the whole recovery period. Greater LF (from the min 5 to 10) and HF (from the min 6 to 10) in the post-exercise period in the H(igh)PT compared to the (Low)PT group (P < 0.05) was observed. These results indicate that exercise training exerts beneficial effects on post-exercise cardiac autonomic control.

Characterizing resonant component in speech: A different view of tracking fundamental frequency

NASA Astrophysics Data System (ADS)

Dong, Bin

2017-05-01

Inspired by the nonlinearity and nonstationarity and the modulations in speech, Hilbert-Huang Transform and cyclostationarity analysis are employed to investigate the speech resonance in vowel in sequence. Cyclostationarity analysis is not directly manipulated on the target vowel, but on its intrinsic mode functions one by one. Thanks to the equivalence between the fundamental frequency in speech and the cyclic frequency in cyclostationarity analysis, the modulation intensity distributions of the intrinsic mode functions provide much information for the estimation of the fundamental frequency. To highlight the relationship between frequency and time, the pseudo-Hilbert spectrum is proposed to replace the Hilbert spectrum here. After contrasting the pseudo-Hilbert spectra of and the modulation intensity distributions of the intrinsic mode functions, it finds that there is usually one intrinsic mode function which works as the fundamental component of the vowel. Furthermore, the fundamental frequency of the vowel can be determined by tracing the pseudo-Hilbert spectrum of its fundamental component along the time axis. The later method is more robust to estimate the fundamental frequency, when meeting nonlinear components. Two vowels [a] and [i], picked up from a speech database FAU Aibo Emotion Corpus, are applied to validate the above findings.

Iterative generalized time-frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions

NASA Astrophysics Data System (ADS)

Chen, Xiaowang; Feng, Zhipeng

2016-12-01

Planetary gearboxes are widely used in many sorts of machinery, for its large transmission ratio and high load bearing capacity in a compact structure. Their fault diagnosis relies on effective identification of fault characteristic frequencies. However, in addition to the vibration complexity caused by intricate mechanical kinematics, volatile external conditions result in time-varying running speed and/or load, and therefore nonstationary vibration signals. This usually leads to time-varying complex fault characteristics, and adds difficulty to planetary gearbox fault diagnosis. Time-frequency analysis is an effective approach to extracting the frequency components and their time variation of nonstationary signals. Nevertheless, the commonly used time-frequency analysis methods suffer from poor time-frequency resolution as well as outer and inner interferences, which hinder accurate identification of time-varying fault characteristic frequencies. Although time-frequency reassignment improves the time-frequency readability, it is essentially subject to the constraints of mono-component and symmetric time-frequency distribution about true instantaneous frequency. Hence, it is still susceptible to erroneous energy reallocation or even generates pseudo interferences, particularly for multi-component signals of highly nonlinear instantaneous frequency. In this paper, to overcome the limitations of time-frequency reassignment, we propose an improvement with fine time-frequency resolution and free from interferences for highly nonstationary multi-component signals, by exploiting the merits of iterative generalized demodulation. The signal is firstly decomposed into mono-components of constant frequency by iterative generalized demodulation. Time-frequency reassignment is then applied to each generalized demodulated mono-component, obtaining a fine time-frequency distribution. Finally, the time-frequency distribution of each signal component is restored and superposed to

Frequency Domain Analysis of Multiwavelength Photoacoustic Signals for Differentiating Tissue Components

NASA Astrophysics Data System (ADS)

Jian, X. H.; Dong, F. L.; Xu, J.; Li, Z. J.; Jiao, Y.; Cui, Y. Y.

2018-05-01

The feasibility of differentiating tissue components by performing frequency domain analysis of photoacoustic images acquired at different wavelengths was studied in this paper. Firstly, according to the basic theory of photoacoustic imaging, a brief theoretical model for frequency domain analysis of multiwavelength photoacoustic signal was deduced. The experiment results proved that the performance of different targets in frequency domain is quite different. Especially, the acoustic spectrum characteristic peaks of different targets are unique, which are 2.93 MHz, 5.37 MHz, 6.83 MHz, and 8.78 MHz for PDMS phantom, while 13.20 MHz, 16.60 MHz, 26.86 MHz, and 29.30 MHz for pork fat. The results indicated that the acoustic spectrum of photoacoustic imaging signals is possible to be utilized for tissue composition characterization.

Multi-component separation and analysis of bat echolocation calls.

PubMed

DiCecco, John; Gaudette, Jason E; Simmons, James A

2013-01-01

The vast majority of animal vocalizations contain multiple frequency modulated (FM) components with varying amounts of non-linear modulation and harmonic instability. This is especially true of biosonar sounds where precise time-frequency templates are essential for neural information processing of echoes. Understanding the dynamic waveform design by bats and other echolocating animals may help to improve the efficacy of man-made sonar through biomimetic design. Bats are known to adapt their call structure based on the echolocation task, proximity to nearby objects, and density of acoustic clutter. To interpret the significance of these changes, a method was developed for component separation and analysis of biosonar waveforms. Techniques for imaging in the time-frequency plane are typically limited due to the uncertainty principle and interference cross terms. This problem is addressed by extending the use of the fractional Fourier transform to isolate each non-linear component for separate analysis. Once separated, empirical mode decomposition can be used to further examine each component. The Hilbert transform may then successfully extract detailed time-frequency information from each isolated component. This multi-component analysis method is applied to the sonar signals of four species of bats recorded in-flight by radiotelemetry along with a comparison of other common time-frequency representations.

Linearized blade row compression component model. Stability and frequency response analysis of a J85-3 compressor

NASA Technical Reports Server (NTRS)

Tesch, W. A.; Moszee, R. H.; Steenken, W. G.

1976-01-01

NASA developed stability and frequency response analysis techniques were applied to a dynamic blade row compression component stability model to provide a more economic approach to surge line and frequency response determination than that provided by time-dependent methods. This blade row model was linearized and the Jacobian matrix was formed. The clean-inlet-flow stability characteristics of the compressors of two J85-13 engines were predicted by applying the alternate Routh-Hurwitz stability criterion to the Jacobian matrix. The predicted surge line agreed with the clean-inlet-flow surge line predicted by the time-dependent method to a high degree except for one engine at 94% corrected speed. No satisfactory explanation of this discrepancy was found. The frequency response of the linearized system was determined by evaluating its Laplace transfer function. The results of the linearized-frequency-response analysis agree with the time-dependent results when the time-dependent inlet total-pressure and exit-flow function amplitude boundary conditions are less than 1 percent and 3 percent, respectively. The stability analysis technique was extended to a two-sector parallel compressor model with and without interstage crossflow and predictions were carried out for total-pressure distortion extents of 180 deg, 90 deg, 60 deg, and 30 deg.

Time/Frequency Analysis of Terrestrial Impack Crater Records

NASA Astrophysics Data System (ADS)

Chang, Heon-Young

2006-09-01

The terrestrial impact cratering record recently has been examined in the time domain by Chang & Moon (2005). It was found that the ˜ 26 Myr periodicity in the impact cratering rate exists over the last ˜ 250 Myrs. Such a periodicity can be found regardless of the lower limit of the diameter up to D ˜ 35 km. It immediately called pros and cons. The aim of this paper is two-fold: (1) to test if reported periodicities can be obtained with an independent method, (2) to see, as attempted earlier, if the phase is modulated. To achieve these goals we employ the time/frequency analysis and for the first time apply this method to the terrestrial impact cratering records. We have confirmed that without exceptions noticeable peaks appear around ˜ 25 Myr, corresponding to a frequency of ˜ 0.04 (Myr)^{-1}. We also find periodicities in the data base including small impact craters, which are longer. Though the time/frequency analysis allows us to observe directly phase variations, we cannot find any indications of such changes. Instead, modes display slow variations of power in time. The time/frequency analysis shows a nonstationary behavior of the modes. The power can grow from just above the noise level and then decrease back to its initial level in a time of order of 10 Myrs.

Time and frequency domain characteristics of detrending-operation-based scaling analysis: Exact DFA and DMA frequency responses

NASA Astrophysics Data System (ADS)

Kiyono, Ken; Tsujimoto, Yutaka

2016-07-01

We develop a general framework to study the time and frequency domain characteristics of detrending-operation-based scaling analysis methods, such as detrended fluctuation analysis (DFA) and detrending moving average (DMA) analysis. In this framework, using either the time or frequency domain approach, the frequency responses of detrending operations are calculated analytically. Although the frequency domain approach based on conventional linear analysis techniques is only applicable to linear detrending operations, the time domain approach presented here is applicable to both linear and nonlinear detrending operations. Furthermore, using the relationship between the time and frequency domain representations of the frequency responses, the frequency domain characteristics of nonlinear detrending operations can be obtained. Based on the calculated frequency responses, it is possible to establish a direct connection between the root-mean-square deviation of the detrending-operation-based scaling analysis and the power spectrum for linear stochastic processes. Here, by applying our methods to DFA and DMA, including higher-order cases, exact frequency responses are calculated. In addition, we analytically investigate the cutoff frequencies of DFA and DMA detrending operations and show that these frequencies are not optimally adjusted to coincide with the corresponding time scale.

Time and frequency domain characteristics of detrending-operation-based scaling analysis: Exact DFA and DMA frequency responses.

PubMed

Kiyono, Ken; Tsujimoto, Yutaka

2016-07-01

We develop a general framework to study the time and frequency domain characteristics of detrending-operation-based scaling analysis methods, such as detrended fluctuation analysis (DFA) and detrending moving average (DMA) analysis. In this framework, using either the time or frequency domain approach, the frequency responses of detrending operations are calculated analytically. Although the frequency domain approach based on conventional linear analysis techniques is only applicable to linear detrending operations, the time domain approach presented here is applicable to both linear and nonlinear detrending operations. Furthermore, using the relationship between the time and frequency domain representations of the frequency responses, the frequency domain characteristics of nonlinear detrending operations can be obtained. Based on the calculated frequency responses, it is possible to establish a direct connection between the root-mean-square deviation of the detrending-operation-based scaling analysis and the power spectrum for linear stochastic processes. Here, by applying our methods to DFA and DMA, including higher-order cases, exact frequency responses are calculated. In addition, we analytically investigate the cutoff frequencies of DFA and DMA detrending operations and show that these frequencies are not optimally adjusted to coincide with the corresponding time scale.

Time-frequency analysis of human motion during rhythmic exercises.

PubMed

Omkar, S N; Vyas, Khushi; Vikranth, H N

2011-01-01

Biomechanical signals due to human movements during exercise are represented in time-frequency domain using Wigner Distribution Function (WDF). Analysis based on WDF reveals instantaneous spectral and power changes during a rhythmic exercise. Investigations were carried out on 11 healthy subjects who performed 5 cycles of sun salutation, with a body-mounted Inertial Measurement Unit (IMU) as a motion sensor. Variance of Instantaneous Frequency (I.F) and Instantaneous Power (I.P) for performance analysis of the subject is estimated using one-way ANOVA model. Results reveal that joint Time-Frequency analysis of biomechanical signals during motion facilitates a better understanding of grace and consistency during rhythmic exercise.

Time-frequency analysis : mathematical analysis of the empirical mode decomposition.

DOT National Transportation Integrated Search

2009-01-01

Invented over 10 years ago, empirical mode : decomposition (EMD) provides a nonlinear : time-frequency analysis with the ability to successfully : analyze nonstationary signals. Mathematical : Analysis of the Empirical Mode Decomposition : is a...

Flutter of High-Speed Civil Transport Flexible Semispan Model: Time-Frequency Analysis

NASA Technical Reports Server (NTRS)

Chabalko, Christopher C.; Hajj, Muhammad R.; Silva, Walter A.

2006-01-01

Time/frequency analysis of fluctuations measured by pressure taps and strain gauges in the experimental studies of the flexible semispan model of a high-speed civil transport wing configuration is performed. The interest is in determining the coupling between the aerodynamic loads and structural motions that led to the hard flutter conditions and loss of the model. The results show that, away from the hard flutter point, the aerodynamic loads at all pressure taps near the wing tip and the structural motions contained the same frequency components. On the other hand, in the flow conditions leading to the hard flutter, the frequency content of the pressure fluctuations near the leading and trailing edges varied significantly. This led to contribution to the structural motions over two frequency ranges. The ratio of these ranges was near 2:1, which suggests the possibility of nonlinear structural coupling.

Using time-frequency analysis to determine time-resolved detonation velocity with microwave interferometry.

PubMed

Kittell, David E; Mares, Jesus O; Son, Steven F

2015-04-01

Two time-frequency analysis methods based on the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used to determine time-resolved detonation velocities with microwave interferometry (MI). The results were directly compared to well-established analysis techniques consisting of a peak-picking routine as well as a phase unwrapping method (i.e., quadrature analysis). The comparison is conducted on experimental data consisting of transient detonation phenomena observed in triaminotrinitrobenzene and ammonium nitrate-urea explosives, representing high and low quality MI signals, respectively. Time-frequency analysis proved much more capable of extracting useful and highly resolved velocity information from low quality signals than the phase unwrapping and peak-picking methods. Additionally, control of the time-frequency methods is mainly constrained to a single parameter which allows for a highly unbiased analysis method to extract velocity information. In contrast, the phase unwrapping technique introduces user based variability while the peak-picking technique does not achieve a highly resolved velocity result. Both STFT and CWT methods are proposed as improved additions to the analysis methods applied to MI detonation experiments, and may be useful in similar applications.

Time-localized frequency analysis of ultrasonic guided waves for nondestructive testing

NASA Astrophysics Data System (ADS)

Shin, Hyeon Jae; Song, Sung-Jin

2000-05-01

A time-localized frequency (TLF) analysis is employed for the guided wave mode identification and improved guided wave applications. For the analysis of time-localized frequency contents of digitized ultrasonic signals, TLF analysis consists of splitting the time domain signal into overlapping segments, weighting each with the hanning window, and forming the columns of discrete Fourier transforms. The result is presented by a frequency versus time domain diagram showing frequency variation along the signal arrival time. For the demonstration of the utility of TLF analysis, an experimental group velocity dispersion pattern obtained by TLF analysis is compared with the dispersion diagram obtained by theory of elasticity. Sample piping is carbon steel piping that is used for the transportation of natural gas underground. Guided wave propagation characteristic on the piping is considered with TLF analysis and wave structure concepts. TLF analysis is used for the detection of simulated corrosion defects and the assessment of weld joint using ultrasonic guided waves. TLF analysis has revealed that the difficulty of mode identification in multi-mode propagation could be overcome. Group velocity dispersion pattern obtained by TLF analysis agrees well with theoretical results.

Carbon financial markets: A time-frequency analysis of CO2 prices

NASA Astrophysics Data System (ADS)

Sousa, Rita; Aguiar-Conraria, Luís; Soares, Maria Joana

2014-11-01

We characterize the interrelation of CO2 prices with energy prices (electricity, gas and coal), and with economic activity. Previous studies have relied on time-domain techniques, such as Vector Auto-Regressions. In this study, we use multivariate wavelet analysis, which operates in the time-frequency domain. Wavelet analysis provides convenient tools to distinguish relations at particular frequencies and at particular time horizons. Our empirical approach has the potential to identify relations getting stronger and then disappearing over specific time intervals and frequencies. We are able to examine the coherency of these variables and lead-lag relations at different frequencies for the time periods in focus.

Phase and amplitude analysis in time-frequency space--application to voluntary finger movement.

PubMed

Ginter, J; Blinowska, K J; Kamiński, M; Durka, P J

2001-09-30

Two methods operating in time-frequency space were applied to analysis of EEG activity accompanying voluntary finger movements. The first one, based on matching pursuit approach provided high-resolution distributions of power in time-frequency space. The phenomena of event related desynchronization (ERD) and synchronization (ERS) were investigated without the need of band-pass filtering. Time evolution of mu- and beta-components was observed in a detailed way. The second method was based on a multichannel autoregressive model (MVAR) adapted for investigation of short-time changes in EEG signal. The direction and spectral content of the EEG activity propagation was estimated by means of short-time directed transfer function (SDTF). The evidence of 'cross-talk' between different areas of motor and sensory cortex was found. The earlier known phenomena, connected with voluntary movements, were confirmed and a new evidence concerning focal ERD/surround ERS and beta activity post-movement synchronization was found.

Orbital component extraction by time-variant sinusoidal modeling.

NASA Astrophysics Data System (ADS)

Sinnesael, Matthias; Zivanovic, Miroslav; De Vleeschouwer, David; Claeys, Philippe; Schoukens, Johan

2016-04-01

Accurately deciphering periodic variations in paleoclimate proxy signals is essential for cyclostratigraphy. Classical spectral analysis often relies on methods based on the (Fast) Fourier Transformation. This technique has no unique solution separating variations in amplitude and frequency. This characteristic makes it difficult to correctly interpret a proxy's power spectrum or to accurately evaluate simultaneous changes in amplitude and frequency in evolutionary analyses. Here, we circumvent this drawback by using a polynomial approach to estimate instantaneous amplitude and frequency in orbital components. This approach has been proven useful to characterize audio signals (music and speech), which are non-stationary in nature (Zivanovic and Schoukens, 2010, 2012). Paleoclimate proxy signals and audio signals have in nature similar dynamics; the only difference is the frequency relationship between the different components. A harmonic frequency relationship exists in audio signals, whereas this relation is non-harmonic in paleoclimate signals. However, the latter difference is irrelevant for the problem at hand. Using a sliding window approach, the model captures time variations of an orbital component by modulating a stationary sinusoid centered at its mean frequency, with a single polynomial. Hence, the parameters that determine the model are the mean frequency of the orbital component and the polynomial coefficients. The first parameter depends on geologic interpretation, whereas the latter are estimated by means of linear least-squares. As an output, the model provides the orbital component waveform, either in the depth or time domain. Furthermore, it allows for a unique decomposition of the signal into its instantaneous amplitude and frequency. Frequency modulation patterns can be used to reconstruct changes in accumulation rate, whereas amplitude modulation can be used to reconstruct e.g. eccentricity-modulated precession. The time-variant sinusoidal model

Astronomical component estimation (ACE v.1) by time-variant sinusoidal modeling

NASA Astrophysics Data System (ADS)

Sinnesael, Matthias; Zivanovic, Miroslav; De Vleeschouwer, David; Claeys, Philippe; Schoukens, Johan

2016-09-01

Accurately deciphering periodic variations in paleoclimate proxy signals is essential for cyclostratigraphy. Classical spectral analysis often relies on methods based on (fast) Fourier transformation. This technique has no unique solution separating variations in amplitude and frequency. This characteristic can make it difficult to correctly interpret a proxy's power spectrum or to accurately evaluate simultaneous changes in amplitude and frequency in evolutionary analyses. This drawback is circumvented by using a polynomial approach to estimate instantaneous amplitude and frequency in orbital components. This approach was proven useful to characterize audio signals (music and speech), which are non-stationary in nature. Paleoclimate proxy signals and audio signals share similar dynamics; the only difference is the frequency relationship between the different components. A harmonic-frequency relationship exists in audio signals, whereas this relation is non-harmonic in paleoclimate signals. However, this difference is irrelevant for the problem of separating simultaneous changes in amplitude and frequency. Using an approach with overlapping analysis frames, the model (Astronomical Component Estimation, version 1: ACE v.1) captures time variations of an orbital component by modulating a stationary sinusoid centered at its mean frequency, with a single polynomial. Hence, the parameters that determine the model are the mean frequency of the orbital component and the polynomial coefficients. The first parameter depends on geologic interpretations, whereas the latter are estimated by means of linear least-squares. As output, the model provides the orbital component waveform, either in the depth or time domain. Uncertainty analyses of the model estimates are performed using Monte Carlo simulations. Furthermore, it allows for a unique decomposition of the signal into its instantaneous amplitude and frequency. Frequency modulation patterns reconstruct changes in

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

PubMed

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C

2004-09-08

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins. Copyright 2004 American Institute of Physics

Application of time series analysis on molecular dynamics simulations of proteins: A study of different conformational spaces by principal component analysis

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C.

2004-09-01

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of α-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Cα coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of α-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of α-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

Full waveform inversion using a decomposed single frequency component from a spectrogram

NASA Astrophysics Data System (ADS)

Ha, Jiho; Kim, Seongpil; Koo, Namhyung; Kim, Young-Ju; Woo, Nam-Sub; Han, Sang-Mok; Chung, Wookeen; Shin, Sungryul; Shin, Changsoo; Lee, Jaejoon

2018-06-01

Although many full waveform inversion methods have been developed to construct velocity models of subsurface, various approaches have been presented to obtain an inversion result with long-wavelength features even though seismic data lacking low-frequency components were used. In this study, a new full waveform inversion algorithm was proposed to recover a long-wavelength velocity model that reflects the inherent characteristics of each frequency component of seismic data using a single-frequency component decomposed from the spectrogram. We utilized the wavelet transform method to obtain the spectrogram, and the decomposed signal from the spectrogram was used as transformed data. The Gauss-Newton method with the diagonal elements of an approximate Hessian matrix was used to update the model parameters at each iteration. Based on the results of time-frequency analysis in the spectrogram, numerical tests with some decomposed frequency components were performed using a modified SEG/EAGE salt dome (A-A‧) line to demonstrate the feasibility of the proposed inversion algorithm. This demonstrated that a reasonable inverted velocity model with long-wavelength structures can be obtained using a single frequency component. It was also confirmed that when strong noise occurs in part of the frequency band, it is feasible to obtain a long-wavelength velocity model from the noise data with a frequency component that is less affected by the noise. Finally, it was confirmed that the results obtained from the spectrogram inversion can be used as an initial velocity model in conventional inversion methods.

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.

PubMed

Pang, Cunsuo; Han, Yan; Hou, Huiling; Liu, Shengheng; Zhang, Nan

2016-09-24

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT's performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.

Complex Signal Kurtosis and Independent Component Analysis for Wideband Radio Frequency Interference Detection

NASA Technical Reports Server (NTRS)

Schoenwald, Adam; Mohammed, Priscilla; Bradley, Damon; Piepmeier, Jeffrey; Wong, Englin; Gholian, Armen

2016-01-01

Radio-frequency interference (RFI) has negatively implicated scientific measurements across a wide variation passive remote sensing satellites. This has been observed in the L-band radiometers SMOS, Aquarius and more recently, SMAP [1, 2]. RFI has also been observed at higher frequencies such as K band [3]. Improvements in technology have allowed wider bandwidth digital back ends for passive microwave radiometry. A complex signal kurtosis radio frequency interference detector was developed to help identify corrupted measurements [4]. This work explores the use of ICA (Independent Component Analysis) as a blind source separation technique to pre-process radiometric signals for use with the previously developed real and complex signal kurtosis detectors.

A statistical package for computing time and frequency domain analysis

NASA Technical Reports Server (NTRS)

Brownlow, J.

1978-01-01

The spectrum analysis (SPA) program is a general purpose digital computer program designed to aid in data analysis. The program does time and frequency domain statistical analyses as well as some preanalysis data preparation. The capabilities of the SPA program include linear trend removal and/or digital filtering of data, plotting and/or listing of both filtered and unfiltered data, time domain statistical characterization of data, and frequency domain statistical characterization of data.

Time-frequency analysis of backscattered signals from diffuse radar targets

NASA Astrophysics Data System (ADS)

Kenny, O. P.; Boashash, B.

1993-06-01

The need for analysis of time-varying signals has led to the formulation of a class of joint time-frequency distributions (TFDs). One of these TFDs, the Wigner-Ville distribution (WVD), has useful properties which can be applied to radar imaging. The authors discuss time-frequency representation of the backscattered signal from a diffuse radar target. It is then shown that for point scatterers which are statistically dependent or for which the reflectivity coefficient has a nonzero mean value, reconstruction using time of flight positron emission tomography on time-frequency images is effective for estimating the scattering function of the target.

Time-frequency analysis of submerged synthetic jet

NASA Astrophysics Data System (ADS)

Kumar, Abhay; Saha, Arun K.; Panigrahi, P. K.

2017-12-01

The coherent structures transport the finite body of fluid mass through rolling which plays an important role in heat transfer, boundary layer control, mixing, cooling, propulsion and other engineering applications. A synthetic jet in the form of a train of vortex rings having coherent structures of different length scales is expected to be useful in these applications. The propagation and sustainability of these coherent structures (vortex rings) in downstream direction characterize the performance of synthetic jet. In the present study, the velocity signal acquired using the S-type hot-film probe along the synthetic jet centerline has been taken for the spectral analysis. One circular and three rectangular orifices of aspect ratio 1, 2 and 4 actuating at 1, 6 and 18 Hz frequency have been used for creating different synthetic jets. The laser induced fluorescence images are used to study the flow structures qualitatively and help in explaining the velocity signal for detection of coherent structures. The study depicts four regions as vortex rollup and suction region (X/D h ≤ 3), steadily translating region (X/D h ≤ 3-8), vortex breakup region (X/Dh ≤ 4-8) and dissipation of small-scale vortices (X/D h ≤ 8-15). The presence of coherent structures localized in physical and temporal domain is analyzed for the characterization of synthetic jet. Due to pulsatile nature of synthetic jet, analysis of velocity time trace or signal in time, frequency and combined time-frequency domain assist in characterizing the signatures of coherent structures. It has been observed that the maximum energy is in the first harmonic of actuation frequency, which decreases slowly in downstream direction at 6 Hz compared to 1 and 18 Hz of actuation.

Time-Frequency Analysis of the Dispersion of Lamb Modes

NASA Technical Reports Server (NTRS)

Prosser, W. H.; Seale, Michael D.; Smith, Barry T.

1999-01-01

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.

Time-Frequency Analysis of the Dispersion of Lamb Modes

NASA Technical Reports Server (NTRS)

Prosser, W. H.; Seale, Michael D.; Smith, Barry T.

1999-01-01

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 AO, A I , So, and S2 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.

Time-frequency vibration analysis for the detection of motor damages caused by bearing currents

NASA Astrophysics Data System (ADS)

Prudhom, Aurelien; Antonino-Daviu, Jose; Razik, Hubert; Climente-Alarcon, Vicente

2017-02-01

Motor failure due to bearing currents is an issue that has drawn an increasing industrial interest over recent years. Bearing currents usually appear in motors operated by variable frequency drives (VFD); these drives may lead to common voltage modes which cause currents induced in the motor shaft that are discharged through the bearings. The presence of these currents may lead to the motor bearing failure only few months after system startup. Vibration monitoring is one of the most common ways for detecting bearing damages caused by circulating currents; the evaluation of the amplitudes of well-known characteristic components in the vibration Fourier spectrum that are associated with race, ball or cage defects enables to evaluate the bearing condition and, hence, to identify an eventual damage due to bearing currents. However, the inherent constraints of the Fourier transform may complicate the detection of the progressive bearing degradation; for instance, in some cases, other frequency components may mask or be confused with bearing defect-related while, in other cases, the analysis may not be suitable due to the eventual non-stationary nature of the captured vibration signals. Moreover, the fact that this analysis implies to lose the time-dimension limits the amount of information obtained from this technique. This work proposes the use of time-frequency (T-F) transforms to analyse vibration data in motors affected by bearing currents. The experimental results obtained in real machines show that the vibration analysis via T-F tools may provide significant advantages for the detection of bearing current damages; among other, these techniques enable to visualise the progressive degradation of the bearing while providing an effective discrimination versus other components that are not related with the fault. Moreover, their application is valid regardless of the operation regime of the machine. Both factors confirm the robustness and reliability of these tools

Detailed Vibration Analysis of Pinion Gear with Time-Frequency Methods

NASA Technical Reports Server (NTRS)

Mosher, Marianne; Pryor, Anna H.; Lewicki, David G.

2003-01-01

In this paper, the authors show a detailed analysis of the vibration signal from the destructive testing of a spiral bevel gear and pinion pair containing seeded faults. The vibration signal is analyzed in the time domain, frequency domain and with four time-frequency transforms: the Short Time Frequency Transform (STFT), the Wigner-Ville Distribution with the Choi-Williams kernel (WV-CW), the Continuous Wavelet' Transform (CWT) and the Discrete Wavelet Transform (DWT). Vibration data of bevel gear tooth fatigue cracks, under a variety of operating load levels and damage conditions, are analyzed using these methods. A new metric for automatic anomaly detection is developed and can be produced from any systematic numerical representation of the vibration signals. This new metric reveals indications of gear damage with all of the time-frequency transforms, as well as time and frequency representations, on this data set. Analysis with the CWT detects changes in the signal at low torque levels not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic anomaly detection and to develop fault detection methods for the metric.

Increasing sensitivity in the measurement of heart rate variability: the method of non-stationary RR time-frequency analysis.

PubMed

Melkonian, D; Korner, A; Meares, R; Bahramali, H

2012-10-01

A novel method of the time-frequency analysis of non-stationary heart rate variability (HRV) is developed which introduces the fragmentary spectrum as a measure that brings together the frequency content, timing and duration of HRV segments. The fragmentary spectrum is calculated by the similar basis function algorithm. This numerical tool of the time to frequency and frequency to time Fourier transformations accepts both uniform and non-uniform sampling intervals, and is applicable to signal segments of arbitrary length. Once the fragmentary spectrum is calculated, the inverse transform recovers the original signal and reveals accuracy of spectral estimates. Numerical experiments show that discontinuities at the boundaries of the succession of inter-beat intervals can cause unacceptable distortions of the spectral estimates. We have developed a measure that we call the "RR deltagram" as a form of the HRV data that minimises spectral errors. The analysis of the experimental HRV data from real-life and controlled breathing conditions suggests transient oscillatory components as functionally meaningful elements of highly complex and irregular patterns of HRV. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

NASA Astrophysics Data System (ADS)

Lu, W. L.; Xie, J. W.; Wang, H. M.; Sheng, C.

2016-09-01

Modern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time-frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time-frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

Frequency- and Time-Domain Methods in Soil-Structure Interaction Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolisetti, Chandrakanth; Whittaker, Andrew S.; Coleman, Justin L.

2015-06-01

Soil-structure interaction (SSI) analysis in the nuclear industry is currently performed using linear codes that function in the frequency domain. There is a consensus that these frequency-domain codes give reasonably accurate results for low-intensity ground motions that result in almost linear response. For higher intensity ground motions, which may result in nonlinear response in the soil, structure or at the vicinity of the foundation, the adequacy of frequency-domain codes is unproven. Nonlinear analysis, which is only possible in the time domain, is theoretically more appropriate in such cases. These methods are available but are rarely used due to the largemore » computational requirements and a lack of experience with analysts and regulators. This paper presents an assessment of the linear frequency-domain code, SASSI, which is widely used in the nuclear industry, and the time-domain commercial finite-element code, LS-DYNA, for SSI analysis. The assessment involves benchmarking the SSI analysis procedure in LS-DYNA against SASSI for linearly elastic models. After affirming that SASSI and LS-DYNA result in almost identical responses for these models, they are used to perform nonlinear SSI analyses of two structures founded on soft soil. An examination of the results shows that, in spite of using identical material properties, the predictions of frequency- and time-domain codes are significantly different in the presence of nonlinear behavior such as gapping and sliding of the foundation.« less

Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications.

PubMed

Fulop, Sean A; Fitz, Kelly

2006-01-01

A modification of the spectrogram (log magnitude of the short-time Fourier transform) to more accurately show the instantaneous frequencies of signal components was first proposed in 1976 [Kodera et al., Phys. Earth Planet. Inter. 12, 142-150 (1976)], and has been considered or reinvented a few times since but never widely adopted. This paper presents a unified theoretical picture of this time-frequency analysis method, the time-corrected instantaneous frequency spectrogram, together with detailed implementable algorithms comparing three published techniques for its computation. The new representation is evaluated against the conventional spectrogram for its superior ability to track signal components. The lack of a uniform framework for either mathematics or implementation details which has characterized the disparate literature on the schemes has been remedied here. Fruitful application of the method is shown in the realms of speech phonation analysis, whale song pitch tracking, and additive sound modeling.

Aurally-adequate time-frequency analysis for scattered sound in auditoria

NASA Astrophysics Data System (ADS)

Norris, Molly K.; Xiang, Ning; Kleiner, Mendel

2005-04-01

The goal of this work was to apply an aurally-adequate time-frequency analysis technique to the analysis of sound scattering effects in auditoria. Time-frequency representations were developed as a motivated effort that takes into account binaural hearing, with a specific implementation of interaural cross-correlation process. A model of the human auditory system was implemented in the MATLAB platform based on two previous models [A. Härmä and K. Palomäki, HUTear, Espoo, Finland; and M. A. Akeroyd, A. Binaural Cross-correlogram Toolbox for MATLAB (2001), University of Sussex, Brighton]. These stages include proper frequency selectivity, the conversion of the mechanical motion of the basilar membrane to neural impulses, and binaural hearing effects. The model was then used in the analysis of room impulse responses with varying scattering characteristics. This paper discusses the analysis results using simulated and measured room impulse responses. [Work supported by the Frank H. and Eva B. Buck Foundation.

Time-frequency analysis of acoustic scattering from elastic objects

NASA Astrophysics Data System (ADS)

Yen, Nai-Chyuan; Dragonette, Louis R.; Numrich, Susan K.

1990-06-01

A time-frequency analysis of acoustic scattering from elastic objects was carried out using the time-frequency representation based on a modified version of the Wigner distribution function (WDF) algorithm. A simple and efficient processing algorithm was developed, which provides meaningful interpretation of the scattering physics. The time and frequency representation derived from the WDF algorithm was further reduced to a display which is a skeleton plot, called a vein diagram, that depicts the essential features of the form function. The physical parameters of the scatterer are then extracted from this diagram with the proper interpretation of the scattering phenomena. Several examples, based on data obtained from numerically simulated models and laboratory measurements for elastic spheres and shells, are used to illustrate the capability and proficiency of the algorithm.

Radar signal analysis of ballistic missile with micro-motion based on time-frequency distribution

NASA Astrophysics Data System (ADS)

Wang, Jianming; Liu, Lihua; Yu, Hua

2015-12-01

The micro-motion of ballistic missile targets induces micro-Doppler modulation on the radar return signal, which is a unique feature for the warhead discrimination during flight. In order to extract the micro-Doppler feature of ballistic missile targets, time-frequency analysis is employed to process the micro-Doppler modulated time-varying radar signal. The images of time-frequency distribution (TFD) reveal the micro-Doppler modulation characteristic very well. However, there are many existing time-frequency analysis methods to generate the time-frequency distribution images, including the short-time Fourier transform (STFT), Wigner distribution (WD) and Cohen class distribution, etc. Under the background of ballistic missile defence, the paper aims at working out an effective time-frequency analysis method for ballistic missile warhead discrimination from the decoys.

Nonlinear analysis of heart rate variability within independent frequency components during the sleep-wake cycle.

PubMed

Vigo, Daniel E; Dominguez, Javier; Guinjoan, Salvador M; Scaramal, Mariano; Ruffa, Eduardo; Solernó, Juan; Siri, Leonardo Nicola; Cardinali, Daniel P

2010-04-19

Heart rate variability (HRV) is a complex signal that results from the contribution of different sources of oscillation related to the autonomic nervous system activity. Although linear analysis of HRV has been applied to sleep studies, the nonlinear dynamics of HRV underlying frequency components during sleep is less known. We conducted a study to evaluate nonlinear HRV within independent frequency components in wake status, slow-wave sleep (SWS, stages III or IV of non-rapid eye movement sleep), and rapid-eye-movement sleep (REM). The sample included 10 healthy adults. Polysomnography was performed to detect sleep stages. HRV was studied globally during each phase and then very low frequency (VLF), low frequency (LF) and high frequency (HF) components were separated by means of the wavelet transform algorithm. HRV nonlinear dynamics was estimated with sample entropy (SampEn). A higher SampEn was found when analyzing global variability (Wake: 1.53+/-0.28, SWS: 1.76+/-0.32, REM: 1.45+/-0.19, p=0.005) and VLF variability (Wake: 0.13+/-0.03, SWS: 0.19+/-0.03, REM: 0.14+/-0.03, p<0.001) at SWS. REM was similar to wake status regarding nonlinear HRV. We propose nonlinear HRV is a useful index of the autonomic activity that characterizes the different sleep-wake cycle stages. 2009 Elsevier B.V. All rights reserved.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Wavelet analysis of near-resonant series RLC circuit with time-dependent forcing frequency

NASA Astrophysics Data System (ADS)

Caccamo, M. T.; Cannuli, A.; Magazù, S.

2018-07-01

In this work, the results of an analysis of the response of a near-resonant series resistance‑inductance‑capacitance (RLC) electric circuit with time-dependent forcing frequency by means of a wavelet cross-correlation approach are reported. In particular, it is shown how the wavelet approach enables frequency and time analysis of the circuit response to be carried out simultaneously—this procedure not being possible by Fourier transform, since the frequency is not stationary in time. A series RLC circuit simulation is performed by using the Simulation Program with Integrated Circuits Emphasis (SPICE), in which an oscillatory sinusoidal voltage drive signal of constant amplitude is swept through the resonant condition by progressively increasing the frequency over a 20-second time window, linearly, from 0.32 Hz to 6.69 Hz. It is shown that the wavelet cross-correlation procedure quantifies the common power between the input signal (represented by the electromotive force) and the output signal, which in the present case is a current, highlighting not only which frequencies are present but also when they occur, i.e. providing a simultaneous time-frequency analysis. The work is directed toward graduate Physics, Engineering and Mathematics students, with the main intention of introducing wavelet analysis into their data analysis toolkit.

Time-frequency analysis of acoustic signals in the audio-frequency range generated during Hadfield's steel friction

NASA Astrophysics Data System (ADS)

Dobrynin, S. A.; Kolubaev, E. A.; Smolin, A. Yu.; Dmitriev, A. I.; Psakhie, S. G.

2010-07-01

Time-frequency analysis of sound waves detected by a microphone during the friction of Hadfield’s steel has been performed using wavelet transform and window Fourier transform methods. This approach reveals a relationship between the appearance of quasi-periodic intensity outbursts in the acoustic response signals and the processes responsible for the formation of wear products. It is shown that the time-frequency analysis of acoustic emission in a tribosystem can be applied, along with traditional approaches, to studying features in the wear and friction process.

A time-frequency classifier for human gait recognition

NASA Astrophysics Data System (ADS)

Mobasseri, Bijan G.; Amin, Moeness G.

2009-05-01

Radar has established itself as an effective all-weather, day or night sensor. Radar signals can penetrate walls and provide information on moving targets. Recently, radar has been used as an effective biometric sensor for classification of gait. The return from a coherent radar system contains a frequency offset in the carrier frequency, known as the Doppler Effect. The movements of arms and legs give rise to micro Doppler which can be clearly detailed in the time-frequency domain using traditional or modern time-frequency signal representation. In this paper we propose a gait classifier based on subspace learning using principal components analysis(PCA). The training set consists of feature vectors defined as either time or frequency snapshots taken from the spectrogram of radar backscatter. We show that gait signature is captured effectively in feature vectors. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Results show that gait classification with high accuracy and short observation window is achievable using the proposed classifier.

Time-frequency representation of a highly nonstationary signal via the modified Wigner distribution

NASA Technical Reports Server (NTRS)

Zoladz, T. F.; Jones, J. H.; Jong, J.

1992-01-01

A new signal analysis technique called the modified Wigner distribution (MWD) is presented. The new signal processing tool has been very successful in determining time frequency representations of highly non-stationary multicomponent signals in both simulations and trials involving actual Space Shuttle Main Engine (SSME) high frequency data. The MWD departs from the classic Wigner distribution (WD) in that it effectively eliminates the cross coupling among positive frequency components in a multiple component signal. This attribute of the MWD, which prevents the generation of 'phantom' spectral peaks, will undoubtedly increase the utility of the WD for real world signal analysis applications which more often than not involve multicomponent signals.

Time-variant random interval natural frequency analysis of structures

NASA Astrophysics Data System (ADS)

Wu, Binhua; Wu, Di; Gao, Wei; Song, Chongmin

2018-02-01

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

Clinical usefulness and feasibility of time-frequency analysis of chemosensory event-related potentials.

PubMed

Huart, C; Rombaux, Ph; Hummel, T; Mouraux, A

2013-09-01

The clinical usefulness of olfactory event-related brain potentials (OERPs) to assess olfactory function is limited by the relatively low signal-to-noise ratio of the responses identified using conventional time-domain averaging. Recently, it was shown that time-frequency analysis of the obtained EEG signals can markedly improve the signal-to-noise ratio of OERPs in healthy controls, because it enhances both phase-locked and non phase-locked EEG responses. The aim of the present study was to investigate the clinical usefulness of this approach and evaluate its feasibility in a clinical setting. We retrospectively analysed EEG recordings obtained from 45 patients (15 anosmic, 15 hyposmic and 15 normos- mic). The responses to olfactory stimulation were analysed using conventional time-domain analysis and joint time-frequency analysis. The ability of the two methods to discriminate between anosmic, hyposmic and normosmic patients was assessed using a Receiver Operating Characteristic analysis. The discrimination performance of OERPs identified using conventional time-domain averaging was poor. In contrast, the discrimination performance of the EEG response identified in the time-frequency domain was relatively high. Furthermore, we found a significant correlation between the magnitude of this response and the psychophysical olfactory score. Time-frequency analysis of the EEG responses to olfactory stimulation could be used as an effective and reliable diagnostic tool for the objective clinical evaluation of olfactory function in patients.

Time Series Decomposition into Oscillation Components and Phase Estimation.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects

DTIC Science & Technology

2017-02-22

AFRL-AFOSR-UK-TR-2017-0023 Linear and Nonlinear Time -Frequency Analysis for Parameter Estimation of Resident Space Objects Marco Martorella...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing   data sources, gathering and maintaining the...Nonlinear Time -Frequency Analysis for Parameter Estimation of Resident Space Objects 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-14-1-0183 5c.  PROGRAM

Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

NASA Astrophysics Data System (ADS)

Zhou, Peng; Zhang, Xi; Sun, Weifeng; Dai, Yongshou; Wan, Yong

2018-01-01

An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment's time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.

Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis.

PubMed

Burnett, Andrew D; Fan, Wenhui; Upadhya, Prashanth C; Cunningham, John E; Hargreaves, Michael D; Munshi, Tasnim; Edwards, Howell G M; Linfield, Edmund H; Davies, A Giles

2009-08-01

Terahertz frequency time-domain spectroscopy has been used to analyse a wide range of samples containing cocaine hydrochloride, heroin and ecstasy--common drugs-of-abuse. We investigated real-world samples seized by law enforcement agencies, together with pure drugs-of-abuse, and pure drugs-of-abuse systematically adulterated in the laboratory to emulate real-world samples. In order to investigate the feasibility of automatic spectral recognition of such illicit materials by terahertz spectroscopy, principal component analysis was employed to cluster spectra of similar compounds.

Characterization of Strombolian events by using independent component analysis

NASA Astrophysics Data System (ADS)

Ciaramella, A.; de Lauro, E.; de Martino, S.; di Lieto, B.; Falanga, M.; Tagliaferri, R.

2004-10-01

We apply Independent Component Analysis (ICA) to seismic signals recorded at Stromboli volcano. Firstly, we show how ICA works considering synthetic signals, which are generated by dynamical systems. We prove that Strombolian signals, both tremor and explosions, in the high frequency band (>0.5 Hz), are similar in time domain. This seems to give some insights to the organ pipe model generation for the source of these events. Moreover, we are able to recognize in the tremor signals a low frequency component (<0.5 Hz), with a well defined peak corresponding to 30s.

CO Component Estimation Based on the Independent Component Analysis

NASA Astrophysics Data System (ADS)

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki; Takeuchi, Tsutomu T.; Fukui, Yasuo

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.

CO component estimation based on the independent component analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ichiki, Kiyotomo; Kaji, Ryohei; Yamamoto, Hiroaki

2014-01-01

Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply FastICA to the component separation problem of the microwave background, including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically, we prepare 100 GHz, 143 GHz, and 217 GHz mock microwave sky maps, which include galactic thermal dust, NANTEN CO line, and the cosmic microwave background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that FastICA can successfully estimate the CO component as the first independentmore » component in our deflection algorithm because its distribution has the largest degree of non-Gaussianity among the components. Thus, FastICA can be a promising technique to extract CO-like components without prior assumptions about their distributions and frequency dependences.« less

Parametric Time-Frequency Analysis and Its Applications in Music Classification

NASA Astrophysics Data System (ADS)

Shen, Ying; Li, Xiaoli; Ma, Ngok-Wah; Krishnan, Sridhar

2010-12-01

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.

Time-frequency techniques in biomedical signal analysis. a tutorial review of similarities and differences.

PubMed

Wacker, M; Witte, H

2013-01-01

This review outlines the methodological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville distribution (WVD) serves as an example of the 'quadratic transforms' class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches. Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the interference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals. The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered 'optimal'. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency

Time-Frequency Analysis Reveals Pairwise Interactions in Insect Swarms

NASA Astrophysics Data System (ADS)

Puckett, James G.; Ni, Rui; Ouellette, Nicholas T.

2015-06-01

The macroscopic emergent behavior of social animal groups is a classic example of dynamical self-organization, and is thought to arise from the local interactions between individuals. Determining these interactions from empirical data sets of real animal groups, however, is challenging. Using multicamera imaging and tracking, we studied the motion of individual flying midges in laboratory mating swarms. By performing a time-frequency analysis of the midge trajectories, we show that the midge behavior can be segmented into two distinct modes: one that is independent and composed of low-frequency maneuvers, and one that consists of higher-frequency nearly harmonic oscillations conducted in synchrony with another midge. We characterize these pairwise interactions, and make a hypothesis as to their biological function.

Adaptive multitaper time-frequency spectrum estimation

NASA Astrophysics Data System (ADS)

Pitton, James W.

1999-11-01

In earlier work, Thomson's adaptive multitaper spectrum estimation method was extended to the nonstationary case. This paper reviews the time-frequency multitaper method and the adaptive procedure, and explores some properties of the eigenvalues and eigenvectors. The variance of the adaptive estimator is used to construct an adaptive smoother, which is used to form a high resolution estimate. An F-test for detecting and removing sinusoidal components in the time-frequency spectrum is also given.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Time-frequency signal analysis and synthesis - The choice of a method and its application

NASA Astrophysics Data System (ADS)

Boashash, Boualem

In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It is shown that a natural approach leads to the introduction of the concepts of the analytic signal and instantaneous frequency. The Wigner-Ville Distribution (WVD) is a method of analysis based upon these concepts and it is shown that an accurate Time-Frequency representation of a signal can be obtained by using the WVD for the analysis of a class of signals referred to as 'asymptotic'. For this class of signals, the instantaneous frequency describes an important physical parameter characteristic of the process under investigation. The WVD procedure for signal analysis and synthesis is outlined and its properties are reviewed for deterministic and random signals.

Time-Frequency Signal Analysis And Synthesis The Choice Of A Method And Its Application

NASA Astrophysics Data System (ADS)

Boashash, Boualem

1988-02-01

In this paper, the problem of choosing a method for time-frequency signal analysis is discussed. It is shown that a natural approach leads to the introduction of the concepts of the analytic signal and in-stantaneous frequency. The Wigner-Ville Distribution (WVD) is a method of analysis based upon these concepts and it is shown that an accurate Time-Frequency representation of a signal can be obtained by using the WVD for the analysis of a class of signals referred to as "asymptotic". For this class of signals, the instantaneous frequency describes an important physical parameter characteristic of the process under investigation. The WVD procedure for signal analysis and synthesis is outlined and its properties are reviewed for deterministic and random signals.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

PubMed

Ergen, Burhan; Tatar, Yetkin; Gulcur, Halil Ozcan

2012-01-01

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Influence of running stride frequency in heart rate variability analysis during treadmill exercise testing.

PubMed

Bailón, Raquel; Garatachea, Nuria; de la Iglesia, Ignacio; Casajús, Jose Antonio; Laguna, Pablo

2013-07-01

The analysis and interpretation of heart rate variability (HRV) during exercise is challenging not only because of the nonstationary nature of exercise, the time-varying mean heart rate, and the fact that respiratory frequency exceeds 0.4 Hz, but there are also other factors, such as the component centered at the pedaling frequency observed in maximal cycling tests, which may confuse the interpretation of HRV analysis. The objectives of this study are to test the hypothesis that a component centered at the running stride frequency (SF) appears in the HRV of subjects during maximal treadmill exercise testing, and to study its influence in the interpretation of the low-frequency (LF) and high-frequency (HF) components of HRV during exercise. The HRV of 23 subjects during maximal treadmill exercise testing is analyzed. The instantaneous power of different HRV components is computed from the smoothed pseudo-Wigner-Ville distribution of the modulating signal assumed to carry information from the autonomic nervous system, which is estimated based on the time-varying integral pulse frequency modulation model. Besides the LF and HF components, the appearance is revealed of a component centered at the running SF as well as its aliases. The power associated with the SF component and its aliases represents 22±7% (median±median absolute deviation) of the total HRV power in all the subjects. Normalized LF power decreases as the exercise intensity increases, while normalized HF power increases. The power associated with the SF does not change significantly with exercise intensity. Consideration of the running SF component and its aliases is very important in HRV analysis since stride frequency aliases may overlap with LF and HF components.

Time-frequency analysis of the bistatic acoustic scattering from a spherical elastic shell.

PubMed

Anderson, Shaun D; Sabra, Karim G; Zakharia, Manell E; Sessarego, Jean-Pierre

2012-01-01

The development of low-frequency sonar systems, using, for instance, a network of autonomous systems in unmanned vehicles, provides a practical means for bistatic measurements (i.e., when the source and receiver are widely separated) allowing for multiple viewpoints of the target of interest. Time-frequency analysis, in particular, Wigner-Ville analysis, takes advantage of the evolution time dependent aspect of the echo spectrum to differentiate a man-made target, such as an elastic spherical shell, from a natural object of the similar shape. A key energetic feature of fluid-loaded and thin spherical shell is the coincidence pattern, also referred to as the mid-frequency enhancement (MFE), that results from antisymmetric Lamb-waves propagating around the circumference of the shell. This article investigates numerically the bistatic variations of the MFE with respect to the monostatic configuration using the Wigner-Ville analysis. The observed time-frequency shifts of the MFE are modeled using a previously derived quantitative ray theory by Zhang et al. [J. Acoust. Soc. Am. 91, 1862-1874 (1993)] for spherical shell's scattering. Additionally, the advantage of an optimal array beamformer, based on joint time delays and frequency shifts is illustrated for enhancing the detection of the MFE recorded across a bistatic receiver array when compared to a conventional time-delay beamformer. © 2012 Acoustical Society of America.

High-frequency ultrasonic methods for determining corrosion layer thickness of hollow metallic components.

PubMed

Liu, Hongwei; Zhang, Lei; Liu, Hong Fei; Chen, Shuting; Wang, Shihua; Wong, Zheng Zheng; Yao, Kui

2018-05-16

Corrosion in internal cavity is one of the most common problems occurs in many hollow metallic components, such as pipes containing corrosive fluids and high temperature turbines in aircraft. It is highly demanded to non-destructively detect the corrosion inside hollow components and determine the corrosion extent from the external side. In this work, we present two high-frequency ultrasonic non-destructive testing (NDT) technologies, including piezoelectric pulse-echo and laser-ultrasonic methods, for detecting corrosion of Ni superalloy from the opposite side. The determination of corrosion layer thickness below ∼100 µm has been demonstrated by both methods, in comparison with X-CT and SEM. With electron microscopic examination, it is found that with multilayer corrosion structure formed over a prolonged corrosion time, the ultrasonic NDT methods can only reliably reveal outer corrosion layer thickness because of the resulting acoustic contrast among the multiple layers due to their respective different mechanical parameters. A time-frequency signal analysis algorithm is employed to effectively enhance the high frequency ultrasonic signal contrast for the piezoelectric pulse-echo method. Finally, a blind test on a Ni superalloy turbine blade with internal corrosion is conducted with the high frequency piezoelectric pulser-receiver method. Copyright © 2018 Elsevier B.V. All rights reserved.

Time-frequency characterisation of paediatric heart sounds

NASA Astrophysics Data System (ADS)

Leung, Terence Sze-Tat

1998-08-01

The operation of the heart can be monitored by the sounds it emits. Structural defects or malfunction of the heart valves will cause additional abnormal sounds such as murmurs and ejection clicks. This thesis aims to characterise the heart sounds of three groups of children who either have an Atrial Septal Defect (ASD), a Ventricular Septal Defect (VSD), or are normal. Two aspects of heart sounds have been specifically investigated; the time-frequency analysis of systolic murmurs and the identification of splitting patterns in the second heart sound. The analysis is based on 42 paediatric heart sound recordings. Murmurs are sounds generated by turbulent flow of blood in the heart. They can be found in patients with both pathological and non-pathological conditions. The acoustic quality of the murmurs generated in each heart condition are different. The first aspect of this work is to characterise the three types of murmurs in the time- frequency domain. Modern time-frequency methods including, the Wigner-Ville Distribution, Smoothed Pseudo Wigner-Ville Distribution, Choi-Williams Distribution and spectrogram have been applied to characterise the murmurs. It was found that the three classes of murmurs exhibited different signatures in their time-frequency representations. By performing Discriminant Analysis, it was shown that spectral features extracted from the time- frequency representations can be used to distinguish between the three classes. The second aspect of the research is to identify splitting patterns in the second heart sound, which consists of two acoustic components due to the closure of the aortic valve and pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as 'split'. The split normally varies in duration over the respiratory cycle. In certain pathologies such as the ASD, the split becomes fixed over the respiration cycle. A technique based on adaptive signal decomposition is developed to

Improving resolution of crosswell seismic section based on time-frequency analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, H.; Li, Y.

1994-12-31

According to signal theory, to improve resolution of seismic section is to extend high-frequency band of seismic signal. In cross-well section, sonic log can be regarded as a reliable source providing high-frequency information to the trace near the borehole. In such case, what to do is to introduce this high-frequency information into the whole section. However, neither traditional deconvolution algorithms nor some new inversion methods such as BCI (Broad Constraint Inversion) are satisfied because of high-frequency noise and nonuniqueness of inversion results respectively. To overcome their disadvantages, this paper presents a new algorithm based on Time-Frequency Analysis (TFA) technology whichmore » has been increasingly received much attention as an useful signal analysis too. Practical applications show that the new method is a stable scheme to improve resolution of cross-well seismic section greatly without decreasing Signal to Noise Ratio (SNR).« less

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of Signals from Gravitational Wave Detectors with Instantaneous Time-Frequency Maps

NASA Technical Reports Server (NTRS)

Stroeer, A.; Blackburn, L.; Camp, J.

2011-01-01

Gravitational wave astronomy relies on the use of multiple detectors, so that coincident detections may distinguish real signals from instrumental artifacts, and also so that relative timing of signals can provide the sky position of sources. We show that the comparison of instantaneous time-frequency and time-amplitude maps provided by the Hilbert-Huang Transform (HHT) can be used effectively for relative signal timing of common signals, to discriminate between the case of identical coincident signals and random noise coincidences and to provide a classification of signals based on their time-frequency trajectories. The comparison is done with a X(sup 2) goodness-offit method which includes contributions from both the instantaneous amplitude and frequency components of the HHT to match two signals in the time domain. This approach naturally allows the analysis of waveforms with strong frequency modulation.

Hydrocarbon Reservoir Prediction Using Bi-Gaussian S Transform Based Time-Frequency Analysis Approach

NASA Astrophysics Data System (ADS)

Cheng, Z.; Chen, Y.; Liu, Y.; Liu, W.; Zhang, G.

2015-12-01

Among those hydrocarbon reservoir detection techniques, the time-frequency analysis based approach is one of the most widely used approaches because of its straightforward indication of low-frequency anomalies from the time-frequency maps, that is to say, the low-frequency bright spots usually indicate the potential hydrocarbon reservoirs. The time-frequency analysis based approach is easy to implement, and more importantly, is usually of high fidelity in reservoir prediction, compared with the state-of-the-art approaches, and thus is of great interest to petroleum geologists, geophysicists, and reservoir engineers. The S transform has been frequently used in obtaining the time-frequency maps because of its better performance in controlling the compromise between the time and frequency resolutions than the alternatives, such as the short-time Fourier transform, Gabor transform, and continuous wavelet transform. The window function used in the majority of previous S transform applications is the symmetric Gaussian window. However, one problem with the symmetric Gaussian window is the degradation of time resolution in the time-frequency map due to the long front taper. In our study, a bi-Gaussian S transform that substitutes the symmetric Gaussian window with an asymmetry bi-Gaussian window is proposed to analyze the multi-channel seismic data in order to predict hydrocarbon reservoirs. The bi-Gaussian window introduces asymmetry in the resultant time-frequency spectrum, with time resolution better in the front direction, as compared with the back direction. It is the first time that the bi-Gaussian S transform is used for analyzing multi-channel post-stack seismic data in order to predict hydrocarbon reservoirs since its invention in 2003. The superiority of the bi-Gaussian S transform over traditional S transform is tested on a real land seismic data example. The performance shows that the enhanced temporal resolution can help us depict more clearly the edge of the

Feature extraction across individual time series observations with spikes using wavelet principal component analysis.

PubMed

Røislien, Jo; Winje, Brita

2013-09-20

Clinical studies frequently include repeated measurements of individuals, often for long periods. We present a methodology for extracting common temporal features across a set of individual time series observations. In particular, the methodology explores extreme observations within the time series, such as spikes, as a possible common temporal phenomenon. Wavelet basis functions are attractive in this sense, as they are localized in both time and frequency domains simultaneously, allowing for localized feature extraction from a time-varying signal. We apply wavelet basis function decomposition of individual time series, with corresponding wavelet shrinkage to remove noise. We then extract common temporal features using linear principal component analysis on the wavelet coefficients, before inverse transformation back to the time domain for clinical interpretation. We demonstrate the methodology on a subset of a large fetal activity study aiming to identify temporal patterns in fetal movement (FM) count data in order to explore formal FM counting as a screening tool for identifying fetal compromise and thus preventing adverse birth outcomes. Copyright © 2013 John Wiley & Sons, Ltd.

Advance in ERG Analysis: From Peak Time and Amplitude to Frequency, Power, and Energy

PubMed Central

Lina, Jean-Marc; Lachapelle, Pierre

2014-01-01

Purpose. To compare time domain (TD: peak time and amplitude) analysis of the human photopic electroretinogram (ERG) with measures obtained in the frequency domain (Fourier analysis: FA) and in the time-frequency domain (continuous (CWT) and discrete (DWT) wavelet transforms). Methods. Normal ERGs (n = 40) were analyzed using traditional peak time and amplitude measurements of the a- and b-waves in the TD and descriptors extracted from FA, CWT, and DWT. Selected descriptors were also compared in their ability to monitor the long-term consequences of disease process. Results. Each method extracted relevant information but had distinct limitations (i.e., temporal and frequency resolutions). The DWT offered the best compromise by allowing us to extract more relevant descriptors of the ERG signal at the cost of lesser temporal and frequency resolutions. Follow-ups of disease progression were more prolonged with the DWT (max 29 years compared to 13 with TD). Conclusions. Standardized time domain analysis of retinal function should be complemented with advanced DWT descriptors of the ERG. This method should allow more sensitive/specific quantifications of ERG responses, facilitate follow-up of disease progression, and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to time domain measurements. PMID:25061605

Real-time, high frequency QRS electrocardiograph

NASA Technical Reports Server (NTRS)

Schlegel, Todd T. (Inventor); DePalma, Jude L. (Inventor); Moradi, Saeed (Inventor)

2006-01-01

Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as RAZs. RAZs are displayed as go, no-go signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.

Time-frequency analysis in optical coherence tomography for technical objects examination

NASA Astrophysics Data System (ADS)

StrÄ kowski, Marcin R.; Kraszewski, Maciej; Trojanowski, Michał; Pluciński, Jerzy

2014-05-01

Optical coherence tomography (OCT) is one of the most advanced optical measurement techniques for complex structure visualization. The advantages of OCT have been used for surface and subsurface defect detection in composite materials, polymers, ceramics, non-metallic protective coatings, and many more. Our research activity has been focused on timefrequency spectroscopic analysis in OCT. It is based on time resolved spectral analysis of the backscattered optical signal delivered by the OCT. The time-frequency method gives spectral characteristic of optical radiation backscattered or backreflected from the particular points inside the tested device. This provides more information about the sample, which are useful for further analysis. Nowadays, the applications of spectroscopic analysis for composite layers characterization or tissue recognition have been reported. During our studies we have found new applications of spectroscopic analysis. We have used this method for thickness estimation of thin films, which are under the resolution of OCT. Also, we have combined the spectroscopic analysis with polarization sensitive OCT (PS-OCT). This approach enables to obtain a multiorder retardation value directly and may become a breakthrough in PS-OCT measurements of highly birefringent media. In this work, we present the time-frequency spectroscopic algorithms and their applications for OCT. Also, the theoretical simulations and measurement validation of this method are shown.

Analysis of click-evoked otoacoustic emissions by concentration of frequency and time: Preliminary results from normal hearing and Ménière's disease ears

NASA Astrophysics Data System (ADS)

Liu, Tzu-Chi; Wu, Hau-Tieng; Chen, Ya-Hui; Chen, Ya-Han; Fang, Te-Yung; Wang, Pa-Chun; Liu, Yi-Wen

2018-05-01

The presence of click-evoked (CE) otoacoustic emissions (OAEs) has been clinically accepted as an indicator of normal cochlear processing of sounds. For treatment and diagnostic purposes, however, clinicians do not typically pay attention to the detailed spectrum and waveform of CEOAEs. A possible reason is due to the lack of noise-robust signal processing tools to estimate physiologically meaningful time-frequency properties of CEOAEs, such as the latency of spectral components. In this on-going study, we applied a modern tool called concentration of frequency and time (ConceFT, [1]) to analyze CEOAE waveforms. Randomly combined orthogonal functions are used as windowing functions for time-frequency analysis. The resulting spectrograms are subject to nonlinear time-frequency reassignment so as to enhance the concentration of time-varying sinusoidal components. The results after reassignment could be further averaged across the random choice of windows. CEOAE waveforms are acquired by a linear averaging paradigm, and longitudinal data are currently being collected from patients with Ménière's disease (MD) and a control group of normal hearing subjects. When CEOAE is present, the ConceFT plots show traces of decreasing but fluctuating instantaneous frequency against time. For comparison purposes, same processing methods are also applied to analyze CEOAE data from cochlear mechanics simulation.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

A new time-frequency method for identification and classification of ball bearing faults

NASA Astrophysics Data System (ADS)

Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel

2017-06-01

In order to fault diagnosis of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery vibration signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing faults. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, fault severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing fault diagnosis system.

Time-dependent reliability analysis of ceramic engine components

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.

1993-01-01

The computer program CARES/LIFE calculates the time-dependent reliability of monolithic ceramic components subjected to thermomechanical and/or proof test loading. This program is an extension of the CARES (Ceramics Analysis and Reliability Evaluation of Structures) computer program. CARES/LIFE accounts for the phenomenon of subcritical crack growth (SCG) by utilizing either the power or Paris law relations. The two-parameter Weibull cumulative distribution function is used to characterize the variation in component strength. The effects of multiaxial stresses are modeled using either the principle of independent action (PIA), the Weibull normal stress averaging method (NSA), or the Batdorf theory. Inert strength and fatigue parameters are estimated from rupture strength data of naturally flawed specimens loaded in static, dynamic, or cyclic fatigue. Two example problems demonstrating proof testing and fatigue parameter estimation are given.

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

Time and frequency domain analysis of sampled data controllers via mixed operation equations

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1981-01-01

Specification of the mathematical equations required to define the dynamic response of a linear continuous plant, subject to sampled data control, is complicated by the fact that the digital components of the control system cannot be modeled via linear ordinary differential equations. This complication can be overcome by introducing two new mathematical operations; namely, the operation of zero order hold and digial delay. It is shown that by direct utilization of these operations, a set of linear mixed operation equations can be written and used to define the dynamic response characteristics of the controlled system. It also is shown how these linear mixed operation equations lead, in an automatable manner, directly to a set of finite difference equations which are in a format compatible with follow on time and frequency domain analysis methods.

Externalizing psychopathology and gain-loss feedback in a simulated gambling task: dissociable components of brain response revealed by time-frequency analysis.

PubMed

Bernat, Edward M; Nelson, Lindsay D; Steele, Vaughn R; Gehring, William J; Patrick, Christopher J

2011-05-01

Externalizing is a broad construct that reflects propensity toward a variety of impulse control problems, including antisocial personality disorder and substance use disorders. Two event-related potential responses known to be reduced among individuals high in externalizing proneness are the P300, which reflects postperceptual processing of a stimulus, and the error-related negativity (ERN), which indexes performance monitoring based on endogenous representations. In the current study, the authors used a simulated gambling task to examine the relation between externalizing proneness and the feedback-related negativity (FRN), a brain response that indexes performance monitoring related to exogenous cues, which is thought to be highly related to the ERN. Time-frequency (TF) analysis was used to disentangle the FRN from the accompanying P300 response to feedback cues by parsing the overall feedback-locked potential into distinctive theta (4-7 Hz) and delta (<3 Hz) TF components. Whereas delta-P300 amplitude was reduced among individuals high in externalizing proneness, theta-FRN response was unrelated to externalizing. These findings suggest that in contrast with previously reported deficits in endogenously based performance monitoring (as indexed by the ERN), individuals prone to externalizing problems show intact monitoring of exogenous cues (as indexed by the FRN). The results also contribute to a growing body of evidence indicating that the P300 is attenuated across a broad range of task conditions in high-externalizing individuals.

Graph Frequency Analysis of Brain Signals

PubMed Central

Huang, Weiyu; Goldsberry, Leah; Wymbs, Nicholas F.; Grafton, Scott T.; Bassett, Danielle S.; Ribeiro, Alejandro

2016-01-01

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity. PMID:28439325

Cross-Modulated Amplitudes and Frequencies Characterize Interacting Components in Complex Systems

NASA Astrophysics Data System (ADS)

Gans, Fabian; Schumann, Aicko Y.; Kantelhardt, Jan W.; Penzel, Thomas; Fietze, Ingo

2009-03-01

The dynamics of complex systems is characterized by oscillatory components on many time scales. To study the interactions between these components we analyze the cross modulation of their instantaneous amplitudes and frequencies, separating synchronous and antisynchronous modulation. We apply our novel technique to brain-wave oscillations in the human electroencephalogram and show that interactions between the α wave and the δ or β wave oscillators as well as spatial interactions can be quantified and related with physiological conditions (e.g., sleep stages). Our approach overcomes the limitation to oscillations with similar frequencies and enables us to quantify directly nonlinear effects such as positive or negative frequency modulation.

Highly sensitive index of sympathetic activity based on time-frequency spectral analysis of electrodermal activity.

PubMed

Posada-Quintero, Hugo F; Florian, John P; Orjuela-Cañón, Álvaro D; Chon, Ki H

2016-09-01

Time-domain indices of electrodermal activity (EDA) have been used as a marker of sympathetic tone. However, they often show high variation between subjects and low consistency, which has precluded their general use as a marker of sympathetic tone. To examine whether power spectral density analysis of EDA can provide more consistent results, we recently performed a variety of sympathetic tone-evoking experiments (43). We found significant increase in the spectral power in the frequency range of 0.045 to 0.25 Hz when sympathetic tone-evoking stimuli were induced. The sympathetic tone assessed by the power spectral density of EDA was found to have lower variation and more sensitivity for certain, but not all, stimuli compared with the time-domain analysis of EDA. We surmise that this lack of sensitivity in certain sympathetic tone-inducing conditions with time-invariant spectral analysis of EDA may lie in its inability to characterize time-varying dynamics of the sympathetic tone. To overcome the disadvantages of time-domain and time-invariant power spectral indices of EDA, we developed a highly sensitive index of sympathetic tone, based on time-frequency analysis of EDA signals. Its efficacy was tested using experiments designed to elicit sympathetic dynamics. Twelve subjects underwent four tests known to elicit sympathetic tone arousal: cold pressor, tilt table, stand test, and the Stroop task. We hypothesize that a more sensitive measure of sympathetic control can be developed using time-varying spectral analysis. Variable frequency complex demodulation, a recently developed technique for time-frequency analysis, was used to obtain spectral amplitudes associated with EDA. We found that the time-varying spectral frequency band 0.08-0.24 Hz was most responsive to stimulation. Spectral power for frequencies higher than 0.24 Hz were determined to be not related to the sympathetic dynamics because they comprised less than 5% of the total power. The mean value of time

Method of Real-Time Principal-Component Analysis

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu

2005-01-01

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Time/frequency systems.

DOT National Transportation Integrated Search

1971-06-01

The report summarizes the work performed at DOT/TSC on the Time/Frequency ATC System study project. Principal emphasis in this report is given to the evaluation and analysis of the technological risk areas. A survey and description of proposed T/F sy...

Correlation between audible noise and corona current generated by AC corona discharge in time and frequency domains

NASA Astrophysics Data System (ADS)

Li, Xuebao; Wang, Jing; Li, Yinfei; Zhang, Qian; Lu, Tiebing; Cui, Xiang

2018-06-01

Corona-generated audible noise is induced by the collisions between space charges and air molecules. It has been proven that there is a close correlation between audible noise and corona current from DC corona discharge. Analysis on the correlation between audible noise and corona current can promote the cognition of the generation mechanism of corona discharge. In this paper, time-domain waveforms of AC corona-generated audible noise and corona current are measured simultaneously. The one-to-one relationship between sound pressure pulses and corona current pulses can be found and is used to remove the interferences from background noise. After the interferences are removed, the linear correlated relationships between sound pressure pulse amplitude and corona current pulse amplitude are obtained through statistical analysis. Besides, frequency components at the harmonics of power frequency (50 Hz) can be found both in the frequency spectrums of audible noise and corona current through frequency analysis. Furthermore, the self-correlation relationships between harmonic components below 400 Hz with the 50 Hz component are analyzed for audible noise and corona current and corresponding empirical formulas are proposed to calculate the harmonic components based on the 50 Hz component. Finally, based on the AC corona discharge process and generation mechanism of audible noise and corona current, the correlation between audible noise and corona current in time domain and frequency domain are interpreted qualitatively. Besides, with the aid of analytical expressions of periodic square waves, sound pressure pulses, and corona current pulses, the modulation effects from the AC voltage on the pulse trains are used to interpret the generation of the harmonic components of audible noise and corona current.

A time-frequency analysis method to obtain stable estimates of magnetotelluric response function based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Cai, Jianhua

2017-05-01

The time-frequency analysis method represents signal as a function of time and frequency, and it is considered a powerful tool for handling arbitrary non-stationary time series by using instantaneous frequency and instantaneous amplitude. It also provides a possible alternative to the analysis of the non-stationary magnetotelluric (MT) signal. Based on the Hilbert-Huang transform (HHT), a time-frequency analysis method is proposed to obtain stable estimates of the magnetotelluric response function. In contrast to conventional methods, the response function estimation is performed in the time-frequency domain using instantaneous spectra rather than in the frequency domain, which allows for imaging the response parameter content as a function of time and frequency. The theory of the method is presented and the mathematical model and calculation procedure, which are used to estimate response function based on HHT time-frequency spectrum, are discussed. To evaluate the results, response function estimates are compared with estimates from a standard MT data processing method based on the Fourier transform. All results show that apparent resistivities and phases, which are calculated from the HHT time-frequency method, are generally more stable and reliable than those determined from the simple Fourier analysis. The proposed method overcomes the drawbacks of the traditional Fourier methods, and the resulting parameter minimises the estimation bias caused by the non-stationary characteristics of the MT data.

Full waveform inversion in the frequency domain using classified time-domain residual wavefields

NASA Astrophysics Data System (ADS)

Son, Woohyun; Koo, Nam-Hyung; Kim, Byoung-Yeop; Lee, Ho-Young; Joo, Yonghwan

2017-04-01

We perform the acoustic full waveform inversion in the frequency domain using residual wavefields that have been separated in the time domain. We sort the residual wavefields in the time domain according to the order of absolute amplitudes. Then, the residual wavefields are separated into several groups in the time domain. To analyze the characteristics of the residual wavefields, we compare the residual wavefields of conventional method with those of our residual separation method. From the residual analysis, the amplitude spectrum obtained from the trace before separation appears to have little energy at the lower frequency bands. However, the amplitude spectrum obtained from our strategy is regularized by the separation process, which means that the low-frequency components are emphasized. Therefore, our method helps to emphasize low-frequency components of residual wavefields. Then, we generate the frequency-domain residual wavefields by taking the Fourier transform of the separated time-domain residual wavefields. With these wavefields, we perform the gradient-based full waveform inversion in the frequency domain using back-propagation technique. Through a comparison of gradient directions, we confirm that our separation method can better describe the sub-salt image than the conventional approach. The proposed method is tested on the SEG/EAGE salt-dome model. The inversion results show that our algorithm is better than the conventional gradient based waveform inversion in the frequency domain, especially for deeper parts of the velocity model.

A time-frequency approach for the analysis of normal and arrhythmia cardiac signals.

PubMed

Mahmoud, Seedahmed S; Fang, Qiang; Davidović, Dragomir M; Cosic, Irena

2006-01-01

Previously, electrocardiogram (ECG) signals have been analyzed in either a time-indexed or spectral form. The reality, is that the ECG and all other biological signals belong to the family of multicomponent nonstationary signals. Due to this reason, the use of time-frequency analysis can be unavoidable for these signals. The Husimi and Wigner distributions are normally used in quantum mechanics for phase space representations of the wavefunction. In this paper, we introduce the Husimi distribution (HD) to analyze the normal and abnormal ECG signals in time-frequency domain. The abnormal cardiac signal was taken from a patient with supraventricular arrhythmia. Simulation results show that the HD has a good performance in the analysis of the ECG signals comparing with the Wigner-Ville distribution (WVD).

Complex demodulation in VLBI estimation of high frequency Earth rotation components

NASA Astrophysics Data System (ADS)

Böhm, S.; Brzeziński, A.; Schuh, H.

2012-12-01

The spectrum of high frequency Earth rotation variations contains strong harmonic signal components mainly excited by ocean tides along with much weaker non-harmonic fluctuations driven by irregular processes like the diurnal thermal tides in the atmosphere and oceans. In order to properly investigate non-harmonic phenomena a representation in time domain is inevitable. We present a method, operating in time domain, which is easily applicable within Earth rotation estimation from Very Long Baseline Interferometry (VLBI). It enables the determination of diurnal and subdiurnal variations, and is still effective with merely diurnal parameter sampling. The features of complex demodulation are used in an extended parameterization of polar motion and universal time which was implemented into a dedicated version of the Vienna VLBI Software VieVS. The functionality of the approach was evaluated by comparing amplitudes and phases of harmonic variations at tidal periods (diurnal/semidiurnal), derived from demodulated Earth rotation parameters (ERP), estimated from hourly resolved VLBI ERP time series and taken from a recently published VLBI ERP model to the terms of the conventional model for ocean tidal effects in Earth rotation recommended by the International Earth Rotation and Reference System Service (IERS). The three sets of tidal terms derived from VLBI observations extensively agree among each other within the three-sigma level of the demodulation approach, which is below 6 μas for polar motion and universal time. They also coincide in terms of differences to the IERS model, where significant deviations primarily for several major tidal terms are apparent. An additional spectral analysis of the as well estimated demodulated ERP series of the ter- and quarterdiurnal frequency bands did not reveal any significant signal structure. The complex demodulation applied in VLBI parameter estimation could be demonstrated a suitable procedure for the reliable reproduction of

Analysis of cardiac signals using spatial filling index and time-frequency domain

PubMed Central

Faust, Oliver; Acharya U, Rajendra; Krishnan, SM; Min, Lim Choo

2004-01-01

Background Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. Methods This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. Results This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. Conclusion Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%. PMID:15361254

Application of Time-Frequency Representations To Non-Stationary Radar Cross Section

DTIC Science & Technology

2009-03-01

The three- dimensional plot produced by a TFR allows one to determine which spectral components of a signal vary with time [25... a range bin ( of width cT 2 ) from the stepped frequency waveform. 2. Cancel the clutter (stationary components) by zeroing out points associated with ...generating an infinite number of bilinear Time Frequency distributions based on a generalized equation and a change- able

Frequency domain analysis of knock images

NASA Astrophysics Data System (ADS)

Qi, Yunliang; He, Xin; Wang, Zhi; Wang, Jianxin

2014-12-01

High speed imaging-based knock analysis has mainly focused on time domain information, e.g. the spark triggered flame speed, the time when end gas auto-ignition occurs and the end gas flame speed after auto-ignition. This study presents a frequency domain analysis on the knock images recorded using a high speed camera with direct photography in a rapid compression machine (RCM). To clearly visualize the pressure wave oscillation in the combustion chamber, the images were high-pass-filtered to extract the luminosity oscillation. The luminosity spectrum was then obtained by applying fast Fourier transform (FFT) to three basic colour components (red, green and blue) of the high-pass-filtered images. Compared to the pressure spectrum, the luminosity spectra better identify the resonant modes of pressure wave oscillation. More importantly, the resonant mode shapes can be clearly visualized by reconstructing the images based on the amplitudes of luminosity spectra at the corresponding resonant frequencies, which agree well with the analytical solutions for mode shapes of gas vibration in a cylindrical cavity.

Cardiorespiratory dynamic response to mental stress: a multivariate time-frequency analysis.

PubMed

Widjaja, Devy; Orini, Michele; Vlemincx, Elke; Van Huffel, Sabine

2013-01-01

Mental stress is a growing problem in our society. In order to deal with this, it is important to understand the underlying stress mechanisms. In this study, we aim to determine how the cardiorespiratory interactions are affected by mental arithmetic stress and attention. We conduct cross time-frequency (TF) analyses to assess the cardiorespiratory coupling. In addition, we introduce partial TF spectra to separate variations in the RR interval series that are linearly related to respiration from RR interval variations (RRV) that are not related to respiration. The performance of partial spectra is evaluated in two simulation studies. Time-varying parameters, such as instantaneous powers and frequencies, are derived from the computed spectra. Statistical analysis is carried out continuously in time to evaluate the dynamic response to mental stress and attention. The results show an increased heart and respiratory rate during stress and attention, compared to a resting condition. Also a fast reduction in vagal activity is noted. The partial TF analysis reveals a faster reduction of RRV power related to (3 s) than unrelated to (30 s) respiration, demonstrating that the autonomic response to mental stress is driven by mechanisms characterized by different temporal scales.

Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

PubMed Central

2013-01-01

Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109

Low-Frequency Components in Rat Pial Arteriolar Rhythmic Diameter Changes.

PubMed

Lapi, Dominga; Mastantuono, Teresa; Di Maro, Martina; Varanini, Maurizio; Colantuoni, Antonio

2017-01-01

This study aimed to analyze the frequency components present in spontaneous rhythmic diameter changes in rat pial arterioles. Pial microcirculation was visualized by fluorescence microscopy. Rhythmic luminal variations were evaluated via computer-assisted methods. Spectral analysis was carried out on 30-min recordings under baseline conditions and after administration of acetylcholine (Ach), papaverine (Pap), Nω-nitro-L-arginine (L-NNA) prior to Ach, indomethacin (INDO), INDO prior to Ach, charybdotoxin and apamin, and charybdotoxin and apamin prior to Ach. Under baseline conditions all arteriolar orders showed 3 frequency components in the ranges of 0.0095-0.02, 0.02-0.06, and 0.06-0.2 Hz, another 2 in the ranges of 0.2-2.0 and 2.5-4.5 Hz, and another ultra-low-frequency component in the range of 0.001-0.0095 Hz. Ach caused a significant increase in the spectral density of the frequency components in the range of 0.001-0.2 Hz. Pap was able to slightly increase spectral density in the ranges of 0.001-0.0095 and 0.0095-0.02 Hz. L-NNA mainly attenuated arteriolar responses to Ach. INDO prior to Ach did not affect the endothelial response to Ach. Charybdotoxin and apamin, suggested as endothelium-derived hyperpolarizing factor inhibitors, reduced spectral density in the range of 0.001-0.0095 Hz before and after Ach administration. In conclusion, regulation of the blood flow distribution is due to several mechanisms, one of which is affected by charibdotoxin and apamin, modulating the vascular tone. © 2017 S. Karger AG, Basel.

Examining the neural correlates of depressive and motor symptoms in Parkinson's disease using Frequency Component Analysis (FCA)

NASA Astrophysics Data System (ADS)

Song, Xiaopeng; Hu, Xiao; Zhou, Shuqin; Liu, Weiguo; Liu, Yijun; Zhu, Huaiqiu; Gao, Jia-Hong

2016-03-01

Depression is prevalent among patients with Parkinson's disease (PD); however the pathophysiology of depression in PD is not well understood. In order to investigate how depression and motor impairments differentially and interactively affect specific brain regions in Parkinson's disease, we introduced a new data driven approach, namely Frequency Component Analysis (FCA), to decompose the resting-state functional magnetic resonance imaging data of 59 subjects with Parkinson's disease into different frequency bands. We then evaluated the main effects of motor severity and depression, and their interactive effects on the BOLD-fMRI signal oscillation energy in these specific frequency components. Our results show that the severity of motor symptoms is more negatively correlated with energy in the frequency band of 0.10-0.25Hz in the bilateral thalamus (THA), but more positively correlated with energy in the frequency band of 0.01-0.027Hz in the bilateral postcentral gyrus (PoCG). In contrast, the severity of depressive symptoms is more associated with the higher energy of the high frequency oscillations (>0.1Hz) but lower energy of 0.01-0.027Hz in the bilateral subgenual gyrus (SGC). Importantly, the interaction between motor and depressive symptoms is negatively correlated with the energy of high frequency oscillations (>0.1Hz) in the substantia nigra/ventral tegmental area (SN/VTA), left hippocampus (HIPP), left inferior orbital frontal cortex (OFC), and left temporoparietal junction (TPJ), but positively correlated with the energy of 0.02-0.05Hz in the left inferior OFC, left TPJ, left inferior temporal gyrus (ITG), and bilateral cerebellum. These results demonstrated that FCA was a promising method in interrogating the neurophysiological implications of different brain rhythms. Our findings further revealed the neural bases underlying the interactions as well the dissociations between motor and depressive symptoms in Parkinson's disease.

Universal distribution of component frequencies in biological and technological systems

PubMed Central

Pang, Tin Yau; Maslov, Sergei

2013-01-01

Bacterial genomes and large-scale computer software projects both consist of a large number of components (genes or software packages) connected via a network of mutual dependencies. Components can be easily added or removed from individual systems, and their use frequencies vary over many orders of magnitude. We study this frequency distribution in genomes of ∼500 bacterial species and in over 2 million Linux computers and find that in both cases it is described by the same scale-free power-law distribution with an additional peak near the tail of the distribution corresponding to nearly universal components. We argue that the existence of a power law distribution of frequencies of components is a general property of any modular system with a multilayered dependency network. We demonstrate that the frequency of a component is positively correlated with its dependency degree given by the total number of upstream components whose operation directly or indirectly depends on the selected component. The observed frequency/dependency degree distributions are reproduced in a simple mathematically tractable model introduced and analyzed in this study. PMID:23530195

Phenomenological analysis of medical time series with regular and stochastic components

NASA Astrophysics Data System (ADS)

Timashev, Serge F.; Polyakov, Yuriy S.

2007-06-01

Flicker-Noise Spectroscopy (FNS), a general approach to the extraction and parameterization of resonant and stochastic components contained in medical time series, is presented. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions), which complement the information of each other. The structural function stochastic component is formed exclusively by "jumps" of the dynamic variable while the power spectrum stochastic component is formed by both spikes and "jumps" on every level of the hierarchy. The information "passport" characteristics that are determined by fitting the derived expressions to the experimental variations for the stochastic components of power spectra and structural functions are interpreted as the correlation times and parameters that describe the rate of "memory loss" on these correlation time intervals for different irregularities. The number of the extracted parameters is determined by the requirements of the problem under study. Application of this approach to the analysis of tremor velocity signals for a Parkinsonian patient is discussed.

Quantitative analysis of sleep EEG microstructure in the time-frequency domain.

PubMed

De Carli, Fabrizio; Nobili, Lino; Beelke, Manolo; Watanabe, Tsuyoshi; Smerieri, Arianna; Parrino, Liborio; Terzano, Mario Giovanni; Ferrillo, Franco

2004-06-30

A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time-frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time-frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.

Components of cross-frequency modulation in health and disease.

PubMed

Allen, Elena A; Liu, Jingyu; Kiehl, Kent A; Gelernter, Joel; Pearlson, Godfrey D; Perrone-Bizzozero, Nora I; Calhoun, Vince D

2011-01-01

The cognitive deficits associated with schizophrenia are commonly believed to arise from the abnormal temporal integration of information, however a quantitative approach to assess network coordination is lacking. Here, we propose to use cross-frequency modulation (cfM), the dependence of local high-frequency activity on the phase of widespread low-frequency oscillations, as an indicator of network coordination and functional integration. In an exploratory analysis based on pre-existing data, we measured cfM from multi-channel EEG recordings acquired while schizophrenia patients (n = 47) and healthy controls (n = 130) performed an auditory oddball task. Novel application of independent component analysis (ICA) to modulation data delineated components with specific spatial and spectral profiles, the weights of which showed covariation with diagnosis. Global cfM was significantly greater in healthy controls (F(1,175) = 9.25, P < 0.005), while modulation at fronto-temporal electrodes was greater in patients (F(1,175) = 17.5, P < 0.0001). We further found that the weights of schizophrenia-relevant components were associated with genetic polymorphisms at previously identified risk loci. Global cfM decreased with copies of 957C allele in the gene for the dopamine D2 receptor (r = -0.20, P < 0.01) across all subjects. Additionally, greater "aberrant" fronto-temporal modulation in schizophrenia patients was correlated with several polymorphisms in the gene for the α2-subunit of the GABA(A) receptor (GABRA2) as well as the total number of risk alleles in GABRA2 (r = 0.45, P < 0.01). Overall, our results indicate great promise for this approach in establishing patterns of cfM in health and disease and elucidating the roles of oscillatory interactions in functional connectivity.

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Detection of changes of high-frequency activity by statistical time-frequency analysis in epileptic spikes

PubMed Central

Kobayashi, Katsuhiro; Jacobs, Julia; Gotman, Jean

2013-01-01

Objective A novel type of statistical time-frequency analysis was developed to elucidate changes of high-frequency EEG activity associated with epileptic spikes. Methods The method uses the Gabor Transform and detects changes of power in comparison to background activity using t-statistics that are controlled by the false discovery rate (FDR) to correct type I error of multiple testing. The analysis was applied to EEGs recorded at 2000 Hz from three patients with mesial temporal lobe epilepsy. Results Spike-related increase of high-frequency oscillations (HFOs) was clearly shown in the FDR-controlled t-spectra: it was most dramatic in spikes recorded from the hippocampus when the hippocampus was the seizure onset zone (SOZ). Depression of fast activity was observed immediately after the spikes, especially consistently in the discharges from the hippocampal SOZ. It corresponded to the slow wave part in case of spike-and-slow-wave complexes, but it was noted even in spikes without apparent slow waves. In one patient, a gradual increase of power above 200 Hz preceded spikes. Conclusions FDR-controlled t-spectra clearly detected the spike-related changes of HFOs that were unclear in standard power spectra. Significance We developed a promising tool to study the HFOs that may be closely linked to the pathophysiology of epileptogenesis. PMID:19394892

Motor monitoring method and apparatus using high frequency current components

DOEpatents

Casada, D.A.

1996-05-21

A motor current analysis method and apparatus for monitoring electrical-motor-driven devices are disclosed. The method and apparatus utilize high frequency portions of the motor current spectra to evaluate the condition of the electric motor and the device driven by the electric motor. The motor current signal produced as a result of an electric motor is monitored and the low frequency components of the signal are removed by a high-pass filter. The signal is then analyzed to determine the condition of the electrical motor and the driven device. 16 figs.

Motor monitoring method and apparatus using high frequency current components

DOEpatents

Casada, Donald A.

1996-01-01

A motor current analysis method and apparatus for monitoring electrical-motor-driven devices. The method and apparatus utilize high frequency portions of the motor current spectra to evaluate the condition of the electric motor and the device driven by the electric motor. The motor current signal produced as a result of an electric motor is monitored and the low frequency components of the signal are removed by a high-pass filter. The signal is then analyzed to determine the condition of the electrical motor and the driven device.

Comparison of time-frequency distribution techniques for analysis of spinal somatosensory evoked potential.

PubMed

Hu, Y; Luk, K D; Lu, W W; Holmes, A; Leong, J C

2001-05-01

Spinal somatosensory evoked potential (SSEP) has been employed to monitor the integrity of the spinal cord during surgery. To detect both temporal and spectral changes in SSEP waveforms, an investigation of the application of time-frequency analysis (TFA) techniques was conducted. SSEP signals from 30 scoliosis patients were analysed using different techniques; short time Fourier transform (STFT), Wigner-Ville distribution (WVD), Choi-Williams distribution (CWD), cone-shaped distribution (CSD) and adaptive spectrogram (ADS). The time-frequency distributions (TFD) computed using these methods were assessed and compared with each other. WVD, ADS, CSD and CWD showed better resolution than STFT. Comparing normalised peak widths, CSD showed the sharpest peak width (0.13+/-0.1) in the frequency dimension, and a mean peak width of 0.70+/-0.12 in the time dimension. Both WVD and CWD produced cross-term interference, distorting the TFA distribution, but this was not seen with CSD and ADS. CSD appeared to give a lower mean peak power bias (10.3%+/-6.2%) than ADS (41.8%+/-19.6%). Application of the CSD algorithm showed both good resolution and accurate spectrograms, and is therefore recommended as the most appropriate TFA technique for the analysis of SSEP signals.

Time-Frequency Domain Analysis of Helicopter Transmission Vibration

DTIC Science & Technology

1991-08-01

Wigner - Ville distribution ( WVD ) have be reported, including speech...FREQUENCY DISTRIBUTIONS . 8 6. THE WIGNER - VILLE DISTRIBUTION . 9 6.1 History. 9 6.2 Definition. 9 6.3 Discrete-Time/Frequency Wigner - Ville Distribution . 10...signals are examined to indicate how various forms of modulation are portrayed using the Wigner - Ville distribution . Practical examples A signal is

The very low-frequency band of heart rate variability represents the slow recovery component after a mental stress task.

PubMed

Usui, Harunobu; Nishida, Yusuke

2017-01-01

The very low-frequency (VLF) band of heart rate variability (HRV) has different characteristics compared with other HRV components. Here we investigated differences in HRV changes after a mental stress task. After the task, the high-frequency (HF) band and ratio of high- to low-frequency bands (LF/HF) immediately returned to baseline. We evaluated the characteristics of VLF band changes after a mental stress task. We hypothesized that the VLF band decreases during the Stroop color word task and there would be a delayed recovery for 2 h after the task (i.e., the VLF change would exhibit a "slow recovery"). Nineteen healthy, young subjects were instructed to rest for 10 min, followed by a Stroop color word task for 20 min. After the task, the subjects were instructed to rest for 120 min. For all subjects, R-R interval data were collected; analysis was performed for VLF, HF, and LF/HF ratio. HRV during the rest time and each 15-min interval of the recovery time were compared. An analysis of the covariance was performed to adjust for the HF band and LF/HF ratio as confounding variables of the VLF component. HF and VLF bands significantly decreased and the LF/HF ratio significantly increased during the task compared with those during rest time. During recovery, the VLF band was significantly decreased compared with the rest time. After the task, the HF band and LF/HF ratio immediately returned to baseline and were not significantly different from the resting values. After adjusting for HF and LF/HF ratio, the VLF band had significantly decreased compared with that during rest. The VLF band is the "slow recovery" component and the HF band and LF/HF ratio are the "quick recovery" components of HRV. This VLF characteristic may clarify the unexplained association of the VLF band in cardiovascular disease prevention.

Safety analytics for integrating crash frequency and real-time risk modeling for expressways.

PubMed

Wang, Ling; Abdel-Aty, Mohamed; Lee, Jaeyoung

2017-07-01

To find crash contributing factors, there have been numerous crash frequency and real-time safety studies, but such studies have been conducted independently. Until this point, no researcher has simultaneously analyzed crash frequency and real-time crash risk to test whether integrating them could better explain crash occurrence. Therefore, this study aims at integrating crash frequency and real-time safety analyses using expressway data. A Bayesian integrated model and a non-integrated model were built: the integrated model linked the crash frequency and the real-time models by adding the logarithm of the estimated expected crash frequency in the real-time model; the non-integrated model independently estimated the crash frequency and the real-time crash risk. The results showed that the integrated model outperformed the non-integrated model, as it provided much better model results for both the crash frequency and the real-time models. This result indicated that the added component, the logarithm of the expected crash frequency, successfully linked and provided useful information to the two models. This study uncovered few variables that are not typically included in the crash frequency analysis. For example, the average daily standard deviation of speed, which was aggregated based on speed at 1-min intervals, had a positive effect on crash frequency. In conclusion, this study suggested a methodology to improve the crash frequency and real-time models by integrating them, and it might inspire future researchers to understand crash mechanisms better. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time-Frequency Analysis And Pattern Recognition Using Singular Value Decomposition Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Lovell, Brian; White, Langford

1988-01-01

Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper.

Dating a tropical ice core by time-frequency analysis of ion concentration depth profiles

NASA Astrophysics Data System (ADS)

Gay, M.; De Angelis, M.; Lacoume, J.-L.

2014-09-01

Ice core dating is a key parameter for the interpretation of the ice archives. However, the relationship between ice depth and ice age generally cannot be easily established and requires the combination of numerous investigations and/or modelling efforts. This paper presents a new approach to ice core dating based on time-frequency analysis of chemical profiles at a site where seasonal patterns may be significantly distorted by sporadic events of regional importance, specifically at the summit area of Nevado Illimani (6350 m a.s.l.), located in the eastern Bolivian Andes (16°37' S, 67°46' W). We used ion concentration depth profiles collected along a 100 m deep ice core. The results of Fourier time-frequency and wavelet transforms were first compared. Both methods were applied to a nitrate concentration depth profile. The resulting chronologies were checked by comparison with the multi-proxy year-by-year dating published by de Angelis et al. (2003) and with volcanic tie points. With this first experiment, we demonstrated the efficiency of Fourier time-frequency analysis when tracking the nitrate natural variability. In addition, we were able to show spectrum aliasing due to under-sampling below 70 m. In this article, we propose a method of de-aliasing which significantly improves the core dating in comparison with annual layer manual counting. Fourier time-frequency analysis was applied to concentration depth profiles of seven other ions, providing information on the suitability of each of them for the dating of tropical Andean ice cores.

Monitoring groundwater-surface water interaction using time-series and time-frequency analysis of transient three-dimensional electrical resistivity changes

USGS Publications Warehouse

Johnson, Timothy C.; Slater, Lee D.; Ntarlagiannis, Dimitris; Day-Lewis, Frederick D.; Elwaseif, Mehrez

2012-01-01

Time-lapse resistivity imaging is increasingly used to monitor hydrologic processes. Compared to conventional hydrologic measurements, surface time-lapse resistivity provides superior spatial coverage in two or three dimensions, potentially high-resolution information in time, and information in the absence of wells. However, interpretation of time-lapse electrical tomograms is complicated by the ever-increasing size and complexity of long-term, three-dimensional (3-D) time series conductivity data sets. Here we use 3-D surface time-lapse electrical imaging to monitor subsurface electrical conductivity variations associated with stage-driven groundwater-surface water interactions along a stretch of the Columbia River adjacent to the Hanford 300 near Richland, Washington, USA. We reduce the resulting 3-D conductivity time series using both time-series and time-frequency analyses to isolate a paleochannel causing enhanced groundwater-surface water interactions. Correlation analysis on the time-lapse imaging results concisely represents enhanced groundwater-surface water interactions within the paleochannel, and provides information concerning groundwater flow velocities. Time-frequency analysis using the Stockwell (S) transform provides additional information by identifying the stage periodicities driving groundwater-surface water interactions due to upstream dam operations, and identifying segments in time-frequency space when these interactions are most active. These results provide new insight into the distribution and timing of river water intrusion into the Hanford 300 Area, which has a governing influence on the behavior of a uranium plume left over from historical nuclear fuel processing operations.

Quantification of frequency-components contributions to the discharge of a karst spring

NASA Astrophysics Data System (ADS)

Taver, V.; Johannet, A.; Vinches, M.; Borrell, V.; Pistre, S.; Bertin, D.

2013-12-01

Karst aquifers represent important underground resources for water supplies, providing it to 25% of the population. Nevertheless such systems are currently underexploited because of their heterogeneity and complexity, which make work fields and physical measurements expensive, and frequently not representative of the whole aquifer. The systemic paradigm appears thus at a complementary approach to study and model karst aquifers in the framework of non-linear system analysis. Its input and output signals, namely rainfalls and discharge contain information about the function performed by the physical process. Therefore, improvement of knowledge about the karst system can be provided using time series analysis, for example Fourier analysis or orthogonal decomposition [1]. Another level of analysis consists in building non-linear models to identify rainfall/discharge relation, component by component [2]. In this context, this communication proposes to use neural networks to first model the rainfall-runoff relation using frequency components, and second to analyze the models, using the KnoX method [3], in order to quantify the importance of each component. Two different neural models were designed: (i) the recurrent model which implements a non-linear recurrent model fed by rainfalls, ETP and previous estimated discharge, (ii) the feed-forward model which implements a non-linear static model fed by rainfalls, ETP and previous observed discharges. The first model is known to better represent the rainfall-runoff relation; the second one to better predict the discharge based on previous discharge observations. KnoX method is based on a variable selection method, which simply considers values of parameters after the training without taking into account the non-linear behavior of the model during functioning. An amelioration of the KnoX method, is thus proposed in order to overcome this inadequacy. The proposed method, leads thus to both a hierarchization and a quantification

Cardiovascular response to acute stress in freely moving rats: time-frequency analysis.

PubMed

Loncar-Turukalo, Tatjana; Bajic, Dragana; Japundzic-Zigon, Nina

2008-01-01

Spectral analysis of cardiovascular series is an important tool for assessing the features of the autonomic control of the cardiovascular system. In this experiment Wistar rats ecquiped with intraarterial catheter for blood pressure (BP) recording were exposed to stress induced by blowing air. The problem of non stationary data was overcomed applying the Smoothed Pseudo Wigner Villle (SPWV) time-frequency distribution. Spectral analysis was done before stress, during stress, immediately after stress and later in recovery. The spectral indices were calculated for both systolic blood pressure (SBP) and pulse interval (PI) series. The time evolution of spectral indices showed perturbed sympathovagal balance.

Neural correlates of multimodal metaphor comprehension: Evidence from event-related potentials and time-frequency decompositions.

PubMed

Ma, Qingguo; Hu, Linfeng; Xiao, Can; Bian, Jun; Jin, Jia; Wang, Qiuzhen

2016-11-01

The present study examined the event-related potential (ERP) and time-frequency components correlates with the comprehension process of multimodal metaphors that were represented by the combination of "a vehicle picture+a written word of an animal". Electroencephalogram data were recorded when participants decided whether the metaphor using an animal word for the vehicle rendered by a picture was appropriate or not. There were two conditions: appropriateness (e.g., sport utility vehicles+tiger) vs. inappropriateness (e.g., sport utility vehicles+cat). The ERP results showed that inappropriate metaphor elicited larger N300 (280-360ms) and N400 (380-460ms) amplitude than appropriate one, which were different from previous exclusively verbal metaphor studies that rarely observed the N300 effect. A P600 (550-750ms) was also observed and larger in appropriate metaphor condition. Besides, the time-frequency principal component analysis revealed that two independent theta activities indexed the separable processes (retrieval of semantic features and semantic integration) underlying the N300 and N400. Delta band was also induced within a later time window and best characterized the integration process underlying P600. These results indicate the specific cognitive mechanism of multimodal metaphor comprehension that is different from verbal metaphor processing, mirrored by several separable processes indexed by ERP components and time-frequency components. The present study extends the metaphor research by uncovering the functional roles of delta and theta as well as their unique contributions to the ERP components during multimodal metaphor comprehension. Copyright © 2016 Elsevier B.V. All rights reserved.

Cost component analysis.

PubMed

Lörincz, András; Póczos, Barnabás

2003-06-01

In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.

Climate signal detected in sub-fossil and living oak trees data. An analysis of signal frequency components

NASA Astrophysics Data System (ADS)

Constantin, Nechita; Francisca, Chiriloaei; Maria, Radoane; Ionel, Popa; Nicoae, Radoane

2016-04-01

This study is focused on analysis the frequency components of the signal detected in living and sub-fossil tree ring series from different time periods. The investigation is oriented to analyze signal frequency components (low and high) of the two categories of trees. The interpretation technique of tree ring width is the instrument most often used to elaborate past climatic reconstructions. The annual resolution, but also, the high capacity of trees to accumulate climatic information are attributes which confer to palaeo-environmental reconstructions the biggest credibility. The main objective of the study refers to the evaluation of climatic signal characteristics, both present day climate and palaeo-climate (last 7000 years BP). Modern dendrochronological methods were applied on 350 samples of sub-fossil trees and 400 living trees. The subfossil trunks were sampled from different fluvial environments (Siret, Suceava, Moldova). Their age was determined using radiocarbon, varying from under 100 years to almost 7000 years BP. The subfossil tree species investigated were Quercus, Alnus, Ulmus. Considering living trees, these were identified on eastern part of Romania, in different actual physico-geographical conditions. The studied living tree species consisted in Quercus species (robur and petraea). Each site was investigated regarding stress factors of the sampled tree. The working methods were applied to the total wood series, both late and early, to detect intra-annual level climate information. Each series has been tested to separate individual trees with climatic signal of other trees with different signals (noises determined by competition between individuals or site stress, or anthropic impact). Comparing dendrochronological series (sub-fossil and living trees) we want to identify what significant causes determined the difference in the signal frequencies. Especially, the human interventions registered in the last 2 centuries will be evaluated by these

Past crops yield dynamics reconstruction from tree-ring chronologies in the forest-steppe zone based on low- and high-frequency components

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Shah, Santosh K.; Zhirnova, Dina F.

2018-05-01

Interrelations of the yield variability of the main crops (wheat, barley, and oats) with hydrothermal regime and growth of conifer trees ( Pinus sylvestris and Larix sibirica) in forest-steppes were investigated in Khakassia, South Siberia. An attempt has been made to understand the role and mechanisms of climatic impact on plants productivity. It was found that amongst variables describing moisture supply, wetness index had maximum impact. Strength of climatic response and correlations with tree growth are different for rain-fed and irrigated crops yield. Separated high-frequency variability components of yield and tree-ring width have more pronounced relationships between each other and with climatic variables than their chronologies per se. Corresponding low-frequency variability components are strongly correlated with maxima observed after 1- to 5-year time shift of tree-ring width. Results of analysis allowed us to develop original approach of crops yield dynamics reconstruction on the base of high-frequency variability component of the growth of pine and low-frequency one of larch.

Detecting fixation on a target using time-frequency distributions of a retinal birefringence scanning signal

PubMed Central

2013-01-01

Background The fovea, which is the most sensitive part of the retina, is known to have birefringent properties, i.e. it changes the polarization state of light upon reflection. Existing devices use this property to obtain information on the orientation of the fovea and the direction of gaze. Such devices employ specific frequency components that appear during moments of fixation on a target. To detect them, previous methods have used solely the power spectrum of the Fast Fourier Transform (FFT), which, unfortunately, is an integral method, and does not give information as to where exactly the events of interest occur. With very young patients who are not cooperative enough, this presents a problem, because central fixation may be present only during very short-lasting episodes, and can easily be missed by the FFT. Method This paper presents a method for detecting short-lasting moments of central fixation in existing devices for retinal birefringence scanning, with the goal of a reliable detection of eye alignment. Signal analysis is based on the Continuous Wavelet Transform (CWT), which reliably localizes such events in the time-frequency plane. Even though the characteristic frequencies are not always strongly expressed due to possible artifacts, simple topological analysis of the time-frequency distribution can detect fixation reliably. Results In all six subjects tested, the CWT allowed precise identification of both frequency components. Moreover, in four of these subjects, episodes of intermittent but definitely present central fixation were detectable, similar to those in Figure 4. A simple FFT is likely to treat them as borderline cases, or entirely miss them, depending on the thresholds used. Conclusion Joint time-frequency analysis is a powerful tool in the detection of eye alignment, even in a noisy environment. The method is applicable to similar situations, where short-lasting diagnostic events need to be detected in time series acquired by means of

Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia.

PubMed

Placek, Michał M; Wachel, Paweł; Iskander, D Robert; Smielewski, Peter; Uryga, Agnieszka; Mielczarek, Arkadiusz; Szczepański, Tomasz A; Kasprowicz, Magdalena

2017-01-01

Classic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment. Continuous recording of CBFV, ABP, ECG, and end-tidal CO2 were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time-frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02-0.07 Hz (VLF), 0.07-0.20 Hz (LF), and 0.20-0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed. The hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF. The time-frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

Time- and frequency-resolved measurements of frequency modulation and switching of a tunable semiconductor laser

NASA Astrophysics Data System (ADS)

Kuznetsov, M.; Stone, J.; Stulz, L. W.

1991-11-01

We report measurements of intensity as a function of both time and frequency for frequency modulation and switching of a tunable semiconductor laser. Because of the uncertainty principle limitations, the measured time-frequency signal can have a complex structure and does not show the simple-minded picture of a laser spectrum whose center frequency varies in time. The observations are explained by a theory of the time-dependent spectral measurements, well known in the field of speech analysis. We discuss implications for channel switching speed and channel interference in switched, frequency-multiplexed optical networks.

Real-time open-loop frequency response analysis of flight test data

NASA Technical Reports Server (NTRS)

Bosworth, J. T.; West, J. C.

1986-01-01

A technique has been developed to compare the open-loop frequency response of a flight test aircraft real time with linear analysis predictions. The result is direct feedback to the flight control systems engineer on the validity of predictions and adds confidence for proceeding with envelope expansion. Further, gain and phase margins can be tracked for trends in a manner similar to the techniques used by structural dynamics engineers in tracking structural modal damping.

Time-Frequency Analysis of Beach Bacteria Variations and its Implication for Recreational Water Quality Modeling

EPA Science Inventory

This paper explores the potential of time-frequency wavelet analysis in resolving beach bacteria concentration and possible explanatory variables across multiple time scales with temporal information still preserved. The wavelet scalograms of E. coli concentrations and the explan...

High-Resolution Audio with Inaudible High-Frequency Components Induces a Relaxed Attentional State without Conscious Awareness.

PubMed

Kuribayashi, Ryuma; Nittono, Hiroshi

2017-01-01

High-resolution audio has a higher sampling frequency and a greater bit depth than conventional low-resolution audio such as compact disks. The higher sampling frequency enables inaudible sound components (above 20 kHz) that are cut off in low-resolution audio to be reproduced. Previous studies of high-resolution audio have mainly focused on the effect of such high-frequency components. It is known that alpha-band power in a human electroencephalogram (EEG) is larger when the inaudible high-frequency components are present than when they are absent. Traditionally, alpha-band EEG activity has been associated with arousal level. However, no previous studies have explored whether sound sources with high-frequency components affect the arousal level of listeners. The present study examined this possibility by having 22 participants listen to two types of a 400-s musical excerpt of French Suite No. 5 by J. S. Bach (on cembalo, 24-bit quantization, 192 kHz A/D sampling), with or without inaudible high-frequency components, while performing a visual vigilance task. High-alpha (10.5-13 Hz) and low-beta (13-20 Hz) EEG powers were larger for the excerpt with high-frequency components than for the excerpt without them. Reaction times and error rates did not change during the task and were not different between the excerpts. The amplitude of the P3 component elicited by target stimuli in the vigilance task increased in the second half of the listening period for the excerpt with high-frequency components, whereas no such P3 amplitude change was observed for the other excerpt without them. The participants did not distinguish between these excerpts in terms of sound quality. Only a subjective rating of inactive pleasantness after listening was higher for the excerpt with high-frequency components than for the other excerpt. The present study shows that high-resolution audio that retains high-frequency components has an advantage over similar and indistinguishable digital sound

Using time-frequency analysis of the photoplethysmographic waveform to detect the withdrawal of 900 mL of blood.

PubMed

Scully, Christopher G; Selvaraj, Nandakumar; Romberg, Frederick W; Wardhan, Richa; Ryan, John; Florian, John P; Silverman, David G; Shelley, Kirk H; Chon, Ki H

2012-07-01

We designed this study to determine if 900 mL of blood withdrawal during spontaneous breathing in healthy volunteers could be detected by examining the time-varying spectral amplitude of the photoplethysmographic (PPG) waveform in the heart rate frequency band and/or in the breathing rate frequency band before significant changes occurred in heart rate or arterial blood pressure. We also identified the best PPG probe site for early detection of blood volume loss by testing ear, finger, and forehead sites. Eight subjects had 900 mL of blood withdrawn followed by reinfusion of 900 mL of blood. Physiological monitoring included PPG waveforms from ear, finger, and forehead probe sites, standard electrocardiogram, and standard blood pressure cuff measurements. The time-varying amplitude sequences in the heart rate frequency band and breathing rate frequency band present in the PPG waveform were extracted from high-resolution time-frequency spectra. These amplitudes were used as a parameter for blood loss detection. Heart rate and arterial blood pressure did not significantly change during the protocol. Using time-frequency analysis of the PPG waveform from ear, finger, and forehead probe sites, the amplitude signal extracted at the frequency corresponding to the heart rate significantly decreased when 900 mL of blood was withdrawn, relative to baseline (all P < 0.05); for the ear, the corresponding signal decreased when only 300 mL of blood was withdrawn. The mean percent decrease in the amplitude of the heart rate component at 900 mL blood loss relative to baseline was 45.2% (38.2%), 42.0% (29.2%), and 42.3% (30.5%) for ear, finger, and forehead probe sites, respectively, with the lower 95% confidence limit shown in parentheses. After 900 mL blood reinfusion, the amplitude signal at the heart rate frequency showed a recovery towards baseline. There was a clear separation of amplitude values at the heart rate frequency between baseline and 900 mL blood withdrawal

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2000-01-01

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

A New Principle of Sound Frequency Analysis

NASA Technical Reports Server (NTRS)

Theodorsen, Theodore

1932-01-01

In connection with the study of aircraft and propeller noises, the National Advisory Committee for Aeronautics has developed an instrument for sound-frequency analysis which differs fundamentally from previous types, and which, owing to its simplicity of principle, construction, and operation, has proved to be of value in this investigation. The method is based on the well-known fact that the Ohmic loss in an electrical resistance is equal to the sum of the losses of the harmonic components of a complex wave, except for the case in which any two components approach or attain vectorial identity, in which case the Ohmic loss is increased by a definite amount. The principle of frequency analysis has been presented mathematically and a number of distinct advantages relative to previous methods have been pointed out. An automatic recording instrument embodying this principle is described in detail. It employs a beat-frequency oscillator as a source of variable frequency. A large number of experiments have verified the predicted superiority of the method. A number of representative records are presented.

Using wavelets to decompose the time frequency effects of monetary policy

NASA Astrophysics Data System (ADS)

Aguiar-Conraria, Luís; Azevedo, Nuno; Soares, Maria Joana

2008-05-01

Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods. To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies.

Quantization of Motor Activity into Primitives and Time-Frequency Atoms Using Independent Component Analysis and Matching Pursuit Algorithms

DTIC Science & Technology

2001-10-25

form: (1) A is a scaling factor, t is time and r a coordinate vector describing the limb configuration. We...combination of limb state and EMG. In our early examination of EMG we detected underlying groups of muscles and phases of activity by inspection and...representations of EEG or other biological signals has been thoroughly explored. Such components might be used as a basis for neuroprosthetic control

Time-frequency analysis of SEMG--with special consideration to the interelectrode spacing.

PubMed

Alemu, M; Kumar, Dinesh Kant; Bradley, Alan

2003-12-01

The surface electromyogram (SEMG) is a complex, nonstationary signal. The spectrum of the SEMG is dependent on the force of contraction being generated and other factors like muscle fatigue and interelectrode distance (IED). The spectrum of the signal is time variant. This paper reports the experimental research conducted to study the influence of force of muscle contraction and IED on the SEMG signal using time-frequency (T-F) analysis. Two T-F techniques have been used: Wigner-Ville distribution (WVD) and Choi-Williams distribution (CWD). The experiment was conducted with the help of ten healthy volunteers (five males and five females) who performed isometric elbow flexions of the active right arm at 20%, 50%, and 80% of their maximal voluntary contraction. The SEMG signal was recorded using surface electrodes placed at a distance of 18 and 36 mm over biceps brachii muscle. The results indicate that the two distributions were spread out across the frequency range at smaller IED. Further, regardless of the spacing, both distributions displayed increased spectral compression with time at higher contraction level.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Recovering Long-wavelength Velocity Models using Spectrogram Inversion with Single- and Multi-frequency Components

NASA Astrophysics Data System (ADS)

Ha, J.; Chung, W.; Shin, S.

2015-12-01

Many waveform inversion algorithms have been proposed in order to construct subsurface velocity structures from seismic data sets. These algorithms have suffered from computational burden, local minima problems, and the lack of low-frequency components. Computational efficiency can be improved by the application of back-propagation techniques and advances in computing hardware. In addition, waveform inversion algorithms, for obtaining long-wavelength velocity models, could avoid both the local minima problem and the effect of the lack of low-frequency components in seismic data. In this study, we proposed spectrogram inversion as a technique for recovering long-wavelength velocity models. In spectrogram inversion, decomposed frequency components from spectrograms of traces, in the observed and calculated data, are utilized to generate traces with reproduced low-frequency components. Moreover, since each decomposed component can reveal the different characteristics of a subsurface structure, several frequency components were utilized to analyze the velocity features in the subsurface. We performed the spectrogram inversion using a modified SEG/SEGE salt A-A' line. Numerical results demonstrate that spectrogram inversion could also recover the long-wavelength velocity features. However, inversion results varied according to the frequency components utilized. Based on the results of inversion using a decomposed single-frequency component, we noticed that robust inversion results are obtained when a dominant frequency component of the spectrogram was utilized. In addition, detailed information on recovered long-wavelength velocity models was obtained using a multi-frequency component combined with single-frequency components. Numerical examples indicate that various detailed analyses of long-wavelength velocity models can be carried out utilizing several frequency components.

Wavelet analysis of frequency chaos game signal: a time-frequency signature of the C. elegans DNA.

PubMed

Messaoudi, Imen; Oueslati, Afef Elloumi; Lachiri, Zied

2014-12-01

Challenging tasks are encountered in the field of bioinformatics. The choice of the genomic sequence's mapping technique is one the most fastidious tasks. It shows that a judicious choice would serve in examining periodic patterns distribution that concord with the underlying structure of genomes. Despite that, searching for a coding technique that can highlight all the information contained in the DNA has not yet attracted the attention it deserves. In this paper, we propose a new mapping technique based on the chaos game theory that we call the frequency chaos game signal (FCGS). The particularity of the FCGS coding resides in exploiting the statistical properties of the genomic sequence itself. This may reflect important structural and organizational features of DNA. To prove the usefulness of the FCGS approach in the detection of different local periodic patterns, we use the wavelet analysis because it provides access to information that can be obscured by other time-frequency methods such as the Fourier analysis. Thus, we apply the continuous wavelet transform (CWT) with the complex Morlet wavelet as a mother wavelet function. Scalograms that relate to the organism Caenorhabditis elegans (C. elegans) exhibit a multitude of periodic organization of specific DNA sequences.

Multibody model reduction by component mode synthesis and component cost analysis

NASA Technical Reports Server (NTRS)

Spanos, J. T.; Mingori, D. L.

1990-01-01

The classical assumed-modes method is widely used in modeling the dynamics of flexible multibody systems. According to the method, the elastic deformation of each component in the system is expanded in a series of spatial and temporal functions known as modes and modal coordinates, respectively. This paper focuses on the selection of component modes used in the assumed-modes expansion. A two-stage component modal reduction method is proposed combining Component Mode Synthesis (CMS) with Component Cost Analysis (CCA). First, each component model is truncated such that the contribution of the high frequency subsystem to the static response is preserved. Second, a new CMS procedure is employed to assemble the system model and CCA is used to further truncate component modes in accordance with their contribution to a quadratic cost function of the system output. The proposed method is demonstrated with a simple example of a flexible two-body system.

Time-frequency analysis of pediatric murmurs

NASA Astrophysics Data System (ADS)

Lombardo, Joseph S.; Blodgett, Lisa A.; Rosen, Ron S.; Najmi, Amir-Homayoon; Thompson, W. Reid

1998-05-01

Technology has provided many new tools to assist in the diagnosis of pathologic conditions of the heart. Echocardiography, Ultrafast CT, and MRI are just a few. While these tools are a valuable resource, they are typically too expensive, large and complex in operation for use in rural, homecare, and physician's office settings. Recent advances in computer performance, miniaturization, and acoustic signal processing, have yielded new technologies that when applied to heart sounds can provide low cost screening for pathologic conditions. The short duration and transient nature of these signals requires processing techniques that provide high resolution in both time and frequency. Short-time Fourier transforms, Wigner distributions, and wavelet transforms have been applied to signals form hearts with various pathologic conditions. While no single technique provides the ideal solution, the combination of tools provides a good representation of the acoustic features of the pathologies selected.

Utilizing time-frequency amplitude and phase synchrony measure to assess feedback processing in a gambling task.

PubMed

Watts, Adreanna T M; Tootell, Anne V; Fix, Spencer T; Aviyente, Selin; Bernat, Edward M

2018-04-29

The neurophysiological mechanisms involved in the evaluation of performance feedback have been widely studied in the ERP literature over the past twenty years, but understanding has been limited by the use of traditional time-domain amplitude analytic approaches. Gambling outcome valence has been identified as an important factor modulating event-related potential (ERP) components, most notably the feedback negativity (FN). Recent work employing time-frequency analysis has shown that processes indexed by the FN are confounded in the time-domain and can be better represented as separable feedback-related processes in the theta (3-7 Hz) and delta (0-3 Hz) frequency bands. In addition to time-frequency amplitude analysis, phase synchrony measures have begun to further our understanding of performance evaluation by revealing how feedback information is processed within and between various brain regions. The current study aimed to provide an integrative assessment of time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony changes following monetary feedback in a gambling task. Results revealed that time-frequency amplitude activity explained separable loss and gain processes confounded in the time-domain. Furthermore, phase synchrony measures explained unique variance above and beyond amplitude measures and demonstrated enhanced functional integration between medial prefrontal and bilateral frontal, motor, and occipital regions for loss relative to gain feedback. These findings demonstrate the utility of assessing time-frequency amplitude, inter-trial phase synchrony, and inter-channel phase synchrony together to better elucidate the neurophysiology of feedback processing. Copyright © 2017. Published by Elsevier B.V.

Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns

NASA Astrophysics Data System (ADS)

Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.

2014-02-01

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

Component Repair Times Obtained from MSPI Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eide, Steven A.; Cadwallader, Lee

Information concerning times to repair or restore equipment to service given a failure is valuable to probabilistic risk assessments (PRAs). Examples of such uses in modern PRAs include estimation of the probability of failing to restore a failed component within a specified time period (typically tied to recovering a mitigating system before core damage occurs at nuclear power plants) and the determination of mission times for support system initiating event (SSIE) fault tree models. Information on equipment repair or restoration times applicable to PRA modeling is limited and dated for U.S. commercial nuclear power plants. However, the Mitigating Systems Performancemore » Index (MSPI) program covering all U.S. commercial nuclear power plants provides up-to-date information on restoration times for a limited set of component types. This paper describes the MSPI program data available and analyzes the data to obtain median and mean component restoration times as well as non-restoration cumulative probability curves. The MSPI program provides guidance for monitoring both planned and unplanned outages of trains of selected mitigating systems deemed important to safety. For systems included within the MSPI program, plants monitor both train UA and component unreliability (UR) against baseline values. If the combined system UA and UR increases sufficiently above established baseline results (converted to an estimated change in core damage frequency or CDF), a “white” (or worse) indicator is generated for that system. That in turn results in increased oversight by the US Nuclear Regulatory Commission (NRC) and can impact a plant’s insurance rating. Therefore, there is pressure to return MSPI program components to service as soon as possible after a failure occurs. Three sets of unplanned outages might be used to determine the component repair durations desired in this article: all unplanned outages for the train type that includes the component of interest, only

Development and application of a time-history analysis for rotorcraft dynamics based on a component approach

NASA Technical Reports Server (NTRS)

Sopher, R.; Hallock, D. W.

1985-01-01

A time history analysis for rotorcraft dynamics based on dynamical substructures, and nonstructural mathematical and aerodynamic components is described. The analysis is applied to predict helicopter ground resonance and response to rotor damage. Other applications illustrate the stability and steady vibratory response of stopped and gimballed rotors, representative of new technology. Desirable attributes expected from modern codes are realized, although the analysis does not employ a complete set of techniques identified for advanced software. The analysis is able to handle a comprehensive set of steady state and stability problems with a small library of components.

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Time frequency analysis of sound from a maneuvering rotorcraft

NASA Astrophysics Data System (ADS)

Stephenson, James H.; Tinney, Charles E.; Greenwood, Eric; Watts, Michael E.

2014-10-01

The acoustic signatures produced by a full-scale, Bell 430 helicopter during steady-level-flight and transient roll-right maneuvers are analyzed by way of time-frequency analysis. The roll-right maneuvers comprise both a medium and a fast roll rate. Data are acquired using a single ground based microphone that are analyzed by way of the Morlet wavelet transform to extract the spectral properties and sound pressure levels as functions of time. The findings show that during maneuvering operations of the helicopter, both the overall sound pressure level and the blade-vortex interaction sound pressure level are greatest when the roll rate of the vehicle is at its maximum. The reduced inflow in the region of the rotor disk where blade-vortex interaction noise originates is determined to be the cause of the increase in noise. A local decrease in inflow reduces the miss distance of the tip vortex and thereby increases the BVI noise signature. Blade loading and advance ratios are also investigated as possible mechanisms for increased sound production, but are shown to be fairly constant throughout the maneuvers.

Frequency multiplexed flux locked loop architecture providing an array of DC SQUIDS having both shared and unshared components

DOEpatents

Ganther, Jr., Kenneth R.; Snapp, Lowell D.

2002-01-01

Architecture for frequency multiplexing multiple flux locked loops in a system comprising an array of DC SQUID sensors. The architecture involves dividing the traditional flux locked loop into multiple unshared components and a single shared component which, in operation, form a complete flux locked loop relative to each DC SQUID sensor. Each unshared flux locked loop component operates on a different flux modulation frequency. The architecture of the present invention allows a reduction from 2N to N+1 in the number of connections between the cryogenic DC SQUID sensors and their associated room temperature flux locked loops. Furthermore, the 1.times.N architecture of the present invention can be paralleled to form an M.times.N array architecture without increasing the required number of flux modulation frequencies.

Time series analysis of collective motions in proteins

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.

2004-01-01

The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

PubMed

Fan, Shu-Han; Chou, Chia-Ching; Chen, Wei-Chen; Fang, Wai-Chi

2015-01-01

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

Time series analysis of ozone data in Isfahan

NASA Astrophysics Data System (ADS)

Omidvari, M.; Hassanzadeh, S.; Hosseinibalam, F.

2008-07-01

Time series analysis used to investigate the stratospheric ozone formation and decomposition processes. Different time series methods are applied to detect the reason for extreme high ozone concentrations for each season. Data was convert into seasonal component and frequency domain, the latter has been evaluated by using the Fast Fourier Transform (FFT), spectral analysis. The power density spectrum estimated from the ozone data showed peaks at cycle duration of 22, 20, 36, 186, 365 and 40 days. According to seasonal component analysis most fluctuation was in 1999 and 2000, but the least fluctuation was in 2003. The best correlation between ozone and sun radiation was found in 2000. Other variables which are not available cause to this fluctuation in the 1999 and 2001. The trend of ozone is increasing in 1999 and is decreasing in other years.

Continuous-variable quantum computing in optical time-frequency modes using quantum memories.

PubMed

Humphreys, Peter C; Kolthammer, W Steven; Nunn, Joshua; Barbieri, Marco; Datta, Animesh; Walmsley, Ian A

2014-09-26

We develop a scheme for time-frequency encoded continuous-variable cluster-state quantum computing using quantum memories. In particular, we propose a method to produce, manipulate, and measure two-dimensional cluster states in a single spatial mode by exploiting the intrinsic time-frequency selectivity of Raman quantum memories. Time-frequency encoding enables the scheme to be extremely compact, requiring a number of memories that are a linear function of only the number of different frequencies in which the computational state is encoded, independent of its temporal duration. We therefore show that quantum memories can be a powerful component for scalable photonic quantum information processing architectures.

Some limitations of frequency as a component of risk: an expository note.

PubMed

Cox, Louis Anthony

2009-02-01

Students of risk analysis are often taught that "risk is frequency times consequence" or, more generally, that risk is determined by the frequency and severity of adverse consequences. But is it? This expository note reviews the concepts of frequency as average annual occurrence rate and as the reciprocal of mean time to failure (MTTF) or mean time between failures (MTBF) in a renewal process. It points out that if two risks (represented as two (frequency, severity) pairs for adverse consequences) have identical values for severity but different values of frequency, then it is not necessarily true that the one with the smaller value of frequency is preferable-and this is true no matter how frequency is defined. In general, there is not necessarily an increasing relation between the reciprocal of the mean time until an event occurs, its long-run average occurrences per year, and other criteria, such as the probability or expected number of times that it will happen over a specific interval of interest, such as the design life of a system. Risk depends on more than frequency and severity of consequences. It also depends on other information about the probability distribution for the time of a risk event that can become lost in simple measures of event "frequency." More flexible descriptions of risky processes, such as point process models can avoid these limitations.

Cross Time-Frequency Analysis of Gastrocnemius Electromyographic Signals in Hypertensive and Nonhypertensive Subjects

NASA Astrophysics Data System (ADS)

Mitchell, Patrick; Krotish, Debra; Shin, Yong-June; Hirth, Victor

2010-12-01

The effects of hypertension are chronic and continuous; it affects gait, balance, and fall risk. Therefore, it is desirable to assess gait health across hypertensive and nonhypertensive subjects in order to prevent or reduce the risk of falls. Analysis of electromyography (EMG) signals can identify age related changes of neuromuscular activation due to various neuropathies and myopathies, but it is difficult to translate these medical changes to clinical diagnosis. To examine and compare geriatrics patients with these gait-altering diseases, we acquire EMG muscle activation signals, and by use of a timesynchronized mat capable of recording pressure information, we localize the EMG data to the gait cycle, ensuring identical comparison across subjects. Using time-frequency analysis on the EMG signal, in conjunction with several parameters obtained from the time-frequency analyses, we can determine the statistical discrepancy between diseases. We base these parameters on physiological manifestations caused by hypertension, as well as other comorbities that affect the geriatrics community. Using these metrics in a small population, we identify a statistical discrepancy between a control group and subjects with hypertension, neuropathy, diabetes, osteoporosis, arthritis, and several other common diseases which severely affect the geriatrics community.

Spatial pattern separation of chemicals and frequency-independent components by terahertz spectroscopic imaging

NASA Astrophysics Data System (ADS)

Watanabe, Yuuki; Kawase, Kodo; Ikari, Tomofumi; Ito, Hiromasa; Ishikawa, Youichi; Minamide, Hiroaki

2003-10-01

We separated the component spatial patterns of frequency-dependent absorption in chemicals and frequency-independent components such as plastic, paper, and measurement noise in terahertz (THz) spectroscopic images, using known spectral curves. Our measurement system, which uses a widely tunable coherent THz-wave parametric oscillator source, can image at a specific frequency in the range 1-2 THz. The component patterns of chemicals can easily be extracted by use of the frequency-independent components. This method could be successfully used for nondestructive inspection for the detection of illegal drugs and devices of bioterrorism concealed, e.g., inside mail and packages.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise.

PubMed

Yan, Jiaquan; Sun, Haixin; Chen, Hailan; Junejo, Naveed Ur Rehman; Cheng, En

2018-03-22

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

PubMed Central

Yan, Jiaquan; Sun, Haixin; Chen, Hailan; Junejo, Naveed Ur Rehman; Cheng, En

2018-01-01

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. PMID:29565288

Segmented frequency-domain fluorescence lifetime measurements: minimizing the effects of photobleaching within a multi-component system.

PubMed

Marwani, Hadi M; Lowry, Mark; Keating, Patrick; Warner, Isiah M; Cook, Robert L

2007-11-01

This study introduces a newly developed frequency segmentation and recombination method for frequency-domain fluorescence lifetime measurements to address the effects of changing fractional contributions over time and minimize the effects of photobleaching within multi-component systems. Frequency segmentation and recombination experiments were evaluated using a two component system consisting of fluorescein and rhodamine B. Comparison of experimental data collected in traditional and segmented fashion with simulated data, generated using different changing fractional contributions, demonstrated the validity of the technique. Frequency segmentation and recombination was also applied to a more complex system consisting of pyrene with Suwannee River fulvic acid reference and was shown to improve recovered lifetimes and fractional intensity contributions. It was observed that photobleaching in both systems led to errors in recovered lifetimes which can complicate the interpretation of lifetime results. Results showed clear evidence that the frequency segmentation and recombination method reduced errors resulting from a changing fractional contribution in a multi-component system, and allowed photobleaching issues to be addressed by commercially available instrumentation.

Extracting Low-Frequency Information from Time Attenuation in Elastic Waveform Inversion

NASA Astrophysics Data System (ADS)

Guo, Xuebao; Liu, Hong; Shi, Ying; Wang, Weihong

2017-03-01

Low-frequency information is crucial for recovering background velocity, but the lack of low-frequency information in field data makes inversion impractical without accurate initial models. Laplace-Fourier domain waveform inversion can recover a smooth model from real data without low-frequency information, which can be used for subsequent inversion as an ideal starting model. In general, it also starts with low frequencies and includes higher frequencies at later inversion stages, while the difference is that its ultralow frequency information comes from the Laplace-Fourier domain. Meanwhile, a direct implementation of the Laplace-transformed wavefield using frequency domain inversion is also very convenient. However, because broad frequency bands are often used in the pure time domain waveform inversion, it is difficult to extract the wavefields dominated by low frequencies in this case. In this paper, low-frequency components are constructed by introducing time attenuation into the recorded residuals, and the rest of the method is identical to the traditional time domain inversion. Time windowing and frequency filtering are also applied to mitigate the ambiguity of the inverse problem. Therefore, we can start at low frequencies and to move to higher frequencies. The experiment shows that the proposed method can achieve a good inversion result in the presence of a linear initial model and records without low-frequency information.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

[Analysis of time domain and frequency domain heart rate variability in fighter pilot before and after upright tilt].

PubMed

Wang, L; Wu, L; Ji, G; Zhang, X; Chen, T; Wang, L

1998-12-01

Effects of upright tilt on mechanism of autonomic nervous regulation of cardiovascular system and characteristics of heart rate variability (HRV) were observed in sixty healthy male pilots. Relation between time domain and frequency domain indexes of short-time HRV (5 min) were analysed before and after upright tilt. The results showed that there are significant difference in short time HRV parameters before and after upright tilt. Significant relationship was formed between time domain and frequency domain indexes of HRV. It suggests that time domain and frequency domain HRV analysis is capable of revealing certain informations embedded in a short series of R-R intervals and can help to evaluate the status of autonomic regulation of cardiovascular function in pilots.

Time-Frequency Learning Machines for Nonstationarity Detection Using Surrogates

NASA Astrophysics Data System (ADS)

Borgnat, Pierre; Flandrin, Patrick; Richard, Cédric; Ferrari, André; Amoud, Hassan; Honeine, Paul

2012-03-01

Time-frequency representations provide a powerful tool for nonstationary signal analysis and classification, supporting a wide range of applications [12]. As opposed to conventional Fourier analysis, these techniques reveal the evolution in time of the spectral content of signals. In Ref. [7,38], time-frequency analysis is used to test stationarity of any signal. The proposed method consists of a comparison between global and local time-frequency features. The originality is to make use of a family of stationary surrogate signals for defining the null hypothesis of stationarity and, based upon this information, to derive statistical tests. An open question remains, however, about how to choose relevant time-frequency features. Over the last decade, a number of new pattern recognition methods based on reproducing kernels have been introduced. These learning machines have gained popularity due to their conceptual simplicity and their outstanding performance [30]. Initiated by Vapnik’s support vector machines (SVM) [35], they offer now a wide class of supervised and unsupervised learning algorithms. In Ref. [17-19], the authors have shown how the most effective and innovative learning machines can be tuned to operate in the time-frequency domain. This chapter follows this line of research by taking advantage of learning machines to test and quantify stationarity. Based on one-class SVM, our approach uses the entire time-frequency representation and does not require arbitrary feature extraction. Applied to a set of surrogates, it provides the domain boundary that includes most of these stationarized signals. This allows us to test the stationarity of the signal under investigation. This chapter is organized as follows. In Section 22.2, we introduce the surrogate data method to generate stationarized signals, namely, the null hypothesis of stationarity. The concept of time-frequency learning machines is presented in Section 22.3, and applied to one-class SVM in order

Detection and characterization of cultural noise sources in magnetotelluric data: individual and joint analysis of the polarization attributes of the electric and magnetic field time-series in the time-frequency domain

NASA Astrophysics Data System (ADS)

Escalas, M.; Queralt, P.; Ledo, J.; Marcuello, A.

2012-04-01

Magnetotelluric (MT) method is a passive electromagnetic technique, which is currently used to characterize sites for the geological storage of CO2. These later ones are usually located nearby industrialized, urban or farming areas, where man-made electromagnetic (EM) signals contaminate the MT data. The identification and characterization of the artificial EM sources which generate the so-called "cultural noise" is an important challenge to obtain the most reliable results with the MT method. The polarization attributes of an EM signal (tilt angle, ellipticity and phase difference between its orthogonal components) are related to the character of its source. In a previous work (Escalas et al. 2011), we proposed a method to distinguish natural signal from cultural noise in the raw MT data. It is based on the polarization analysis of the MT time-series in the time-frequency domain, using a wavelet scheme. We developed an algorithm to implement the method, and was tested with both synthetic and field data. In 2010, we carried out a controlled-source electromagnetic (CSEM) experiment in the Hontomín site (the Research Laboratory on Geological Storage of CO2 in Spain). MT time-series were contaminated at different frequencies with the signal emitted by a controlled artificial EM source: two electric dipoles (1 km long, arranged in North-South and East-West directions). The analysis with our algorithm of the electric field time-series acquired in this experiment was successful: the polarization attributes of both the natural and artificial signal were obtained in the time-frequency domain, highlighting their differences. The processing of the magnetic field time-series acquired in the Hontomín experiment has been done in the present work. This new analysis of the polarization attributes of the magnetic field data has provided additional information to detect the contribution of the artificial source in the measured data. Moreover, the joint analysis of the

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

Real-time, high frequency QRS electrocardiograph with reduced amplitude zone detection

NASA Technical Reports Server (NTRS)

Schlegel, Todd T. (Inventor); DePalma, Jude L. (Inventor); Moradi, Saeed (Inventor)

2009-01-01

Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as ''RAZs''. RAZs are displayed as ''go, no-go'' signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.

On the Analysis Methods for the Time Domain and Frequency Domain Response of a Buried Objects*

NASA Astrophysics Data System (ADS)

Poljak, Dragan; Šesnić, Silvestar; Cvetković, Mario

2014-05-01

There has been a continuous interest in the analysis of ground-penetrating radar systems and related applications in civil engineering [1]. Consequently, a deeper insight of scattering phenomena occurring in a lossy half-space, as well as the development of sophisticated numerical methods based on Finite Difference Time Domain (FDTD) method, Finite Element Method (FEM), Boundary Element Method (BEM), Method of Moments (MoM) and various hybrid methods, is required, e.g. [2], [3]. The present paper deals with certain techniques for time and frequency domain analysis, respectively, of buried conducting and dielectric objects. Time domain analysis is related to the assessment of a transient response of a horizontal straight thin wire buried in a lossy half-space using a rigorous antenna theory (AT) approach. The AT approach is based on the space-time integral equation of the Pocklington type (time domain electric field integral equation for thin wires). The influence of the earth-air interface is taken into account via the simplified reflection coefficient arising from the Modified Image Theory (MIT). The obtained results for the transient current induced along the electrode due to the transmitted plane wave excitation are compared to the numerical results calculated via an approximate transmission line (TL) approach and the AT approach based on the space-frequency variant of the Pocklington integro-differential approach, respectively. It is worth noting that the space-frequency Pocklington equation is numerically solved via the Galerkin-Bubnov variant of the Indirect Boundary Element Method (GB-IBEM) and the corresponding transient response is obtained by the aid of inverse fast Fourier transform (IFFT). The results calculated by means of different approaches agree satisfactorily. Frequency domain analysis is related to the assessment of frequency domain response of dielectric sphere using the full wave model based on the set of coupled electric field integral

Definitions of Frequency and Timing Terms, Satellite Navigation and Timing Systems, and the Behavior and Analyses of Precision Crystal and Atomic Frequency Standards and their Characteristics

DTIC Science & Technology

2009-05-01

time transfer techniques has largely been due to the improvement in frequency standards. In this document, an effort was made to provide substantial...of RCC Document 214-94, contains definitions of frequency and timing terms, time transfer techniques and analysis, and behavior of crystal and atomic...Characteristics, May 2009 viii TTG Telecommunications and Timing Group TWSTFT Two-Way Satellite Time and Frequency Transfer U.S. United States USNO

Time-Frequency Analysis of Chemosensory Event-Related Potentials to Characterize the Cortical Representation of Odors in Humans

PubMed Central

Huart, Caroline; Legrain, Valéry; Hummel, Thomas; Rombaux, Philippe; Mouraux, André

2012-01-01

Background The recording of olfactory and trigeminal chemosensory event-related potentials (ERPs) has been proposed as an objective and non-invasive technique to study the cortical processing of odors in humans. Until now, the responses have been characterized mainly using across-trial averaging in the time domain. Unfortunately, chemosensory ERPs, in particular, olfactory ERPs, exhibit a relatively low signal-to-noise ratio. Hence, although the technique is increasingly used in basic research as well as in clinical practice to evaluate people suffering from olfactory disorders, its current clinical relevance remains very limited. Here, we used a time-frequency analysis based on the wavelet transform to reveal EEG responses that are not strictly phase-locked to onset of the chemosensory stimulus. We hypothesized that this approach would significantly enhance the signal-to-noise ratio of the EEG responses to chemosensory stimulation because, as compared to conventional time-domain averaging, (1) it is less sensitive to temporal jitter and (2) it can reveal non phase-locked EEG responses such as event-related synchronization and desynchronization. Methodology/Principal Findings EEG responses to selective trigeminal and olfactory stimulation were recorded in 11 normosmic subjects. A Morlet wavelet was used to characterize the elicited responses in the time-frequency domain. We found that this approach markedly improved the signal-to-noise ratio of the obtained EEG responses, in particular, following olfactory stimulation. Furthermore, the approach allowed characterizing non phase-locked components that could not be identified using conventional time-domain averaging. Conclusion/Significance By providing a more robust and complete view of how odors are represented in the human brain, our approach could constitute the basis for a robust tool to study olfaction, both for basic research and clinicians. PMID:22427997

Independent component analysis algorithm FPGA design to perform real-time blind source separation

NASA Astrophysics Data System (ADS)

Meyer-Baese, Uwe; Odom, Crispin; Botella, Guillermo; Meyer-Baese, Anke

2015-05-01

The conditions that arise in the Cocktail Party Problem prevail across many fields creating a need for of Blind Source Separation. The need for BSS has become prevalent in several fields of work. These fields include array processing, communications, medical signal processing, and speech processing, wireless communication, audio, acoustics and biomedical engineering. The concept of the cocktail party problem and BSS led to the development of Independent Component Analysis (ICA) algorithms. ICA proves useful for applications needing real time signal processing. The goal of this research was to perform an extensive study on ability and efficiency of Independent Component Analysis algorithms to perform blind source separation on mixed signals in software and implementation in hardware with a Field Programmable Gate Array (FPGA). The Algebraic ICA (A-ICA), Fast ICA, and Equivariant Adaptive Separation via Independence (EASI) ICA were examined and compared. The best algorithm required the least complexity and fewest resources while effectively separating mixed sources. The best algorithm was the EASI algorithm. The EASI ICA was implemented on hardware with Field Programmable Gate Arrays (FPGA) to perform and analyze its performance in real time.

Time-frequency analysis of functional optical mammographic images

NASA Astrophysics Data System (ADS)

Barbour, Randall L.; Graber, Harry L.; Schmitz, Christoph H.; Tarantini, Frank; Khoury, Georges; Naar, David J.; Panetta, Thomas F.; Lewis, Theophilus; Pei, Yaling

2003-07-01

We have introduced working technology that provides for time-series imaging of the hemoglobin signal in large tissue structures. In this study we have explored our ability to detect aberrant time-frequency responses of breast vasculature for subjects with Stage II breast cancer at rest and in response to simple provocations. The hypothesis being explored is that time-series imaging will be sensitive to the known structural and functional malformations of the tumor vasculature. Mammographic studies were conducted using an adjustable hemisheric measuring head containing 21 source and 21 detector locations (441 source-detector pairs). Simultaneous dual-wavelength studies were performed at 760 and 830 nm at a framing rate of ~2.7 Hz. Optical measures were performed on women lying prone with the breast hanging in a pendant position. Two class of measures were performed: (1) 20- minute baseline measure wherein the subject was at rest; (2) provocation studies wherein the subject was asked to perform some simple breathing maneuvers. Collected data were analyzed to identify the time-frequency structure and central tendencies of the detector responses and those of the image time series. Imaging data were generated using the Normalized Difference Method (Pei et al., Appl. Opt. 40, 5755-5769, 2001). Results obtained clearly document three classes of anomalies when compared to the normal contralateral breast. 1) Breast tumors exhibit altered oxygen supply/demand imbalance in response to an oxidative challenge (breath hold). 2) The vasomotor response of the tumor vasculature is mainly depressed and exhibits an altered modulation. 3) The affected area of the breast wherein the altered vasomotor signature is seen extends well beyond the limits of the tumor itself.

Precession missile feature extraction using sparse component analysis of radar measurements

NASA Astrophysics Data System (ADS)

Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des

2012-12-01

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

Developing a complex independent component analysis technique to extract non-stationary patterns from geophysical time-series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen

2016-04-01

Geodetic/geophysical observations, such as the time series of global terrestrial water storage change or sea level and temperature change, represent samples of physical processes and therefore contain information about complex physical interactionswith many inherent time scales. Extracting relevant information from these samples, for example quantifying the seasonality of a physical process or its variability due to large-scale ocean-atmosphere interactions, is not possible by rendering simple time series approaches. In the last decades, decomposition techniques have found increasing interest for extracting patterns from geophysical observations. Traditionally, principal component analysis (PCA) and more recently independent component analysis (ICA) are common techniques to extract statistical orthogonal (uncorrelated) and independent modes that represent the maximum variance of observations, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the auto-covariance matrix or diagonalizing higher (than two)-order statistical tensors from centered time series. However, the stationary assumption is obviously not justifiable for many geophysical and climate variables even after removing cyclic components e.g., the seasonal cycles. In this paper, we present a new decomposition method, the complex independent component analysis (CICA, Forootan, PhD-2014), which can be applied to extract to non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA (Forootan and Kusche, JoG-2012), where we (i) define a new complex data set using a Hilbert transformation. The complex time series contain the observed values in their real part, and the temporal rate of variability in their imaginary part. (ii) An ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex data set in (i

Demodulation Algorithms for the Ofdm Signals in the Time- and Frequency-Scattering Channels

NASA Astrophysics Data System (ADS)

Bochkov, G. N.; Gorokhov, K. V.; Kolobkov, A. V.

2016-06-01

We consider a method based on the generalized maximum-likelihood rule for solving the problem of reception of the signals with orthogonal frequency division multiplexing of their harmonic components (OFDM signals) in the time- and frequency-scattering channels. The coherent and incoherent demodulators effectively using the time scattering due to the fast fading of the signal are developed. Using computer simulation, we performed comparative analysis of the proposed algorithms and well-known signal-reception algorithms with equalizers. The proposed symbolby-symbol detector with decision feedback and restriction of the number of searched variants is shown to have the best bit-error-rate performance. It is shown that under conditions of the limited accuracy of estimating the communication-channel parameters, the incoherent OFDMsignal detectors with differential phase-shift keying can ensure a better bit-error-rate performance compared with the coherent OFDM-signal detectors with absolute phase-shift keying.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

Twitter data analysis: temporal and term frequency analysis with real-time event

NASA Astrophysics Data System (ADS)

Yadav, Garima; Joshi, Mansi; Sasikala, R.

2017-11-01

From the past few years, World Wide Web (www) has become a prominent and huge source for user generated content and opinionative data. Among various social media, Twitter gained popularity as it offers a fast and effective way of sharing users’ perspective towards various critical and other issues in different domain. As the data is hugely generated on cloud, it has opened doors for the researchers in the field of data science and analysis. There are various domains such as ‘Political’ domain, ‘Entertainment’ domain and ‘Business’ domain. Also there are various APIs that Twitter provides for developers 1) Search API, focus on the old tweets 2) Rest API, focuses on user details and allow to collect the user profile, friends and followers 3) Streaming API, which collects details like tweets, hashtags, geo locations. In our work we are accessing Streaming API in order to fetch real-time tweets for the dynamic happening event. For this we are focusing on ‘Entertainment’ domain especially ‘Sports’ as IPL-T20 is currently the trending on-going event. We are collecting these numerous amounts of tweets and storing them in MongoDB database where the tweets are stored in JSON document format. On this document we are performing time-series analysis and term frequency analysis using different techniques such as filtering, information extraction for text-mining that fulfils our objective of finding interesting moments for temporal data in the event and finding the ranking among the players or the teams based on popularity which helps people in understanding key influencers on the social media platform.

Time-frequency and advanced frequency estimation techniques for the investigation of bat echolocation calls.

PubMed

Kopsinis, Yannis; Aboutanios, Elias; Waters, Dean A; McLaughlin, Steve

2010-02-01

In this paper, techniques for time-frequency analysis and investigation of bat echolocation calls are studied. Particularly, enhanced resolution techniques are developed and/or used in this specific context for the first time. When compared to traditional time-frequency representation methods, the proposed techniques are more capable of showing previously unseen features in the structure of bat echolocation calls. It should be emphasized that although the study is focused on bat echolocation recordings, the results are more general and applicable to many other types of signal.

Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan.

PubMed

Wang, Ven-Shing; Lo, Ei-Wen; Liang, Chih-Hsiang; Chao, Keh-Ping; Bao, Bo-Ying; Chang, Ta-Yuan

2016-12-01

Road traffic noise exposure has been associated with auditory and non-auditory health effects, but few studies report noise characteristics. This study determines 24-h noise levels and analyzes their frequency components to investigate associations between seasons, meteorology, land-use types, and traffic. We set up 50 monitoring stations covering ten different land-use types and conducted measurements at three times of the year to obtain 24-h-average A-weighted equivalent noise levels (L Aeq , 24h ) and frequency analyses from 2013 to 2014 in Taichung, Taiwan. Information on land-use types, road parameters, traffic flow rates, and meteorological variables was also collected for analysis with the annual averages of road traffic noise and its frequency components. The annual average L Aeq , 24h in Taichung was 66.4 ± 4.7 A-weighed decibels (dBA). Significant differences in L Aeq , 24h and frequency components were observed between land-use types (all p-values < 0.001), but not between seasons, with the highest two noise levels of 71.2 ± 1.0 dBA and 70.0 ± 2.6 dBA measured in stream-channel and commercial areas, with the highest component being 61.4 ± 5.3 dBA at 1000 Hz. Road width, traffic flow rates, and land-use types were significantly associated with annual average L Aeq , 24h (all p-values < 0.050). Noise levels at 125 Hz had the highest correlation with total traffic (Spearman's coefficient = 0.795) and the highest prediction in the multiple linear regression (R 2  = 0.803; adjusted R 2  = 0.765). These findings reveal the spatial variation in road traffic noise exposure in Taichung. The highest correlation and predictive capacity was observed between this variation and noise levels at 125 Hz. We recommend that governmental agencies should take actions to reduce noise levels from traffic vehicles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Engineering Design Handbook: Timing Systems and Components

DTIC Science & Technology

1975-12-01

23-1 23-2 Modular Components 23-2 23—3 Integrated Circuits 23—2 23—4 Matching Techniques 23-5 23-5 DC and AC Systems 23-7 23-6 Hybrid...Assembly Illustrating Modular Design . . 23—4 23-3 Characteristics of the Source 23—6 23—4 Characteristics of the Load 23—6 23—5 Matching Source and...4-1 INTRODUCTION There is a continuous demand for increased precision and accuracy in frequency control. Today fast time pulses are used in

Component analysis of somatosensory evoked potentials for identifying spinal cord injury location.

PubMed

Wang, Yazhou; Li, Guangsheng; Luk, Keith D K; Hu, Yong

2017-05-24

This study aims to determine whether the time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) can be used to identify the specific location of a compressive spinal cord injury using a classification technique. Waveforms of SEPs after compressive injuries at various locations (C4, C5 and C6) in rat spinal cords were decomposed into a series of TFCs using a high-resolution time-frequency analysis method. A classification method based on support vector machine (SVM) was applied to the distributions of these TFCs among different pathological locations. The difference among injury locations manifests itself in different categories of SEP TFCs. High-energy TFCs of normal-state SEPs have significantly higher power and frequency than those of injury-state SEPs. The location of C5 is characterized by a unique distribution pattern of middle-energy TFCs. The difference between C4 and C6 is evidenced by the distribution pattern of low-energy TFCs. The proposed classification method based on SEP TFCs offers a discrimination accuracy of 80.2%. In this study, meaningful information contained in various SEP components was investigated and used to propose a new application of SEPs for identification of the location of pathological changes in the cervical spinal cord.

[Simulation of speech perception with cochlear implants : Influence of frequency and level of fundamental frequency components with electronic acoustic stimulation].

PubMed

Rader, T; Fastl, H; Baumann, U

2017-03-01

After implantation of cochlear implants with hearing preservation for combined electronic acoustic stimulation (EAS), the residual acoustic hearing ability relays fundamental speech frequency information in the low frequency range. With the help of acoustic simulation of EAS hearing perception the impact of frequency and level fine structure of speech signals can be systematically examined. The aim of this study was to measure the speech reception threshold (SRT) under various noise conditions with acoustic EAS simulation by variation of the frequency and level information of the fundamental frequency f0 of speech. The study was carried out to determine to what extent the SRT is impaired by modification of the f0 fine structure. Using partial tone time pattern analysis an acoustic EAS simulation of the speech material from the Oldenburg sentence test (OLSA) was generated. In addition, determination of the f0 curve of the speech material was conducted. Subsequently, either the parameter frequency or level of f0 was fixed in order to remove one of the two fine contour information of the speech signal. The processed OLSA sentences were used to determine the SRT in background noise under various test conditions. The conditions "f0 fixed frequency" and "f0 fixed level" were tested under two different situations, under "amplitude modulated background noise" and "continuous background noise" conditions. A total of 24 subjects with normal hearing participated in the study. The SRT in background noise for the condition "f0 fixed frequency" was more favorable in continuous noise with 2.7 dB and in modulated noise with 0.8 dB compared to the condition "f0 fixed level" with 3.7 dB and 2.9 dB, respectively. In the simulation of speech perception with cochlear implants and acoustic components, the level information of the fundamental frequency had a stronger impact on speech intelligibility than the frequency information. The method of simulation of transmission of

Adaptive noise cancelling and time-frequency techniques for rail surface defect detection

NASA Astrophysics Data System (ADS)

Liang, B.; Iwnicki, S.; Ball, A.; Young, A. E.

2015-03-01

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.

Investigation into the bistatic evolution of the acoustic scattering from a cylindrical shell using time-frequency analysis

NASA Astrophysics Data System (ADS)

Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-01-01

The time and frequency analyses of the acoustic scattering by an elastic cylindrical shell in bistatic method show that the arrival times of the echoes and the resonance frequencies of the elastic waves propagating in and around the cylindrical shell are a function of the bistatic angle, β, between the emitter and receiver transducers. The aim of this work is to explain the observed results in time and frequency domains using time-frequency analysis and graphical interpretations. The performance of four widely used time-frequency representations, the Smoothed Pseudo Wigner-Ville (SPWV), the Spectrogram (SP), the reassignment SPWV, and the reassignment SP, are studied. The investigation into the evolution of the time-frequency plane as a function of the bistatic angle β shows that there are the waves propagating in counter-clockwise direction (labeled wave+) and the waves which propagate in clockwise direction (labeled waves-). In this paper the A, S0, and A1 circumferential waves are investigated. The graphical interpretations are used to explain the formation mechanism of these waves and the acoustic scattering in monostatic and bistatic configurations. The delay between the echoes of the waves+ and those of the waves- is expressed in the case of the circumnavigating wave (Scholte-Stoneley wave). This study shows that the observed waves at β = 0 ° and β = 18 0 ° are the result of the constructive interferences between the waves+ and the waves-. A comparative study of the physical properties (group velocity dispersion and cut-off frequency) of the waves+, the waves- and the waves observed in monostatic configuration is conducted. Furthermore, it is shown that the ability of the time-frequency representation to highlight the waves+ and the waves- is very useful, for example, for the detection and the localization of defaults, the classification purposes, etc.

Research on criticality analysis method of CNC machine tools components under fault rate correlation

NASA Astrophysics Data System (ADS)

Gui-xiang, Shen; Xian-zhuo, Zhao; Zhang, Ying-zhi; Chen-yu, Han

2018-02-01

In order to determine the key components of CNC machine tools under fault rate correlation, a system component criticality analysis method is proposed. Based on the fault mechanism analysis, the component fault relation is determined, and the adjacency matrix is introduced to describe it. Then, the fault structure relation is hierarchical by using the interpretive structure model (ISM). Assuming that the impact of the fault obeys the Markov process, the fault association matrix is described and transformed, and the Pagerank algorithm is used to determine the relative influence values, combined component fault rate under time correlation can obtain comprehensive fault rate. Based on the fault mode frequency and fault influence, the criticality of the components under the fault rate correlation is determined, and the key components are determined to provide the correct basis for equationting the reliability assurance measures. Finally, taking machining centers as an example, the effectiveness of the method is verified.

Connecting Time and Frequency in the RC Circuit

NASA Astrophysics Data System (ADS)

Moya, A. A.

2017-04-01

Charging and discharging processes of a capacitor through a resistor, as well as the concept of impedance in alternating current circuits, are topics covered in introductory physics courses. The experimental study of the charge and discharge of a capacitor through a resistor is a well-established lab exercise that is used to introduce concepts such as exponential increase or decrease and time constant. Determining the time constant of the RC circuit has important practical applications because, for example, it can be used to measure unknown values of resistance or capacitance. The transient experiment can be done by using a voltmeter and stopwatch, signal generator and oscilloscope, or even low-cost data acquisition systems such as Arduino. An equivalent topic when studying alternating current circuits arises from the characterization of the impedance of the series or parallel combination of the capacitor and the resistor as a function of frequency. Determining the time constant of the RC circuit by means of impedance measurements for different frequencies is a known experimental technique that can be done using not only LCR meters but also basic instrumentation in the physics lab such as a signal generator, frequency counter, and multimeter. However, lab exercises dealing with RC circuits in alternating current usually focus on their use as filters, and the potential applications in the field of the electrical characterization of material systems are ignored. In this work, we describe a simple exercise showing how the time constant of the RC circuit can easily be determined in the introductory physics lab by means of impedance measurements as a function of frequency. This exercise allows students to learn experimental techniques that find application to characterize the time constants of the charge transport processes in material systems. Moreover, comparison of the time constants obtained from transient and frequency analysis allows us to relate the time and

Time and frequency applications.

PubMed

Hellwig, H

1993-01-01

An overview is given of the capabilities of atomic clocks and quartz crystal oscillators in terms of available precision of time and frequency signals. The generation, comparison, and dissemination of time and frequency is then discussed. The principal focus is to survey uses of time and frequency in navigation, communication, and science. The examples given include the Global Positioning System, a satellite-based global navigation system, and general and dedicated communication networks, as well as experiments in general relativity and radioastronomy. The number of atomic clocks and crystal oscillators that are in actual use worldwide is estimated.

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

PubMed Central

Hu, L.; Zhang, Z.G.; Mouraux, A.; Iannetti, G.D.

2015-01-01

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

Postural analysis in time and frequency domains in patients with Ehlers-Danlos syndrome.

PubMed

Galli, Manuela; Rigoldi, Chiara; Celletti, Claudia; Mainardi, Luca; Tenore, Nunzio; Albertini, Giorgio; Camerota, Filippo

2011-01-01

The goal of this work is to analyze postural control in Ehlers-Danlos syndrome (EDS) participants in time and frequency domain. This study considered a pathological group composed by 22 EDS participants performing a postural test consisting in maintaining standing position over a force platform for 30s in two conditions: open eyes (OE) and closed eyes (CE). In order to compare pathological group we acquired in the same conditions a control group composed by 20 healthy participants. The obtained center of pressure (COP) signal was analyzed in time and frequency domain using an AR model. Results revealed differences between pathological and control group: EDS participants pointed out difficulties in controlling COP displacements trying to keep it inside the BOS in AP direction and for this reason increased the use of ML mechanism in order to avoid the risk of fall. Also in CE conditions they demonstrated more difficulties in maintaining posture revealing the proprioceptive system is impaired, due to ligament laxity that characterized EDS participants. Frequency domain analysis showed no differences between the two groups, affirming that the changes in time domain reflected really the impairment to the postural control mechanism and not a different strategy assumed by EDS participants. These data could help in decision-making process to establish a correct rehabilitation approach, based on the reinforcing of muscle tone to supply the ligament laxity in order to prevent risks of falls and its consequences. Copyright © 2010 Elsevier Ltd. All rights reserved.

Time-Frequency and Non-Laplacian Phenomena at Radio Frequencies

DTIC Science & Technology

2017-01-22

Unlimited UU UU UU UU 22-01-2017 30-Sep-2012 30-Sep-2016 Final Report: Time -Frequency and Non-Laplacian Phenomena at Radio Frequencies The views...average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data... Time ‐Frequency and Non‐Laplacian Phenomena at Radio Frequencies  U.S. Army Research Office grant W911NF‐12‐1‐0526  Michael B. Steer  Department of

Frequency-phase analysis of resting-state functional MRI

PubMed Central

Goelman, Gadi; Dan, Rotem; Růžička, Filip; Bezdicek, Ondrej; Růžička, Evžen; Roth, Jan; Vymazal, Josef; Jech, Robert

2017-01-01

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. PMID:28272522

Surface atrial frequency analysis in patients with atrial fibrillation: a tool for evaluating the effects of intervention.

PubMed

Raine, Dan; Langley, Philip; Murray, Alan; Dunuwille, Asunga; Bourke, John P

2004-09-01

The aims of this study were to evaluate (1) principal component analysis as a technique for extracting the atrial signal waveform from the standard 12-lead ECG and (2) its ability to distinguish changes in atrial fibrillation (AF) frequency parameters over time and in response to pharmacologic manipulation using drugs with different effects on atrial electrophysiology. Twenty patients with persistent AF were studied. Continuous 12-lead Holter ECGs were recorded for 60 minutes, first, in the drug-free state. Mean and variability of atrial waveform frequency were measured using an automated computer technique. This extracted the atrial signal by principal component analysis and identified the main frequency component using Fourier analysis. Patients were then allotted sequentially to receive 1 of 4 drugs intravenously (amiodarone, flecainide, sotalol, or metoprolol), and changes induced in mean and variability of atrial waveform frequency measured. Mean and variability of atrial waveform frequency did not differ within patients between the two 30-minute sections of the drug-free state. As hypothesized, significant changes in mean and variability of atrial waveform frequency were detected after manipulation with amiodarone (mean: 5.77 vs 4.86 Hz; variability: 0.55 vs 0.31 Hz), flecainide (mean: 5.33 vs 4.72 Hz; variability: 0.71 vs 0.31 Hz), and sotalol (mean: 5.94 vs 4.90 Hz; variability: 0.73 vs 0.40 Hz) but not with metoprolol (mean: 5.41 vs 5.17 Hz; variability: 0.81 vs 0.82 Hz). A technique for continuously analyzing atrial frequency characteristics of AF from the surface ECG has been developed and validated.

Dynamic analysis of a flexible spacecraft with rotating components. Volume 1: Analytical developments

NASA Technical Reports Server (NTRS)

Bodley, C. S.; Devers, A. D.; Park, A. C.

1975-01-01

Analytical procedures and digital computer code are presented for the dynamic analysis of a flexible spacecraft with rotating components. Topics, considered include: (1) nonlinear response in the time domain, and (2) linear response in the frequency domain. The spacecraft is assumed to consist of an assembly of connected rigid or flexible subassemblies. The total system is not restricted to a topological connection arrangement and may be acting under the influence of passive or active control systems and external environments. The analytics and associated digital code provide the user with the capability to establish spacecraft system nonlinear total response for specified initial conditions, linear perturbation response about a calculated or specified nominal motion, general frequency response and graphical display, and spacecraft system stability analysis.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly

Eulerian frequency analysis of structural vibrations from high-speed video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Venanzoni, Andrea; Siemens Industry Software NV, Interleuvenlaan 68, B-3001 Leuven; De Ryck, Laurent

An approach for the analysis of the frequency content of structural vibrations from high-speed video recordings is proposed. The techniques and tools proposed rely on an Eulerian approach, that is, using the time history of pixels independently to analyse structural motion, as opposed to Lagrangian approaches, where the motion of the structure is tracked in time. The starting point is an existing Eulerian motion magnification method, which consists in decomposing the video frames into a set of spatial scales through a so-called Laplacian pyramid [1]. Each scale — or level — can be amplified independently to reconstruct a magnified motionmore » of the observed structure. The approach proposed here provides two analysis tools or pre-amplification steps. The first tool provides a representation of the global frequency content of a video per pyramid level. This may be further enhanced by applying an angular filter in the spatial frequency domain to each frame of the video before the Laplacian pyramid decomposition, which allows for the identification of the frequency content of the structural vibrations in a particular direction of space. This proposed tool complements the existing Eulerian magnification method by amplifying selectively the levels containing relevant motion information with respect to their frequency content. This magnifies the displacement while limiting the noise contribution. The second tool is a holographic representation of the frequency content of a vibrating structure, yielding a map of the predominant frequency components across the structure. In contrast to the global frequency content representation of the video, this tool provides a local analysis of the periodic gray scale intensity changes of the frame in order to identify the vibrating parts of the structure and their main frequencies. Validation cases are provided and the advantages and limits of the approaches are discussed. The first validation case consists of the frequency

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

NASA Astrophysics Data System (ADS)

Huang, Liang; Ni, Xuan; Ditto, William L.; Spano, Mark; Carney, Paul R.; Lai, Ying-Cheng

2017-01-01

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

Time Frequency Analysis of Spacecraft Propellant Tank Spinning Slosh

NASA Technical Reports Server (NTRS)

Green, Steven T.; Burkey, Russell C.; Sudermann, James

2010-01-01

Many spacecraft are designed to spin about an axis along the flight path as a means of stabilizing the attitude of the spacecraft via gyroscopic stiffness. Because of the assembly requirements of the spacecraft and the launch vehicle, these spacecraft often spin about an axis corresponding to a minor moment of inertia. In such a case, any perturbation of the spin axis will cause sloshing motions in the liquid propellant tanks that will eventually dissipate enough kinetic energy to cause the spin axis nutation (wobble) to grow further. This spinning slosh and resultant nutation growth is a primary design problem of spinning spacecraft and one that is not easily solved by analysis or simulation only. Testing remains the surest way to address spacecraft nutation growth. This paper describes a test method and data analysis technique that reveal the resonant frequency and damping behavior of liquid motions in a spinning tank. Slosh resonant frequency and damping characteristics are necessary inputs to any accurate numerical dynamic simulation of the spacecraft.

Note: A component-level frequency tunable isolator for vibration-sensitive chips using SMA beams.

PubMed

Zhang, Xiaoyong; Ding, Xin; Wu, Di; Qi, Junlei; Wang, Ruixin; Lu, Siwei; Yan, Xiaojun

2016-06-01

This note presents a component-level frequency tunable isolator for vibration-sensitive chips. The isolator employed 8 U-shaped shape memory alloy (SMA) beams to support an isolation island (used for mounting chips). Due to the temperature-induced Young's modulus variation of SMA, the system stiffness of the isolator can be controlled through heating the SMA beams. In such a way, the natural frequency of the isolator can be tuned. A prototype was fabricated to evaluate the concept. The test results show that the natural frequency of the isolator can be tuned in the range of 64 Hz-97 Hz by applying different heating strategies. Moreover, resonant vibration can be suppressed significantly (the transmissibility decreases about 65% near the resonant frequency) using a real-time tuning method.

Solid perception mechanism by a shading pattern: spatial frequency components in a corrugated wave pattern.

PubMed

Nameda, N

1988-01-01

Illumination allows solid object perception to be obtained and depicted by a shading pattern produced by lighting. The shading cue, as one of solid perception cues (Gibson 1979), was investigated in regard to a white corrugated wave shape, using computer graphic device: Tospix-2. The reason the corrugated wave was chosen, is that an alternately bright and dark pattern, produced by shading, can be conveniently analyzed into contained spatial frequencies. This paper reports spatial frequency properties contained in the shading pattern. The shading patterns, input into the computer graphic device, are analyzed by Fourier Transformation by the same device. After the filtration by various spatial frequency low and high pass filters, Inverse Fourier Transformation is carried out for the residual components. The result of the analysis indicates that the third through higher harmonics components are important in regard to presenting a solid reality feeling in solid perception. Sakata (1983) also reported that an edged pattern, superimposed onto a lower sinusoidal pattern, was important in solid perception. The third through higher harmonics components express the changing position of luminance on the pattern, and a slanted plane relating to the light direction. Detection of a solid shape, constructed with flat planes, is assumed to be on the bottom of the perfect curved solid perception mechanism. Apparent evidence for this assumption, in difficult visual conditions, is that a flat paneled solid is seen before the curved solid. This mechanism is explained by two spatial frequency neural network systems, assumed as having correspondence with higher spatial frequency detection and lower spatial frequency detection.

Cluster analysis of word frequency dynamics

NASA Astrophysics Data System (ADS)

Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

2015-01-01

This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

[An EMD based time-frequency distribution and its application in EEG analysis].

PubMed

Li, Xiaobing; Chu, Meng; Qiu, Tianshuang; Bao, Haiping

2007-10-01

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.

A component analysis of positive behaviour support plans.

PubMed

McClean, Brian; Grey, Ian

2012-09-01

Positive behaviour support (PBS) emphasises multi-component interventions by natural intervention agents to help people overcome challenging behaviours. This paper investigates which components are most effective and which factors might mediate effectiveness. Sixty-one staff working with individuals with intellectual disability and challenging behaviours completed longitudinal competency-based training in PBS. Each staff participant conducted a functional assessment and developed and implemented a PBS plan for one prioritised individual. A total of 1,272 interventions were available for analysis. Measures of challenging behaviour were taken at baseline, after 6 months, and at an average of 26 months follow-up. There was a significant reduction in the frequency, management difficulty, and episodic severity of challenging behaviour over the duration of the study. Escape was identified by staff as the most common function, accounting for 77% of challenging behaviours. The most commonly implemented components of intervention were setting event changes and quality-of-life-based interventions. Only treatment acceptability was found to be related to decreases in behavioural frequency. No single intervention component was found to have a greater association with reductions in challenging behaviour.

The noseleaf of Rhinolophus formosae focuses the Frequency Modulated (FM) component of the calls

PubMed Central

Vanderelst, Dieter; Lee, Ya-Fu; Geipel, Inga; Kalko, Elisabeth K. V.; Kuo, Yen-Min; Peremans, Herbert

2013-01-01

Bats of the family Rhinolophidae emit their echolocation calls through their nostrils and feature elaborate noseleaves shaping the directionality of the emissions. The calls of these bats consist of a long constant-frequency component preceded and/or followed by short frequency-modulated sweeps. While Rhinolophidae are known for their physiological specializations for processing the constant frequency part of the calls, previous evidence suggests that the noseleaves of these animals are tuned to the frequencies in the frequency modulated components of the calls. In this paper, we seek further support for this hypothesis by simulating the emission beam pattern of the bat Rhinolophus formosae. Filling the furrows of lancet and removing the basal lappets (i.e., two flaps on the noseleaf) we find that these conspicuous features of the noseleaf focus the emitted energy mostly for frequencies in the frequency-modulated components. Based on the assumption that this component of the call is used by the bats for ranging, we develop a qualitative model to assess the increase in performance due to the furrows and/or the lappets. The model confirms that both structures decrease the ambiguity in selecting relevant targets for ranging. The lappets and the furrows shape the emission beam for different spatial regions and frequency ranges. Therefore, we conclude that the presented evidence is in line with the hypothesis that different parts of the noseleaves of Rhinolophidae are tuned to different frequency ranges with at least some of the most conspicuous ones being tuned to the frequency modulated components of the calls—thus yielding strong evidence for the sensory importance of the component. PMID:23882226

Real time recognition of explosophorous group and explosive material using laser induced photoacoustic spectroscopy associated with novel algorithm for time and frequency domain analysis.

PubMed

El-Sharkawy, Yasser H; Elbasuney, Sherif

2018-06-07

Energy-rich bonds such as nitrates (NO 3 - ) and percholorates (ClO 4 - ) have an explosive nature; they are frequently encountered in high energy materials. These bonds encompass two highly electronegative atoms competing for electrons. Common explosive materials including urea nitrate, ammonium nitrate, and ammonium percholorates were subjected to photoacoustic spectroscopy. The captured signal was processed using novel digital algorithm designed for time and frequency domain analysis. Frequency domain analysis offered not only characteristic frequencies for NO 3 - and ClO 4 - groups; but also characteristic fingerprint spectra (based on thermal, acoustical, and optical properties) for different materials. The main outcome of this study is that phase-shift domain analysis offered an outstanding signature for each explosive material, with novel discrimination between explosive and similar non-explosive material. Photoacoustic spectroscopy offered different characteristic signatures that can be employed for real time detection with stand-off capabilities. There is no two materials could have the same optical, thermal, and acoustical properties. Copyright © 2018 Elsevier B.V. All rights reserved.

Blind source separation based on time-frequency morphological characteristics for rigid acoustic scattering by underwater objects

NASA Astrophysics Data System (ADS)

Yang, Yang; Li, Xiukun

2016-06-01

Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation (BSS) can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution (WVD) observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. An experimental simulation has been used, with changes in the pulse width of the transmitted signal, the relative amplitude and the time delay parameter, in order to analyzing the feasibility of this new method. Simulation results show that the new method is not only able to separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.

Psychophysical and physiological responses to gratings with luminance and chromatic components of different spatial frequencies.

PubMed

Cooper, Bonnie; Sun, Hao; Lee, Barry B

2012-02-01

Gratings that contain luminance and chromatic components of different spatial frequencies were used to study the segregation of signals in luminance and chromatic pathways. Psychophysical detection and discrimination thresholds to these compound gratings, with luminance and chromatic components of the one either half or double the spatial frequency of the other, were measured in human observers. Spatial frequency tuning curves for detection of compound gratings followed the envelope of those for luminance and chromatic gratings. Different grating types were discriminable at detection threshold. Fourier analysis of physiological responses of macaque retinal ganglion cells to compound waveforms showed chromatic information to be restricted to the parvocellular pathway and luminance information to the magnocellular pathway. Taken together, the human psychophysical and macaque physiological data support the strict segregation of luminance and chromatic information in independent channels, with the magnocellular and parvocellular pathways, respectively, serving as likely the physiological substrates. © 2012 Optical Society of America

Direct measurement of group delay with joint time-frequency analysis of a white-light spectral interferogram.

PubMed

Deng, Yuqiang; Yang, Weijian; Zhou, Chun; Wang, Xi; Tao, Jun; Kong, Weipeng; Zhang, Zhigang

2008-12-01

We propose and demonstrate an analysis method to directly extract the group delay rather than the phase from the white-light spectral interferogram. By the joint time-frequency analysis technique, group delay is directly read from the ridge of wavelet transform, and group-delay dispersion is easily obtained by additional differentiation. The technique shows reasonable potential for the characterization of ultra-broadband chirped mirrors.

Payload and Components Real-Time Automated Test System (PACRATS), Data Acquisition of Leak Rate and Pressure Data Test Procedure

NASA Technical Reports Server (NTRS)

Rinehart, Maegan L.

2011-01-01

The purpose of this activity is to provide the Mechanical Components Test Facility (MCTF) with the capability to obtain electronic leak test and proof pressure data, Payload and Components Real-time Automated Test System (PACRATS) data acquisition software will be utilized to display real-time data. It will record leak rates and pressure/vacuum level(s) simultaneously. This added functionality will provide electronic leak test and pressure data at specified sampling frequencies. Electronically stored data will provide ES61 with increased data security, analysis, and accuracy. The tasks performed in this procedure are to verify PACRATS only, and are not intended to provide verifications for MCTF equipment.

Time-domain representation of frequency-dependent foundation impedance functions

USGS Publications Warehouse

Safak, E.

2006-01-01

Foundation impedance functions provide a simple means to account for soil-structure interaction (SSI) when studying seismic response of structures. Impedance functions represent the dynamic stiffness of the soil media surrounding the foundation. The fact that impedance functions are frequency dependent makes it difficult to incorporate SSI in standard time-history analysis software. This paper introduces a simple method to convert frequency-dependent impedance functions into time-domain filters. The method is based on the least-squares approximation of impedance functions by ratios of two complex polynomials. Such ratios are equivalent, in the time-domain, to discrete-time recursive filters, which are simple finite-difference equations giving the relationship between foundation forces and displacements. These filters can easily be incorporated into standard time-history analysis programs. Three examples are presented to show the applications of the method.

Steerable Principal Components for Space-Frequency Localized Images*

PubMed Central

Landa, Boris; Shkolnisky, Yoel

2017-01-01

As modern scientific image datasets typically consist of a large number of images of high resolution, devising methods for their accurate and efficient processing is a central research task. In this paper, we consider the problem of obtaining the steerable principal components of a dataset, a procedure termed “steerable PCA” (steerable principal component analysis). The output of the procedure is the set of orthonormal basis functions which best approximate the images in the dataset and all of their planar rotations. To derive such basis functions, we first expand the images in an appropriate basis, for which the steerable PCA reduces to the eigen-decomposition of a block-diagonal matrix. If we assume that the images are well localized in space and frequency, then such an appropriate basis is the prolate spheroidal wave functions (PSWFs). We derive a fast method for computing the PSWFs expansion coefficients from the images' equally spaced samples, via a specialized quadrature integration scheme, and show that the number of required quadrature nodes is similar to the number of pixels in each image. We then establish that our PSWF-based steerable PCA is both faster and more accurate then existing methods, and more importantly, provides us with rigorous error bounds on the entire procedure. PMID:29081879

Effects of reward context on feedback processing as indexed by time-frequency analysis.

PubMed

Watts, Adreanna T M; Bernat, Edward M

2018-05-11

The role of reward context has been investigated as an important factor in feedback processing. Previous work has demonstrated that the amplitude of the feedback negativity (FN) depends on the value of the outcome relative to the range of possible outcomes in a given context, not the objective value of the outcome. However, some research has shown that the FN does not scale with loss magnitude in loss-only contexts, suggesting that some contexts do not show a pattern of context dependence. Methodologically, time-frequency decomposition techniques have proven useful for isolating time-domain ERP activity as separable processes indexed in delta (< 3 Hz) and theta (3-7 Hz). Thus, the current study assessed the role of context in a modified gambling feedback task using time-frequency analysis to better isolate the underlying processes. Results revealed that theta was more context dependent and reflected a binary evaluation of bad versus good outcomes in the gain and even contexts. Delta was more context independent: good outcomes scaled linearly with reward magnitude and good-bad differences scaled with context valence. Our findings reveal that theta and delta are differentially sensitive to context and that context valence may play a critical role in determining how the brain processes feedback. © 2018 Society for Psychophysiological Research.

Seismic signal time-frequency analysis based on multi-directional window using greedy strategy

NASA Astrophysics Data System (ADS)

Chen, Yingpin; Peng, Zhenming; Cheng, Zhuyuan; Tian, Lin

2017-08-01

Wigner-Ville distribution (WVD) is an important time-frequency analysis technology with a high energy distribution in seismic signal processing. However, it is interfered by many cross terms. To suppress the cross terms of the WVD and keep the concentration of its high energy distribution, an adaptive multi-directional filtering window in the ambiguity domain is proposed. This begins with the relationship of the Cohen distribution and the Gabor transform combining the greedy strategy and the rotational invariance property of the fractional Fourier transform in order to propose the multi-directional window, which extends the one-dimensional, one directional, optimal window function of the optimal fractional Gabor transform (OFrGT) to a two-dimensional, multi-directional window in the ambiguity domain. In this way, the multi-directional window matches the main auto terms of the WVD more precisely. Using the greedy strategy, the proposed window takes into account the optimal and other suboptimal directions, which also solves the problem of the OFrGT, called the local concentration phenomenon, when encountering a multi-component signal. Experiments on different types of both the signal models and the real seismic signals reveal that the proposed window can overcome the drawbacks of the WVD and the OFrGT mentioned above. Finally, the proposed method is applied to a seismic signal's spectral decomposition. The results show that the proposed method can explore the space distribution of a reservoir more precisely.

Fault detection of gearbox using time-frequency method

NASA Astrophysics Data System (ADS)

Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

2017-04-01

This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

Meal frequency and timing in health and disease

PubMed Central

Mattson, Mark P.; Allison, David B.; Fontana, Luigi; Harvie, Michelle; Longo, Valter D.; Malaisse, Willy J.; Mosley, Michael; Notterpek, Lucia; Ravussin, Eric; Scheer, Frank A. J. L.; Seyfried, Thomas N.; Varady, Krista A.; Panda, Satchidananda

2014-01-01

Although major research efforts have focused on how specific components of foodstuffs affect health, relatively little is known about a more fundamental aspect of diet, the frequency and circadian timing of meals, and potential benefits of intermittent periods with no or very low energy intakes. The most common eating pattern in modern societies, three meals plus snacks every day, is abnormal from an evolutionary perspective. Emerging findings from studies of animal models and human subjects suggest that intermittent energy restriction periods of as little as 16 h can improve health indicators and counteract disease processes. The mechanisms involve a metabolic shift to fat metabolism and ketone production, and stimulation of adaptive cellular stress responses that prevent and repair molecular damage. As data on the optimal frequency and timing of meals crystalizes, it will be critical to develop strategies to incorporate those eating patterns into health care policy and practice, and the lifestyles of the population. PMID:25404320

Application of time-frequency analysis to the evaluation of the condition of car suspension

NASA Astrophysics Data System (ADS)

Szymański, G. M.; Josko, M.; Tomaszewski, F.; Filipiak, R.

2015-06-01

The article presents possibilities of use of vibration signal parameters for the evaluation of elements' clearance in the car suspension system. The time-spectrum analysis has been proposed to determine the frequency band connected with car body free vibration generated by impacts of suspension elements in case of clearance in suspension elements fixing to the car body. Diagnostic models allowing evaluation of shock absorber fastening to the car body are described in this work.

Isolating the Energetic Component of Speech-on-Speech Masking With Ideal Time-Frequency Segregation

DTIC Science & Technology

2006-12-01

Auditory Scene Analysis MIT Press, Cambridge, MA. Bronkhorst, A., and Plomp, R. 1992. “Effects of multiple speechlike maskers on binaural speech...C. J. 1994. “Perception and computational sepa- ration of simultaneous vowels: Cues arising from low frequency beating ,” J. Acoust. Soc. Am. 95...Litovsky, R., and Culling, J. 2004. “The benefit of binaural hearing in a cocktail party: Effects of location and type of interferer,” J. Acoust. Soc

CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

PubMed

Krishnamurthy, Krish

2013-12-01

The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.

Hilbert-Huang spectral analysis for characterizing the intrinsic time-scales of variability in decennial time-series of surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2016-04-01

An analysis of the variability of the surface solar irradiance (SSI) at different local time-scales is presented in this study. Since geophysical signals, such as long-term measurements of the SSI, are often produced by the non-linear interaction of deterministic physical processes that may also be under the influence of non-stationary external forcings, the Hilbert-Huang transform (HHT), an adaptive, noise-assisted, data-driven technique, is employed to extract locally - in time and in space - the embedded intrinsic scales at which a signal oscillates. The transform consists of two distinct steps. First, by means of the Empirical Mode Decomposition (EMD), the time-series is "de-constructed" into a finite number - often small - of zero-mean components that have distinct temporal scales of variability, termed hereinafter the Intrinsic Mode Functions (IMFs). The signal model of the components is an amplitude modulation - frequency modulation (AM - FM) one, and can also be thought of as an extension of a Fourier series having both time varying amplitude and frequency. Following the decomposition, Hilbert spectral analysis is then employed on the IMFs, yielding a time-frequency-energy representation that portrays changes in the spectral contents of the original data, with respect to time. As measurements of surface solar irradiance may possibly be contaminated by the manifestation of different type of stochastic processes (i.e. noise), the identification of real, physical processes from this background of random fluctuations is of interest. To this end, an adaptive background noise null hypothesis is assumed, based on the robust statistical properties of the EMD when applied to time-series of different classes of noise (e.g. white, red or fractional Gaussian). Since the algorithm acts as an efficient constant-Q dyadic, "wavelet-like", filter bank, the different noise inputs are decomposed into components having the same spectral shape, but that are translated to the

High Frequency Resolution TOA Analysis for ELF/VLFWave Generation Experiments at HAARP

NASA Astrophysics Data System (ADS)

Ruddle, J. D.; Moore, R. C.

2014-12-01

Modulated HF heating of the ionosphere in the presence of natural ionospheric current sources has been used as a method to generate electromagnetic ELF/VLF waves since the 1970's. In the ~1-5 kHz band, the amplitude and phase of the received ELF/VLF signal depends on the amplitude and phase of the conductivity modulation generated throughout the HF-heated ionospheric body, as well as on the signal propagation parameters (i.e., the attenuation and phase constants) between each of the current sources and the receiver. Recent signal processing advances have produced an accurate ELF/VLF time-of-arrival (TOA) analysis technique that differentiates line-of-sight and ionospherically-reflected signal components, determining the amplitude and phase of each component observed at the receiver. This TOA method requires a wide bandwidth (> 2.5 kHz) and therefore is relatively insensitive to the frequency-dependent nature of ELF/VLF wave propagation. In this paper, we present an improved ELF/VLF TOA method that is capable of providing high frequency resolution. The new analysis technique is applied to experimental observations of ELF/VLF signals generated by modulated heating at HAARP. We present measurements of the amplitude and phase of the received ELF/VLF signal as a function of frequency and compare the results with the predictions of an HF heating model.

Improved time-frequency analysis of ASDEX Upgrade reflectometry data using the reassigned spectrogram technique.

PubMed

Varela, P; Silva, A; da Silva, F; da Graça, S; Manso, M E; Conway, G D

2010-10-01

The spectrogram is one of the best-known time-frequency distributions suitable to analyze signals whose energy varies both in time and frequency. In reflectometry, it has been used to obtain the frequency content of FM-CW signals for density profile inversion and also to study plasma density fluctuations from swept and fixed frequency data. Being implemented via the short-time Fourier transform, the spectrogram is limited in resolution, and for that reason several methods have been developed to overcome this problem. Among those, we focus on the reassigned spectrogram technique that is both easily automated and computationally efficient requiring only the calculation of two additional spectrograms. In each time-frequency window, the technique reallocates the spectrogram coordinates to the region that most contributes to the signal energy. The application to ASDEX Upgrade reflectometry data results in better energy concentration and improved localization of the spectral content of the reflected signals. When combined with the automatic (data driven) window length spectrogram, this technique provides improved profile accuracy, in particular, in regions where frequency content varies most rapidly such as the edge pedestal shoulder.

Signal analysis by means of time-frequency (Wigner-type) distributions -- Applications to sonar and radar echoes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaunaurd, G.; Strifors, H.C.

1996-09-01

Time series data have been traditionally analyzed in either the time or the frequency domains. For signals with a time-varying frequency content, the combined time-frequency (TF) representations, based on the Cohen class of (generalized) Wigner distributions (WD`s) offer a powerful analysis tool. Using them, it is possible to: (1) trace the time-evolution of the resonance features usually present in a standard sonar cross section (SCS), or in a radar cross section (RCS) and (2) extract target information that may be difficult to even notice in an ordinary SCS or RCS. After a brief review of the fundamental properties of themore » WD, the authors discuss ways to reduce or suppress the cross term interference that appears in the WD of multicomponent systems. These points are illustrated with a variety of three-dimensional (3-D) plots of Wigner and pseudo-Wigner distributions (PWD), in which the strength of the distribution is depicted as the height of a Wigner surface with height scales measured by various color shades or pseudocolors. The authors also review studies they have made of the echoes returned by conducting or dielectric targets in the atmosphere, when they are illuminated by broadband radar pings. A TF domain analysis of these impulse radar returns demonstrates their superior informative content. These plots allow the identification of targets in an easier and clearer fashion than by the conventional RCS of narrowband systems. The authors show computed and measured plots of WD and PWD of various types of aircraft to illustrate the classification advantages of the approach at any aspect angle. They also show analogous results for metallic objects buried underground, in dielectric media, at various depths.« less

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats.

PubMed

Richard, Nelly; Laursen, Bettina; Grupe, Morten; Drewes, Asbjørn M; Graversen, Carina; Sørensen, Helge B D; Bastlund, Jesper F

2017-04-01

Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats. An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task. While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones. The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

NASA Astrophysics Data System (ADS)

Richard, Nelly; Laursen, Bettina; Grupe, Morten; Drewes, Asbjørn M.; Graversen, Carina; Sørensen, Helge B. D.; Bastlund, Jesper F.

2017-04-01

Objective. Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats. Approach. An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task. Main results. While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones. Significance. The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.

Applying matching pursuit decomposition time-frequency processing to UGS footstep classification

NASA Astrophysics Data System (ADS)

Larsen, Brett W.; Chung, Hugh; Dominguez, Alfonso; Sciacca, Jacob; Kovvali, Narayan; Papandreou-Suppappola, Antonia; Allee, David R.

2013-06-01

The challenge of rapid footstep detection and classification in remote locations has long been an important area of study for defense technology and national security. Also, as the military seeks to create effective and disposable unattended ground sensors (UGS), computational complexity and power consumption have become essential considerations in the development of classification techniques. In response to these issues, a research project at the Flexible Display Center at Arizona State University (ASU) has experimented with footstep classification using the matching pursuit decomposition (MPD) time-frequency analysis method. The MPD provides a parsimonious signal representation by iteratively selecting matched signal components from a pre-determined dictionary. The resulting time-frequency representation of the decomposed signal provides distinctive features for different types of footsteps, including footsteps during walking or running activities. The MPD features were used in a Bayesian classification method to successfully distinguish between the different activities. The computational cost of the iterative MPD algorithm was reduced, without significant loss in performance, using a modified MPD with a dictionary consisting of signals matched to cadence temporal gait patterns obtained from real seismic measurements. The classification results were demonstrated with real data from footsteps under various conditions recorded using a low-cost seismic sensor.

The time-frequency method of signal analysis in internal combustion engine diagnostics

NASA Astrophysics Data System (ADS)

Avramchuk, V. S.; Kazmin, V. P.; Faerman, V. A.; Le, V. T.

2017-01-01

The paper presents the results of the study of applicability of time-frequency correlation functions to solving the problems of internal combustion engine fault diagnostics. The proposed methods are theoretically justified and experimentally tested. In particular, the method’s applicability is illustrated by the example of specially generated signals that simulate the vibration of an engine both during the normal operation and in the case of a malfunction in the system supplying fuel to the cylinders. This method was confirmed during an experiment with an automobile internal combustion engine. The study offers the main findings of the simulation and the experiment and highlights certain characteristic features of time-frequency autocorrelation functions that allow one to identify malfunctions in an engine’s cylinder. The possibility in principle of using time-frequency correlation functions in function testing of the internal combustion engine is demonstrated. The paper’s conclusion proposes further research directions including the application of the method to diagnosing automobile gearboxes.

Tonal frequency affects amplitude but not topography of rhesus monkey cranial EEG components.

PubMed

Teichert, Tobias

2016-06-01

The rhesus monkey is an important model of human auditory function in general and auditory deficits in neuro-psychiatric diseases such as schizophrenia in particular. Several rhesus monkey studies have described homologs of clinically relevant auditory evoked potentials such as pitch-based mismatch negativity, a fronto-central negativity that can be observed when a series of regularly repeating sounds is disrupted by a sound of different tonal frequency. As a result it is well known how differences of tonal frequency are represented in rhesus monkey EEG. However, to date there is no study that systematically quantified how absolute tonal frequency itself is represented. In particular, it is not known if frequency affects rhesus monkey EEG component amplitude and topography in the same way as previously shown for humans. A better understanding of the effect of frequency may strengthen inter-species homology and will provide a more solid foundation on which to build the interpretation of frequency MMN in the rhesus monkey. Using arrays of up to 32 cranial EEG electrodes in 4 rhesus macaques we identified 8 distinct auditory evoked components including the N85, a fronto-central negativity that is the presumed homolog of the human N1. In line with human data, the amplitudes of most components including the N85 peaked around 1000 Hz and were strongly attenuated above ∼1750 Hz. Component topography, however, remained largely unaffected by frequency. This latter finding may be consistent with the known absence of certain anatomical structures in the rhesus monkey that are believed to cause the changes in topography in the human by inducing a rotation of generator orientation as a function of tonal frequency. Overall, the findings are consistent with the assumption of a homolog representation of tonal frequency in human and rhesus monkey EEG. Copyright © 2016 Elsevier B.V. All rights reserved.

A real-time algorithm for the harmonic estimation and frequency tracking of dominant components in fusion plasma magnetic diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alves, D.; Coelho, R.; Collaboration: JET-EFDA Contributors

2013-08-15

The real-time tracking of instantaneous quantities such as frequency, amplitude, and phase of components immerse in noisy signals has been a common problem in many scientific and engineering fields such as power systems and delivery, telecommunications, and acoustics for the past decades. In magnetically confined fusion research, extracting this sort of information from magnetic signals can be of valuable assistance in, for instance, feedback control of detrimental magnetohydrodynamic modes and disruption avoidance mechanisms by monitoring instability growth or anticipating mode-locking events. This work is focused on nonlinear Kalman filter based methods for tackling this problem. Similar methods have already provenmore » their merits and have been successfully employed in this scientific domain in applications such as amplitude demodulation for the motional Stark effect diagnostic. In the course of this work, three approaches are described, compared, and discussed using magnetic signals from the Joint European Torus tokamak plasma discharges for benchmarking purposes.« less

Time frequency analysis of olfactory induced EEG-power change.

PubMed

Schriever, Valentin Alexander; Han, Pengfei; Weise, Stefanie; Hösel, Franziska; Pellegrino, Robert; Hummel, Thomas

2017-01-01

The objective of the present study was to investigate the usefulness of time-frequency analysis (TFA) of olfactory-induced EEG change with a low-cost, portable olfactometer in the clinical investigation of smell function. A total of 78 volunteers participated. The study was composed of three parts where olfactory stimuli were presented using a custom-built olfactometer. Part I was designed to optimize the stimulus as well as the recording conditions. In part II EEG-power changes after olfactory/trigeminal stimulation were compared between healthy participants and patients with olfactory impairment. In Part III the test-retest reliability of the method was evaluated in healthy subjects. Part I indicated that the most effective paradigm for stimulus presentation was cued stimulus, with an interstimulus interval of 18-20s at a stimulus duration of 1000ms with each stimulus quality presented 60 times in blocks of 20 stimuli each. In Part II we found that central processing of olfactory stimuli analyzed by TFA differed significantly between healthy controls and patients even when controlling for age. It was possible to reliably distinguish patients with olfactory impairment from healthy individuals at a high degree of accuracy (healthy controls vs anosmic patients: sensitivity 75%; specificity 89%). In addition we could show a good test-retest reliability of TFA of chemosensory induced EEG-power changes in Part III. Central processing of olfactory stimuli analyzed by TFA reliably distinguishes patients with olfactory impairment from healthy individuals at a high degree of accuracy. Importantly this can be achieved with a simple olfactometer.

Instantaneous frequency time analysis of physiology signals: The application of pregnant women’s radial artery pulse signals

NASA Astrophysics Data System (ADS)

Su, Zhi-Yuan; Wang, Chuan-Chen; Wu, Tzuyin; Wang, Yeng-Tseng; Tang, Feng-Cheng

2008-01-01

This study used the Hilbert-Huang transform, a recently developed, instantaneous frequency-time analysis, to analyze radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy. The acquired instantaneous frequency-time spectrum (Hilbert spectrum) is further compared with the Morlet wavelet spectrum. Results indicate that the Hilbert spectrum is especially suitable for analyzing the time series of non-stationary radial artery pulse signals since, in the Hilbert-Huang transform, signals are decomposed into different mode functions in accordance with signal’s local time scale. Therefore, the Hilbert spectrum contains more detailed information than the Morlet wavelet spectrum. From the Hilbert spectrum, we can see that radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy have different patterns. This approach could be applied to facilitate non-invasive diagnosis of fetus’ physiological signals in the future.

Use of a Principal Components Analysis for the Generation of Daily Time Series.

NASA Astrophysics Data System (ADS)

Dreveton, Christine; Guillou, Yann

2004-07-01

A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.

PubMed

Tošić, Tamara; Sellers, Kristin K; Fröhlich, Flavio; Fedotenkova, Mariia; Beim Graben, Peter; Hutt, Axel

2015-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain.

Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

PubMed Central

Tošić, Tamara; Sellers, Kristin K.; Fröhlich, Flavio; Fedotenkova, Mariia; beim Graben, Peter; Hutt, Axel

2016-01-01

For decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system's dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the δ-band activity is much less recurrent than α-band activity. Moreover, α-activity is susceptible to pre-stimuli, while δ-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain. PMID:26834580

Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions

NASA Astrophysics Data System (ADS)

Naritomi, Yusuke; Fuchigami, Sotaro

2011-02-01

Protein dynamics on a long time scale was investigated using all-atom molecular dynamics (MD) simulation and time-structure based independent component analysis (tICA). We selected the lysine-, arginine-, ornithine-binding protein (LAO) as a target protein and focused on its domain motions in the open state. A MD simulation of the LAO in explicit water was performed for 600 ns, in which slow and large-amplitude domain motions of the LAO were observed. After extracting domain motions by rigid-body domain analysis, the tICA was applied to the obtained rigid-body trajectory, yielding slow modes of the LAO's domain motions in order of decreasing time scale. The slowest mode detected by the tICA represented not a closure motion described by a largest-amplitude mode determined by the principal component analysis but a twist motion with a time scale of tens of nanoseconds. The slow dynamics of the LAO were well described by only the slowest mode and were characterized by transitions between two basins. The results show that tICA is promising for describing and analyzing slow dynamics of proteins.

Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: the case of domain motions.

PubMed

Naritomi, Yusuke; Fuchigami, Sotaro

2011-02-14

Protein dynamics on a long time scale was investigated using all-atom molecular dynamics (MD) simulation and time-structure based independent component analysis (tICA). We selected the lysine-, arginine-, ornithine-binding protein (LAO) as a target protein and focused on its domain motions in the open state. A MD simulation of the LAO in explicit water was performed for 600 ns, in which slow and large-amplitude domain motions of the LAO were observed. After extracting domain motions by rigid-body domain analysis, the tICA was applied to the obtained rigid-body trajectory, yielding slow modes of the LAO's domain motions in order of decreasing time scale. The slowest mode detected by the tICA represented not a closure motion described by a largest-amplitude mode determined by the principal component analysis but a twist motion with a time scale of tens of nanoseconds. The slow dynamics of the LAO were well described by only the slowest mode and were characterized by transitions between two basins. The results show that tICA is promising for describing and analyzing slow dynamics of proteins.

Fine structure of the low-frequency spectra of heart rate and blood pressure.

PubMed

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-10-13

The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R-R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time-frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order--the most crucial factor when using this method--with the help of FFT and WVD methods. Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 +/- 0.003 (mean +/- SD) Hz, 0.076 +/- 0.012 Hz, and 0.117 +/- 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP-RRI phase relationship was found. The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04-0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological and computational questions regarding

An online input force time history reconstruction algorithm using dynamic principal component analysis

NASA Astrophysics Data System (ADS)

Prawin, J.; Rama Mohan Rao, A.

2018-01-01

The knowledge of dynamic loads acting on a structure is always required for many practical engineering problems, such as structural strength analysis, health monitoring and fault diagnosis, and vibration isolation. In this paper, we present an online input force time history reconstruction algorithm using Dynamic Principal Component Analysis (DPCA) from the acceleration time history response measurements using moving windows. We also present an optimal sensor placement algorithm to place limited sensors at dynamically sensitive spatial locations. The major advantage of the proposed input force identification algorithm is that it does not require finite element idealization of structure unlike the earlier formulations and therefore free from physical modelling errors. We have considered three numerical examples to validate the accuracy of the proposed DPCA based method. Effects of measurement noise, multiple force identification, different kinds of loading, incomplete measurements, and high noise levels are investigated in detail. Parametric studies have been carried out to arrive at optimal window size and also the percentage of window overlap. Studies presented in this paper clearly establish the merits of the proposed algorithm for online load identification.

Performance comparison of ISAR imaging method based on time frequency transforms

NASA Astrophysics Data System (ADS)

Xie, Chunjian; Guo, Chenjiang; Xu, Jiadong

2013-03-01

Inverse synthetic aperture radar (ISAR) can image the moving target, especially the target in the air, so it is important in the air defence and missile defence system. Time-frequency Transform was applied to ISAR imaging process widely. Several time frequency transforms were introduced. Noise jamming methods were analysed, and when these noise jamming were added to the echo of the ISAR receiver, the image can become blur even can't to be identify. But the effect is different to the different time frequency analysis. The results of simulation experiment show the Performance Comparison of the method.

High frequency resolution terahertz time-domain spectroscopy

NASA Astrophysics Data System (ADS)

Sangala, Bagvanth Reddy

2013-12-01

A new method for the high frequency resolution terahertz time-domain spectroscopy is developed based on the characteristic matrix method. This method is useful for studying planar samples or stack of planar samples. The terahertz radiation was generated by optical rectification in a ZnTe crystal and detected by another ZnTe crystal via electro-optic sampling method. In this new characteristic matrix based method, the spectra of the sample and reference waveforms will be modeled by using characteristic matrices. We applied this new method to measure the optical constants of air. The terahertz transmission through the layered systems air-Teflon-air-Quartz-air and Nitrogen gas-Teflon-Nitrogen gas-Quartz-Nitrogen gas was modeled by the characteristic matrix method. A transmission coefficient is derived from these models which was optimized to fit the experimental transmission coefficient to extract the optical constants of air. The optimization of an error function involving the experimental complex transmission coefficient and the theoretical transmission coefficient was performed using patternsearch algorithm of MATLAB. Since this method takes account of the echo waveforms due to reflections in the layered samples, this method allows analysis of longer time-domain waveforms giving rise to very high frequency resolution in the frequency-domain. We have presented the high frequency resolution terahertz time-domain spectroscopy of air and compared the results with the literature values. We have also fitted the complex susceptibility of air to the Lorentzian and Gaussian functions to extract the linewidths.

Spectrometer employing optical fiber time delays for frequency resolution

DOEpatents

Schuss, Jack J.; Johnson, Larry C.

1979-01-01

This invention provides different length glass fibers for providing a broad range of optical time delays for short incident chromatic light pulses for the selective spatial and frequency analysis of the light with a single light detector. To this end, the frequencies of the incident light are orientated and matched with the different length fibers by dispersing the separate frequencies in space according to the respective fiber locations and lengths at the input terminal of the glass fibers. This makes the different length fibers useful in the field of plasma physics. To this end the short light pulses can be scattered by a plasma and then passed through the fibers for analyzing and diagnosing the plasma while it varies rapidly with time.

A straightforward frequency-estimation technique for GPS carrier-phase time transfer.

PubMed

Hackman, Christine; Levine, Judah; Parker, Thomas E; Piester, Dirk; Becker, Jürgen

2006-09-01

Although Global Positioning System (GPS) carrier-phase time transfer (GPSCPTT) offers frequency stability approaching 10-15 at averaging times of 1 d, a discontinuity occurs in the time-transfer estimates between the end of one processing batch (1-3 d in length) and the beginning of the next. The average frequency over a multiday analysis period often has been computed by first estimating and removing these discontinuities, i.e., through concatenation. We present a new frequency-estimation technique in which frequencies are computed from the individual batches then averaged to obtain the mean frequency for a multiday period. This allows the frequency to be computed without the uncertainty associated with the removal of the discontinuities and requires fewer computational resources. The new technique was tested by comparing the fractional frequency-difference values it yields to those obtained using a GPSCPTT concatenation method and those obtained using two-way satellite time-and-frequency transfer (TWSTFT). The clocks studied were located in Braunschweig, Germany, and in Boulder, CO. The frequencies obtained from the GPSCPTT measurements using either method agreed with those obtained from TWSTFT at several parts in 1016. The frequency values obtained from the GPSCPTT data by use of the new method agreed with those obtained using the concatenation technique at 1-4 x 10(-16).

Time-Frequency Approach for Stochastic Signal Detection

NASA Astrophysics Data System (ADS)

Ghosh, Ripul; Akula, Aparna; Kumar, Satish; Sardana, H. K.

2011-10-01

The detection of events in a stochastic signal has been a subject of great interest. One of the oldest signal processing technique, Fourier Transform of a signal contains information regarding frequency content, but it cannot resolve the exact onset of changes in the frequency, all temporal information is contained in the phase of the transform. On the other hand, Spectrogram is better able to resolve temporal evolution of frequency content, but has a trade-off in time resolution versus frequency resolution in accordance with the uncertainty principle. Therefore, time-frequency representations are considered for energetic characterisation of the non-stationary signals. Wigner Ville Distribution (WVD) is the most prominent quadratic time-frequency signal representation and used for analysing frequency variations in signals.WVD allows for instantaneous frequency estimation at each data point, for a typical temporal resolution of fractions of a second. This paper through simulations describes the way time frequency models are applied for the detection of event in a stochastic signal.

78 FR 19311 - Certain Radio Frequency Identification (“RFID”) Products And Components Thereof; Institution of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-29

... Identification (``RFID'') Products And Components Thereof; Institution of Investigation Pursuant to 19 U.S.C... sale within the United States after importation of certain radio frequency identification (``RFID... after importation of certain radio frequency identification (``RFID'') products and components thereof...

Short-time fractional Fourier methods for the time-frequency representation of chirp signals.

PubMed

Capus, Chris; Brown, Keith

2003-06-01

The fractional Fourier transform (FrFT) provides a valuable tool for the analysis of linear chirp signals. This paper develops two short-time FrFT variants which are suited to the analysis of multicomponent and nonlinear chirp signals. Outputs have similar properties to the short-time Fourier transform (STFT) but show improved time-frequency resolution. The FrFT is a parameterized transform with parameter, a, related to chirp rate. The two short-time implementations differ in how the value of a is chosen. In the first, a global optimization procedure selects one value of a with reference to the entire signal. In the second, a values are selected independently for each windowed section. Comparative variance measures based on the Gaussian function are given and are shown to be consistent with the uncertainty principle in fractional domains. For appropriately chosen FrFT orders, the derived fractional domain uncertainty relationship is minimized for Gaussian windowed linear chirp signals. The two short-time FrFT algorithms have complementary strengths demonstrated by time-frequency representations for a multicomponent bat chirp, a highly nonlinear quadratic chirp, and an output pulse from a finite-difference sonar model with dispersive change. These representations illustrate the improvements obtained in using FrFT based algorithms compared to the STFT.

Characterization of Deficiencies in the Frequency Domain Forced Response Analysis Technique for Turbine Bladed Disks

NASA Technical Reports Server (NTRS)

Brown, Andrew M.; Schmauch, Preston

2012-01-01

Turbine blades in rocket and jet engine turbomachinery experience enormous harmonic loading conditions. These loads result from the integer number of upstream and downstream stator vanes as well as the other turbine stages. The standard technique for forced response analysis to assess structural integrity is to decompose a CFD generated flow field into its harmonic components, and to then perform a frequency response analysis at the problematic natural frequencies. Recent CFD analysis and water-flow testing at NASA/MSFC, though, indicates that this technique may miss substantial harmonic and non-harmonic excitation sources that become present in complex flows. These complications suggest the question of whether frequency domain analysis is capable of capturing the excitation content sufficiently. Two studies comparing frequency response analysis with transient response analysis, therefore, have been performed. The first is of a bladed disk with each blade modeled by simple beam elements. It was hypothesized that the randomness and other variation from the standard harmonic excitation would reduce the blade structural response, but the results showed little reduction. The second study was of a realistic model of a bladed-disk excited by the same CFD used in the J2X engine program. The results showed that the transient analysis results were up to 10% higher for "clean" nodal diameter excitations and six times larger for "messy" excitations, where substantial Fourier content around the main harmonic exists.

Time-Frequency Approach for Stochastic Signal Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Ripul; Akula, Aparna; Kumar, Satish

2011-10-20

The detection of events in a stochastic signal has been a subject of great interest. One of the oldest signal processing technique, Fourier Transform of a signal contains information regarding frequency content, but it cannot resolve the exact onset of changes in the frequency, all temporal information is contained in the phase of the transform. On the other hand, Spectrogram is better able to resolve temporal evolution of frequency content, but has a trade-off in time resolution versus frequency resolution in accordance with the uncertainty principle. Therefore, time-frequency representations are considered for energetic characterisation of the non-stationary signals. Wigner Villemore » Distribution (WVD) is the most prominent quadratic time-frequency signal representation and used for analysing frequency variations in signals.WVD allows for instantaneous frequency estimation at each data point, for a typical temporal resolution of fractions of a second. This paper through simulations describes the way time frequency models are applied for the detection of event in a stochastic signal.« less

A pipeline VLSI design of fast singular value decomposition processor for real-time EEG system based on on-line recursive independent component analysis.

PubMed

Huang, Kuan-Ju; Shih, Wei-Yeh; Chang, Jui Chung; Feng, Chih Wei; Fang, Wai-Chi

2013-01-01

This paper presents a pipeline VLSI design of fast singular value decomposition (SVD) processor for real-time electroencephalography (EEG) system based on on-line recursive independent component analysis (ORICA). Since SVD is used frequently in computations of the real-time EEG system, a low-latency and high-accuracy SVD processor is essential. During the EEG system process, the proposed SVD processor aims to solve the diagonal, inverse and inverse square root matrices of the target matrices in real time. Generally, SVD requires a huge amount of computation in hardware implementation. Therefore, this work proposes a novel design concept for data flow updating to assist the pipeline VLSI implementation. The SVD processor can greatly improve the feasibility of real-time EEG system applications such as brain computer interfaces (BCIs). The proposed architecture is implemented using TSMC 90 nm CMOS technology. The sample rate of EEG raw data adopts 128 Hz. The core size of the SVD processor is 580×580 um(2), and the speed of operation frequency is 20MHz. It consumes 0.774mW of power during the 8-channel EEG system per execution time.

Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.

PubMed

Gong, Anmin; Liu, Jianping; Chen, Si; Fu, Yunfa

2018-01-01

To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.

A New Approach in Time-Frequency Analysis with Applications to Experimental High Range Resolution Radar Data

DTIC Science & Technology

2003-11-01

Distributions In contrast to the linear time-frequency transforms such as the short-time Fourier transform, the Wigner - Ville distribution ( WVD ) is...23 9 Results of nine TFDs: (a) Wigner - Ville distribution , (b) Born-Jordan distribution , (c) Choi-Williams distribution , (d) bilinear TFD...are applied in the Wigner - Ville class of time-frequency transforms and the reassignment methods, which are applied to any time-frequency distribution

Predictability and Prediction of Low-Frequency Rainfall Over the Lower Reaches of the Yangtze River Valley on the Time Scale of 20 to 30 days

NASA Astrophysics Data System (ADS)

Yang, Qiuming

2018-01-01

This paper presents a predictability study of the 20-30-day low-frequency rainfall over the lower reaches of the Yangtze River valley (LYRV). This study relies on an extended complex autoregressive (ECAR) model method, which is based on the principal components of the global 850 hPa low-frequency meridional wind. ECAR is a recently advanced climate forecast method, based on data-driven models. It not only reflects the lagged variations information between the leading low-frequency components of the global circulation and rainfall in a complex space, but also displays the ability to describe the synergy variations of low-frequency components of a climate system in a low dimensional space. A 6-year forecast experiment is conducted on the low-frequency rainfall over the LYRV for the extended-range daily forecasts during 2009-2014, based on the time-varying high-order ECAR. These experimental results demonstrate that the useful skills of the real-time forecasts are achieved for an extended lead-time up to 28 days with a fifth-order model, and are also shown to be 27-day lead for forecasts which are initiated from weak intraseasonal oscillation (ISO). This high-order ECAR displays the ability to significantly improve the predictions of the ISO. The analysis of the 20-30-day ISO predictability reveals a predictability limit of about 28-40 days. Therefore, the forecast framework used in this study is determined to have the potential to assist in improving the real-time forecasts for the 20-30-day oscillations related to the heavy rainfall over the LYRV in summer.

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

NASA Astrophysics Data System (ADS)

Nasanbat, E.; Sharav, S.; Sanjaa, T.; Lkhamjav, O.; Magsar, E.; Tuvdendorj, B.

2018-04-01

This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

Hydrograph structure informed calibration in the frequency domain with time localization

NASA Astrophysics Data System (ADS)

Kumarasamy, K.; Belmont, P.

2015-12-01

Complex models with large number of parameters are commonly used to estimate sediment yields and predict changes in sediment loads as a result of changes in management or conservation practice at large watershed (>2000 km2) scales. As sediment yield is a strongly non-linear function that responds to channel (peak or mean) velocity or flow depth, it is critical to accurately represent flows. The process of calibration in such models (e.g., SWAT) generally involves the adjustment of several parameters to obtain better estimates of goodness of fit metrics such as Nash Sutcliff Efficiency (NSE). However, such indicators only provide a global view of model performance, potentially obscuring accuracy of the timing or magnitude of specific flows of interest. We describe an approach for streamflow calibration that will greatly reduce the black-box nature of calibration, when response from a parameter adjustment is not clearly known. Fourier Transform or the Short Term Fourier Transform could be used to characterize model performance in the frequency domain as well, however, the ambiguity of a Fourier transform with regards to time localization renders its implementation in a model calibration setting rather useless. Brief and sudden changes (e.g. stream flow peaks) in signals carry the most interesting information from parameter adjustments, which are completely lost in the transform without time localization. Wavelet transform captures the frequency component in the signal without compromising time and is applied to contrast changes in signal response to parameter adjustments. Here we employ the mother wavelet called the Mexican hat wavelet and apply a Continuous Wavelet Transform to understand the signal in the frequency domain. Further, with the use of the cross-wavelet spectrum we examine the relationship between the two signals (prior or post parameter adjustment) in the time-scale plane (e.g., lower scales correspond to higher frequencies). The non-stationarity of

Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates.

PubMed

Fraiwan, Luay; Lweesy, Khaldon; Khasawneh, Natheer; Fraiwan, Mohammad; Wenz, Heinrich; Dickhaus, Hartmut

2011-08-01

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.

Extraction of fast neuronal changes from multichannel functional near-infrared spectroscopy signals using independent component analysis

NASA Astrophysics Data System (ADS)

Morren, Geert; Wolf, Martin; Lemmerling, Philippe; Wolf, Ursula; Choi, Jee H.; Gratton, Enrico; De Lathauwer, Lieven; Van Huffel, Sabine

2002-06-01

Fast changes in the range of milliseconds in the optical properties of cerebral tissue, which are associated with brain activity, can be detected using non-invasive near-infrared spectroscopy (NIRS). These changes in light scattering are due to an alteration in the refractive index at neuronal membranes. The aim of this study was to develop highly sensitive data analysis algorithms to detect this fast signal, which is small compared to other physiological signals. A frequency-domain tissue oximeter, whose laser diodes were modulated at 110MHz was used. The amplitude, mean intensity and phase of the modulated optical signal was measured at 96Hz sample rate. The probe consisting of 4 crossed source detector pairs was placed above the motor cortex, contralateral to the hand performing a tapping exercise consisting of alternating rest- and tapping periods of 20s each. The tapping frequency, which was set to 3.55Hz or 2.5 times the heart rate of the subject to avoid the influence of harmonics on the signal, could not be observed in any of the individual signals measured by the detectors. An adaptive filter was used to remove the arterial pulsatility from the optical signals. Independent Component Analysis allowed to separate signal components in which the tapping frequency was clearly visible.

Systematic study of anharmonic features in a principal component analysis of gramicidin A.

PubMed

Kurylowicz, Martin; Yu, Ching-Hsing; Pomès, Régis

2010-02-03

We use principal component analysis (PCA) to detect functionally interesting collective motions in molecular-dynamics simulations of membrane-bound gramicidin A. We examine the statistical and structural properties of all PCA eigenvectors and eigenvalues for the backbone and side-chain atoms. All eigenvalue spectra show two distinct power-law scaling regimes, quantitatively separating large from small covariance motions. Time trajectories of the largest PCs converge to Gaussian distributions at long timescales, but groups of small-covariance PCs, which are usually ignored as noise, have subdiffusive distributions. These non-Gaussian distributions imply anharmonic motions on the free-energy surface. We characterize the anharmonic components of motion by analyzing the mean-square displacement for all PCs. The subdiffusive components reveal picosecond-scale oscillations in the mean-square displacement at frequencies consistent with infrared measurements. In this regime, the slowest backbone mode exhibits tilting of the peptide planes, which allows carbonyl oxygen atoms to provide surrogate solvation for water and cation transport in the channel lumen. Higher-frequency modes are also apparent, and we describe their vibrational spectra. Our findings expand the utility of PCA for quantifying the essential features of motion on the anharmonic free-energy surface made accessible by atomistic molecular-dynamics simulations. Copyright (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time

Common time-frequency analysis of local field potential and pyramidal cell activity in seizure-like events of the rat hippocampus

NASA Astrophysics Data System (ADS)

Cotic, M.; Chiu, A. W. L.; Jahromi, S. S.; Carlen, P. L.; Bardakjian, B. L.

2011-08-01

To study cell-field dynamics, physiologists simultaneously record local field potentials and the activity of individual cells from animals performing cognitive tasks, during various brain states or under pathological conditions. However, apart from spike shape and spike timing analyses, few studies have focused on elucidating the common time-frequency structure of local field activity relative to surrounding cells across different periods of phenomena. We have used two algorithms, multi-window time frequency analysis and wavelet phase coherence (WPC), to study common intracellular-extracellular (I-E) spectral features in spontaneous seizure-like events (SLEs) from rat hippocampal slices in a low magnesium epilepsy model. Both algorithms were applied to 'pairs' of simultaneously observed I-E signals from slices in the CA1 hippocampal region. Analyses were performed over a frequency range of 1-100 Hz. I-E spectral commonality varied in frequency and time. Higher commonality was observed from 1 to 15 Hz, and lower commonality was observed in the 15-100 Hz frequency range. WPC was lower in the non-SLE region compared to SLE activity; however, there was no statistical difference in the 30-45 Hz band between SLE and non-SLE modes. This work provides evidence of strong commonality in various frequency bands of I-E SLEs in the rat hippocampus, not only during SLEs but also immediately before and after.

The role of spatial frequency information for ERP components sensitive to faces and emotional facial expression.

PubMed

Holmes, Amanda; Winston, Joel S; Eimer, Martin

2005-10-01

To investigate the impact of spatial frequency on emotional facial expression analysis, ERPs were recorded in response to low spatial frequency (LSF), high spatial frequency (HSF), and unfiltered broad spatial frequency (BSF) faces with fearful or neutral expressions, houses, and chairs. In line with previous findings, BSF fearful facial expressions elicited a greater frontal positivity than BSF neutral facial expressions, starting at about 150 ms after stimulus onset. In contrast, this emotional expression effect was absent for HSF and LSF faces. Given that some brain regions involved in emotion processing, such as amygdala and connected structures, are selectively tuned to LSF visual inputs, these data suggest that ERP effects of emotional facial expression do not directly reflect activity in these regions. It is argued that higher order neocortical brain systems are involved in the generation of emotion-specific waveform modulations. The face-sensitive N170 component was neither affected by emotional facial expression nor by spatial frequency information.

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y., E-mail: thuzhangyu@foxmail.com; Huang, S. L., E-mail: huangsling@tsinghua.edu.cn; Wang, S.

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency formore » all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.« less

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals.

PubMed

Zhang, Y; Huang, S L; Wang, S; Zhao, W

2016-05-01

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert-Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.

Time-invariant component-based normalization for a simultaneous PET-MR scanner.

PubMed

Belzunce, M A; Reader, A J

2016-05-07

Component-based normalization is a method used to compensate for the sensitivity of each of the lines of response acquired in positron emission tomography. This method consists of modelling the sensitivity of each line of response as a product of multiple factors, which can be classified as time-invariant, time-variant and acquisition-dependent components. Typical time-variant factors are the intrinsic crystal efficiencies, which are needed to be updated by a regular normalization scan. Failure to do so would in principle generate artifacts in the reconstructed images due to the use of out of date time-variant factors. For this reason, an assessment of the variability and the impact of the crystal efficiencies in the reconstructed images is important to determine the frequency needed for the normalization scans, as well as to estimate the error obtained when an inappropriate normalization is used. Furthermore, if the fluctuations of these components are low enough, they could be neglected and nearly artifact-free reconstructions become achievable without performing a regular normalization scan. In this work, we analyse the impact of the time-variant factors in the component-based normalization used in the Biograph mMR scanner, but the work is applicable to other PET scanners. These factors are the intrinsic crystal efficiencies and the axial factors. For the latter, we propose a new method to obtain fixed axial factors that was validated with simulated data. Regarding the crystal efficiencies, we assessed their fluctuations during a period of 230 d and we found that they had good stability and low dispersion. We studied the impact of not including the intrinsic crystal efficiencies in the normalization when reconstructing simulated and real data. Based on this assessment and using the fixed axial factors, we propose the use of a time-invariant normalization that is able to achieve comparable results to the standard, daily updated, normalization factors used in this

Time-invariant component-based normalization for a simultaneous PET-MR scanner

NASA Astrophysics Data System (ADS)

Belzunce, M. A.; Reader, A. J.

2016-05-01

Component-based normalization is a method used to compensate for the sensitivity of each of the lines of response acquired in positron emission tomography. This method consists of modelling the sensitivity of each line of response as a product of multiple factors, which can be classified as time-invariant, time-variant and acquisition-dependent components. Typical time-variant factors are the intrinsic crystal efficiencies, which are needed to be updated by a regular normalization scan. Failure to do so would in principle generate artifacts in the reconstructed images due to the use of out of date time-variant factors. For this reason, an assessment of the variability and the impact of the crystal efficiencies in the reconstructed images is important to determine the frequency needed for the normalization scans, as well as to estimate the error obtained when an inappropriate normalization is used. Furthermore, if the fluctuations of these components are low enough, they could be neglected and nearly artifact-free reconstructions become achievable without performing a regular normalization scan. In this work, we analyse the impact of the time-variant factors in the component-based normalization used in the Biograph mMR scanner, but the work is applicable to other PET scanners. These factors are the intrinsic crystal efficiencies and the axial factors. For the latter, we propose a new method to obtain fixed axial factors that was validated with simulated data. Regarding the crystal efficiencies, we assessed their fluctuations during a period of 230 d and we found that they had good stability and low dispersion. We studied the impact of not including the intrinsic crystal efficiencies in the normalization when reconstructing simulated and real data. Based on this assessment and using the fixed axial factors, we propose the use of a time-invariant normalization that is able to achieve comparable results to the standard, daily updated, normalization factors used in this

The possible influence of noise frequency components on the health of exposed industrial workers--a review.

PubMed

Mahendra Prashanth, K V; Venugopalachar, Sridhar

2011-01-01

Noise is a common occupational health hazard in most industrial settings. An assessment of noise and its adverse health effects based on noise intensity is inadequate. For an efficient evaluation of noise effects, frequency spectrum analysis should also be included. This paper aims to substantiate the importance of studying the contribution of noise frequencies in evaluating health effects and their association with physiological behavior within human body. Additionally, a review of studies published between 1988 and 2009 that investigate the impact of industrial/occupational noise on auditory and non-auditory effects and the probable association and contribution of noise frequency components to these effects is presented. The relevant studies in English were identified in Medknow, Medline, Wiley, Elsevier, and Springer publications. Data were extracted from the studies that fulfilled the following criteria: title and/or abstract of the given study that involved industrial/occupational noise exposure in relation to auditory and non-auditory effects or health effects. Significant data on the study characteristics, including noise frequency characteristics, for assessment were considered in the study. It is demonstrated that only a few studies have considered the frequency contributions in their investigations to study auditory effects and not non-auditory effects. The data suggest that significant adverse health effects due to industrial noise include auditory and heart-related problems. The study provides a strong evidence for the claims that noise with a major frequency characteristic of around 4 kHz has auditory effects and being deficient in data fails to show any influence of noise frequency components on non-auditory effects. Furthermore, specific noise levels and frequencies predicting the corresponding health impacts have not yet been validated. There is a need for advance research to clarify the importance of the dominant noise frequency contribution in

Analysis of the circumferential acoustic waves backscattered by a tube using the time-frequency representation of Wigner-Ville

NASA Astrophysics Data System (ADS)

Latif, R.; Aassif, E.; Maze, G.; Decultot, D.; Moudden, A.; Faiz, B.

2000-01-01

This paper presents a study of the group velocity dispersion of some circumferential waves propagating around an elastic tube. The dispersive character of the circumferential waves is theoretically known, but the experimental measurement of the group velocity in a dispersive medium is still a complex operation. We have determined the characteristics of the circumferential wave dispersion for aluminium and steel tubes using a time-frequency representation. Among these time-frequency techniques, the Wigner-Ville distribution (WVD) is used here for its interesting properties in terms of acoustic applications. The WVD is applied to the analysis of the dispersion of S0 symmetric and A1 antisymmetric circumferential waves propagating around a tube with a radii ratio equal to 0.95 (internal radius:external radius). This allowed us to determine their group velocities and reduced cutoff frequencies. The results obtained are in good agreement with the calculated values using the proper modes theory.

Frequency-domain-independent vector analysis for mode-division multiplexed transmission

NASA Astrophysics Data System (ADS)

Liu, Yunhe; Hu, Guijun; Li, Jiao

2018-04-01

In this paper, we propose a demultiplexing method based on frequency-domain independent vector analysis (FD-IVA) algorithm for mode-division multiplexing (MDM) system. FD-IVA extends frequency-domain independent component analysis (FD-ICA) from unitary variable to multivariate variables, and provides an efficient method to eliminate the permutation ambiguity. In order to verify the performance of FD-IVA algorithm, a 6 ×6 MDM system is simulated. The simulation results show that the FD-IVA algorithm has basically the same bit-error-rate(BER) performance with the FD-ICA algorithm and frequency-domain least mean squares (FD-LMS) algorithm. Meanwhile, the convergence speed of FD-IVA algorithm is the same as that of FD-ICA. However, compared with the FD-ICA and the FD-LMS, the FD-IVA has an obviously lower computational complexity.

Statistical Analysis of Solar PV Power Frequency Spectrum for Optimal Employment of Building Loads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olama, Mohammed M; Sharma, Isha; Kuruganti, Teja

In this paper, a statistical analysis of the frequency spectrum of solar photovoltaic (PV) power output is conducted. This analysis quantifies the frequency content that can be used for purposes such as developing optimal employment of building loads and distributed energy resources. One year of solar PV power output data was collected and analyzed using one-second resolution to find ideal bounds and levels for the different frequency components. The annual, seasonal, and monthly statistics of the PV frequency content are computed and illustrated in boxplot format. To examine the compatibility of building loads for PV consumption, a spectral analysis ofmore » building loads such as Heating, Ventilation and Air-Conditioning (HVAC) units and water heaters was performed. This defined the bandwidth over which these devices can operate. Results show that nearly all of the PV output (about 98%) is contained within frequencies lower than 1 mHz (equivalent to ~15 min), which is compatible for consumption with local building loads such as HVAC units and water heaters. Medium frequencies in the range of ~15 min to ~1 min are likely to be suitable for consumption by fan equipment of variable air volume HVAC systems that have time constants in the range of few seconds to few minutes. This study indicates that most of the PV generation can be consumed by building loads with the help of proper control strategies, thereby reducing impact on the grid and the size of storage systems.« less

Time and frequency technology at NIST

NASA Technical Reports Server (NTRS)

Sullivan, D. B.

1994-01-01

The state of development of advanced timing systems at NIST is described. The work on cesium and rubidium frequency standards, stored-ion frequency standards, diode lasers used to pump such standards, time transfer, and methods for characterizing clocks, oscillators, and time distribution systems is presented. The emphasis is on NIST-developed technology rather than the general state of the art in this field.

Frequency accurate coherent electro-optic dual-comb spectroscopy in real-time.

PubMed

Martín-Mateos, Pedro; Jerez, Borja; Largo-Izquierdo, Pedro; Acedo, Pablo

2018-04-16

Electro-optic dual-comb spectrometers have proved to be a promising technology for sensitive, high-resolution and rapid spectral measurements. Electro-optic combs possess very attractive features like simplicity, reliability, bright optical teeth, and typically moderate but quickly tunable optical spans. Furthermore, in a dual-comb arrangement, narrowband electro-optic combs are generated with a level of mutual coherence that is sufficiently high to enable optical multiheterodyning without inter-comb stabilization or signal processing systems. However, this valuable tool still presents several limitations; for instance, on most systems, absolute frequency accuracy and long-term stability cannot be guaranteed; likewise, interferometer-induced phase noise restricts coherence time and limits the attainable signal-to-noise ratio. In this paper, we address these drawbacks and demonstrate a cost-efficient absolute electro-optic dual-comb instrument based on a frequency stabilization mechanism and a novel adaptive interferogram acquisition approach devised for electro-optic dual-combs capable of operating in real-time. The spectrometer, completely built from commercial components, provides sub-ppm frequency uncertainties and enables a signal-to-noise ratio of 10000 (intensity noise) in 30 seconds of integration time.

Multi-frequency data analysis in AFM by wavelet transform

NASA Astrophysics Data System (ADS)

Pukhova, V.; Ferrini, G.

2017-10-01

Interacting cantilevers in AFM experiments generate non-stationary, multi-frequency signals consisting of numerous excited flexural and torsional modes and their harmonics. The analysis of such signals is challenging, requiring special methodological approaches and a powerful mathematical apparatus. The most common approach to the signal analysis is to apply Fourier transform analysis. However, FT gives accurate spectra for stationary signals, and for signals changing their spectral content over time, FT provides only an averaged spectrum. Hence, for non-stationary and rapidly varying signals, such as those from interacting cantilevers, a method that shows the spectral evolution in time is needed. One of the most powerful techniques, allowing detailed time-frequency representation of signals, is the wavelet transform. It is a method of analysis that allows representation of energy associated to the signal at a particular frequency and time, providing correlation between the spectral and temporal features of the signal, unlike FT. This is particularly important in AFM experiments because signals nonlinearities contains valuable information about tip-sample interactions and consequently surfaces properties. The present work is aimed to show the advantages of wavelet transform in comparison with FT using as an example the force curve analysis in dynamic force spectroscopy.

Time-Frequency Characterization of Cerebral Hemodynamics of Migraine Sufferers as Assessed by NIRS Signals

NASA Astrophysics Data System (ADS)

Molinari, Filippo; Rosati, Samanta; Liboni, William; Negri, Emanuela; Mana, Ornella; Allais, Gianni; Benedetto, Chiara

2010-12-01

Near-infrared spectroscopy (NIRS) is a noninvasive system for the real-time monitoring of the concentration of oxygenated ([InlineEquation not available: see fulltext.]) and reduced (HHb) hemoglobin in the brain cortex. [InlineEquation not available: see fulltext.] and HHb concentrations vary in response to cerebral autoregulation. Sixty-eight women (14 migraineurs without aura, 49 migraineurs with aura, and 5 controls) performed breath-holding and hyperventilation during NIRS recordings. Signals were processed using the Choi-Williams time-frequency transform in order to measure the power variation of the very-low frequencies (VLF: 20-40 mHz) and of the low frequencies (LF: 40-140 mHz). Results showed that migraineurs without aura present different LF and VLF power levels than controls and migraineurs with aura. The accurate power measurement of the time-frequency analysis allowed for the discrimination of the subjects' hemodynamic patterns. The time-frequency analysis of NIRS signals can be used in clinical practice to assess cerebral hemodynamics.

Natural time analysis on the ultra-low frequency magnetic field variations prior to the 2016 Kumamoto (Japan) earthquakes

NASA Astrophysics Data System (ADS)

Potirakis, Stelios M.; Schekotov, Alexander; Asano, Tomokazu; Hayakawa, Masashi

2018-04-01

On 15 April 2016 a very strong and shallow earthquake (EQ) (MW = 7.0 , depth ∼ 10 km) occurred in Southwest Japan under the city of Kumamoto, while two very strong foreshocks (MW = 6.2 and MW = 6.0) preceded by about one day. The Kumamoto EQs being very catastrophic, have already attracted much attention among the scientific community in a quest for understanding the generation mechanism, as well as for reporting any preseismic anomalies in various observables and assessing the effectivity of the current early warning systems. In the present article we report precursory behavior of the ground-based observed ultra-low frequency (ULF) magnetic field variations before the Kumamoto EQs. By analyzing specific ULF magnetic field characteristics in terms of the recently introduced natural time (NT) analysis method, we identified that ULF magnetic field variations presented critical features from 2 weeks up to 1 month before the Kumamoto EQs. Specifically, the ULF magnetic field characteristics Fh , Fz , Dh and δDep were analyzed. The first two represent variations of the horizontal and vertical components of the geomagnetic field. The third and fourth characteristics correspond to the depression (decrease) and a relative depression of the horizontal magnetic field variations, respectively. The latter depends on the degree of ionospheric disturbance. All of them were found to reach criticality before the Kumamoto EQs; however, in different time periods for each characteristic.

Scale-free dynamics of the synchronization between sleep EEG power bands and the high frequency component of heart rate variability in normal men and patients with sleep apnea-hypopnea syndrome.

PubMed

Dumont, Martine; Jurysta, Fabrice; Lanquart, Jean-Pol; Noseda, André; van de Borne, Philippe; Linkowski, Paul

2007-12-01

To investigate the dynamics of the synchronization between heart rate variability and sleep electroencephalogram power spectra and the effect of sleep apnea-hypopnea syndrome. Heart rate and sleep electroencephalogram signals were recorded in controls and patients with sleep apnea-hypopnea syndrome that were matched for age, gender, sleep parameters, and blood pressure. Spectral analysis was applied to electrocardiogram and electroencephalogram sleep recordings to obtain power values every 20s. Synchronization likelihood was computed between time series of the normalized high frequency spectral component of RR-intervals and all electroencephalographic frequency bands. Detrended fluctuation analysis was applied to the synchronizations in order to qualify their dynamic behaviors. For all sleep bands, the fluctuations of the synchronization between sleep EEG and heart activity appear scale free and the scaling exponent is close to one as for 1/f noise. We could not detect any effect due to sleep apnea-hypopnea syndrome. The synchronizations between the high frequency component of heart rate variability and all sleep power bands exhibited robust fluctuations characterized by self-similar temporal behavior of 1/f noise type. No effects of sleep apnea-hypopnea syndrome were observed in these synchronizations. Sleep apnea-hypopnea syndrome does not affect the interdependence between the high frequency component of heart rate variability and all sleep power bands as measured by synchronization likelihood.

Time-frequency analyses of fluid-solid interaction under sinusoidal translational shear deformation of the viscoelastic rat cerebrum

NASA Astrophysics Data System (ADS)

Leahy, Lauren N.; Haslach, Henry W.

2018-02-01

During normal extracellular fluid (ECF) flow in the brain glymphatic system or during pathological flow induced by trauma resulting from impacts and blast waves, ECF-solid matter interactions result from sinusoidal shear waves in the brain and cranial arterial tissue, both heterogeneous biological tissues with high fluid content. The flow in the glymphatic system is known to be forced by pulsations of the cranial arteries at about 1 Hz. The experimental shear stress response to sinusoidal translational shear deformation at 1 Hz and 25% strain amplitude and either 0% or 33% compression is compared for rat cerebrum and bovine aortic tissue. Time-frequency analyses aim to correlate the shear stress signal frequency components over time with the behavior of brain tissue constituents to identify the physical source of the shear nonlinear viscoelastic response. Discrete fast Fourier transformation analysis and the novel application to the shear stress signal of harmonic wavelet decomposition both show significant 1 Hz and 3 Hz components. The 3 Hz component in brain tissue, whose magnitude is much larger than in aortic tissue, may result from interstitial fluid induced drag forces. The harmonic wavelet decomposition locates 3 Hz harmonics whose magnitudes decrease on subsequent cycles perhaps because of bond breaking that results in easier fluid movement. Both tissues exhibit transient shear stress softening similar to the Mullins effect in rubber. The form of a new mathematical model for the drag force produced by ECF-solid matter interactions captures the third harmonic seen experimentally.

Time Correlations and the Frequency Spectrum of Sound Radiated by Turbulent Flows

NASA Technical Reports Server (NTRS)

Rubinstein, Robert; Zhou, Ye

1997-01-01

Theories of turbulent time correlations are applied to compute frequency spectra of sound radiated by isotropic turbulence and by turbulent shear flows. The hypothesis that Eulerian time correlations are dominated by the sweeping action of the most energetic scales implies that the frequency spectrum of the sound radiated by isotropic turbulence scales as omega(exp 4) for low frequencies and as omega(exp -3/4) for high frequencies. The sweeping hypothesis is applied to an approximate theory of jet noise. The high frequency noise again scales as omega(exp -3/4), but the low frequency spectrum scales as omega(exp 2). In comparison, a classical theory of jet noise based on dimensional analysis gives omega(exp -2) and omega(exp 2) scaling for these frequency ranges. It is shown that the omega(exp -2) scaling is obtained by simplifying the description of turbulent time correlations. An approximate theory of the effect of shear on turbulent time correlations is developed and applied to the frequency spectrum of sound radiated by shear turbulence. The predicted steepening of the shear dominated spectrum appears to be consistent with jet noise measurements.

TIME-FREQUENCY ANALYSIS OF THE SUPERORBITAL MODULATION OF THE X-RAY BINARY SMC X-1 USING THE HILBERT-HUANG TRANSFORM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Chin-Ping; Chou, Yi; Yang, Ting-Chang

2011-10-20

The high-mass X-ray binary SMC X-1 exhibits a superorbital modulation with a dramatically varying period ranging between {approx}40 days and {approx}60 days. This research studies the time-frequency properties of the superorbital modulation of SMC X-1 based on the observations made by the All-Sky Monitor (ASM) onboard the Rossi X-ray Timing Explorer (RXTE). We analyzed the entire ASM database collected since 1996. The Hilbert-Huang transform (HHT), developed for non-stationary and nonlinear time-series analysis, was adopted to derive the instantaneous superorbital frequency. The resultant Hilbert spectrum is consistent with the dynamic power spectrum as it shows more detailed information in both themore » time and frequency domains. The RXTE observations show that the superorbital modulation period was mostly between {approx}50 days and {approx}65 days, whereas it changed to {approx}45 days around MJD 50,800 and MJD 54,000. Our analysis further indicates that the instantaneous frequency changed to a timescale of hundreds of days between {approx}MJD 51,500 and {approx}MJD 53,500. Based on the instantaneous phase defined by HHT, we folded the ASM light curve to derive a superorbital profile, from which an asymmetric feature and a low state with barely any X-ray emissions (lasting for {approx}0.3 cycles) were observed. We also calculated the correlation between the mean period and the amplitude of the superorbital modulation. The result is similar to the recently discovered relationship between the superorbital cycle length and the mean X-ray flux for Her X-1.« less

Applying the new method of time-frequency transforms to the analysis of the characteristics of geomagnetic Pc5 pulsations

NASA Astrophysics Data System (ADS)

Zelinsky, N. R.; Kleimenova, N. G.; Gromova, L. I.

2017-09-01

This study considers the possibility of using the new methods of time-frequency transforms, such as chirplet and warblet transforms, to analyze the digital observational data of geomagnetic pulsations of Pc5 type. For this purpose, necessary algorithms of calculation and appropriate software were developed. The chirplet transform method (CT) is used to analyze signals with a linear frequency modulation. A chirplet variation, the so-called warblet transform, is used to analyze signals with a nonlinear frequency modulation. Since, in studying geomagnetic pulsations, it is difficult to make assumptions on the character of the behavior of the instantaneous frequency of the signal, the special generalized warblet transform (GWT) was used for the analysis. The GWT has a high spatiotemporal resolution and was developed to analyze oscillations both with a periodic and nonperiodic change of the instantaneous frequency. The software developed for GWT calculation was used to study daytime geomagnetic Pc5 pulsations with durations of several hours that were detected via the network of ground-based magnetometers of the Scandinavian IMAGE profile during the magnetic storm of May 29-30, 2003. For the first time, temporal variations of the instantaneous frequency of geomagnetic pulsations are determined and their possible use in studying the fine spatial structure of Pc5 waves is shown.

Interpretations of Frequency Domain Analyses of Neural Entrainment: Periodicity, Fundamental Frequency, and Harmonics.

PubMed

Zhou, Hong; Melloni, Lucia; Poeppel, David; Ding, Nai

2016-01-01

Brain activity can follow the rhythms of dynamic sensory stimuli, such as speech and music, a phenomenon called neural entrainment. It has been hypothesized that low-frequency neural entrainment in the neural delta and theta bands provides a potential mechanism to represent and integrate temporal information. Low-frequency neural entrainment is often studied using periodically changing stimuli and is analyzed in the frequency domain using the Fourier analysis. The Fourier analysis decomposes a periodic signal into harmonically related sinusoids. However, it is not intuitive how these harmonically related components are related to the response waveform. Here, we explain the interpretation of response harmonics, with a special focus on very low-frequency neural entrainment near 1 Hz. It is illustrated why neural responses repeating at f Hz do not necessarily generate any neural response at f Hz in the Fourier spectrum. A strong neural response at f Hz indicates that the time scales of the neural response waveform within each cycle match the time scales of the stimulus rhythm. Therefore, neural entrainment at very low frequency implies not only that the neural response repeats at f Hz but also that each period of the neural response is a slow wave matching the time scale of a f Hz sinusoid.

Real-Time 12-Lead High-Frequency QRS Electrocardiography for Enhanced Detection of Myocardial Ischemia and Coronary Artery Disease

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Kulecz, Walter B.; DePalma, Jude L.; Feiveson, Alan H.; Wilson, John S.; Rahman, M. Atiar; Bungo, Michael W.

2004-01-01

Several studies have shown that diminution of the high-frequency (HF; 150-250 Hz) components present within the central portion of the QRS complex of an electrocardiogram (ECG) is a more sensitive indicator for the presence of myocardial ischemia than are changes in the ST segments of the conventional low-frequency ECG. However, until now, no device has been capable of displaying, in real time on a beat-to-beat basis, changes in these HF QRS ECG components in a continuously monitored patient. Although several software programs have been designed to acquire the HF components over the entire QRS interval, such programs have involved laborious off-line calculations and postprocessing, limiting their clinical utility. We describe a personal computer-based ECG software program developed recently at the National Aeronautics and Space Administration (NASA) that acquires, analyzes, and displays HF QRS components in each of the 12 conventional ECG leads in real time. The system also updates these signals and their related derived parameters in real time on a beat-to-beat basis for any chosen monitoring period and simultaneously displays the diagnostic information from the conventional (low-frequency) 12-lead ECG. The real-time NASA HF QRS ECG software is being evaluated currently in multiple clinical settings in North America. We describe its potential usefulness in the diagnosis of myocardial ischemia and coronary artery disease.

Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy patients.

PubMed

Clariá, F; Vallverdú, M; Baranowski, R; Chojnowska, L; Caminal, P

2008-03-01

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

PubMed Central

Peláez-Coca, M. D.; Orini, M.; Lázaro, J.; Bailón, R.; Gil, E.

2013-01-01

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration. PMID:24363777

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

NASA Astrophysics Data System (ADS)

Forootan, Ehsan; Kusche, Jürgen; Talpe, Matthieu; Shum, C. K.; Schmidt, Michael

2017-12-01

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission

A High-Spin Rate Measurement Method for Projectiles Using a Magnetoresistive Sensor Based on Time-Frequency Domain Analysis

PubMed Central

Shang, Jianyu; Deng, Zhihong; Fu, Mengyin; Wang, Shunting

2016-01-01

Traditional artillery guidance can significantly improve the attack accuracy and overall combat efficiency of projectiles, which makes it more adaptable to the information warfare of the future. Obviously, the accurate measurement of artillery spin rate, which has long been regarded as a daunting task, is the basis of precise guidance and control. Magnetoresistive (MR) sensors can be applied to spin rate measurement, especially in the high-spin and high-g projectile launch environment. In this paper, based on the theory of a MR sensor measuring spin rate, the mathematical relationship model between the frequency of MR sensor output and projectile spin rate was established through a fundamental derivation. By analyzing the characteristics of MR sensor output whose frequency varies with time, this paper proposed the Chirp z-Transform (CZT) time-frequency (TF) domain analysis method based on the rolling window of a Blackman window function (BCZT) which can accurately extract the projectile spin rate. To put it into practice, BCZT was applied to measure the spin rate of 155 mm artillery projectile. After extracting the spin rate, the impact that launch rotational angular velocity and aspect angle have on the extraction accuracy of the spin rate was analyzed. Simulation results show that the BCZT TF domain analysis method can effectively and accurately measure the projectile spin rate, especially in a high-spin and high-g projectile launch environment. PMID:27322266

A High-Spin Rate Measurement Method for Projectiles Using a Magnetoresistive Sensor Based on Time-Frequency Domain Analysis.

PubMed

Shang, Jianyu; Deng, Zhihong; Fu, Mengyin; Wang, Shunting

2016-06-16

Traditional artillery guidance can significantly improve the attack accuracy and overall combat efficiency of projectiles, which makes it more adaptable to the information warfare of the future. Obviously, the accurate measurement of artillery spin rate, which has long been regarded as a daunting task, is the basis of precise guidance and control. Magnetoresistive (MR) sensors can be applied to spin rate measurement, especially in the high-spin and high-g projectile launch environment. In this paper, based on the theory of a MR sensor measuring spin rate, the mathematical relationship model between the frequency of MR sensor output and projectile spin rate was established through a fundamental derivation. By analyzing the characteristics of MR sensor output whose frequency varies with time, this paper proposed the Chirp z-Transform (CZT) time-frequency (TF) domain analysis method based on the rolling window of a Blackman window function (BCZT) which can accurately extract the projectile spin rate. To put it into practice, BCZT was applied to measure the spin rate of 155 mm artillery projectile. After extracting the spin rate, the impact that launch rotational angular velocity and aspect angle have on the extraction accuracy of the spin rate was analyzed. Simulation results show that the BCZT TF domain analysis method can effectively and accurately measure the projectile spin rate, especially in a high-spin and high-g projectile launch environment.

Independent component analysis decomposition of hospital emergency department throughput measures

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Henry

2016-05-01

We present a method adapted from medical sensor data analysis, viz. independent component analysis of electroencephalography data, to health system analysis. Timely and effective care in a hospital emergency department is measured by throughput measures such as median times patients spent before they were admitted as an inpatient, before they were sent home, before they were seen by a healthcare professional. We consider a set of five such measures collected at 3,086 hospitals distributed across the U.S. One model of the performance of an emergency department is that these correlated throughput measures are linear combinations of some underlying sources. The independent component analysis decomposition of the data set can thus be viewed as transforming a set of performance measures collected at a site to a collection of outputs of spatial filters applied to the whole multi-measure data. We compare the independent component sources with the output of the conventional principal component analysis to show that the independent components are more suitable for understanding the data sets through visualizations.

Filter-based multiscale entropy analysis of complex physiological time series.

PubMed

Xu, Yuesheng; Zhao, Liang

2013-08-01

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Detection and Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of GPS Time Series

NASA Astrophysics Data System (ADS)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2014-12-01

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies. Indeed, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we present the application of the vbICA technique to GPS position time series. First, we use vbICA on synthetic data that simulate a seismic cycle

Real-time and high accuracy frequency measurements for intermediate frequency narrowband signals

NASA Astrophysics Data System (ADS)

Tian, Jing; Meng, Xiaofeng; Nie, Jing; Lin, Liwei

2018-01-01

Real-time and accurate measurements of intermediate frequency signals based on microprocessors are difficult due to the computational complexity and limited time constraints. In this paper, a fast and precise methodology based on the sigma-delta modulator is designed and implemented by first generating the twiddle factors using the designed recursive scheme. This scheme requires zero times of multiplications and only half amounts of addition operations by using the discrete Fourier transform (DFT) and the combination of the Rife algorithm and Fourier coefficient interpolation as compared with conventional methods such as DFT and Fast Fourier Transform. Experimentally, when the sampling frequency is 10 MHz, the real-time frequency measurements with intermediate frequency and narrowband signals have a measurement mean squared error of ±2.4 Hz. Furthermore, a single measurement of the whole system only requires approximately 0.3 s to achieve fast iteration, high precision, and less calculation time.

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2001-01-01

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Satellite time and frequency transfer (STIFT)

NASA Technical Reports Server (NTRS)

Vessot, R. F. C.

1983-01-01

The concept of placing a hydrogen maser high stability clock in Earth orbit to provide accurate time and frequency comparisons worldwide to major timing centers and to a large number of radio observatory antenna sites involved in VLBI measurements was studied. The proposal was chiefly directed toward studies and initial hardware designs for time comparisons between hydrogen maser frequency standards and to modifications of the hydrogen maser for long-term use in space.

The Benefits of Using Time-Frequency Analysis with Synthetic Aperture Focusing Technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albright, Austin P; Clayton, Dwight A

2015-01-01

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results producedmore » using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band s interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m x 2m x 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus

The benefits of using time-frequency analysis with synthetic aperture focusing technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albright, Austin, E-mail: albrightap@ornl.gov, E-mail: claytonda@ornl.gov; Clayton, Dwight, E-mail: albrightap@ornl.gov, E-mail: claytonda@ornl.gov

2015-03-31

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results producedmore » using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus

The benefits of using time-frequency analysis with synthetic aperture focusing technique

NASA Astrophysics Data System (ADS)

Albright, Austin; Clayton, Dwight

2015-03-01

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results produced using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

PubMed

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not

Least-dependent-component analysis based on mutual information

NASA Astrophysics Data System (ADS)

Stögbauer, Harald; Kraskov, Alexander; Astakhov, Sergey A.; Grassberger, Peter

2004-12-01

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of “independent” component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

KvN mechanics approach to the time-dependent frequency harmonic oscillator.

PubMed

Ramos-Prieto, Irán; Urzúa-Pineda, Alejandro R; Soto-Eguibar, Francisco; Moya-Cessa, Héctor M

2018-05-30

Using the Ermakov-Lewis invariants appearing in KvN mechanics, the time-dependent frequency harmonic oscillator is studied. The analysis builds upon the operational dynamical model, from which it is possible to infer quantum or classical dynamics; thus, the mathematical structure governing the evolution will be the same in both cases. The Liouville operator associated with the time-dependent frequency harmonic oscillator can be transformed using an Ermakov-Lewis invariant, which is also time dependent and commutes with itself at any time. Finally, because the solution of the Ermakov equation is involved in the evolution of the classical state vector, we explore some analytical and numerical solutions.

Frequency and time properties of decimeter narrowband spikes in solar flares

NASA Astrophysics Data System (ADS)

Wang, Shujuan

2013-07-01

In this paper, we focus to study the frequency and time properties of a group of spikes recorded by the 1.08-2.04 GHz spectrometer of NAOC on 27 October 2003. At the first we calculate the mean and minimum bandwidth of the spikes. We apply two different methods based on the wavelet analysis according to Messmer & Benz (2000). The first method determines the dominant spike bandwidth scale based on their scalegram, and the second method is a feature detection algorithm in the time-frequency plane. Secondly the time profile of each single spike was fitted and analyzed. In particular, we determined the e-folding rise and decay times corresponding to the ascending and decaying parts of the time profile, respectively. Several important correlations were studied and compared with the results in earlier literature, i.e. those between duration and frequency, e-folding rise time and decay time, e-folding decay time and duration, and e-folding decay time and peak flux. Finally some parameters of source region were estimated and the possible decaying mechanism was discussed.

Reducing Undue Conservatism in "Higher Frequency" Structural Design Loads in Aerospace Components

NASA Technical Reports Server (NTRS)

Knight, J. Brent

2012-01-01

This study is intended to investigate the frequency dependency of significant strain due to vibratory loads in aerospace vehicle components. The notion that "higher frequency" dynamic loads applied as static loads is inherently conservative is perceived as widely accepted. This effort is focused on demonstrating that principle and attempting to evolve methods to capitalize on it to mitigate undue conservatism. It has been suggested that observations of higher frequency modes that resulted in very low corresponding strain did so due to those modes not being significant. Two avionics boxes, one with its first significant mode at 341 Hz and the other at 857 Hz, were attached to a flat panel installed on a curved orthogrid panel which was driven acoustically in tests performed at NASA/MSFC. Strain and acceleration were measured at select locations on each of the boxes. When possible, strain gage rosettes and accelerometers were installed on either side of a given structural member so that measured strain and acceleration data would directly correspond to one another. Ultimately, a frequency above which vibratory loads can be disregarded for purposes of static structural analyses and sizing of typical robust aerospace components is sought.

Regional teleseismic body-wave tomography with component-differential finite-frequency sensitivity kernels

NASA Astrophysics Data System (ADS)

Yu, Y.; Shen, Y.; Chen, Y. J.

2015-12-01

By using ray theory in conjunction with the Born approximation, Dahlen et al. [2000] computed 3-D sensitivity kernels for finite-frequency seismic traveltimes. A series of studies have been conducted based on this theory to model the mantle velocity structure [e.g., Hung et al., 2004; Montelli et al., 2004; Ren and Shen, 2008; Yang et al., 2009; Liang et al., 2011; Tang et al., 2014]. One of the simplifications in the calculation of the kernels is the paraxial assumption, which may not be strictly valid near the receiver, the region of interest in regional teleseismic tomography. In this study, we improve the accuracy of traveltime sensitivity kernels of the first P arrival by eliminating the paraxial approximation. For calculation efficiency, the traveltime table built by the Fast Marching Method (FMM) is used to calculate both the wave vector and the geometrical spreading at every grid in the whole volume. The improved kernels maintain the sign, but with different amplitudes at different locations. We also find that when the directivity of the scattered wave is being taken into consideration, the differential sensitivity kernel of traveltimes measured at the vertical and radial component of the same receiver concentrates beneath the receiver, which can be used to invert for the structure inside the Earth. Compared with conventional teleseismic tomography, which uses the differential traveltimes between two stations in an array, this method is not affected by instrument response and timing errors, and reduces the uncertainty caused by the finite dimension of the model in regional tomography. In addition, the cross-dependence of P traveltimes to S-wave velocity anomaly is significant and sensitive to the structure beneath the receiver. So with the component-differential finite-frequency sensitivity kernel, the anomaly of both P-wave and S-wave velocity and Vp/Vs ratio can be achieved at the same time.

Time-frequency dynamics of superluminal pulse transition to the subluminal regime.

PubMed

Dorrah, Ahmed H; Ramakrishnan, Abhinav; Mojahedi, Mo

2015-03-01

Spectral reshaping and nonuniform phase delay associated with an electromagnetic pulse propagating in a temporally dispersive medium may lead to interesting observations in which the group velocity becomes superluminal or even negative. In such cases, the finite bandwidth of the superluminal region implies the inevitable existence of a cutoff distance beyond which a superluminal pulse becomes subluminal. In this paper, we derive a closed-form analytic expression to estimate this cutoff distance in abnormal dispersive media with gain. Moreover, the method of steepest descent is used to track the time-frequency dynamics associated with the evolution of the center of mass of a superluminal pulse to the subluminal regime. This evolution takes place at longer propagation depths as a result of the subluminal components affecting the behavior of the pulse. Finally, the analysis presents the fundamental limitations of superluminal propagation in light of factors such as the medium depth, pulse width, and the medium dispersion strength.

An RFI Detection Algorithm for Microwave Radiometers Using Sparse Component Analysis

NASA Technical Reports Server (NTRS)

Mohammed-Tano, Priscilla N.; Korde-Patel, Asmita; Gholian, Armen; Piepmeier, Jeffrey R.; Schoenwald, Adam; Bradley, Damon

2017-01-01

Radio Frequency Interference (RFI) is a threat to passive microwave measurements and if undetected, can corrupt science retrievals. The sparse component analysis (SCA) for blind source separation has been investigated to detect RFI in microwave radiometer data. Various techniques using SCA have been simulated to determine detection performance with continuous wave (CW) RFI.

Network Analyses for Space-Time High Frequency Wind Data

NASA Astrophysics Data System (ADS)

Laib, Mohamed; Kanevski, Mikhail

2017-04-01

Recently, network science has shown an important contribution to the analysis, modelling and visualization of complex time series. Numerous existing methods have been proposed for constructing networks. This work studies spatio-temporal wind data by using networks based on the Granger causality test. Furthermore, a visual comparison is carried out with several frequencies of data and different size of moving window. The main attention is paid to the temporal evolution of connectivity intensity. The Hurst exponent is applied on the provided time series in order to explore if there is a long connectivity memory. The results explore the space time structure of wind data and can be applied to other environmental data. The used dataset presents a challenging case study. It consists of high frequency (10 minutes) wind data from 120 measuring stations in Switzerland, for a time period of 2012-2013. The distribution of stations covers different geomorphological zones and elevation levels. The results are compared with the Person correlation network as well.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

PubMed

Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin

2013-11-01

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools

Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

NASA Astrophysics Data System (ADS)

Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

2015-04-01

A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources

High frequency oscillations evoked by peripheral magnetic stimulation.

PubMed

Biller, S; Simon, L; Fiedler, P; Strohmeier, D; Haueisen, J

2011-01-01

The analysis of somatosensory evoked potentials (SEP) and / or fields (SEF) is a well-established and important tool for investigating the functioning of the peripheral and central human nervous system. A standard technique to evoke SEPs / SEFs is the stimulation of the median nerve by using a bipolar electrical stimulus. We aim at an alternative stimulation technique enabling stimulation of deep nerve structures while reducing patient stress and error susceptibility. In the current study, we apply a commercial transcranial magnetic stimulation system for peripheral magnetic stimulation of the median nerve. We compare the results of simultaneously recorded EEG signals to prove applicability of our technique to evoke SEPs including low frequency components (LFC) as well as high frequency oscillations (HFO). Therefore, we compare amplitude, latency and time-frequency characteristics of the SEP of 14 healthy volunteers after electric and magnetic stimulation. Both low frequency components and high frequency oscillations were detected. The HFOs were superimposed onto the primary cortical response N20. Statistical analysis revealed significantly lower amplitudes and increased latencies for LFC and HFO components after magnetic stimulation. The differences indicate the inability of magnetic stimulation to elicit supramaximal responses. A psycho-perceptual evaluation showed that magnetic stimulation was less unpleasant for 12 out of the 14 volunteers. In conclusion, we showed that LFC and HFO components related to median nerve stimulation can be evoked by peripheral magnetic stimulation.

Time Domain and Frequency Domain Deterministic Channel Modeling for Tunnel/Mining Environments.

PubMed

Zhou, Chenming; Jacksha, Ronald; Yan, Lincan; Reyes, Miguel; Kovalchik, Peter

2017-01-01

Understanding wireless channels in complex mining environments is critical for designing optimized wireless systems operated in these environments. In this paper, we propose two physics-based, deterministic ultra-wideband (UWB) channel models for characterizing wireless channels in mining/tunnel environments - one in the time domain and the other in the frequency domain. For the time domain model, a general Channel Impulse Response (CIR) is derived and the result is expressed in the classic UWB tapped delay line model. The derived time domain channel model takes into account major propagation controlling factors including tunnel or entry dimensions, frequency, polarization, electrical properties of the four tunnel walls, and transmitter and receiver locations. For the frequency domain model, a complex channel transfer function is derived analytically. Based on the proposed physics-based deterministic channel models, channel parameters such as delay spread, multipath component number, and angular spread are analyzed. It is found that, despite the presence of heavy multipath, both channel delay spread and angular spread for tunnel environments are relatively smaller compared to that of typical indoor environments. The results and findings in this paper have application in the design and deployment of wireless systems in underground mining environments.

Time-Frequency Analysis of Rocket Nozzle Wall Pressures During Start-up Transients

NASA Technical Reports Server (NTRS)

Baars, Woutijn J.; Tinney, Charles E.; Ruf, Joseph H.

2011-01-01

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.

Time-Frequency Analyses of Tide-Gauge Sensor Data

PubMed Central

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors’ data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented. PMID:22163829

Time-frequency analyses of tide-gauge sensor data.

PubMed

Erol, Serdar

2011-01-01

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors' data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.

Fine structure of the low-frequency spectra of heart rate and blood pressure

PubMed Central

Kuusela, Tom A; Kaila, Timo J; Kähönen, Mika

2003-01-01

Background The aim of this study was to explore the principal frequency components of the heart rate and blood pressure variability in the low frequency (LF) and very low frequency (VLF) band. The spectral composition of the R–R interval (RRI) and systolic arterial blood pressure (SAP) in the frequency range below 0.15 Hz were carefully analyzed using three different spectral methods: Fast Fourier transform (FFT), Wigner-Ville distribution (WVD), and autoregression (AR). All spectral methods were used to create time–frequency plots to uncover the principal spectral components that are least dependent on time. The accurate frequencies of these components were calculated from the pole decomposition of the AR spectral density after determining the optimal model order – the most crucial factor when using this method – with the help of FFT and WVD methods. Results Spectral analysis of the RRI and SAP of 12 healthy subjects revealed that there are always at least three spectral components below 0.15 Hz. The three principal frequency components are 0.026 ± 0.003 (mean ± SD) Hz, 0.076 ± 0.012 Hz, and 0.117 ± 0.016 Hz. These principal components vary only slightly over time. FFT-based coherence and phase-function analysis suggests that the second and third components are related to the baroreflex control of blood pressure, since the phase difference between SAP and RRI was negative and almost constant, whereas the origin of the first component is different since no clear SAP–RRI phase relationship was found. Conclusion The above data indicate that spontaneous fluctuations in heart rate and blood pressure within the standard low-frequency range of 0.04–0.15 Hz typically occur at two frequency components rather than only at one as widely believed, and these components are not harmonically related. This new observation in humans can help explain divergent results in the literature concerning spontaneous low-frequency oscillations. It also raises methodological

Time and Frequency-Domain Cross-Verification of SLS 6DOF Trajectory Simulations

NASA Technical Reports Server (NTRS)

Johnson, Matthew; McCullough, John

2017-01-01

The Space Launch System (SLS) Guidance, Navigation, and Control (GNC) team and its partners have developed several time- and frequency-based simulations for development and analysis of the proposed SLS launch vehicle. The simulations differ in fidelity and some have unique functionality that allows them to perform specific analyses. Some examples of the purposes of the various models are: trajectory simulation, multi-body separation, Monte Carlo, hardware in the loop, loads, and frequency domain stability analyses. While no two simulations are identical, many of the models are essentially six degree-of-freedom (6DOF) representations of the SLS plant dynamics, hardware implementation, and flight software. Thus at a high level all of those models should be in agreement. Comparison of outputs from several SLS trajectory and stability analysis tools are ongoing as part of the program's current verification effort. The purpose of these comparisons is to highlight modeling and analysis differences, verify simulation data sources, identify inconsistencies and minor errors, and ultimately to verify output data as being a good representation of the vehicle and subsystem dynamics. This paper will show selected verification work in both the time and frequency domain from the current design analysis cycle of the SLS for several of the design and analysis simulations. In the time domain, the tools that will be compared are MAVERIC, CLVTOPS, SAVANT, STARS, ARTEMIS, and POST 2. For the frequency domain analysis, the tools to be compared are FRACTAL, SAVANT, and STARS. The paper will include discussion of these tools including their capabilities, configurations, and the uses to which they are put in the SLS program. Determination of the criteria by which the simulations are compared (matching criteria) requires thoughtful consideration, and there are several pitfalls that may occur that can severely punish a simulation if not considered carefully. The paper will discuss these

Frequency Dynamics of the First Heart Sound

NASA Astrophysics Data System (ADS)

Wood, John Charles

Cardiac auscultation is a fundamental clinical tool but first heart sound origins and significance remain controversial. Previous clinical studies have implicated resonant vibrations of both the myocardium and the valves. Accordingly, the goals of this thesis were threefold, (1) to characterize the frequency dynamics of the first heart sound, (2) to determine the relative contribution of the myocardium and the valves in determining first heart sound frequency, and (3) to develop new tools for non-stationary signal analysis. A resonant origin for first heart sound generation was tested through two studies in an open-chest canine preparation. Heart sounds were recorded using ultralight acceleration transducers cemented directly to the epicardium. The first heart sound was observed to be non-stationary and multicomponent. The most dominant feature was a powerful, rapidly-rising frequency component that preceded mitral valve closure. Two broadband components were observed; the first coincided with mitral valve closure while the second significantly preceded aortic valve opening. The spatial frequency of left ventricular vibrations was both high and non-stationary which indicated that the left ventricle was not vibrating passively in response to intracardiac pressure fluctuations but suggested instead that the first heart sound is a propagating transient. In the second study, regional myocardial ischemia was induced by left coronary circumflex arterial occlusion. Acceleration transducers were placed on the ischemic and non-ischemic myocardium to determine whether ischemia produced local or global changes in first heart sound amplitude and frequency. The two zones exhibited disparate amplitude and frequency behavior indicating that the first heart sound is not a resonant phenomenon. To objectively quantify the presence and orientation of signal components, Radon transformation of the time -frequency plane was performed and found to have considerable potential for pattern

Aircraft Fault Detection Using Real-Time Frequency Response Estimation

NASA Technical Reports Server (NTRS)

Grauer, Jared A.

2016-01-01

A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

Probabilistic Structural Analysis Methods (PSAM) for Select Space Propulsion System Components

NASA Technical Reports Server (NTRS)

1999-01-01

Probabilistic Structural Analysis Methods (PSAM) are described for the probabilistic structural analysis of engine components for current and future space propulsion systems. Components for these systems are subjected to stochastic thermomechanical launch loads. Uncertainties or randomness also occurs in material properties, structural geometry, and boundary conditions. Material property stochasticity, such as in modulus of elasticity or yield strength, exists in every structure and is a consequence of variations in material composition and manufacturing processes. Procedures are outlined for computing the probabilistic structural response or reliability of the structural components. The response variables include static or dynamic deflections, strains, and stresses at one or several locations, natural frequencies, fatigue or creep life, etc. Sample cases illustrates how the PSAM methods and codes simulate input uncertainties and compute probabilistic response or reliability using a finite element model with probabilistic methods.

Linear and nonlinear frequency- and time-domain spectroscopy with multiple frequency combs.

PubMed

Bennett, Kochise; Rouxel, Jeremy R; Mukamel, Shaul

2017-09-07

Two techniques that employ equally spaced trains of optical pulses to map an optical high frequency into a low frequency modulation of the signal that can be detected in real time are compared. The development of phase-stable optical frequency combs has opened up new avenues to metrology and spectroscopy. The ability to generate a series of frequency spikes with precisely controlled separation permits a fast, highly accurate sampling of the material response. Recently, pairs of frequency combs with slightly different repetition rates have been utilized to down-convert material susceptibilities from the optical to microwave regime where they can be recorded in real time. We show how this one-dimensional dual comb technique can be extended to multiple dimensions by using several combs. We demonstrate how nonlinear susceptibilities can be quickly acquired using this technique. In a second class of techniques, sequences of ultrafast mode locked laser pulses are used to recover pathways of interactions contributing to nonlinear susceptibilities by using a photo-acoustic modulation varying along the sequences. We show that these techniques can be viewed as a time-domain analog of the multiple frequency comb scheme.

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

PubMed Central

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method. PMID:27382610

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals.

PubMed

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

Real time analysis of voiced sounds

NASA Technical Reports Server (NTRS)

Hong, J. P. (Inventor)

1976-01-01

A power spectrum analysis of the harmonic content of a voiced sound signal is conducted in real time by phase-lock-loop tracking of the fundamental frequency, (f sub 0) of the signal and successive harmonics (h sub 1 through h sub n) of the fundamental frequency. The analysis also includes measuring the quadrature power and phase of each frequency tracked, differentiating the power measurements of the harmonics in adjacent pairs, and analyzing successive differentials to determine peak power points in the power spectrum for display or use in analysis of voiced sound, such as for voice recognition.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

A new method for gravity field recovery based on frequency analysis of spherical harmonics

NASA Astrophysics Data System (ADS)

Cai, Lin; Zhou, Zebing

2017-04-01

All existing methods for gravity field recovery are mostly based on the space-wise and time-wise approach, whose core processes are constructing the observation equations and solving them by the least square method. It's should be pointed that the least square method means the approximation. On the other hand, we can directly and precisely obtain the coefficients of harmonics by computing the Fast Fourier Transform (FFT) when we do 1-D data (time series) analysis. So the question whether we directly and precisely obtain the coefficients of spherical harmonic by computing 2-D FFT of measurements of satellite gravity mission is of great significance, since this may guide us to a new understanding of the signal components of gravity field and make us determine it quickly by taking advantage of FFT. Like the 1-D data analysis, the 2-D FFT of measurements of satellite can be computed rapidly. If we can determine the relationship between spherical harmonics and 2-D Fourier frequencies and the transfer function from measurements to spherical coefficients, the question mentioned above can be solved. So the objective of this research project is to establish a new method based on frequency analysis of spherical harmonic, which directly compute the confidents of spherical harmonic of gravity field, which is differ from recovery by least squares. There is a one to one correspondence between frequency spectrum and the time series in 1-D FFT. The 2-D FFT has a similar relationship to 1-D FFT. Owing to the fact that any degree or order (higher than one) of spherical function has multi frequencies and these frequencies may be aliased. Fortunately, the elements and ratio of these frequencies of spherical function can be determined, and we can compute the coefficients of spherical function from 2-D FFT. This relationship can be written as equations and equivalent to a matrix, which is solid and can be derived in advance. Until now the relationship has be determined. Some preliminary

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

Full-time versus part-time employment: Does it influence frequency of grandparental childcare?

PubMed

Lakomý, Martin; Kreidl, Martin

2015-12-01

The impact of grandparents' employment on grandparental childcare has been examined repeatedly, but the findings have so far been inconsistent. We contend that these inconsistencies may have resulted from variations in model specification and crude measurement of employment status. Furthermore, we assert that earlier research overlooked gender differences in the ability to combine paid employment and caregiving as well as variations between maternal and paternal grandparents. We also question the causal interpretation of earlier findings that were based on cross-sectional data. We revisit the issue of the impact of the intensity of employment and analyze SHARE data from 19 countries. We find a significant positive association between part-time employment (as compared to full-time employment) and the frequency of grandparental childcare in a cross-sectional sample, but only among paternal grandmothers. Capitalizing on the panel component of SHARE, we use a within-person estimator to show that this association is unlikely to reflect a causal effect of the intensity of labor market attachment on the frequency of the care of grandchildren, but more probably results from omitted variable bias. We argue that grandparents most likely to provide (intensive) childcare are also most likely to adjust their employment in anticipation of caregiving. The paper documents the usefulness of role strain theory among grandparents and highlights that part-time jobs may reduce role conflict and may thus make grandparenting a more easily manageable experience.

Generalized Structured Component Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Takane, Yoshio

2004-01-01

We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…

Time-frequency analysis of the EEG mu rhythm as a measure of sensorimotor integration in the later stages of swallowing.

PubMed

Cuellar, M; Harkrider, A W; Jenson, D; Thornton, D; Bowers, A; Saltuklaroglu, T

2016-07-01

Electroencephalography (EEG) was used to map the temporal dynamics of sensorimotor integration relative to the strength and timing of muscular activity during swallowing. 64-channel EEG data and surface electromyographic (sEMG) data were recorded from 25 neurologically-healthy adults during swallowing and tongue-tapping. Events were demarcated so that sensorimotor activity primarily from the pharyngeal and esophageal phases of swallowing could be compared to activity resulting from tongue tapping. Independent component analysis identified bilateral clusters of sensorimotor mu components localized to the premotor and primary motor cortices as well as an infrahyoid myogenic cluster. Subsequent event-related spectral perturbations (ERSP) analyses showed event-related desynchronization (ERD) in the spectral power in the alpha (8-13Hz) and beta (15-25Hz) frequency bands of the mu clusters in both tasks. Mu ERD was stronger during swallowing when compared to tongue tapping (pFDR<.05) and the differences in sensorimotor processing between conditions was greater in the right hemisphere than the left, suggesting stronger right hemisphere lateralization for swallowing than tongue-tapping. Mu activity was interpreted as representing a normal feed forward and feedback driven sensorimotor loop during the later stages of swallowing. Results support further use of this novel neuroimaging technique to concurrently map neural and muscle activity during swallowing in clinical populations using EEG. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Warped frequency transform analysis of ultrasonic guided waves in long bones

NASA Astrophysics Data System (ADS)

De Marchi, L.; Baravelli, E.; Xu, K.; Ta, D.; Speciale, N.; Marzani, A.; Viola, E.

2010-03-01

Long bones can be seen as irregular hollow tubes, in which, for a given excitation frequency, many ultrasonic Guided Waves (GWs) can propagate. The analysis of GWs is potential to reflect more information on both geometry and material properties of the bone than any other method (such as dual-energy X-ray absorptiometry, or quantitative computed tomography), and can be used in the assessment of osteoporosis and in the evaluation of fracture healing. In this study, time frequency representations (TFRs) were used to gain insights into the expected behavior of GWs in bones. To this aim, we implemented a dedicated Warped Frequency Transform (WFT) which decomposes the spectrotemporal components of the different propagating modes by selecting an appropriate warping map to reshape the frequency axis. The map can be designed once the GWs group velocity dispersion curves can be predicted. To this purpose, the bone is considered as a hollow cylinder with inner and outer diameter of 16.6 and 24.7 mm, respectively, and linear poroelastic material properties in agreement with the low level of stresses induced by the waves. Timetransient events obtained experimentally, via a piezoelectric ultrasonic set-up applied to bovine tibiae, are analyzed. The results show that WFT limits interference patterns which appear with others TFRs (such as scalograms or warpograms) and produces a sparse representation suitable for characterization purposes. In particular, the mode-frequency combinations propagating with minimal losses are identified.

Roller Bearing Health Monitoring Using CPLE Frequency Analysis Method

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi; Jones, Jess H.

2007-01-01

This paper describes a unique vibration signature analysis technique Coherence Phase Line Enhancer (CPLE) Frequency Analysis - for roller bearing health monitoring. Defects of roller bearing (e.g. wear, foreign debris, crack in bearing supporting structure, etc.) can cause small bearing characteristic frequency shifts due to minor changes in bearing geometry. Such frequency shifts are often too small to detect by the conventional Power Spectral Density (PSD) due to its frequency bandwidth limitation. This Coherent Phase Line Enhancer technology has been evolving over the last few years and has culminated in the introduction of a new and novel frequency spectrum which is fully described in this paper. This CPLE technology uses a "key phasor" or speed probe as a preprocessor for this analysis. With the aid of this key phasor, this CPLE technology can develop a two dimensional frequency spectrum that preserves both amplitude and phase that is not normally obtained using conventional frequency analysis. This two-dimensional frequency transformation results in several newly defined spectral functions; i. e. CPLE-PSD, CPLE-Coherence and the CPLE-Frequency. This paper uses this CPLE frequency analysis to detect subtle, low level bearing related signals in the High Pressure Fuel Pump (HPFP) of the Space Shuttle Main Engine (SSME). For many rotating machinery applications, a key phasor is an essential measurement that is used in the detection of bearing related signatures. There are times however, when a key phasor is not available; i. e. during flight of any of the SSME turbopumps or on the SSME High Pressure Oxygen Turbopump (HPOTP) where no speed probe is present. In this case, the CPLE analysis approach can still be achieved using a novel Pseudo Key Phasor (PKP) technique to reconstruct a 1/Rev PKP signal directly from external vibration measurements. This paper develops this Pseudo Key Phasor technique and applies it to the SSME vibration data.

DynPeak: An Algorithm for Pulse Detection and Frequency Analysis in Hormonal Time Series

PubMed Central

Vidal, Alexandre; Zhang, Qinghua; Médigue, Claire; Fabre, Stéphane; Clément, Frédérique

2012-01-01

The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. As a result, the pattern of plasma LH may be not so clearly pulsatile. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. Hence, there is a real need for robust IPI detection algorithms. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm. PMID:22802933

Time frequency power profile of QRS complex obtained with wavelet transform in spontaneously hypertensive rats.

PubMed

Takano, Nami K; Tsutsumi, Takeshi; Suzuki, Hiroshi; Okamoto, Yoshiwo; Nakajima, Toshiaki

2012-02-01

We evaluated whether frequency analysis could detect the development of interstitial fibrosis in rats. SHR/Izm and age-matched WKY/Izm were used. Limb lead II electrocardiograms were recorded. Continuous wavelet transform (CWT) was applied for the time-frequency analysis. The integrated time-frequency power (ITFP) between QRS complexes was measured and compared between groups. The ITFP at low-frequency bands (≤125Hz) was significantly higher in SHR/Izm. The percent change of ITFP showed the different patterns between groups. Prominent interstitial fibrosis with an increase in TIMP-1 mRNA expression was also observed in SHR/Izm. These results were partly reproduced in a computer simulation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Space-time latent component modeling of geo-referenced health data.

PubMed

Lawson, Andrew B; Song, Hae-Ryoung; Cai, Bo; Hossain, Md Monir; Huang, Kun

2010-08-30

Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made. Copyright (c) 2010 John Wiley & Sons, Ltd.

Arthropod Surveillance Programs: Basic Components, Strategies, and Analysis.

PubMed

Cohnstaedt, Lee W; Rochon, Kateryn; Duehl, Adrian J; Anderson, John F; Barrera, Roberto; Su, Nan-Yao; Gerry, Alec C; Obenauer, Peter J; Campbell, James F; Lysyk, Tim J; Allan, Sandra A

2012-03-01

Effective entomological surveillance planning stresses a careful consideration of methodology, trapping technologies, and analysis techniques. Herein, the basic principles and technological components of arthropod surveillance plans are described, as promoted in the symposium "Advancements in arthropod monitoring technology, techniques, and analysis" presented at the 58th annual meeting of the Entomological Society of America in San Diego, CA. Interdisciplinary examples of arthropod monitoring for urban, medical, and veterinary applications are reviewed. Arthropod surveillance consists of the three components: 1) sampling method, 2) trap technology, and 3) analysis technique. A sampling method consists of selecting the best device or collection technique for a specific location and sampling at the proper spatial distribution, optimal duration, and frequency to achieve the surveillance objective. Optimized sampling methods are discussed for several mosquito species (Diptera: Culicidae) and ticks (Acari: Ixodidae). The advantages and limitations of novel terrestrial and aerial insect traps, artificial pheromones and kairomones are presented for the capture of red flour beetle (Coleoptera: Tenebrionidae), small hive beetle (Coleoptera: Nitidulidae), bed bugs (Hemiptera: Cimicidae), and Culicoides (Diptera: Ceratopogonidae) respectively. After sampling, extrapolating real world population numbers from trap capture data are possible with the appropriate analysis techniques. Examples of this extrapolation and action thresholds are given for termites (Isoptera: Rhinotermitidae) and red flour beetles.

Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time-frequency analysis.

PubMed

Orini, M; Laguna, P; Mainardi, L T; Bailón, R

2012-03-01

In this study, a framework for the characterization of the dynamic interactions between RR variability (RRV) and systolic arterial pressure variability (SAPV) is proposed. The methodology accounts for the intrinsic non-stationarity of the cardiovascular system and includes the assessment of both the strength and the prevalent direction of local coupling. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used to estimate the time-frequency (TF) power, coherence, and phase-difference spectra with fine TF resolution. The interactions between the signals are quantified by time-varying indices, including the local coupling, phase differences, time delay, and baroreflex sensitivity (BRS). Every index is extracted from a specific TF region, localized by combining information from the different spectra. In 14 healthy subjects, a head-up tilt provoked an abrupt decrease in the cardiovascular coupling; a rapid change in the phase difference (from 0.37 ± 0.23 to -0.27 ± 0.22 rad) and time delay (from 0.26 ± 0.14 to -0.16 ± 0.16 s) in the high-frequency band; and a decrease in the BRS (from 23.72 ± 7.66 to 6.92 ± 2.51 ms mmHg(-1)). In the low-frequency range, during a head-up tilt, restoration of the baseline level of cardiovascular coupling took about 2 min and SAPV preceded RRV by about 0.85 s during the whole test. The analysis of the Eurobavar data set, which includes subjects with intact as well as impaired baroreflex, showed that the presented methodology represents an improved TF generalization of traditional time-invariant methodologies and can reveal dysfunctions in subjects with baroreflex impairment. Additionally, the results also suggest the use of non-stationary signal-processing techniques to analyze signals recorded under conditions that are usually supposed to be stationary.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

PubMed

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Adaptive signal processing and higher order time- frequency analysis for acoustic and vibration signatures in condition monitoring

NASA Astrophysics Data System (ADS)

Lee, Sang-Kwon

This thesis is concerned with the development of a useful engineering technique to detect and analyse faults in rotating machinery. The methods developed are based on the advanced signal processing such as the adaptive signal processing and higher-order time frequency methods. The two-stage Adaptive Line Enhancer (ALE), using adaptive signal processing, has been developed for increasing the Signal to Noise Ratio of impulsive signals. The enhanced signal can then be analysed using time frequency methods to identify fault characteristics. However, if after pre-processing by the two stage ALE, the SNR of the signals is low, the residual noise often hinders clear identification of the fault characteristics in the time-frequency domain. In such cases, higher order time-frequency methods have been proposed and studied. As examples of rotating machinery, the internal combustion engine and an industrial gear box are considered in this thesis. The noise signal from an internal combustion engine and vibration signal measured on a gear box are studied in detail. Typically an impulsive signal manifests itself when the fault occurs in the machinery and is embedded in background noise, such as the fundamental frequency and its harmonic orders of the rotation speed and broadband noise. The two-stage ALE is developed for reducing this background noise. Conditions for the choice of adaptive filter parameters are studied and suitable adaptive algorithms given. The enhanced impulsive signal is analysed in the time- frequency domain using the Wigner higher order moment spectra (WHOMS) and the multi-time WHOMS (which is a dual form of the WHOMS). The WHOMS suffers from unwanted cross-terms, which increase dramatically as the order increases. Novel expressions for the cross-terms in WHOMS have been presented. The number of cross-terms can be reduced by taking the principal slice of the WHOMS. The residual cross-terms are smoothed by using a general class of kernel functions and the �

Differential neural responses to acupuncture revealed by MEG using wavelet-based time-frequency analysis: a pilot study.

PubMed

You, Youbo; Bai, Lijun; Dai, Ruwei; Xue, Ting; Zhong, Chongguang; Feng, Yuanyuan; Wang, Hu; Liu, Zhenyu; Tian, Jie

2011-01-01

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, still faces many controversies. As previous neuroimaging studies on acupuncture mainly adopted relatively low time-resolution functional magnetic resonance imaging (fMRI) technology and inappropriate block-designed experimental paradigm due to sustained effect, in the current study, we employed a single block-designed paradigm together with high temporal-resolution magnetoencephalography (MEG) technology. We applied time-frequency analysis based upon Morlet wavelet transforming approach to detect differential oscillatory brain dynamics induced by acupuncture at Stomach Meridian 36 (ST36) using a nearby nonacupoint (NAP) as control condition. We observed that frequency power changes were mainly restricted to delta band for both ST36 group and NAP group. Consistently increased delta band power in contralateral temporal regions and decreased power in the counterparts of ipsilateral hemisphere were detected following stimulation at ST36 on the right leg. Compared with ST36, no significant delta ranges were found in temporal regions in NAP group, illustrating different oscillatory brain patterns. Our results may provide additional evidence to support the specificity of acupuncture modulation effects.

The Role of Time and Frequency in Future Systems

NASA Technical Reports Server (NTRS)

Stein, Samuel R.; Gifford, Al; Celano, Tom

1996-01-01

Over the past twenty years, the Global Positioning System (GPS) has revolutionized the performance and the geographical availability of time and frequency discrimination, while at the same time reducing the cost to the individual user. This paper examines the question of what comes next for time and frequency dissemination. The question has two motivations: How can improved performance be achieved in the future, and how can redundant sources of time and frequency be provided to critical systems? A model is developed for time and frequency dissemination based on the time management performed in GPS. Several candidate systems for future time and frequency distribution are identified. One system - SONET telecommunications - is discussed in detail. Performance requirements and hardware implementation are presented.

The instantaneous frequency rate spectrogram

NASA Astrophysics Data System (ADS)

Czarnecki, Krzysztof

2016-01-01

An accelerogram of the instantaneous phase of signal components referred to as an instantaneous frequency rate spectrogram (IFRS) is presented as a joint time-frequency distribution. The distribution is directly obtained by processing the short-time Fourier transform (STFT) locally. A novel approach to amplitude demodulation based upon the reassignment method is introduced as a useful by-product. Additionally, an estimator of energy density versus the instantaneous frequency rate (IFR) is proposed and referred to as the IFR profile. The energy density is estimated based upon both the classical energy spectrogram and the IFRS smoothened by the median filter. Moreover, the impact of an analyzing window width, additive white Gaussian noise and observation time is tested. Finally, the introduced method is used for the analysis of the acoustic emission of an automotive engine. The recording of the engine of a Lamborghini Gallardo is analyzed as an example.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Statistical properties and time-frequency analysis of temperature, salinity and turbidity measured by the MAREL Carnot station in the coastal waters of Boulogne-sur-Mer (France)

NASA Astrophysics Data System (ADS)

Kbaier Ben Ismail, Dhouha; Lazure, Pascal; Puillat, Ingrid

2016-10-01

In marine sciences, many fields display high variability over a large range of spatial and temporal scales, from seconds to thousands of years. The longer recorded time series, with an increasing sampling frequency, in this field are often nonlinear, nonstationary, multiscale and noisy. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. The objective of this paper is to highlight time series analysis methods already applied in econometrics, signal processing, health, etc. to the environmental marine domain, assess advantages and inconvenients and compare classical techniques with more recent ones. Temperature, turbidity and salinity are important quantities for ecosystem studies. The authors here consider the fluctuations of sea level, salinity, turbidity and temperature recorded from the MAREL Carnot system of Boulogne-sur-Mer (France), which is a moored buoy equipped with physico-chemical measuring devices, working in continuous and autonomous conditions. In order to perform adequate statistical and spectral analyses, it is necessary to know the nature of the considered time series. For this purpose, the stationarity of the series and the occurrence of unit-root are addressed with the Augmented-Dickey Fuller tests. As an example, the harmonic analysis is not relevant for temperature, turbidity and salinity due to the nonstationary condition, except for the nearly stationary sea level datasets. In order to consider the dominant frequencies associated to the dynamics, the large number of data provided by the sensors should enable the estimation of Fourier spectral analysis. Different power spectra show a complex variability and reveal an influence of environmental factors such as tides. However, the previous classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data. Interpolating the time series introduces numerous artifacts to the

Cross-frequency and band-averaged response variance prediction in the hybrid deterministic-statistical energy analysis method

NASA Astrophysics Data System (ADS)

Reynders, Edwin P. B.; Langley, Robin S.

2018-08-01

The hybrid deterministic-statistical energy analysis method has proven to be a versatile framework for modeling built-up vibro-acoustic systems. The stiff system components are modeled deterministically, e.g., using the finite element method, while the wave fields in the flexible components are modeled as diffuse. In the present paper, the hybrid method is extended such that not only the ensemble mean and variance of the harmonic system response can be computed, but also of the band-averaged system response. This variance represents the uncertainty that is due to the assumption of a diffuse field in the flexible components of the hybrid system. The developments start with a cross-frequency generalization of the reciprocity relationship between the total energy in a diffuse field and the cross spectrum of the blocked reverberant loading at the boundaries of that field. By making extensive use of this generalization in a first-order perturbation analysis, explicit expressions are derived for the cross-frequency and band-averaged variance of the vibrational energies in the diffuse components and for the cross-frequency and band-averaged variance of the cross spectrum of the vibro-acoustic field response of the deterministic components. These expressions are extensively validated against detailed Monte Carlo analyses of coupled plate systems in which diffuse fields are simulated by randomly distributing small point masses across the flexible components, and good agreement is found.

Time Domain and Frequency Domain Deterministic Channel Modeling for Tunnel/Mining Environments

PubMed Central

Zhou, Chenming; Jacksha, Ronald; Yan, Lincan; Reyes, Miguel; Kovalchik, Peter

2018-01-01

Understanding wireless channels in complex mining environments is critical for designing optimized wireless systems operated in these environments. In this paper, we propose two physics-based, deterministic ultra-wideband (UWB) channel models for characterizing wireless channels in mining/tunnel environments — one in the time domain and the other in the frequency domain. For the time domain model, a general Channel Impulse Response (CIR) is derived and the result is expressed in the classic UWB tapped delay line model. The derived time domain channel model takes into account major propagation controlling factors including tunnel or entry dimensions, frequency, polarization, electrical properties of the four tunnel walls, and transmitter and receiver locations. For the frequency domain model, a complex channel transfer function is derived analytically. Based on the proposed physics-based deterministic channel models, channel parameters such as delay spread, multipath component number, and angular spread are analyzed. It is found that, despite the presence of heavy multipath, both channel delay spread and angular spread for tunnel environments are relatively smaller compared to that of typical indoor environments. The results and findings in this paper have application in the design and deployment of wireless systems in underground mining environments.† PMID:29457801

Frequency analysis of gaze points with CT colonography interpretation using eye gaze tracking system

NASA Astrophysics Data System (ADS)

Tsutsumi, Shoko; Tamashiro, Wataru; Sato, Mitsuru; Okajima, Mika; Ogura, Toshihiro; Doi, Kunio

2017-03-01

It is important to investigate eye tracking gaze points of experts, in order to assist trainees in understanding of image interpretation process. We investigated gaze points of CT colonography (CTC) interpretation process, and analyzed the difference in gaze points between experts and trainees. In this study, we attempted to understand how trainees can be improved to a level achieved by experts in viewing of CTC. We used an eye gaze point sensing system, Gazefineder (JVCKENWOOD Corporation, Tokyo, Japan), which can detect pupil point and corneal reflection point by the dark pupil eye tracking. This system can provide gaze points images and excel file data. The subjects are radiological technologists who are experienced, and inexperienced in reading CTC. We performed observer studies in reading virtual pathology images and examined observer's image interpretation process using gaze points data. Furthermore, we examined eye tracking frequency analysis by using the Fast Fourier Transform (FFT). We were able to understand the difference in gaze points between experts and trainees by use of the frequency analysis. The result of the trainee had a large amount of both high-frequency components and low-frequency components. In contrast, both components by the expert were relatively low. Regarding the amount of eye movement in every 0.02 second we found that the expert tended to interpret images slowly and calmly. On the other hand, the trainee was moving eyes quickly and also looking for wide areas. We can assess the difference in the gaze points on CTC between experts and trainees by use of the eye gaze point sensing system and based on the frequency analysis. The potential improvements in CTC interpretation for trainees can be evaluated by using gaze points data.

Classification of alloys using laser induced breakdown spectroscopy with principle component analysis

NASA Astrophysics Data System (ADS)

Syuhada Mangsor, Aneez; Haider Rizvi, Zuhaib; Chaudhary, Kashif; Safwan Aziz, Muhammad

2018-05-01

The study of atomic spectroscopy has contributed to a wide range of scientific applications. In principle, laser induced breakdown spectroscopy (LIBS) method has been used to analyse various types of matter regardless of its physical state, either it is solid, liquid or gas because all elements emit light of characteristic frequencies when it is excited to sufficiently high energy. The aim of this work was to analyse the signature spectrums of each element contained in three different types of samples. Metal alloys of Aluminium, Titanium and Brass with the purities of 75%, 80%, 85%, 90% and 95% were used as the manipulated variable and their LIBS spectra were recorded. The characteristic emission lines of main elements were identified from the spectra as well as its corresponding contents. Principal component analysis (PCA) was carried out using the data from LIBS spectra. Three obvious clusters were observed in 3-dimensional PCA plot which corresponding to the different group of alloys. Findings from this study showed that LIBS technology with the help of principle component analysis could conduct the variety discrimination of alloys demonstrating the capability of LIBS-PCA method in field of spectro-analysis. Thus, LIBS-PCA method is believed to be an effective method for classifying alloys with different percentage of purifications, which was high-cost and time-consuming before.

Genome-wide selection components analysis in a fish with male pregnancy.

PubMed

Flanagan, Sarah P; Jones, Adam G

2017-04-01

A major goal of evolutionary biology is to identify the genome-level targets of natural and sexual selection. With the advent of next-generation sequencing, whole-genome selection components analysis provides a promising avenue in the search for loci affected by selection in nature. Here, we implement a genome-wide selection components analysis in the sex role reversed Gulf pipefish, Syngnathus scovelli. Our approach involves a double-digest restriction-site associated DNA sequencing (ddRAD-seq) technique, applied to adult females, nonpregnant males, pregnant males, and their offspring. An F ST comparison of allele frequencies among these groups reveals 47 genomic regions putatively experiencing sexual selection, as well as 468 regions showing a signature of differential viability selection between males and females. A complementary likelihood ratio test identifies similar patterns in the data as the F ST analysis. Sexual selection and viability selection both tend to favor the rare alleles in the population. Ultimately, we conclude that genome-wide selection components analysis can be a useful tool to complement other approaches in the effort to pinpoint genome-level targets of selection in the wild. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

A time and frequency synchronization method for CO-OFDM based on CMA equalizers

NASA Astrophysics Data System (ADS)

Ren, Kaixuan; Li, Xiang; Huang, Tianye; Cheng, Zhuo; Chen, Bingwei; Wu, Xu; Fu, Songnian; Ping, Perry Shum

2018-06-01

In this paper, an efficient time and frequency synchronization method based on a new training symbol structure is proposed for polarization division multiplexing (PDM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The coarse timing synchronization is achieved by exploiting the correlation property of the first training symbol, and the fine timing synchronization is accomplished by using the time-domain symmetric conjugate of the second training symbol. Furthermore, based on these training symbols, a constant modulus algorithm (CMA) is proposed for carrier frequency offset (CFO) estimation. Theoretical analysis and simulation results indicate that the algorithm has the advantages of robustness to poor optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). The frequency offset estimation range can achieve [ -Nsc/2 ΔfN , + Nsc/2 ΔfN ] GHz with the mean normalized estimation error below 12 × 10-3 even under the condition of OSNR as low as 10 dB.