Joint time-frequency domain identification of nonlinearly controlled structures
NASA Astrophysics Data System (ADS)
Jin, Gang; Sain, Michael K.; Spencer, Billie F., Jr.; Pham, Khanh D.
2006-05-01
This paper introduces a 3-step approach for the identification of a linear structure that is controlled by nonlinear damping devices. First, the structure with the integrated nonlinear damper is subjected to random vibration test and the frequency response function (FRF) of the structure is calculated from the input-output data of the physical system. Based on the frequency domain data, a state space model is then estimated using a recently developed FRF curve-fitting technique that is designed especially for lightly damped structures with control inputs. Finally an iterative process is used to optimize the model performance in the time domain and an integrated model of the nonlinearly controlled structure is derived by interconnecting the structure model with that of the nonlinear damper. The complete approach is illustrated by the modeling of a base-isolated structure controlled by a magnetorheological (MR) fluid damper.
A Robust Image Watermarking in the Joint Time-Frequency Domain
NASA Astrophysics Data System (ADS)
Öztürk, Mahmut; Akan, Aydın; Çekiç, Yalçın
2010-12-01
With the rapid development of computers and internet applications, copyright protection of multimedia data has become an important problem. Watermarking techniques are proposed as a solution to copyright protection of digital media files. In this paper, a new, robust, and high-capacity watermarking method that is based on spatiofrequency (SF) representation is presented. We use the discrete evolutionary transform (DET) calculated by the Gabor expansion to represent an image in the joint SF domain. The watermark is embedded onto selected coefficients in the joint SF domain. Hence, by combining the advantages of spatial and spectral domain watermarking methods, a robust, invisible, secure, and high-capacity watermarking method is presented. A correlation-based detector is also proposed to detect and extract any possible watermarks on an image. The proposed watermarking method was tested on some commonly used test images under different signal processing attacks like additive noise, Wiener and Median filtering, JPEG compression, rotation, and cropping. Simulation results show that our method is robust against all of the attacks.
Synthetic aperture sonar imaging using joint time-frequency analysis
NASA Astrophysics Data System (ADS)
Wang, Genyuan; Xia, Xiang-Gen
1999-03-01
The non-ideal motion of the hydrophone usually induces the aperture error of the synthetic aperture sonar (SAS), which is one of the most important factors degrading the SAS imaging quality. In the SAS imaging, the return signals are usually nonstationary due to the non-ideal hydrophone motion. In this paper, joint time-frequency analysis (JTFA), as a good technique for analyzing nonstationary signals, is used in the SAS imaging. Based on the JTFA of the sonar return signals, a novel SAS imaging algorithm is proposed. The algorithm is verified by simulation examples.
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
Estimation of modal parameters using bilinear joint time frequency distributions
NASA Astrophysics Data System (ADS)
Roshan-Ghias, A.; Shamsollahi, M. B.; Mobed, M.; Behzad, M.
2007-07-01
In this paper, a new method is proposed for modal parameter estimation using time-frequency representations. Smoothed Pseudo Wigner-Ville distribution which is a member of the Cohen's class distributions is used to decouple vibration modes completely in order to study each mode separately. This distribution reduces cross-terms which are troublesome in Wigner-Ville distribution and retains the resolution as well. The method was applied to highly damped systems, and results were superior to those obtained via other conventional methods.
Joint time-frequency analysis of high-bandwidth low-resolution ISAR imaging systems
NASA Astrophysics Data System (ADS)
Ghogomu, Patrick; Testorf, Markus E.
2003-09-01
Joint time-frequency analysis is applied to radar imaging problems. Special attention is given to imaging applications, for which the resolution is severely limited due the available bandwidth of the radar signal both in range and cross-range. This includes the detection of landmines as well as foliage penetration radar imaging. Motivated by this type of imaging problem a new joint time-frequency method, the STPDFT algorithm is introduced and compared with existing methods. The performance of all methods is illustrated with synthetic test signals. In addition, preliminary results are presented which demonstrate the performance of joint-time frequency transforms, if applied to low resolution imaging problems.
Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals
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.
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
Real time frequency domain fibreoptic temperature sensor using ruby crystals.
Alcala, J R; Liao, S C; Zheng, J
1996-01-01
The excited state phosphorescence lifetime of ruby crystals is used to monitor temperature in the physiological range from 15 degrees to 45 degrees C with precision and accuracy less than 1 degree C, in real time. Precision of 0.1 degree C is attained with 3 min integration times. A 500 micron cubic ruby crystal bounded to the distal end of an optical fibre of similar core dimensions is excited with pulsed Ne-He laser light of about 9 microW average power. The instrument uses a sampler for data acquisition, and frequency domain methods for data fitting. The instrument amplifies the a.c. components of the detector output and band limits the signal to 800 Hz. The fundamental frequency of the excitation is set to 24.41 Hz to obtain 32 or less harmonics. This band-limited signal is sampled and averaged between 20 and 100 cycles to obtain temperature measurements in real time. The frequency domain representation of the data is obtained by employing fast Fourier transform algorithms. The phase delay and the modulation ratio, of each sampled harmonic, is then computed. Five to 32 values of the phase and modulation are averaged before computing the sensor lifetime. The technique is capable of measuring precise and accurate excited state lifetimes from subpicowatt luminescent signals in plastic optical fibres. A least squares fit yields the lifetimes of single exponentials. A component of zero lifetime is introduced to account for the backscatter excitation seen by the photodetector leaking through optical interference filters. The phosphorescence lifetimes measured reproducibly to about six parts in 1000, with a 2 s integration time, are used to monitor physiological temperature. Temperatures are computed employing empirical polynomials. The system drift is 3% over 5 h of continuous operation. The instrumentation and methods allow 2.7 s update times and 50 s full response times. PMID:8771039
Bubble Pulse Cancelation in the Time-Frequency Domain Using Warping Operators
NASA Astrophysics Data System (ADS)
Niu, Hai-Qiang; Zhang, Ren-He; Li, Zheng-Lin; Guo, Yong-Gang; He, Li
2013-08-01
The received shock waves produced by explosive charges are often polluted by bubble pulses in underwater acoustic experiments. A method based on warping operators is proposed to cancel the bubble pulses in the time-frequency domain. This is applied to the explosive data collected during the Yellow Sea experiment in November 2000. The original received signal is first transformed into a warped signal by warping operators. Then, the warped signal is analyzed in the time-frequency domain. Due to the different features between the shock waves and the bubble pulses in the time-frequency domain for the warped signal, the bubble pulses can be easily filtered out. Furthermore, the shock waves in the original time domain can be retrieved by the inverse warping transformation. The autocorrelation functions and the time-frequency representation show that the bubble pulses can be canceled effectively.
NASA Astrophysics Data System (ADS)
Ghoraani, Behnaz; Krishnan, Sridhar
2009-12-01
The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD) technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved.
NASA Astrophysics Data System (ADS)
Zhang, Shengli; Tang, Jiong
2016-04-01
Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.
Hybrid time-frequency domain equalization for LED nonlinearity mitigation in OFDM-based VLC systems.
Li, Jianfeng; Huang, Zhitong; Liu, Xiaoshuang; Ji, Yuefeng
2015-01-12
A novel hybrid time-frequency domain equalization scheme is proposed and experimentally demonstrated to mitigate the white light emitting diode (LED) nonlinearity in visible light communication (VLC) systems based on orthogonal frequency division multiplexing (OFDM). We handle the linear and nonlinear distortion separately in a nonlinear OFDM system. The linear part is equalized in frequency domain and the nonlinear part is compensated by an adaptive nonlinear time domain equalizer (N-TDE). The experimental results show that with only a small number of parameters the nonlinear equalizer can efficiently mitigate the LED nonlinearity. With the N-TDE the modulation index (MI) and BER performance can be significantly enhanced. PMID:25835706
NASA Astrophysics Data System (ADS)
Dan, Lilin; Xiao, Yue; Ni, Wei; Li, Shaoqian
In this letter, a low complexity transmitter is proposed for the downlinks of orthogonal frequency code division multiplexing (OFCDM) systems. The principle is based on a joint time-frequency spreading and inverse fast Fourier transform (TFS-IFFT), which combines the frequency spreading with partial stages of IFFT, so as to simplify the real-time processing. Compared with the conventional one, the proposed OFCDM transmitter is of lower real-time computational complexity, especially for those with large spreading factor or low modulation level. Furthermore, the proposed TFS-IFFT can also be applied to other frequency spreading systems, such as MC-CDMA, for complexity reduction.
Characterization of the Korotkoff sounds using joint time-frequency analysis.
Allen, John; Gehrke, Tobias; O'Sullivan, John J; King, Susan T; Murray, Alan
2004-02-01
The sounds associated with the five classical Korotkoff phases are clinically important for measuring systolic and diastolic blood pressures. The frequency ranges of the sounds have already been described simply using the overall peak frequencies within each phase by Fourier methods. However, such analysis may be missing potentially useful clinical information. The aim of this study was to compare features associated with the different phases of the Korotkoff sounds obtained during blood pressure measurement using a joint time-frequency analysis (JTFA) technique. A single operator recorded Korotkoff sounds from 25 healthy subjects using a measurement system comprising cardiology stethoscope, microphone, amplifier and recording system for computer sound digitization, and a MiniDisc system for playback to the cardiologist for Korotkoff phase classification. We have shown that using this system the phase classification by the cardiologist is repeatable, with no significant differences found in the number of sounds allocated to phases on two separate recording assessments. The digitized sounds were processed using a MATLAB-based short-time Fourier transform JTFA technique and differences in time, frequency and amplitude characteristics between the phases compared. It was found that on average, phase III had the largest overall amplitude and high frequency energy. Phase II had the greatest high frequency component and longest murmur, and was visibly the most complex phase in terms of time and frequency content. In contrast, phases IV and V had the lowest amplitude and frequency components. Overall, the statistically significant transitions between phases were: phase I to II with increases in high frequency (224 to 275 Hz) (p < 0.01) and sound duration (49 to 98 ms) (p < 0.0001), II to III with a significant decrease in sound duration (to 37 ms) (p < 0.0001), III to IV with decreases in maximum amplitude (0.95 to 0.25), highest frequency (262 to 95 Hz), and relative high
NASA Astrophysics Data System (ADS)
Diallo, M. S.; Kulesh, M.; Holschneider, M.; Scherbaum, F.
2004-12-01
In this contribution we propose a method of wave field separation from multicomponent data sets based on the continuous wavelet transform (CWT). We present different approaches for obtaining the time-frequency dependent instantaneous polarization attributes for multicomponent data sets (2C, 3C or more). Using these attributes, we show how to construct filters tailored to separate (filter) different wave types followed by an inverse wavelet transform to obtain the desired wave type in the time domain. The proposed methods are applied on synthetic and experimental data for illustration.
Chládek, J; Brázdil, M; Halámek, J; Plešinger, F; Jurák, P
2013-01-01
We present an off-line analysis procedure for exploring brain activity recorded from intra-cerebral electroencephalographic data (SEEG). The objective is to determine the statistical differences between different types of stimulations in the time-frequency domain. The procedure is based on computing relative signal power change and subsequent statistical analysis. An example of characteristic statistically significant event-related de/synchronization (ERD/ERS) detected across different frequency bands following different oddball stimuli is presented. The method is used for off-line functional classification of different brain areas. PMID:24109865
Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application
NASA Astrophysics Data System (ADS)
Molina, Gary N. Garcia; Ebrahimi, Touradj; Vesin, Jean-Marc
2003-12-01
Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG) signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.
Iterative Receiver in Time-Frequency Domain for Shallow Water Acoustic Channel
NASA Astrophysics Data System (ADS)
Zhao, Liang; Ge, Jianhua
2012-03-01
Inter-symbol interference (ISI) caused by multi-path propagation, especially in shallow water channel, degrades the performance of underwater acoustic (UWA) communication systems. In this paper, we combine soft minimum mean squared error (MMSE) equalization and the serially concatenated trellis coded modulation (SCTCM) decoding to develop an iterative receiver in time-frequency domain (TFD) for underwater acoustic point to point communications. Based on sound speed profile (SSP) measured in the lake and finite-element ray (FER) tracing method (Bellhop), the shallow water channel is constructed to evaluate the performance of the proposed iterative receiver. The results suggest that the proposed iterative receiver can reduce the calculation complexity of the equalizer and obtain better performance using less receiving elements.
Non-stationary frequency domain system identification using time-frequency representations
NASA Astrophysics Data System (ADS)
Guo, Yanlin; Kareem, Ahsan
2016-05-01
System properties of buildings and bridges may vary with time due to temperature changes, aging or extreme loadings. To identify these time-varying system properties, this study proposes a new output-only non-stationary system identification (SI) framework based on instantaneous or marginal spectra derived from the time-frequency representation, e.g., short time Fourier or wavelet transform. Spectra derived from these time-frequency representations are very popular in tracking time-varying frequencies; however, they have seldom been used to identify the time-varying damping ratio because a short window needed to capture the time-varying information amplifies the bandwidth significantly, which may lead to considerably overestimating the damping ratio. To overcome this shortcoming, this study modifies the theoretical frequency response function (FRF) to explicitly account for the windowing effect, and therefore enables SI directly using instantaneous or marginal spectra derived from the wavelet or short time Fourier transform. The response spectrum estimated using the short time window and the modified FRF are both influenced by the same time window, thus the instantaneous or time-localized marginal spectrum of response can be fitted to the modified FRF to identify frequency and damping ratio at each time instant. This spectral-based SI framework can reliably identify damping in time-varying systems under non-stationary excitations. The efficacy of the proposed framework is demonstrated by both numerical and full-scale examples, and also compared to the time-domain SI method, stochastic subspace identification (SSI), since the time-domain SI approaches and their extensions are popular in identifying time-varying systems utilizing recursive algorithms or moving windows.
A method for efficient fractional sample delay generation for real-time frequency-domain beamformers
Breeding, J.E.; Karnowski, T.P.
1995-07-01
This paper presents an efficient method for fractional delay filter generation for frequency-domain beamformers. A common misunderstanding regarding frequency-domain beamforming is that any fractional time shift can be achieved using the delay property of the discrete Fourier transform (DFT). Blind application of the DFT delay property introduces circular convolution errors that may adversely affect the beam`s time series. The method presented avoids these errors while enabling real-time processing.
Al-Fahoum, Amjed S; Al-Fraihat, Ausilah A
2014-01-01
Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance. PMID:24967316
Al-Fahoum, Amjed S.; Al-Fraihat, Ausilah A.
2014-01-01
Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance. PMID:24967316
EEG biometric identification: a thorough exploration of the time-frequency domain
NASA Astrophysics Data System (ADS)
DelPozo-Banos, Marcos; Travieso, Carlos M.; Weidemann, Christoph T.; Alonso, Jesús B.
2015-10-01
Objective. Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG. Approach. We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied. Main results. Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time. Significance. Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.
Real-time frequency domain temperature and oxygen sensor with a single optical fiber.
Liao, S C; Xu, Z; Izatt, J A; Alcala, J R
1997-11-01
The combined excited-state phosphorescence life-times of an alexandrite crystal and platinum tetraphenylporphyrin Pt(TPP) in a single-fiber sensor are used to monitor temperature and oxygen concentration in the physiological range from 15-45 degrees C and 0-50% O2 with precision of 0.24 degree C and 0.15% O2 and accuracy of 0.28 degree C and 0.2% O2. A 500-micron cubic alexandrite crystal bound to the distal end of a 750-micron-diameter optical fiber core and the Pt(TPP) coated circumferentially with a length of 1 cm from the end of the same fiber are excited with pulsed super-bright blue LED light. This apparatus uses a 125-kHz sampler for data acquisition and frequency domain methods for signal processing. The instrument amplifies both the dc and ac components of the photomultiplier output and band limits the signal to 20 kHz. The fundamental frequency of the excitation is set to 488.3 Hz and the highest harmonic used is the 35th. This bandlimited signal is sampled and averaged over a few hundred cycles in the time domain. The frequency domain representation of the data is obtained by employing fast Fourier transform algorithms. The phase delay and the modulation ratio of each sampled harmonic are then computed. At least four log-spaced harmonic phases or modulations are averaged before decoding the two lifetimes of temperature and oxygen phosphorescent sensors. A component of zero lifetime is introduced to account for the excitation backscatter leakage through optical interference filters seen by the photodetector. Linear and second-order empirical polynomials are employed to compute the temperatures and oxygen concentrations from the inverse lifetimes. In the situation of constant oxygen concentration, the lifetime of Pt(TPP) changes with temperature but can be compensated using the measured temperature lifetime. The system drift is 0.24 degree C for the temperature measurement and 0.59% for the oxygen concentration measurement over 30 h of continuous operation
Experimental research on anti-vibration interferometry based on time-frequency-domain analysis
NASA Astrophysics Data System (ADS)
Hu, Yao; Hao, Qun; Zhang, Fanghua; Tian, Yuhan
2013-10-01
Phase-shifting interferometry is a non-contact precision precise measuring method for optical surface, but it is highly sensitive to external vibrations. A time-and-frequency-domain (TFD) anti-noise phase-shifting interferometry is proposed to eliminate the effect of vibrations and improve the precision of measurement. According to simulations and preliminary experiments, active phase-shifting speed as well as interferogram capture speed should be increased to improve the anti-vibration capability of the TFD method. In this paper, a fast phase-shifting approach based on PZT actuator and interferogram detection with high-speed camera is proposed. Preliminary experimental results are given to demonstrate the approach.
NASA Technical Reports Server (NTRS)
Palumbo, Dan
2008-01-01
The lifetimes of coherent structures are derived from data correlated over a 3 sensor array sampling streamwise sidewall pressure at high Reynolds number (> 10(exp 8)). The data were acquired at subsonic, transonic and supersonic speeds aboard a Tupolev Tu-144. The lifetimes are computed from a variant of the correlation length termed the lifelength. Characteristic lifelengths are estimated by fitting a Gaussian distribution to the sensors cross spectra and are shown to compare favorably with Efimtsov s prediction of correlation space scales. Lifelength distributions are computed in the time/frequency domain using an interval correlation technique on the continuous wavelet transform of the original time data. The median values of the lifelength distributions are found to be very close to the frequency averaged result. The interval correlation technique is shown to allow the retrieval and inspection of the original time data of each event in the lifelength distributions, thus providing a means to locate and study the nature of the coherent structure in the turbulent boundary layer. The lifelength data are converted to lifetimes using the convection velocity. The lifetime of events in the time/frequency domain are displayed in Lifetime Maps. The primary purpose of the paper is to validate these new analysis techniques so that they can be used with confidence to further characterize the behavior of coherent structures in the turbulent boundary layer.
Kim, Keo Sik; Seo, Jeong Hwan; Kang, Jin U; Song, Chul Gyu
2009-05-01
Vibroarthrographic (VAG) signals, generated by human knee movement, are non-stationary and multi-component in nature and their time-frequency distribution (TFD) provides a powerful means to analyze such signals. The objective of this paper is to improve the classification accuracy of the features, obtained from the TFD of normal and abnormal VAG signals, using segmentation by the dynamic time warping (DTW) and denoising algorithm by the singular value decomposition (SVD). VAG and knee angle signals, recorded simultaneously during one flexion and one extension of the knee, were segmented and normalized at 0.5 Hz by the DTW method. Also, the noise within the TFD of the segmented VAG signals was reduced by the SVD algorithm, and a back-propagation neural network (BPNN) was used to classify the normal and abnormal VAG signals. The characteristic parameters of VAG signals consist of the energy, energy spread, frequency and frequency spread parameter extracted by the TFD. A total of 1408 segments (normal 1031, abnormal 377) were used for training and evaluating the BPNN. As a result, the average classification accuracy was 91.4 (standard deviation +/-1.7) %. The proposed method showed good potential for the non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis. PMID:19217685
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
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
NASA Astrophysics Data System (ADS)
Van De Vijver, Ellen; De Pue, Jan; Cornelis, Wim; Van Meirvenne, Marc
2015-04-01
A stepped frequency continuous wave (SFCW) ground penetrating radar (GPR) system produces waveforms consisting of a sequence of sine waves with linearly increasing frequency. By adopting a wide frequency bandwidth, SFCW GPR systems offer an optimal resolution at each achievable measurement depth. Furthermore, these systems anticipate an improved penetration depth and signal-to-noise ratio (SNR) as compared to time-domain impulse GPRs, because energy is focused in one single frequency at a time and the phase and amplitude of the reflected signal is recorded for each discrete frequency step. However, the search for the optimal practical implementation of SFCW GPR technology to fulfil these theoretical advantages is still ongoing. In this study we compare the performance of a SFCW GPR system for air-launched and ground-coupled antenna configurations. The first is represented by a 3d-Radar Geoscope GS3F system operated with a V1213 antenna array. This array contains 7 transmitting and 7 receiving antennae resulting in 13 measurement channels at a spacing of 0.075 m and providing a total scan width of 0.975 m. The ground-coupled configuration is represented by 3d-Radar's latest-generation SFCW system, GeoScope Mk IV, operated with a DXG1212 antenna array. With 6 transmitting and 5 receiving antennae this array provides 12 measurement channels and an effective scan width of 0.9 m. Both systems were tested on several sites representative of various application environments, including a test site with different road specimens (Belgian Road Research Centre) and two test areas in different agricultural fields in Flanders, Belgium. For each test, data acquisition was performed using the full available frequency bandwidth of the systems (50 to 3000 MHz). Other acquisition parameters such as the frequency step and dwell time were varied in different tests. Analyzing the data of the different tests in time, frequency and wavelet domain allows to evaluate different performance
Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R
2012-01-01
This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases. PMID:22448233
Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R.
2012-01-01
This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases. PMID:22448233
NASA Astrophysics Data System (ADS)
Lai, W. L.; Kou, S. C.; Poon, C. S.
2012-07-01
SummaryThis paper describes an experimental method to characterize the soil's unsaturated zone by constructing a scenario in which transient downward water infiltration took place from the topsoil to the bottom soil continuously. During the water infiltration, GPR waveforms and side-view grayscale images of the soil column were simultaneously and continuously captured. The GPR wavelets associated with the wetting front were analyzed using short time fourier transform (STFT) algorithm. The downward wetting front and the stretch of unsaturated transition zone decelerated and eased the wetting front's reflection in the time domain; as well as reduced the peak frequency and attenuated the frequency spectra in the frequency domain. The subsequent drying process further attenuated but accelerated the wetting front's reflection in both time and frequency domains. These observations were correlated with the image pixel profiles, from which GPR velocity profiles at different lapsed times were generated after computation via a complex refractive index model (CRIM). The CRIM method is entirely non-invasive and not only offers very detailed measurement of the water saturation profile of the transition zone in laboratory scale, but also is potentially useful for the further study of a variety of vadose zone properties.
Moukadem, Ali; Schmidt, Samuel; Dieterlen, Alain
2015-01-01
This paper considers the problem of classification of the first and the second heart sounds (S1 and S2) under cardiac stress test. The main objective is to classify these sounds without electrocardiogram (ECG) reference and without taking into consideration the systolic and the diastolic time intervals criterion which can become problematic and useless in several real life settings as severe tachycardia and tachyarrhythmia or in the case of subjects being under cardiac stress activity. First, the heart sounds are segmented by using a modified time-frequency based envelope. Then, to distinguish between the first and the second heart sounds, new features, named αopt, β, and γ, based on high order statistics and energy concentration measures of the Stockwell transform (S-transform) are proposed in this study. A study of the variation of the high frequency content of S1 and S2 over the HR (heart rate) is also discussed. The proposed features are validated on a database that contains 2636 S1 and S2 sounds corresponding to 62 heart signals and 8 subjects under cardiac stress test collected from healthy subjects. Results and comparisons with existing methods in the literature show a large superiority for our proposed features. PMID:26089957
NASA Astrophysics Data System (ADS)
Zhang, Fanghua; Hao, Qun; Hu, Yao; Zhu, Qiudong
2012-11-01
This paper introduces a time-and-frequency-domain (TFD) anti-noise phase-shifting interferometry, and designs an experimental system to test the anti-vibration ability of this method. In the system, a plane mirror is measured under the external vibrations simulated by the standard mirror propelled by PZT. During the measurement, each of the key parameters is assigned different values. By analyzing the testing results, the law of the parameters' influence on system anti-vibration capability can be obtained. According to the law, the optimization parameters can be determined so that the system has the maximum anti- vibration capability.
Singh, Karan; Singhvi, Akshit; Pathangay, Vinod
2015-08-01
Acquiring fingertip ECG (electrocardiogram) signal using dry contact electrodes is challenging due to the presence of noise and interference by EMG (electromyogram) potentials. In this paper, we propose a method for using the fingertip ECG signal for biometric authentication. The noisy segments of the signal are segmented out using a variance-based heuristic and the clean signal is used for subsequent processing. By applying baseline correction and band pass filtering, the filtered signal is used for beat feature extraction. The features are used to train a support vector machine (SVM) classifier. Experimental results are presented to show the optimum filter parameters and feature sets for best classification performance. The performance of the proposed method with the optimum parameters was evaluated on a public domain CYBHi dataset with 126 subjects and the beat level EER of 3.4% was obtained. PMID:26736315
Almeida, Pedro R; Ferreira-Santos, Fernando; Chaves, Pedro L; Paiva, Tiago O; Barbosa, Fernando; Marques-Teixeira, João
2016-01-01
Findings concerning the emotional modulation of the N170 component of the visual event-related potential are mixed. In the present report we tested the hypothesis that the emotional modulation of the N170 may be driven by the perceived emotional arousal of the stimuli, rather than by specific emotional categories. Fifty-four participants viewed facial expressions of anger, disgust, fear and happiness, plus low arousal neutral faces. All emotional categories were matched in arousal, while stimuli within each category varied parametrically in this dimension. The modulation of the electrocortical activity on the N170 time-window was analyzed in the time domain and via time-frequency decomposition. The effects of emotion and arousal were analyzed separately. In the time domain N170 amplitudes co-varied parametrically with perceived arousal, regardless of emotional category. This modulation was linearly associated with the power of the theta, alpha, and beta frequency bands. Moreover, fear was associated with a trend for increased N170 amplitudes, enhanced alpha power, and increased broad band inter-trial phase coherence. These results support the views that a) the activity in N170 time window is fundamentally modulated by perceived arousal, b) the modulation of the N170 may be the product of an increased evoked response, rather than the result of phase resetting processes, and c) facial expressions of fear retain some processing primacy, that may be related to their increased value as environmental cues. PMID:26659012
Time-frequency filtering for classifying targets in nonstationary clutter
NASA Astrophysics Data System (ADS)
Gomatam, Vikram Thiruneermalai; Loughlin, Patrick
2014-06-01
Classifying underwater targets from their sonar backscatter is often complicated by induced or self-noise (i.e. clutter, reverberation) arising from the scattering of the sonar pulse from non-target objects. Because clutter is inherently nonstationary, and because the propagation environment can induce nonstationarities as well, in addition to any nonstationarities / time-varying spectral components of the target echo itself, a joint phase space approach to target classification has been explored. In this paper, we apply a previously developed minimum mean square time-frequency spectral estimation method to design a bank of time-frequency filters from training data to distinguish targets from clutter. The method is implemented in the ambiguity domain in order to reduce computational requirements. In this domain, the optimal filter (more commonly called a "kernel" in the time-frequency literature) multiples the ambiguity function of the received signal, and then the mean squared distance to each target class is computed. Simulations demonstrate that the class-specific optimal kernel better separates each target from the clutter and other targets, compared to a simple mean-squared distance measure with no kernel processing.
NASA Astrophysics Data System (ADS)
Siqueira, M.; Katul, G.; Sampson, D. A.; Stoy, P.; Juang, J.; Oren, R.
2004-12-01
Ecosystem processes relevant to carbon transfer and storage are known to vary over many time and space scales. In the time domain, processes ranging from seconds, such as turbulent transport, to seasons, such as plant phenology, affect assimilation and respiration, which in turn, control carbon allocation over time scales of days to years. These inter-related processes contribute to the forest development (often measured in years to decades) and long-term carbon sequestration. To date, no single model captures the entire spectrum of variability of these processes; rather, a modular approach is adopted in which the forcing and response variables are mechanistically coupled over an inherent or assumed time scale that is then integrated to longer time scales. The effect of such modular parameterization of the "fast" processes and their cross-scale interaction with the slowly varying processes on long-term carbon sequestration remains a subject of investigation. We address this problem in two ways. First, we perform a multi-model inter-comparison in the time and frequency domains to assess how different parameterizations of photosynthesis and water vapor fluxes in forest growth models (e.g. BGC, SECRETS, PnET and 3PG) reproduce the observed spectrum of these two fluxes from hours to years. These models were chosen because they significantly vary in complexity and integration time step, thereby "filtering" the flux spectrum differently. Next, we explore the consequences of this filtering on cross-scale information flow using a newly proposed nested scheme that employs multi-species allocation routines with assimilation calculated with CANVEG. CANVEG is a multi-layer and multi-species model that resolves the entire canopy microclimate and uses a dynamic leaf area density as an input. The analysis is done in a cost-benefit fashion evaluating the gain in predictive skills of long-term carbon sequestration as result of extra model complexity and added parameterizations. As
NASA Astrophysics Data System (ADS)
Strifors, H. C.; Abrahamson, S.; Andersson, T.; Gaunaurd, G. C.
2006-05-01
Ultra-wideband ground penetrating radar (GPR) systems have proved useful for extracting and displaying information for target recognition purposes. Target signatures whether in the time, frequency, or joint time-frequency domains, will substantially depend on the target's burial conditions such as the type of soil, burial depth, and the soil's moisture content. That dependence can be utilized for target recognition purposes as we have demonstrated previously. The signature template of each target was computed in the time-frequency domain from the returned echo when the target was buried at a known depth in the soil with a known moisture content. Then, for any returned echo the relative difference between the similarly computed target signature and a selected signature template was computed. A global optimization method together with our (approximate) target translation method (TTM) that signature difference, chosen as object function, was minimized by adjusting the depth and moisture content, now taken to be unknown parameters. The template that gave the smallest value of the minimized object function for the returned echo was taken as target classification and the corresponding values of the depth and moisture parameters as estimates of the target's burial conditions. This optimization technique can also be applied to time-series data, avoiding the need for time-frequency analysis. It is then of interest to evaluate the relative merits of time data and time-frequency data for target recognition. Such a comparison is here preformed using signals returned from dummy mines buried underground. The results of the analysis serve to assess the intrinsic worth of data in the time domain and in the time-frequency domain for identifying subsurface targets using a GPR. The targets are buried in a test field at the Swedish Explosive Ordnance Disposal and Demining Center (SWEDEC) at Eksjo, Sweden.
Holographic security system based on image domain joint transform correlator
NASA Astrophysics Data System (ADS)
Borisov, Michael; Odinokov, Sergey B.; Bondarev, Leonid A.; Kurakin, Sergey V.; Matveyev, Sergey V.; Belyaev, V. S.
2002-04-01
We describe holographic security system providing machine reading of the holographic information and matching it with the reference one by optical means. The security holographic mark includes several test holograms and should be applied to a carrier: ID-card, paper seal etc. Each of the holograms stores a part of entire image, stored in the reference hologram. Image domain JTC is used to match the images retrieved from the holograms. Being recorded and retrieved, the images provides correlation peaks with special positions, with a strict dependence on the tested and reference holograms mutual shifts. The system proposed works like usual JTC with a few useful differences. The image domain recognizing is a result of Fresnel holographic technique of the images recording. It provides more effective usage of the light addressed SLM (LASLM) work pupil and resolution in more simple and compact device. Few correlation peaks enhances the device recognizing probability. We describe the real-time experimental arrangement based on LASLM. The experimental results are in a good correspondence with computer simulations. We also show in practice that good results may be obtained while using the image domain JTC technique in despite of the low LASLM resolution and the device compact size.
Time-frequency featured co-movement between the stock and prices of crude oil and gold
NASA Astrophysics Data System (ADS)
Huang, Shupei; An, Haizhong; Gao, Xiangyun; Huang, Xuan
2016-02-01
The nonlinear relationships among variables caused by the hidden frequency information complicate the time series analysis. To shed more light on this nonlinear issue, we examine their relationships in joint time-frequency domain with multivariate framework, and the analyses in the time domain and frequency domain serve as comparisons. The daily Brent oil prices, London gold fixing price and Shanghai Composite index from January 1991 to September 2014 are adopted as example. First, they have long-term cointegration relationship in time domain from holistic perspective. Second, the Granger causality tests in different frequency bands are heterogeneous. Finally, the comparison between results from wavelet coherence and multiple wavelet coherence in the joint time-frequency domain indicates that in the high (1-14 days) and medium frequency (14-128 days) bands, the combination of Brent and gold prices has stronger correlation with the stock. In the low frequency band (256-512 days), year 2003 is the structure broken point before which Brent and oil are ideal choice for hedging the risk of the stock market. Thus, this paper offers more details between the Chinese stock market and the commodities markets of crude oil and gold, which suggests that the decisions for different time and frequencies should consider the corresponding benchmark information.
Separating More Sources Than Sensors Using Time-Frequency Distributions
NASA Astrophysics Data System (ADS)
Linh-Trung, Nguyen; Belouchrani, Adel; Abed-Meraim, Karim; Boashash, Boualem
2005-12-01
We examine the problem of blind separation of nonstationary sources in the underdetermined case, where there are more sources than sensors. Since time-frequency (TF) signal processing provides effective tools for dealing with nonstationary signals, we propose a new separation method that is based on time-frequency distributions (TFDs). The underlying assumption is that the original sources are disjoint in the time-frequency (TF) domain. The successful method recovers the sources by performing the following four main procedures. First, the spatial time-frequency distribution (STFD) matrices are computed from the observed mixtures. Next, the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then, the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources; each class, now containing the auto-source points of only one source, gives an estimation of the TFD of this source. Finally, the source waveforms are recovered from their TFD estimates using TF synthesis. Simulated experiments indicate the success of the proposed algorithm in different scenarios. We also contribute with two other modified versions of the algorithm to better deal with auto-source point selection.
Demultiplexing based on frequency-domain joint decision MMA for MDM system
NASA Astrophysics Data System (ADS)
Caili, Gong; Li, Li; Guijun, Hu
2016-06-01
In this paper, we propose a demultiplexing method based on frequency-domain joint decision multi-modulus algorithm (FD-JDMMA) for mode division multiplexing (MDM) system. The performance of FD-JDMMA is compared with frequency-domain multi-modulus algorithm (FD-MMA) and frequency-domain least mean square (FD-LMS) algorithm. The simulation results show that FD-JDMMA outperforms FD-MMA in terms of BER and convergence speed in the cases of mQAM (m=4, 16 and 64) formats. And it is also demonstrated that FD-JDMMA achieves better BER performance and converges faster than FD-LMS in the cases of 16QAM and 64QAM. Furthermore, FD-JDMMA maintains similar computational complexity as the both equalization algorithms.
Brain connectivity study of joint attention using frequency-domain optical imaging technique
NASA Astrophysics Data System (ADS)
Chaudhary, Ujwal; Zhu, Banghe; Godavarty, Anuradha
2010-02-01
Autism is a socio-communication brain development disorder. It is marked by degeneration in the ability to respond to joint attention skill task, from as early as 12 to 18 months of age. This trait is used to distinguish autistic from nonautistic populations. In this study, diffuse optical imaging is being used to study brain connectivity for the first time in response to joint attention experience in normal adults. The prefrontal region of the brain was non-invasively imaged using a frequency-domain based optical imager. The imaging studies were performed on 11 normal right-handed adults and optical measurements were acquired in response to joint-attention based video clips. While the intensity-based optical data provides information about the hemodynamic response of the underlying neural process, the time-dependent phase-based optical data has the potential to explicate the directional information on the activation of the brain. Thus brain connectivity studies are performed by computing covariance/correlations between spatial units using this frequency-domain based optical measurements. The preliminary results indicate that the extent of synchrony and directional variation in the pattern of activation varies in the left and right frontal cortex. The results have significant implication for research in neural pathways associated with autism that can be mapped using diffuse optical imaging tools in the future.
NASA Astrophysics Data System (ADS)
Klose, C. D.; Kim, H. K.; Netz, U.; Blaschke, S.; Zwaka, P. A.; Mueller, G. A.; Beuthan, J.; Hielscher, A. H.
2009-02-01
Novel methods that can help in the diagnosis and monitoring of joint disease are essential for efficient use of novel arthritis therapies that are currently emerging. Building on previous studies that involved continuous wave imaging systems we present here first clinical data obtained with a new frequency-domain imaging system. Three-dimensional tomographic data sets of absorption and scattering coefficients were generated for 107 fingers. The data were analyzed using ANOVA, MANOVA, Discriminant Analysis DA, and a machine-learning algorithm that is based on self-organizing mapping (SOM) for clustering data in 2-dimensional parameter spaces. Overall we found that the SOM algorithm outperforms the more traditional analysis methods in terms of correctly classifying finger joints. Using SOM, healthy and affected joints can now be separated with a sensitivity of 0.97 and specificity of 0.91. Furthermore, preliminary results suggest that if a combination of multiple image properties is used, statistical significant differences can be found between RA-affected finger joints that show different clinical features (e.g. effusion, synovitis or erosion).
Fahmy, Gamal; Black, John; Panchanathan, Sethuraman
2006-06-01
Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented. PMID:16764265
NASA Astrophysics Data System (ADS)
Wen, Xiang; Zheng, Xiangquan
2011-10-01
Joint scheduling of cross-domain communication resource based on grid-enabled networking is an efficient solution to better support grid application and provide communication service capability for on-demand cross-domain traffic delivery. This paper presents a grid-enabled communication network simulation system, and carries out study on joint scheduling of cross-domain communication resource in grid-enabled communication network from the point view of feasibility and effectiveness. The result of simulation shows that, by adopting the method of on-demand sharing and flexible composition of communication resource in grid-enable networking, grid application providing quality-guaranteed service could be better supported.
Multipixel system for gigahertz frequency-domain optical imaging of finger joints
NASA Astrophysics Data System (ADS)
Netz, Uwe J.; Beuthan, Jürgen; Hielscher, Andreas H.
2008-03-01
Frequency-domain optical imaging systems have shown great promise for characterizing blood oxygenation, hemodynamics, and other physiological parameters in human and animal tissues. However, most of the frequency domain systems presented so far operate with source modulation frequencies below 150MHz. At these low frequencies, their ability to provide accurate data for small tissue geometries such as encountered in imaging of finger joints or rodents is limited. Here, we present a new system that can provide data up to 1GHz using an intensity modulated charged coupled device camera. After data processing, the images show the two-dimensional distribution of amplitude and phase of the light modulation on the finger surface. The system performance was investigated and test measurements on optical tissue phantoms were taken to investigate whether higher frequencies yield better signal-to-noise ratios (SNRs). It could be shown that local changes in optical tissue properties, as they appear in the initial stages of rheumatoid arthritis in a finger joint, are detectable by simple image evaluation, with the range of modulation frequency around 500MHz proving to yield the highest SNR.
Complexity in congestive heart failure: A time-frequency approach
NASA Astrophysics Data System (ADS)
Banerjee, Santo; Palit, Sanjay K.; Mukherjee, Sayan; Ariffin, MRK; Rondoni, Lamberto
2016-03-01
Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure—gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.
Practical implementation of the image domain joint transform correlator for holographic security
NASA Astrophysics Data System (ADS)
Borisov, Michael V.; Odinokov, Sergey B.; Bondarev, Leonid A.; Kurakin, Sergey V.
2003-05-01
We describe the experimental setup of the image domain joint transform correlator intended for holographic security application. The security verification routine demands two channels. The first one corresponds to the reference hologram stored in the security device. The other is a security holographic mark with several test sub-holograms, applied to a carrier: ID-card, paper seal etc. Each of the holograms stores a part of entire image, stored in the reference hologram. Image domain JTC is used to match the images retrieved from the holograms. The images are recorded by a light addressed spatial light modulator (LASLM). Being recorded and retrieved, the images provides correlation peaks with special positions, with a strict dependence on the tested and reference holograms mutual shifts. We prove experimentally that the image domain recognizing provides as more effective usage of the LASLM work pupil and resolution as a less device size. The system also has a good tolerance to shift and rotation of the security holographic mark. Few correlation peaks respected to test holograms enhances the device recognizing probability. We provide computer simulations based on the mathematical analysis of the optical signal transforming. The real-time experimental results corresponded with computer simulations are presented.
High-resolution time-frequency distributions for fall detection
NASA Astrophysics Data System (ADS)
Amin, Moeness G.; Zhang, Yimin D.; Boashash, Boualem
2015-05-01
In this paper, we examine the role of high-resolution time-frequency distributions (TFDs) of radar micro-Doppler signatures for fall detection. The work supports the recent and rising interest in using emerging radar technology for elderly care and assisted living. Spectrograms have been the de facto joint-variable signal representation, depicting the signal power in both time and frequency. Although there have been major advances in designing quadratic TFDs which are superior to spectrograms in terms of detailing the local signal behavior, the contributions of these distributions in the area of human motion classifications and their offerings in enhanced feature extractions have not yet been properly evaluated. The main purpose of this paper is to show the effect of using high-resolution TFD kernels, in lieu of spectrogram, on fall detection. We focus on the extended modified B-distribution (EMBD) and exploit the level of details it provides as compared with the coarse and smoothed time-frequency signatures offered by spectrograms.
Reducing noise in the time-frequency representation using sparsity promoting kernel design
NASA Astrophysics Data System (ADS)
Jokanović, Branka; Amin, Moeness G.; Zhang, Yimin D.
2014-05-01
Missing samples in the time domain introduce noise-like artifacts in the ambiguity domain due to their de facto zero values assumed by the bilinear transform. These artifacts clutter the dual domain of the time-frequency signal representation and obscures the time-frequency signature of single and multicomponent signals. In order to suppress the artifacts influence, we formulate a problem based on the sparsity aware kernel. The proposed kernel design is more robust to the artifacts caused by the missing samples.
Gadaleta, Matteo; Giorgio, Agostino
2012-01-01
This study proposes a method for ventricular late potentials (VLPs) detection using time-frequency representation and wavelet denoising in high-resolution electrocardiography (HRECG). The analysis is performed both with the signal averaged electrocardiography (SAECG) and in real time. A comparison between the temporal and the time-frequency analysis is also reported. In the first analysis the standard parameters QRSd, LAS40, and RMS40 were used; in the second normalized energy in time-frequency domain was calculated. The algorithm was tested adding artificial VLPs to real ECGs. PMID:22957271
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.
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.
NASA Astrophysics Data System (ADS)
Haimovich, Alexander M.; Peckham, C. D.; Teti, Joseph G., Jr.
1994-06-01
It is well known that targets moving along track within a Synthetic Aperture Radar (SAR) field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus not only hindering target feature extraction, but also reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The processing is carried out on the range compressed data but before azimuth compression. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). This paper investigates the application of three different time-frequency analysis techniques to estimate the instantaneous Doppler frequency of range compressed SAR data. In particular, we compare the Wigner-Ville distribution, the Gabor expansion and the Short-Time Fourier transform with respect to their performance in noisy SAR data. Criteria are suggested to quantify the performance of each method in the joint time- frequency domain. It is shown that these methods exhibit sharp signal-to-noise threshold effects, i.e., a certain SNR below which the accuracy of the velocity estimation deteriorates rapidly. It is also shown that the methods differ with respect to their representation of the SAR data.
Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering.
Qian, Pengjiang; Jiang, Yizhang; Deng, Zhaohong; Hu, Lingzhi; Sun, Shouwei; Wang, Shitong; Muzic, Raymond F
2016-01-01
The classical maximum entropy clustering (MEC) algorithm usually cannot achieve satisfactory results in the situations where the data is insufficient, incomplete, or distorted. To address this problem, inspired by transfer learning, the specific cluster prototypes and fuzzy memberships jointly leveraged (CPM-JL) framework for cross-domain MEC (CDMEC) is firstly devised in this paper, and then the corresponding algorithm referred to as CPM-JL-CDMEC and the dedicated validity index named fuzzy memberships-based cross-domain difference measurement (FM-CDDM) are concurrently proposed. In general, the contributions of this paper are fourfold: 1) benefiting from the delicate CPM-JL framework, CPM-JL-CDMEC features high-clustering effectiveness and robustness even in some complex data situations; 2) the reliability of FM-CDDM has been demonstrated to be close to well-established external criteria, e.g., normalized mutual information and rand index, and it does not require additional label information. Hence, using FM-CDDM as a dedicated validity index significantly enhances the applicability of CPM-JL-CDMEC under realistic scenarios; 3) the performance of CPM-JL-CDMEC is generally better than, at least equal to, that of MEC because CPM-JL-CDMEC can degenerate into the standard MEC algorithm after adopting the proper parameters, and which avoids the issue of negative transfer; and 4) in order to maximize privacy protection, CPM-JL-CDMEC employs the known cluster prototypes and their associated fuzzy memberships rather than the raw data in the source domain as prior knowledge. The experimental studies thoroughly evaluated and demonstrated these advantages on both synthetic and real-life transfer datasets. PMID:26684257
Time frequency analyses of vibrations of wind turbine towers
NASA Astrophysics Data System (ADS)
Chiang, Chih-Hung; Huang, Chi-Luen; Hsu, Keng-Tseng; Cheng, Chia-Chi; Yu, Chih-Peng; Lai, Jiunnren
2015-04-01
Transient vibrations of the tower supporting a horizontal-axis wind turbine were recorded using a microwave interferometer. Variations in dominant frequencies have been reported in the previous study. Signal analyses aiming to uncouple different frequency components were performed using reassigned spectrogram, a time-frequency representation based on time-corrected short time Fourier transform. Optimal resolutions in both time and frequency domains were first investigated using synthetic signals. The goal was to seek out the favorable combinations of window size and overlapping portions of adjacent windows for a data sequence at a given sampling rate. The dominant frequency found in reassigned spectrogram agrees with that obtained using Fourier spectrum of the same transient measurements of the wind turbine tower under investigation.
Adaptive multimode signal reconstruction from time-frequency representations.
Meignen, Sylvain; Oberlin, Thomas; Depalle, Philippe; Flandrin, Patrick; McLaughlin, Stephen
2016-04-13
This paper discusses methods for the adaptive reconstruction of the modes of multicomponent AM-FM signals by their time-frequency (TF) representation derived from their short-time Fourier transform (STFT). The STFT of an AM-FM component or mode spreads the information relative to that mode in the TF plane around curves commonly called ridges. An alternative view is to consider a mode as a particular TF domain termed a basin of attraction. Here we discuss two new approaches to mode reconstruction. The first determines the ridge associated with a mode by considering the location where the direction of the reassignment vector sharply changes, the technique used to determine the basin of attraction being directly derived from that used for ridge extraction. A second uses the fact that the STFT of a signal is fully characterized by its zeros (and then the particular distribution of these zeros for Gaussian noise) to deduce an algorithm to compute the mode domains. For both techniques, mode reconstruction is then carried out by simply integrating the information inside these basins of attraction or domains. PMID:26953184
NASA Astrophysics Data System (ADS)
Li, Yanyan; Wang, Qing; Chen, Jianping; Han, Lili; Zhang, Wen; Ruan, Yunkai
2014-12-01
This paper presents an application of the Kolmogorov-Smirnov and Wilcoxon rank sum nonparametric statistical tests for identifying structural domain boundaries in jointed rock masses. In the method, the upper hemispherical surface is divided into 100 nearly equal-area windows. The similarity between two samples of joint orientations is measured by comparing the frequencies or the number of joint poles occurring in the windows. Over 2400 joints collected from 8 adjacent exploration tunnels at the Songta dam site in southwest China are used to demonstrate the method. By applying the technique to the study area, structural domain boundaries in the rock mass are determined. Our results suggest that the study area, with an area of approximately 17,850 m2, can be classified into four structural domains. However, the traditional method with the correlation coefficient fails to reveal the structural changes. Since the correlation coefficient is only a measure of strength of the linear relation between two samples, it has limitations in measuring the similarity between joint orientation distributions. A comparison between the proposed method and previous methods indicates that the new technique could provide more reliable results. Besides, the new method can be applied to structural populations with small sample sizes.
Postural tachycardia syndrome: time frequency mapping.
Novak, V; Novak, P; Opfer-Gehrking, T L; Low, P A
1996-12-14
Orthostatic tachycardia is common but its specificity remains uncertain. Our preliminary work suggested that using autonomic function testing in conjunction with time-frequency mapping (TFM), it might be possible to characterize a subset of the postural tachycardia syndrome (POTS), that is due to a restricted autonomic neuropathy. We describe 20 patients (17 women and 3 men, aged 14-43 years) with florid POTS and 20 controls (14 women and 6 men, aged 20-41 years). Autonomic failure was quantified by its distribution (cardiovagal, adrenergic and sudomotor) and severity, a symptom profile was generated, and spectral indices, based on modified Wigner distribution during rest and head-up tilt (80 degrees) were evaluated. During tilt-up POTS patients differed from controls by an excessive heart rate (> 130 bpm) (P < 0.001), and higher diastolic pressure (P < 0.01). During rest, cardiovagal oscillations (at respiratory frequencies [RF]) and slow rhythms at nonrespiratory frequencies (NONRF) (from 0.01 to 0.07 Hz) in R-R intervals (RRI) (P < 0.01) were reduced. Both RF and NONRF rhythms in RRI were further blunted with tilt-up (P < 0.001). Slow adrenergic vasomotor rhythms in blood pressure (BP) (approximately 0.07 Hz) surged with tilt-up and returned to normal levels afterwards. The index of sympatho-vagal balance (NONRF-Systolic BP (SBP)/RF-RRI) was dramatically increased in POTS (P < 0.001). Distal postganglionic sudomotor failure was observed, and impairment of the BP responses to the Valsalva maneuver (phase II) suggested peripheral adrenergic dysfunction. Persistent orthostatic dizziness, tiredness, gastrointestinal symptoms and palpitations were common in POTS patients. It is possible to identify a subset of POTS patients who have a length-dependent autonomic neuropathy, affecting the peripheral adrenergic and cardiovagal fibers, with relative preservation of cardiac adrenergic fibers. PMID:8988490
Postural tachycardia syndrome: time frequency mapping
NASA Technical Reports Server (NTRS)
Novak, V.; Novak, P.; Opfer-Gehrking, T. L.; Low, P. A.
1996-01-01
Orthostatic tachycardia is common but its specificity remains uncertain. Our preliminary work suggested that using autonomic function testing in conjunction with time-frequency mapping (TFM), it might be possible to characterize a subset of the postural tachycardia syndrome (POTS), that is due to a restricted autonomic neuropathy. We describe 20 patients (17 women and 3 men, aged 14-43 years) with florid POTS and 20 controls (14 women and 6 men, aged 20-41 years). Autonomic failure was quantified by its distribution (cardiovagal, adrenergic and sudomotor) and severity, a symptom profile was generated, and spectral indices, based on modified Wigner distribution during rest and head-up tilt (80 degrees) were evaluated. During tilt-up POTS patients differed from controls by an excessive heart rate (> 130 bpm) (P < 0.001), and higher diastolic pressure (P < 0.01). During rest, cardiovagal oscillations (at respiratory frequencies [RF]) and slow rhythms at nonrespiratory frequencies (NONRF) (from 0.01 to 0.07 Hz) in R-R intervals (RRI) (P < 0.01) were reduced. Both RF and NONRF rhythms in RRI were further blunted with tilt-up (P < 0.001). Slow adrenergic vasomotor rhythms in blood pressure (BP) (approximately 0.07 Hz) surged with tilt-up and returned to normal levels afterwards. The index of sympatho-vagal balance (NONRF-Systolic BP (SBP)/RF-RRI) was dramatically increased in POTS (P < 0.001). Distal postganglionic sudomotor failure was observed, and impairment of the BP responses to the Valsalva maneuver (phase II) suggested peripheral adrenergic dysfunction. Persistent orthostatic dizziness, tiredness, gastrointestinal symptoms and palpitations were common in POTS patients. It is possible to identify a subset of POTS patients who have a length-dependent autonomic neuropathy, affecting the peripheral adrenergic and cardiovagal fibers, with relative preservation of cardiac adrenergic fibers.
Maximum-likelihood methods for array processing based on time-frequency distributions
NASA Astrophysics Data System (ADS)
Zhang, Yimin; Mu, Weifeng; Amin, Moeness G.
1999-11-01
This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non- stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi- dimensional optimizations. Compared to the recently proposed time- frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.
NASA Astrophysics Data System (ADS)
Walther, Julia; Koch, Edmund
2011-06-01
Recently, a new method called joint spectral and time domain optical coherence tomography (STdOCT) for flow velocity measurement in spectral domain OCT (SD OCT) was presented. This method analyzes the detected timeresolved interference fringe spectra by using a two-dimensional fast Fourier transformation (2D FFT) to determine directly the Doppler frequency shift instead of calculating the phase difference at each depth position of adjacent A-scans. There, it was found that STdOCT is more robust for measurements with low signal to noise ratio than the classic phase-resolved Doppler OCT (DOCT) making it attractive first for imaging fast flow velocities at which a strong Doppler angle dependent signal damping occurs due to interference fringe washout and second for investigating large blood vessels with a big diameter and a highly damped signal of blood with increasing depth due to strong scattering and absorption in the near-infrared wavelength range. In the present study, we would like to introduce an enhanced algorithm for STdOCT permitting a more precise flow velocity measurement in comparison to the conventional STdOCT. The new method determines the amplitude of the broadened Doppler frequency shift by calculating the center of gravity via the complex analytical signal as a result of the second FFT instead of detecting the maximum intensity signal. Furthermore, the comparison with phase-resolved DOCT was done experimentally by using a flow phantom consisting of a 1% Intralipid emulsion and a 320 μm glass capillary. As a result, the enhanced STdOCT and DOCT processed data are completely equivalent.
[EMD Time-Frequency Analysis of Raman Spectrum and NIR].
Zhao, Xiao-yu; Fang, Yi-ming; Tan, Feng; Tong, Liang; Zhai, Zhe
2016-02-01
This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method. The empirical mode decomposition spectrum becomes intrinsic mode functions, which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component, while the NIR's low order intrinsic mode functions only undertakes fewer primary spectroscopic effective information. Both the real spectrum and numerical experiments show that the empirical mode decomposition (EMD) regard Raman spectrum as the amplitude-modulated signal, which possessed with high frequency adsorption property; and EMD regards NIR as the frequency-modulated signal, which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions. The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum, modal aliasing happened. Through further analysis of corn leaf's NIR in time-frequency domain, after EMD, the first and second orders components of low energy are cut off, and reconstruct spectral signal by using the remaining intrinsic mode functions, the root-mean-square error is 1.001 1, and the correlation coefficient is 0.981 3, both of these two indexes indicated higher accuracy in re-construction; the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band; and the Hilbert transform of characteristic modal component displays, 657 cm⁻¹ is the specific frequency by the corn leaf stress spectrum, which could be regarded as characteristic frequency for identification. PMID:27209743
Measurement Epistemology and Time-Frequency Conjugate Spaces
NASA Astrophysics Data System (ADS)
Roychoudhuri, Chandrasekhar
2010-05-01
We present the critical steps involved in any measurement process, which tell us that force-free and intervention-free measurements are not possible. We add to this the NIW-principle, Non-Interference of Waves, which has been neglected by us for centuries even though it is obvious from careful observations of crossing of all material based waves and light beams. Then we underscore that the foundational assumption behind the time-frequency Fourier theorem does not represent any physical reality even though mathematical computation does give the desired results. It assumes that simple superposition of monochromatic Fourier waves, by themselves, can generate time finite pulses due to interference. Unfortunately, the NIW-principle forbids it. Founders of quantum physics, oblivious of the existence of the NIW-principle, assumed that superposition of light beams produce the observed fringes. In reality, the superposition effects become observable because the quantized detectors carry out the summation of the joint stimulations. Thus, quantum physicists mistakenly assigned the quantum behavior of detectors on to light (photons). Based on these observations, we underscore that the ultimate purpose of physical theories is to facilitate the visualization of the invisible interaction processes, rather than simply model the measured data, as is customary now.
Minimum entropy approach to denoising time-frequency distributions
NASA Astrophysics Data System (ADS)
Aviyente, Selin; Williams, William J.
2001-11-01
Signals used in time-frequency analysis are usually corrupted by noise. Therefore, denoising the time-frequency representation is a necessity for producing readable time-frequency images. Denoising is defined as the operation of smoothing a noisy signal or image for producing a noise free representation. Linear smoothing of time-frequency distributions (TFDs) suppresses noise at the expense of considerable smearing of the signal components. For this reason, nonlinear denoising has been preferred. A common example to nonlinear denoising methods is the wavelet thresholding. In this paper, we introduce an entropy based approach to denoising time-frequency distributions. This new approach uses the spectrogram decomposition of time-frequency kernels proposed by Cunningham and Williams.In order to denoise the time-frequency distribution, we combine those spectrograms with smallest entropy values, thus ensuring that each spectrogram is well concentrated on the time-frequency plane and contains as little noise as possible. Renyi entropy is used as the measure to quantify the complexity of each spectrogram. The threshold for the number of spectrograms to combine is chosen adaptively based on the tradeoff between entropy and variance. The denoised time-frequency distributions for several signals are shown to demonstrate the effectiveness of the method. The improvement in performance is quantitatively evaluated.
Time-frequency beamforming for nondestructive evaluations of plate using ultrasonic Lamb wave
NASA Astrophysics Data System (ADS)
Han, Je-Heon; Kim, Yong-Joe
2015-03-01
The objective of this study is to detect structural defect locations in a plate by exciting the plate with a specific ultrasonic Lamb wave and recording reflective wave signals using a piezoelectric transducer array. For the purpose of eliminating the effects of the direct excitation signals as well as the boundary-reflected wave signals, it is proposed to improve a conventional MUSIC beamforming procedure by processing the measured signals in the time-frequency domain. In addition, a normalized, damped, cylindrical 2-D steering vector is proposed to increase the spatial resolution of time-frequency MUSIC power results. A cross-shaped array is selected to further improve the spatial resolution and to avoid mirrored virtual image effects. Here, it is experimentally demonstrated that the proposed time-frequency MUSIC beamforming procedure can be used to identify structural defect locations on an aluminum plate by distinguishing the defect-induced waves from the excitation-generated and boundary-reflected waves.
Stankovic, Srdjan; Orovic, Irena; Zaric, Nikola
2010-03-01
A watermarking approach based on multidimensional time-frequency analysis is proposed. It represents a unified concept that can be used for different types of data such as audio, speech signals, images or video. Time-frequency analysis is employed for speech signals, while space/spatial-frequency analysis is used for images. Their combination is applied for video signals. Particularly, we focus on the 2-D case: space/spatial-frequency based image watermarking procedure that will be subsequently extended to video signal. A method that selects coefficients for watermarking by estimating the local frequency content is proposed. In order to provide watermark imperceptibility, the nonstationary filtering is used to model the watermark which corresponds to the host signal components. Furthermore, the watermark detection within the multidimensional time-frequency domain is proposed. The efficiency and robustness of the procedure in the presence of various attacks is proven experimentally. PMID:20172773
Time-frequency analysis of event-related potentials: a brief tutorial.
Herrmann, Christoph S; Rach, Stefan; Vosskuhl, Johannes; Strüber, Daniel
2014-07-01
Event-related potentials (ERPs) reflect cognitive processes and are usually analyzed in the so-called time domain. Additional information on cognitive functions can be assessed when analyzing ERPs in the frequency domain and treating them as event-related oscillations (EROs). This procedure results in frequency spectra but lacks information about the temporal dynamics of EROs. Here, we describe a method-called time-frequency analysis-that allows analyzing both the frequency of an ERO and its evolution over time. In a brief tutorial, the reader will learn how to use wavelet analysis in order to compute time-frequency transforms of ERP data. Basic steps as well as potential artifacts are described. Rather than in terms of formulas, descriptions are in textual form (written text) with numerous figures illustrating the topics. Recommendations on how to present frequency and time-frequency data in journal articles are provided. Finally, we briefly review studies that have applied time-frequency analysis to mismatch negativity paradigms. The deviant stimulus of such a paradigm evokes an ERO in the theta frequency band that is stronger than for the standard stimulus. Conversely, the standard stimulus evokes a stronger gamma-band response than does the deviant. This is interpreted in the context of the so-called match-and-utilization model. PMID:24194116
Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima
2014-01-01
We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint
Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment
Liu, Rui; Cheng, Wei; Tong, Hanghang; Wang, Wei; Zhang, Xiang
2016-01-01
Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist multiple related networks, in which each network may be constructed from a different domain and instances in one domain may be related to instances in other domains. In this paper, we propose a robust algorithm, MCA, for multi-network clustering that takes into account cross-domain relationships between instances. MCA has several advantages over the existing single network clustering methods. First, it is able to detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networks by leveraging the duality between clustering individual networks and inferring cross-network cluster alignment. Finally, it provides a multi-network clustering solution that is more robust to noise and errors. We perform extensive experiments on a variety of real and synthetic networks to demonstrate the effectiveness and efficiency of MCA.
ERIC Educational Resources Information Center
Lapierre, Laurent M.; Bonaccio, Silvia; Allen, Tammy D.
2009-01-01
The purpose of our study was to further elucidate how employees should behave at work to increase their chances of being mentored by their immediate supervisor. To that end, we experimentally tested how three domains of employee performance [task performance (TP), organizational citizenship behavior (OCB) targeting the supervisor, and…
Time-frequency signature sparse reconstruction using chirp dictionary
NASA Astrophysics Data System (ADS)
Nguyen, Yen T. H.; Amin, Moeness G.; Ghogho, Mounir; McLernon, Des
2015-05-01
This paper considers local sparse reconstruction of time-frequency signatures of windowed non-stationary radar returns. These signals can be considered instantaneously narrow-band, thus the local time-frequency behavior can be recovered accurately with incomplete observations. The typically employed sinusoidal dictionary induces competing requirements on window length. It confronts converse requests on the number of measurements for exact recovery, and sparsity. In this paper, we use chirp dictionary for each window position to determine the signal instantaneous frequency laws. This approach can considerably mitigate the problems of sinusoidal dictionary, and enable the utilization of longer windows for accurate time-frequency representations. It also reduces the picket fence by introducing a new factor, the chirp rate α. Simulation examples are provided, demonstrating the superior performance of local chirp dictionary over its sinusoidal counterpart.
Electrocardiogram Signal and Linear Time-Frequency Transforms
NASA Astrophysics Data System (ADS)
Krishna, B. T.
2014-12-01
The diagnostic analysis of non-stationary multi component signals such as electrocardiogram (ECG) involves the use of time-frequency transforms. So, the application of time-frequency transforms to an ECG signal is an important problem of research. In this paper, initially, linear transforms like short time Fourier transform, continuous wavelet transforms, s-transform etc. are revisited. Then the application of these transforms to normal and abnormal ECG signals is illustrated. It has been observed that s-transform provides better time and frequency resolution compared to other linear transforms. The fractional Fourier transform provides rotation to the spectrogram representation.
Precision frequency synthesizing sources with excellent time/frequency performances
NASA Technical Reports Server (NTRS)
Zhou, Liren; Lin, Hai
1994-01-01
Precision frequency synthesizing sources are needed in the time / frequency measuring system, atomic frequency standards, telemetry, communication, and radar systems. This kind of frequency synthesizing source possesses high frequency accuracy and excellent long term and short term frequency stability. Several precision frequency synthesizing sources developed by Beijing Institute of Radio Metrology and Measurement (BIRMM) which have been successfully applied to the time / frequency measuring system, atomic frequency standards system, and radar system are described. In addition, the working principle, implementation approach, and the main technical specifications of the frequency synthesizing sources are also given.
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.
Bouslimi, D; Coatrieux, G; Roux, Ch
2011-01-01
In this paper, we propose a new joint watermarking/encryption algorithm for the purpose of verifying the reliability of medical images in both encrypted and spatial domains. It combines a substitutive watermarking algorithm, the quantization index modulation (QIM), with a block cipher algorithm, the Advanced Encryption Standard (AES), in CBC mode of operation. The proposed solution gives access to the outcomes of the image integrity and of its origins even though the image is stored encrypted. Experimental results achieved on 8 bits encoded Ultrasound images illustrate the overall performances of the proposed scheme. By making use of the AES block cipher in CBC mode, the proposed solution is compliant with or transparent to the DICOM standard. PMID:22256213
Character Recognition Method by Time-Frequency Analyses Using Writing Pressure
NASA Astrophysics Data System (ADS)
Watanabe, Tatsuhito; Katsura, Seiichiro
With the development of information and communication technology, personal verification becomes more and more important. In the future ubiquitous society, the development of terminals handling personal information requires the personal verification technology. The signature is one of the personal verification methods; however, the number of characters is limited in the case of the signature and therefore false signature is used easily. Thus, personal identification is difficult from handwriting. This paper proposes a “haptic pen” that extracts the writing pressure, and shows a character recognition method by time-frequency analyses. Although the figures of characters written by different amanuenses are similar, the differences appear in the time-frequency domain. As a result, it is possible to use the proposed character recognition for personal identification more exactly. The experimental results showed the viability of the proposed method.
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.
Dortmans, L; Jans, H; Sauren, A; Huson, A
1991-11-01
A description is given of the results obtained for step excitation for two human knee joint specimens using a time-domain analysis technique. As was expected from the results of a previous study, the magnitude of the dynamic load applied has a marked influence upon the stiffness and damping values for the two observed vibration modes. Deliberate damaging of selected joint elements also yields a well observable change in the dynamic behavior of the joint although these changes are difficult to interpret. Here the use of a nonlinear dynamic numerical model of the knee joint seems indispensable. An important observation is, however, that the experimental method discussed here enables to quantify the behavior of the joint and therefore may provide a valuable tool for validation of such a model. PMID:1762435
Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination
NASA Astrophysics Data System (ADS)
Yin, Haifan; Cottatellucci, Laura; Gesbert, David; Muller, Ralf R.; He, Gaoning
2016-06-01
We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods.
Isolate Speech Recognition Based on Time-Frequency Analysis Methods
NASA Astrophysics Data System (ADS)
Mantilla-Caeiros, Alfredo; Nakano Miyatake, Mariko; Perez-Meana, Hector
A feature extraction method for isolate speech recognition is proposed, which is based on a time frequency analysis using a critical band concept similar to that performed in the inner ear model; which emulates the inner ear behavior by performing signal decomposition, similar to carried out by the basilar membrane. Evaluation results show that the proposed method performs better than other previously proposed feature extraction methods when it is used to characterize normal as well as esophageal speech signal.
Sabra, Karim G; Anderson, Shaun D
2014-05-01
Structural echoes of underwater elastic targets, used for detection and classification purposes, can be highly localized in the time-frequency domain and can be aspect-dependent. Hence such structural echoes recorded along a distributed (synthetic) aperture, e.g., using a moving receiver platform, would not meet the stationarity and multiple snapshots requirements of common subspace array processing methods used for denoising array data based on their estimated covariance matrix. To address this issue, this article introduces a subspace array processing method based on the space-time-frequency distribution (STFD) of single-snapshots of non-stationary signals. This STFD is obtained by computing Cohen's class time-frequency distributions between all pairwise combination of the recorded signals along an arbitrary aperture array. This STFD is interpreted as a generalized array covariance matrix which automatically accounts for the inherent coherence across the time-frequency plane of the received nonstationary echoes emanating from the same target. Hence, identifying the signal's subspace from the eigenstructure of this STFD provides a means for denoising these non-stationary structural echoes by spreading the clutter and noise power in the time-frequency domain; as demonstrated here numerically and experimentally using the structural echoes of a thin steel spherical shell measured along a synthetic aperture. PMID:24815264
Cuspineda-Bravo, Elena R; Martínez-Montes, Eduardo; Farach-Fumero, Miguel; Machado-Curbelo, Calixto
2015-04-01
The combination of recently developed methods for electroencephalographic (EEG) space-time-frequency analysis can provide noninvasive functional neuroimages necessary for obtaining an accurate localization of the epileptogenic zone. The aim of this study was to determine if time-frequency (TF) analysis, followed by EEG source localization, would improve the detection and identification of epileptogenic and related activity. Seventeen patients with refractory frontal lobe epilepsy (FLE) were studied using video EEG recording. TF analysis identified the first epileptogenic EEG changes. Using the Bayesian model averaging (BMA) approach, we compared brain electromagnetic tomographic (BET) images, constructed from the TF domain, with BET images constructed from the time domain only. We determined if the localization identified by BET images was concordant with the localization from medical history and video EEG recording. TF analysis provided a clear display of subtle EEG features, including EEG lateralization, and more concordant and delimited epileptogenic zones, compared with time-domain source analysis. In conclusion, EEG TF analysis improves source localization. After a thorough validation, this methodology could become a useful noninvasive tool for localizing the epileptogenic zone in clinical practice. PMID:24879437
Time-Frequency Approach for Stochastic Signal Detection
Ghosh, Ripul; Akula, Aparna; Kumar, Satish; Sardana, H. K.
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 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.
Vibration-based machine condition monitoring with attention to the use of time-frequency methods
NASA Astrophysics Data System (ADS)
Rehorn, Adam G. J.; Orban, Peter E.; Jiang, Jin
2004-03-01
To enable lightly staffed or fully autonomous machining operations, it is essential that both the condition of the cutter and the health of the machine tool system be known. In this paper, the health of the spindle positioning drive (Z axis) on a Proteo D/94 precision machining center is investigated using time, frequency and time-frequency techniques. Investigated is a cogging phenomenon produced as a result of the DC servomotor brushes sticking due to poor design. This incipient fault reduces the accuracy and controllability of the machine tool, and always leads to total drive failure. Thus, it is important to determine the fault signature of the drive so that corrective action may be taken before failure can occur, permanently damaging both the motor and the workpiece. The vibratory signatures of both a healthy and a faulty spindle during translation are analyzed. It is shown that a spindle under fault conditions behaves differently from a healthy one, and that time and time-frequency domain methods provide useful information on the status of the system. This paper lays the groundwork for the development of a future machine condition monitoring system, which can be easily retrofitted to any machine tool system.
Time-frequency manifold correlation matching for periodic fault identification in rotating machines
NASA Astrophysics Data System (ADS)
He, Qingbo; Wang, Xiangxiang
2013-05-01
For rotating machines, the localized faults of key components generally represent as periodic transient impulses in vibration signals. The existence of background noise will corrupt transient impulses in practice, and will thus increase the difficulty to identify specific faults. This paper combines the concepts of time-frequency manifold (TFM) and image template matching, and proposes a novel TFM correlation matching method to enhance identification of the periodic faults. This method is to conduct correlation matching of a vibration signal in the time-frequency domain by using the TFM with a short duration as a template. By this method, the time-frequency distribution (TFD) of a vibration signal is firstly achieved by the Smoothed Pseudo-Wigner-Ville distribution (SPWVD) method. Then the TFM template is learned to do correlation matching with the TFD of the analyzed signal. Finally, the ridge is extracted from the correlation matching image and the ridge coefficients are analyzed for periodic fault identification. The proposed method takes advantages of the TFM in noise suppression and template matching in object enhancement, and can enhance the fault impulses of interest in a unified scale. The novel method is verified to be superior to traditional enveloping method with providing smoother and clearer fault impulse component via applications to gearbox fault detection and bearing defect identification.
Weak-coherent-state-based time-frequency quantum key distribution
NASA Astrophysics Data System (ADS)
Zhang, Yequn; Djordjevic, Ivan B.; Neifeld, Mark A.
2015-11-01
We study large-alphabet quantum key distribution (QKD) based on the use of weak-coherent states and the time-frequency uncertainty relation. The large alphabet is achieved by dividing time and spectrum into M bins resulting in a frame similar to traditional pulse-position modulation (in time domain). However, the non-uniform occurrence of a photon prepared in a time/frequency bin creates the space for eavesdropping. By analysis, we show that a new intercept-resend attack strategy exists, which is stronger than that has been reported in the literature and hence the secret key rate of time-frequency QKD (TF-QKD) can be more tightly bounded. We then analyse the secret key rates of TF-QKD under various practical issues, such as channel loss, background noise, jitter and atmospheric turbulence in order to better understand the applicability of TF-QKD. Further, we discuss the information reconciliation for TF-QKD. Specifically, we investigate the layered coding scheme for TF-QKD based on quasi-cyclic low-density parity-check codes against jitter and atmospheric turbulence. By simulation, we demonstrate that information reconciliation can be efficiently achieved.
Human Time-Frequency Acuity Beats the Fourier Uncertainty Principle
NASA Astrophysics Data System (ADS)
Oppenheim, Jacob N.; Magnasco, Marcelo O.
2013-01-01
The time-frequency uncertainty principle states that the product of the temporal and frequency extents of a signal cannot be smaller than 1/(4π). We study human ability to simultaneously judge the frequency and the timing of a sound. Our subjects often exceeded the uncertainty limit, sometimes by more than tenfold, mostly through remarkable timing acuity. Our results establish a lower bound for the nonlinearity and complexity of the algorithms employed by our brains in parsing transient sounds, rule out simple “linear filter” models of early auditory processing, and highlight timing acuity as a central feature in auditory object processing.
Adaptive time-frequency parametrization of epileptic spikes
NASA Astrophysics Data System (ADS)
Durka, Piotr J.
2004-05-01
Adaptive time-frequency approximations of signals have proven to be a valuable tool in electroencephalogram (EEG) analysis and research, where it is believed that oscillatory phenomena play a crucial role in the brain’s information processing. This paper extends this paradigm to the nonoscillating structures such as the epileptic EEG spikes, and presents the advantages of their parametrization in general terms such as amplitude and half-width. A simple detector of epileptic spikes in the space of these parameters, tested on a limited data set, gives very promising results. It also provides a direct distinction between randomly occurring spikes or spike/wave complexes and rhythmic discharges.
Fault detection in rotor bearing systems using time frequency techniques
NASA Astrophysics Data System (ADS)
Chandra, N. Harish; Sekhar, A. S.
2016-05-01
Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.
Time-frequency methods for signal analysis in wind turbines
NASA Astrophysics Data System (ADS)
Kalista, Karel; Liska, Jindrich
2015-11-01
Since wind turbines became one of the most often source of renewable energy, appropriate health and condition monitoring systems are required. Especially proper monitoring of offshore plants is very significant because the accessibility is difficult and inspections are very costly. In comparison with conventional rotating machine vibration monitoring, where steady conditions and stationary signal are usually assumed, the wind turbines are characterized by unsteady conditions due to variable rotational speed. Hence the vibration signal is non-stationary and interpretation of signal signatures may be more complex. The common approach to analyze such non-stationary signals is the use of a time-frequency method, usually Short-Time Fourier Transform, which is the most popular one due to its simplicity. Nevertheless, there are other methods which can give a different view at the analyzed data and provide new information. This article investigates the potential use of some other time-frequency methods, namely Wavelet Transform, Wigner-Ville distribution and Hilbert-Huang transform in wind plants monitoring systems and apply these methods to real measured data with additional simulated bearing fault signal. Finally, the mentioned methods are compared based on computational complexity, readability and interpretability. Though the last two criteria are very subjective, Short-Time Fourier Transform was finally chosen as the most effective method followed by Wavelet Transform.
The Application of Time-Frequency Methods to HUMS
NASA Technical Reports Server (NTRS)
Pryor, Anna H.; Mosher, Marianne; Lewicki, David G.; Norvig, Peter (Technical Monitor)
2001-01-01
This paper reports the study of four time-frequency transforms applied to vibration signals and presents a new metric for comparing them for fault detection. The four methods to be described and compared are the Short Time Frequency Transform (STFT), the Choi-Williams Distribution (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, are analyzed using these methods. The new metric for automatic fault 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 methods on this data set. Analysis with the CWT detects mechanical problems with the test rig 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 fault detection and to develop methods of setting the threshold for the metric.
A visual parallel-BCI speller based on the time-frequency coding strategy
NASA Astrophysics Data System (ADS)
Xu, Minpeng; Chen, Long; Zhang, Lixin; Qi, Hongzhi; Ma, Lan; Tang, Jiabei; Wan, Baikun; Ming, Dong
2014-04-01
Objective. Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. Approach. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Main results. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min-1, with an average of 54.0 bit min-1 and 43.0 bit min-1 in the three rounds and five rounds, respectively. Significance. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
Time-frequency analysis of the bistatic acoustic scattering from a spherical elastic shell.
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. PMID:22280581
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.
Time-Frequency Analysis Reveals Pairwise Interactions in Insect Swarms.
Puckett, James G; Ni, Rui; Ouellette, Nicholas T
2015-06-26
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. PMID:26197145
Time-Frequency Methods for Structural Health Monitoring †
Pyayt, Alexander L.; Kozionov, Alexey P.; Mokhov, Ilya I.; Lang, Bernhard; Meijer, Robert J.; Krzhizhanovskaya, Valeria V.; Sloot, Peter M. A.
2014-01-01
Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany) and “strange” behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany). PMID:24625740
High-resolution signal synthesis for time-frequency distributions
Cunningham, G.S.; Williams, W.J.
1993-03-01
Bilinear time-frequency distributions (TFDs) offer improved resolution over linear nine-frequency representations (TFRs), but many TFDs are costly to evaluate and are not associated with signal synthesis algorithms. Recently, the spectrogram (SP) decomposition and weighted reversal correlator decomposition have been used to define low-cost, high-resolution TFDs. In this paper, we show that the vector-valued ``square-root`` of a TFD (VVTFR) provides a representational underpinning for the TFD. By synthesizing signals from modified VVTFRs, we define high-resolution signal synthesis algorithms associated with TFDs. The signal analysis and synthesis packages can be implemented as weighted sums of SP/short-time Fourier Transform signal analysis and synthesis packages, which are widely available, allowing the interested non-specialist easy access to high-resolution methods.
High-resolution signal synthesis for time-frequency distributions
Cunningham, G.S. ); Williams, W.J. . Dept. of Electrical Engineering and Computer Science)
1993-01-01
Bilinear time-frequency distributions (TFDs) offer improved resolution over linear nine-frequency representations (TFRs), but many TFDs are costly to evaluate and are not associated with signal synthesis algorithms. Recently, the spectrogram (SP) decomposition and weighted reversal correlator decomposition have been used to define low-cost, high-resolution TFDs. In this paper, we show that the vector-valued square-root'' of a TFD (VVTFR) provides a representational underpinning for the TFD. By synthesizing signals from modified VVTFRs, we define high-resolution signal synthesis algorithms associated with TFDs. The signal analysis and synthesis packages can be implemented as weighted sums of SP/short-time Fourier Transform signal analysis and synthesis packages, which are widely available, allowing the interested non-specialist easy access to high-resolution methods.
Time-frequency analysis of synthetic aperture radar signals
Johnston, B.
1996-08-01
Synthetic aperture radar (SAR) has become an important tool for remote sensing of the environment. SAR is a set of digital signal processing algorithms that are used to focus the signal returned to the radar because radar systems in themselves cannot produce the high resolution images required in remote sensing applications. To reconstruct an image, several parameters must be estimated and the quality of output image depends on the degree of accuracy of these parameters. In this thesis, we derive the fundamental SAR algorithms and concentrate on the estimation of one of its critical parameters. We show that the common technique for estimating this particular parameter can sometimes lead to erroneous results and reduced quality images. We also employ time-frequency analysis techniques to examine variations in the radar signals caused by platform motion and show how these results can be used to improve output image quality.
Time-frequency methods for structural health monitoring.
Pyayt, Alexander L; Kozionov, Alexey P; Mokhov, Ilya I; Lang, Bernhard; Meijer, Robert J; Krzhizhanovskaya, Valeria V; Sloot, Peter M A
2014-01-01
Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany) and "strange" behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany). PMID:24625740
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.
Faster learning algorithm convergence utilizing a combined time-frequency representation as basis
NASA Astrophysics Data System (ADS)
Hendriks, A. J.; Uys, Hermann; du Plessis, Anton; Steenkamp, Christine
2013-10-01
Light is capable of directly manipulating and probing molecular dynamics at its most fundamental level. One versatile approach to influencing such dynamics exploits temporally shaped femtosecond laser pulses. Oftentimes the control mechanisms necessary to induce a desired reaction cannot be determined theoretically a priori. However under certain circumstances these mechanisms can be extracted experimentally through trial and error. This can be implemented systematically by using an evolutionary learning algorithm (LA) with closed loop feedback. Most frequently, pulse shaping algorithms operate within either the time or frequency domain, however seldom both. This may influence the physical insight gained due to dependence on the search basis, as well as influence the speed the algorithm takes to converge. As an alternative to the Fourier domain basis, we make use of a combined time-frequency representation known as the von Neumann basis where we observe temporal and spectral effects at the same time. We report on the numerical and experimental results obtained using the Fourier, as well as the von Neumann basis to maximize the second harmonic generation (SHG) output in a non-linear crystal. We show that the von Neumann representation converges faster than the Fourier domain when compared to searches in the Fourier domain. We also show a reduced parameter space is required for the Fourier domain to converge efficiently, but not for von Neumann domain. Finally we show the highest SHG signal is not only a consequence of the shortest pulse, but that the pulse central frequency also plays a key role. Taken together these results suggest that the von Neumann basis can be used as a viable alternative to the Fourier domain with improved convergence time and potentially deeper physical insight.
Batista, Arnaldo G; Najdi, Shirin; Godinho, Daniela M; Martins, Catarina; Serrano, Fátima C; Ortigueira, Manuel D; Rato, Raul T
2016-09-01
The uterine electromyogram, also called electrohysterogram (EHG), is an electrical signal generated by the uterine contractile activity. The EHG has been considered a promising biomarker for labour and preterm labour prediction, for which there is a demand for accurate estimation methods. Preterm labour is a significant public health concern and one of the major causes of neonatal mortality and morbidity [1]. Given the non-stationary properties of the EHG signal, time-frequency domain analysis can be used. For real life signals it is not generally possible to determine a priori the suitable quadratic time-frequency kernel or the appropriate wavelet family and relative parameters, regarding, for instance, the adequate detection of the signal frequency variation in time. There has been a lack of a comprehensive software tool for the selection of the appropriate time frequency representation of a multichannel EHG signal and extraction of relevant spectral and temporal information. The presented toolbox (Uterine Explorer) has been specifically designed for the EHG analysis and exploration in view of the characterisation of its components. The starting point is the multichannel scalogram or spectrogram representation from which frequency and time marginals, instantaneous frequency and bandwidth are obtained as EHG features. From this point the detected components undergo parametric and non-parametric spectral estimation and wavelet packet analysis. Intrauterine pressure estimation (IUP) is obtained using the Teager, RMS, wavelet marginal and Hilbert operators over the EHG. This toolbox has been tested to build up a dictionary of 288 EHG components [2], useful for research in preterm labour prediction. PMID:27474810
NASA Astrophysics Data System (ADS)
Yang, Y.; Dong, X. J.; Peng, Z. K.; Zhang, W. M.; Meng, G.
2015-01-01
In real application, when rotary machinery frequently involves variable-speed, unsteady load and defect, it will produce non-stationary vibration signal. Such signal can be characterized by mono- or multi-component frequency modulation (FM) and its internal instantaneous patterns are closely related to operation condition of the rotary machinery. For example, instantaneous frequency (IF) and instantaneous amplitude (IA) of a non-stationary signal are two important time-frequency features to be inspected. For vibration signal analysis of the rotary machinery, time-frequency analysis (TFA), known for analyzing the signal in the time and frequency domain simultaneously, has been accepted as a key signal processing tool. Particularly, parameterized TFA, among various TFAs, has shown great potential to investigate time-frequency features of non-stationary signals. It attracts more attention for improving time-frequency representation (TFR) with signal-dependent transform parameters. However, the parameter estimation and component separation are two problems to tackle with while using the parameterized TFA to extract time-frequency features from non-stationary vibration signal of varying-speed rotary machinery. In this paper, we propose a procedure for the parameterized TFA to analyze the non-stationary vibration signal of varying-speed rotary machinery. It basically includes four steps: initialization, estimation of transform parameter, component separation and parameterized TFA, as well as feature extraction. To demonstrate the effectiveness of the proposed method in analyzing mono- and multi-component signals, it is first used to analyze the vibration response of a laboratory rotor during a speed-up and run-down process, and then extract the instantaneous time-frequency signatures of a hydro-turbine rotor in a hydroelectric power station during a shut-down stage. In addition, the results are compared with several traditional TFAs and the proposed method outperforms
NASA Astrophysics Data System (ADS)
Zhou, Huai-lai; Wang, Chang-cheng; Marfurt, Kurt J.; Jiang, Yi-wei; Bi, Jian-xia
2016-04-01
Maximizing vertical resolution is a key objective in seismic data processing. Early deconvolution and spectral balancing algorithms assumed that the seismic source wavelet was temporally invariant, or stationary. In practice, seismic scattering and attenuation give rise to non-stationary seismic source wavelets. To address this issue, most conventional time-varying deconvolution wavelet shaping and spectral modelling techniques using the stationary polynomial fitting assume the wavelet to be locally stationary within a small number of overlapping analysis windows while the fitting coefficients are invariant with all the frequencies. In this paper, we show an improvement obtained by modelling smoothly varying spectra of the seismic wavelet using non-stationary polynomial fitting in the time-frequency domain. We first decompose each seismic trace using a generalized S-transform that provides a good time-frequency distribution for the estimation of the time-varying wavelet spectra. We then model the slowly varying source wavelet spectrum at each time sample by a smooth low-order polynomial. Finally, we spectrally balance the modelled wavelet to flatten the seismic response, thereby increasing vertical resolution. We calibrate the algorithm on a simple synthetic and then apply it to a 3-D land survey acquired in western China, showing the value on both vertical slices through seismic amplitude and attribute time slices. Our new algorithm significantly improves the vertical resolution of the seismic signal, while not increasing the noise.
Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling
Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng
2016-01-01
Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath. PMID:27428974
Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling.
Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng
2016-01-01
Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath. PMID:27428974
NASA Astrophysics Data System (ADS)
Li, Chuan; Liang, Ming
2012-01-01
The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time-frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated-frequency modulated (AM-FM) gearbox fault signal usually cannot be directly extracted from the blurred time-frequency representation (TFR) caused by the time-varying frequency and noisy multicomponent measurement. As such, we propose to use a generalized synchrosqueezing transform (GST)-based TF method to detect and diagnose gearbox faults. With this method, the original vibration signal is first mapped into another analytical signal to facilitate synchrosqueezing of the TF picture. A time-scale domain restoration process is then applied to recover the instantaneous frequency profile with concentrated TFR. The gearbox fault, if any, can then be detected by observing the presence of the meshing frequency and sideband components in the TFR. The faulty gear can be identified via frequency relation analysis of AM-FM components. The proposed method is evaluated using both simulated and experimental gearbox vibration signals. The results show that the proposed approach is effective for gearbox condition monitoring.
Libbrecht, Maxwell W.; Ay, Ferhat; Hoffman, Michael M.; Gilbert, David M.; Bilmes, Jeffrey A.; Noble, William Stafford
2015-01-01
The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term “specific expression domains.” We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data. PMID:25677182
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
Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D
2015-05-01
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
Time frequency analysis of Jovian and Saturnian radio spectral patterns
NASA Astrophysics Data System (ADS)
Boudjada, Mohammed Y.; Galopeau, Patrick H. M.; Al-Haddad, Emad; Lammer, Helmut
2016-04-01
Prominent radio spectral patterns were observed by the Cassini Radio and Plasma Wave Science experiment (RPWS) principally at Jupiter and Saturn. The spectral shapes are displayed in the usual dynamic spectra showing the flux density versus the time and the frequency. Those patterns exhibit well-organized shapes in the time-frequency plane connected with the rotation of the planet. We consider in this analysis the auroral emissions which occurred in the frequency range between 10 kHz and approximately 3 MHz. It concerns the Jovian hectometric emission (HOM) and the Saturnian kilometric radiation (SKR). We show in the case of Jupiter's HOM that the spectral patterns are well-arranged arc structures with curvatures depending on the Jovian rotation. Regarding the SKR emission, the spectral shapes exhibit generally complex patterns, and only sometimes arc structures are observed. We emphasize the curve alterations from vertex-early to vertex-late arcs (and vice versa) and we study their dependences, or not, on the planetary rotations. We also discuss the common physical process at the origin of the HOM and SKR emissions, specifically the spectral patterns created by the interaction between planetary satellites (e.g. Io or Dione) and the Jovian and Saturnian magnetospheres.
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.
Sparse time-frequency decomposition based on dictionary adaptation.
Hou, Thomas Y; Shi, Zuoqiang
2016-04-13
In this paper, we propose a time-frequency analysis method to obtain instantaneous frequencies and the corresponding decomposition by solving an optimization problem. In this optimization problem, the basis that is used to decompose the signal is not known a priori. Instead, it is adapted to the signal and is determined as part of the optimization problem. In this sense, this optimization problem can be seen as a dictionary adaptation problem, in which the dictionary is adaptive to one signal rather than a training set in dictionary learning. This dictionary adaptation problem is solved by using the augmented Lagrangian multiplier (ALM) method iteratively. We further accelerate the ALM method in each iteration by using the fast wavelet transform. We apply our method to decompose several signals, including signals with poor scale separation, signals with outliers and polluted by noise and a real signal. The results show that this method can give accurate recovery of both the instantaneous frequencies and the intrinsic mode functions. PMID:26953172
Time-frequency analysis of railway bridge response in forced vibration
NASA Astrophysics Data System (ADS)
Cantero, Daniel; Ülker-Kaustell, Mahir; Karoumi, Raid
2016-08-01
This paper suggests the use of the Continuous Wavelet Transform in combination with the Modified Littlewood-Paley basis to analyse bridge responses exited by traversing trains. The analysis provides an energy distribution map in the time-frequency domain that offers a better resolution compared to previous published studies. This is demonstrated with recorded responses of the Skidträsk Bridge, a 36 m long composite bridge located in Sweden. It is shown to be particularly useful to understand the evolution of the energy content during a vehicle crossing event. With this information it is possible to distinguish the effect of several of the governing factors involved in the dynamic response including vehicle's speed and axle configuration as well as non-linear behaviour of the structure.
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
Naval Space Surveillance Center uses of time, frequency, and phase
NASA Technical Reports Server (NTRS)
Hayden, Carroll C.; Knowles, Stephen H.
1992-01-01
The Naval Space Surveillance Center (NAVSPASUR) is an operational naval command that has the mission of determining the location of all manmade objects in space and transmitting information on objects of interest to the fleet. NAVSPASUR operates a 217 MHz radar fence that has 9 transmitting and receiving stations deployed in a line across southern Continental United States (CONUS). This surveillance fence provides unalerted detection of satellites overflying CONUS. NAVSPASUR also maintains a space catalog of all orbiting space objects. NAVSPASUR plays an important role as operational alternate to the primary national Space Surveillance Center (SSC) and Space Defence Operations Center (SPADOC). In executing these responsibilities, NAVSPASUR needs precise and/or standardized time and frequency in a number of applications. These include maintenance of the radar fence references to specification, and coordination with other commands and agencies for data receipt and dissemination. Precise time and frequency must be maintained within each site to enable proper operation of the interferometry phasing technique used. Precise time-of-day clocking must exist between sites for proper intersite coordination. Phase may be considered a derivative of time and frequency. Its control within each transmitter or receiver site is of great importance to NAVSPASUR because of the operation of the sensor as an interferometer system, with source direction angles as the primary observable. Determination of the angular position of a satellite is directly dependent on the accuracy with which the differential phase between spaced subarrays can be measured at each receiver site. Various aspects of the NAVSPASUR are discussed with respect to time, frequency, and phase.
Time-Frequency Analyses of Tide-Gauge Sensor Data
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
United time-frequency spectroscopy for dynamics and global structure.
Marian, Adela; Stowe, Matthew C; Lawall, John R; Felinto, Daniel; Ye, Jun
2004-12-17
Ultrashort laser pulses have thus far been used in two distinct modes. In the time domain, the pulses have allowed probing and manipulation of dynamics on a subpicosecond time scale. More recently, phase stabilization has produced optical frequency combs with absolute frequency reference across a broad bandwidth. Here we combine these two applications in a spectroscopic study of rubidium atoms. A wide-bandwidth, phase-stabilized femtosecond laser is used to monitor the real-time dynamic evolution of population transfer. Coherent pulse accumulation and quantum interference effects are observed and well modeled by theory. At the same time, the narrow linewidth of individual comb lines permits a precise and efficient determination of the global energy-level structure, providing a direct connection among the optical, terahertz, and radio-frequency domains. The mechanical action of the optical frequency comb on the atomic sample is explored and controlled, leading to precision spectroscopy with an appreciable reduction in systematic errors. PMID:15550622
Time-Frequency Variability of Kuroshio Meanders in Tokara Strait
NASA Astrophysics Data System (ADS)
Nakamura, H.; Yamashiro, T.; Nishina, A.; Ichikawa, H.
2006-12-01
The Kuroshio path in the northern Okinawa Trough, Japan, located between the continental slope and Tokara Strait, exhibits meandering motions with largest displacements in the East China Sea; these motions have dominant periods in the broad range of 30-90 days. Understanding the dynamic nature of such meanders is crucial to predicting small and large meanders of the Kuroshio path off the south coast of Japan. Previous numerical simulations suggest that the Kuroshio path meanders in the northern Okinawa Trough become nonstationary in variance because of changes in background states of the Kuroshio in the northern Okinawa Trough, but a detailed analysis based on observed data has yet to be performed. The purpose of the present study is to provide a detailed description of the time-frequency variability of Kuroshio path meanders observed in Tokara Strait. Three Kuroshio indicators were subjected to wavelet analysis for the period 1984-2004: the Kuroshio Position Index (KPI) in Tokara Strait, Kuroshio Volume Transport (KVT) in Tokara Strait, and the basal current velocity of the Kuroshio on the continental slope in the northern Okinawa Trough. The 30-90 day variance of the KPI shows a season-fixed nature, with larger amplitudes in the period December-July. The amplitude of the variance in this phenomenon is also modulated by interannual variations, with small variance recorded during 1989-1992, large variance during 1993-1998, and a return to small variance from 1999-2003. This interannual variation is positively correlated with that of the KVT. The largest variance of the KPI during February-April precedes the largest volume transport in April-May by about 1 month, suggesting that eddy vorticity flux strengthens the mean current field. Previous numerical simulations reproduce the recirculation gyre as a cyclonic eddy in the area between the continental slope and Tokara Strait; this gyre is analogous to the northern recirculation gyre associated with the eastward jet
NASA Astrophysics Data System (ADS)
Zhang, H.; Fang, H.; Yao, H.; Maceira, M.; van der Hilst, R. D.
2014-12-01
Recently, Zhang et al. (2014, Pure and Appiled Geophysics) have developed a joint inversion code incorporating body-wave arrival times and surface-wave dispersion data. The joint inversion code was based on the regional-scale version of the double-difference tomography algorithm tomoDD. The surface-wave inversion part uses the propagator matrix solver in the algorithm DISPER80 (Saito, 1988) for forward calculation of dispersion curves from layered velocity models and the related sensitivities. The application of the joint inversion code to the SAFOD site in central California shows that the fault structure is better imaged in the new model, which is able to fit both the body-wave and surface-wave observations adequately. Here we present a new joint inversion method that solves the model in the wavelet domain constrained by sparsity regularization. Compared to the previous method, it has the following advantages: (1) The method is both data- and model-adaptive. For the velocity model, it can be represented by different wavelet coefficients at different scales, which are generally sparse. By constraining the model wavelet coefficients to be sparse, the inversion in the wavelet domain can inherently adapt to the data distribution so that the model has higher spatial resolution in the good data coverage zone. Fang and Zhang (2014, Geophysical Journal International) have showed the superior performance of the wavelet-based double-difference seismic tomography method compared to the conventional method. (2) For the surface wave inversion, the joint inversion code takes advantage of the recent development of direct inversion of surface wave dispersion data for 3-D variations of shear wave velocity without the intermediate step of phase or group velocity maps (Fang et al., 2014, Geophysical Journal International). A fast marching method is used to compute, at each period, surface wave traveltimes and ray paths between sources and receivers. We will test the new joint
Matrix factorization to time-frequency distribution for structural health monitoring
NASA Astrophysics Data System (ADS)
Chang, Chia-Ming; Huang, Shieh-Kung
2016-04-01
Structural health monitoring enables structural information to be acquired through sensing technology, and is of need to early detect problems and damages in structures. Health monitoring strategies are often realized through a combination of qualitative sensing systems and high-performance structural integrity assessment methods. Structural deviations can be then effectively identified by interpreting the raw sensor measurements using signal processing techniques. The objective of this study is to develop a new structural health monitoring method that applies a matrix factorization algorithm to a time-frequency representation of multi-channel signals measured from a structure. This method processes vibrational input and/or output responses of structures to improve raw data quality, to estimate structural responses, to derive signal features, and to detect structural variations. For example, the proposed method can reduce the signal noise by utilizing first few principle vectors to reconstruct the measured signals. For frequency-domain responses, this method can smooth the phase to obtain a better input-output relationship of a structure. Additionally, the method removes abnormal signals in time series, allowing better understanding of structural behavior. Due to communication loss, this method is able to recover lost data from other channel measurements in a structure. Moreover, the proposed method transforms the signal components into a specific domain and then yield meaningful characteristics. All these features are numerically verified using experimental data, and the proposed method permits more detailed investigation of structural behavior.
TIME-FREQUENCY Analysis of a Suspension Bridge Based on GPS
NASA Astrophysics Data System (ADS)
XU, L.; GUO, J. J.; JIANG, J. J.
2002-06-01
This paper describes the results obtained from full-scale measurements of Humen bridge, which is the second longest suspension bridge in China. A real-time kinematic (RTK) global positioning system (GPS) has been developed and installed on the Humen bridge for on-line monitoring of bridge deck movements. The field wind-induced vibration data were measured by this monitoring system. Three system identification techniques are then adopted in the modal analysis of the wind-induced vibration response: the time-frequency Wigner distribution (WD) technique, the frequency-domain fast Fourier transform (FFT) technique and the time-domain auto-regressive moving average vector (ARMAV) technique. The WD technique can recognize close modal coupling and non-stationary response. The FFT technique can on site verify the quality of the measurements, but its frequency resolution is low and damping estimates are unreliable. The ARMAV method allows for gaining high-frequency resolution. However, it is strictly related to the stationary hypothesis. It is a general conclusion that we can improve the quality of the analysis and get more precise characteristics of the signal by these three methods. In addition, the WD combined with ARMAV seems to be the best case in quantitative analysis of fast-changing vibration signals.
NASA Astrophysics Data System (ADS)
Bernat, Edward M.; Nelson, Lindsay D.; Holroyd, Clay B.; Gehring, William J.; Patrick, Christopher J.
2008-08-01
Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signal processing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.
ERIC Educational Resources Information Center
Chiaburu, Dan S.; Huang, Jason L.; Hutchins, Holly M.; Gardner, Richard G.
2014-01-01
Trainees' knowledge gains represent an important outcome in human resource development. In this research, we tested a model examining the joint influence of social desirability (impression management, self-deception) and motives (need for power, need for approval) on trainees' self-reported knowledge gain. We conducted a study with…
Pierobon, Alberto; DiZio, Paul; Lackner, James R.
2013-01-01
We tested an innovative method to estimate joint stiffness and damping during multijoint unfettered arm movements. The technique employs impulsive perturbations and a time-frequency analysis to estimate the arm's mechanical properties along a reaching trajectory. Each single impulsive perturbation provides a continuous estimation on a single-reach basis, making our method ideal to investigate motor adaptation in the presence of force fields and to study the control of movement in impaired individuals with limited kinematic repeatability. In contrast with previous dynamic stiffness studies, we found that stiffness varies during movement, achieving levels higher than during static postural control. High stiffness was associated with elevated reflexive activity. We observed a decrease in stiffness and a marked reduction in long-latency reflexes around the reaching movement velocity peak. This pattern could partly explain the difference between the high stiffness reported in postural studies and the low stiffness measured in dynamic estimation studies, where perturbations are typically applied near the peak velocity point. PMID:23945781
An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers.
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
An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers
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
NASA Astrophysics Data System (ADS)
Zhang, Haibin; Lu, Siliang; He, Qingbo; Kong, Fanrang
2016-03-01
The diagnosis of train bearing defects based on the acoustic signal acquired by a trackside microphone plays a significant role in the transport system. However, the wayside acoustic signal suffers from the Doppler distortion due to the high moving speed and also contains the multi-source signals from different train bearings. This paper proposes a novel solution to overcome the two difficulties in trackside acoustic diagnosis. In the method a pseudo time-frequency analysis (PTFA) based on an improved Dopplerlet transform (IDT) is presented to acquire the time centers for different bearings. With the time centers, we design a series of Dopplerlet filters (DF) in time-frequency domain to work on the signal's time-frequency distribution (TFD) gained by the short time Fourier transform (STFT). Then an inverse STFT (ISTFT) is utilized to get the separated signals for each sound source which means bearing here. Later the resampling method based on certain motion parameters eliminates the Doppler Effect and finally the diagnosis can be made effectively according to the envelope spectrum of each separated signal. With the effectiveness of the technique validated by both simulated and experimental cases, the proposed wayside acoustic diagnostic scheme is expected to be available in wayside defective bearing detection.
Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations
Gramfort, A.; Strohmeier, D.; Haueisen, J.; Hämäläinen, M.; Kowalski, M.
2013-01-01
Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain imaging with high temporal resolution. While solving the inverse problem independently at every time point can give an image of the active brain at every millisecond, such a procedure does not capitalize on the temporal dynamics of the signal. Linear inverse methods (Minimum-norm, dSPM, sLORETA, beamformers) typically assume that the signal is stationary: regularization parameter and data covariance are independent of time and the time varying signal-to-noise ratio (SNR). Other recently proposed non-linear inverse solvers promoting focal activations estimate the sources in both space and time while also assuming stationary sources during a time interval. However such an hypothesis only holds for short time intervals. To overcome this limitation, we propose time-frequency mixed-norm estimates (TF-MxNE), which use time-frequency analysis to regularize the ill-posed inverse problem. This method makes use of structured sparse priors defined in the time-frequency domain, offering more accurate estimates by capturing the non-stationary and transient nature of brain signals. State-of-the-art convex optimization procedures based on proximal operators are employed, allowing the derivation of a fast estimation algorithm. The accuracy of the TF-MxNE is compared to recently proposed inverse solvers with help of simulations and by analyzing publicly available MEG datasets. PMID:23291276
Lindgren, Johan; Hulkko, Eero; Pettersson, Mika; Kiljunen, Toni
2011-12-14
Numerical wave packet simulations are performed for studying coherent anti-Stokes Raman scattering (CARS) for CN radicals. Electronic coherence is created by femtosecond laser pulses between the X(2)Σ and B(2)Σ states. Due to the large energy separation of vibrational states, the wave packets are superpositions of rotational states only. This allows for a specially detailed inspection of the second- and third-order coherences by a two-dimensional imaging approach. We present the time-frequency domain images to illustrate the intra- and intermolecular interferences, and discuss the procedure to rationally control and experimentally detect the interferograms in solid Xe environment. PMID:22168710
Zhang, Zhe; Leong, Philip H W
2015-08-01
We propose a novel online algorithm for computing least-square based periodograms, otherwise known as the Lomb-Scargle Periodogram. Our spectral analysis technique has been shown to be superior to traditional discrete Fourier transform (DFT) based methods, and we introduce an algorithm which has O(N) time complexity per input sample. The technique is suitable for real-time embedded implementations and its utility is demonstrated through an application to the high resolution time-frequency domain analysis of heart rate variability (HRV). PMID:26736732
NASA Astrophysics Data System (ADS)
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-frequency Analysis on low-resistivity Shielding Layer in TEM Soundings
NASA Astrophysics Data System (ADS)
Shi, Xianxin; Wu, Kai
The transient electromagnetic (TEM) method will be influenced by shielding effect of the low-resistivity overburden layer. By adopting the smooth pseudo Wigner-Ville distribution (SPWD), the responses simulated with a finite-difference time-domain method (FDTD) of D- and G-type models by a 2D line source and H-, A-, K- and Q-type models by a loop source are transformed to the time-frequency (T-F) plane. It is indicated that in low-resistivity, the TEM wave aggregates and will consume more energy, it transmits faster in high-resistivity layers but slower in low-resistivity ones. For A-type models widely in distribution of the North China type coalfield of our country, energy of the TEM field has been almost depleted when arriving at the bottom interface (interfaces of Ordovician limestone and coal series) during the TEM exploration in this area, influence of the low resistive shielding layer shall be taken into full consideration, and relatively longer observing time window shall be selected to ensure the detection depth and high-power instruments shall be adopted to increase the signal-noise ratio during construction design.
Study on the Fault Location Method for Power Cables using the Time-frequency Analysis
NASA Astrophysics Data System (ADS)
Kobayashi, Shin'ichi; Morimoto, Nozomi; Miyajima, Kazuhisa; Hozumi, Naohiro
The pulse radar method is one of fault location methods for power cables. It locates the breakdown point by measuring the delay time of the echo or the discharge signal coming from the breakdown point. The equipment for the pulse radar method is more compact compared with the Murray loop bridge, and its operation is more simple because sensitive adjustments of proportion are not needed. However the signal propagating through the cable is distorted depending on the distance and frequency, leading to a poor accuracy for the location. In this report, signal processing in the time-frequency domain is proposed to solve this problem. The pulse waveforms received at two different terminals of the cable were extracted by a window function, and subsequently Fourier transformed in order to calculate the phase difference at an appropriate frequency. A special care was taken for un-wrapping the folded phase spectrum. The phase difference was interpreted as the time lag at an identical frequency. The technique was applied to the fault location for a full size XLPE cable line.
Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali
2015-08-01
In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier. PMID:26736619
Trace-transform-based time-frequency filtering for seismic signal enhancement in Northeast China
NASA Astrophysics Data System (ADS)
Wu, Ning; Li, Yue; Tian, Yanan; Zhong, Tie
2016-05-01
In this paper, a trace-transform-based radial trace time frequency peak-filtering method (RT-TFPF) with temporal-spatial directions is proposed. It utilizes the similarity of data along the reflection event and computes the temporal-spatial radial directions by seeking the local maximum value of a constructed trace function. This method takes advantage of the TFPF in non-stationary signal estimation, especially with no prior knowledge. Furthermore, applying the filtering in the temporal-spatial domain results in less biased TFPF estimation. Within the framework of the trace transform, the specified trace function first calculates the centroid and then accumulates the energy of the reflected signal along the trajectory, helping to find the locally optimal filtering directions automatically. Experiments on both synthetic record and field data in North-East China demonstrates good performance-strong random noise can be attenuated, while at the same time, the estimated reflection signal is more accurate for use in interpretation.
Keissar, K; Maestri, R; Pinna, G D; La Rovere, M T; Gilad, O
2010-07-01
A novel approach for the estimation of baroreflex sensitivity (BRS) is introduced based on time-frequency analysis of the transfer function (TF). The TF method (TF-BRS) is a well-established non-invasive technique which assumes stationarity. This condition is difficult to meet, especially in cardiac patients. In this study, the classical TF was replaced with a wavelet transfer function (WTF) and the classical coherence was replaced with wavelet transform coherence (WTC), adding the time domain as an additional degree of freedom with dynamic error estimation. Error analysis and comparison between WTF-BRS and TF-BRS were performed using simulated signals with known transfer function and added noise. Similar comparisons were performed for ECG and blood pressure signals, in the supine position, of 19 normal subjects, 44 patients with a history of previous myocardial infarction (MI) and 45 patients with chronic heart failure. This yielded an excellent linear association (R > 0.94, p < 0.001) for time-averaged WTF-BRS, validating the new method as consistent with a known method. The additional advantage of dynamic analysis of coherence and TF estimates was illustrated in two physiological examples of supine rest and change of posture showing the evolution of BRS synchronized with its error estimations and sympathovagal balance. PMID:20585147
NASA Astrophysics Data System (ADS)
He, Qingbo; Ding, Xiaoxi
2016-05-01
The transients caused by the localized fault are important measurement information for bearing fault diagnosis. Thus it is crucial to extract the transients from the bearing vibration or acoustic signals that are always corrupted by a large amount of background noise. In this paper, an iterative transient feature extraction approach is proposed based on time-frequency (TF) domain sparse representation. The approach is realized by presenting a new method, called local TF template matching. In this method, the TF atoms are constructed based on the TF distribution (TFD) of the Morlet wavelet bases and local TF templates are formulated from the TF atoms for the matching process. The instantaneous frequency (IF) ridge calculated from the TFD of an analyzed signal provides the frequency parameter values for the TF atoms as well as an effective template matching path on the TF plane. In each iteration, local TF templates are employed to do correlation with the TFD of the analyzed signal along the IF ridge tube for identifying the optimum parameters of transient wavelet model. With this iterative procedure, transients can be extracted in the TF domain from measured signals one by one. The final signal can be synthesized by combining the extracted TF atoms and the phase of the raw signal. The local TF template matching builds an effective TF matching-based sparse representation approach with the merit of satisfying the native pulse waveform structure of transients. The effectiveness of the proposed method is verified by practical defective bearing signals. Comparison results also show that the proposed method is superior to traditional methods in transient feature extraction.
Lin, Chin-Feng; Zhu, Jin-De
2012-03-01
Hilbert-Huang transformation, wavelet transformation, and Fourier transformation are the principal time-frequency analysis methods. These transformations can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively. The Hilbert-Huang transformation is a combination of empirical mode decomposition and Hilbert spectral analysis. The empirical mode decomposition uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions. Hilbert transforms are then used to transform the intrinsic mode functions into instantaneous frequencies, to obtain the signal's time-frequency-energy distributions and features. Hilbert-Huang transformation-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined Hilbert-Huang transformation-based time-frequency analysis of the electroencephalogram FPI signals of clinical alcoholics, and 'sharp I' wave-based Hilbert-Huang transformation time-frequency features. In this paper, we discuss the application of Hilbert-Huang transformation-based time-frequency analysis to biomedical signals, such as electroencephalogram, electrocardiogram signals, electrogastrogram recordings, and speech signals. PMID:22558835
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.
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-01-01
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the “non-progressing” and “progressing” glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection. PMID:25606299
NASA Astrophysics Data System (ADS)
Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.
2014-03-01
Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
Analysis and design of modified window shapes for S-transform to improve time-frequency localization
NASA Astrophysics Data System (ADS)
Ma, Jianping; Jiang, Jin
2015-06-01
This paper deals with window design issues for modified S-transform (MST) to improve the performance of time-frequency analysis (TFA). After analyzing the drawbacks of existing window functions, a window design technique is proposed. The technique uses a sigmoid function to control the window width in frequency domain. By proper selection of certain tuning parameters of a sigmoid function, windows with different width profiles can be obtained for multi-component signals. It is also interesting to note that the MST algorithm can be considered as a special case of a generalized method that adds a tunable shaping function to the standard window in frequency domain to meet specific frequency localization needs. The proposed design technique has been validated on a physical vibration test system using signals with different characteristics. The results have demonstrated that the proposed MST algorithm has superior time-frequency localization capabilities over standard ST, as well as other classical TFA methods. Subsequently, the proposed MST algorithm is applied to vibration monitoring of pipes in a water supply process controlled by a diaphragm pump for fault detection purposes.
IGS/BIPM pilot project: GPS carrier phase for time/frequency transfer and timescale formation
NASA Astrophysics Data System (ADS)
Ray, J.; Senior, K.
2003-06-01
The development within the International GPS Service (IGS) of a suite of clock products, for both satellites and tracking receivers, offers some experiences which mirror the operations of the Bureau International des Poids et Mesures (BIPM) in its formation of TAI/UTC but some aspects differ markedly. The IGS relies exclusively on the carrier phase-based geodetic technique whereas BIPM time/frequency transfers use only common-view and two-way satellite (TWSTFT) methods. The carrier-phase approach has the potential of very high precision but suitable instrumental calibration procedures are only in the initial phases of deployment; the current BIPM techniques are more mature and widely used among timing labs, but are either less precise (common-view) or much more expensive (TWSTFT). In serving its geodetic users, the essential requirement for IGS clock products is that they be fully self-consistent in relative terms and also fully consistent with all other IGS products, especially the satellite orbits, in order to permit an isolated user to apply them with accuracy of a few centimetres. While there is no other strong requirement for the IGS timescale except to be reasonably close to broadcast GPS time, it is nonetheless very desirable for the IGS clock products to possess additional properties, such as being highly stable and being accurately relatable to UTC. These qualities enhance the value of IGS clock products for applications other than pure geodesy, especially for timing operations. The jointly sponsored `IGS/BIPM Pilot Project to Study Accurate Time and Frequency Comparisons using GPS Phase and Code Measurements' is developing operational strategies to exploit geodetic GPS methods for improved global time/frequency comparisons to the mutual benefit of both organizations. While helping the IGS to refine its clock products and link them to UTC, this collaboration will also provide new time transfer results for the BIPM that may eventually improve the formation
NASA Astrophysics Data System (ADS)
Chen, H. X.; Xue, Y. J.; Cao, J.
2015-12-01
Empirical mode decomposition (EMD), which is a data-driven adaptive decomposition method and is not limited by time-frequency uncertainty spreading, is proved to be more suitable for seismic signals which are nonlinear and non-stationary. Compared with other Fourier-based and wavelet-based time-frequency methods, EMD-based time-frequency methods have higher temporal and spatial resolution and yield hydrocarbon interpretations with more statistical significance. Empirical mode decomposition algorithm has now evolved from EMD to Ensemble EMD (EEMD) to Complete Ensemble EMD (CEEMD). Even though EMD-based time-frequency methods offer many promising features for analyzing and processing geophysical data, there are some limitations or defects in EMD-based time-frequency methods. This presentation will present a comparative study on hydrocarbon detection using seven EMD-based time-frequency analysis methods, which include: (1) first, EMD combined with Hilbert transform (HT) as a time-frequency analysis method is used for hydrocarbon detection; and (2) second, Normalized Hilbert transform (NHT) and HU Methods respectively combined with HT as improved time-frequency analysis methods are applied for hydrocarbon detection; and (3) three, EMD combined with Teager-Kaiser energy (EMD/TK) is investigated for hydrocarbon detection; and (4) four, EMD combined with wavelet transform (EMDWave) as a seismic attenuation estimation method is comparatively studied; and (5) EEMD- and CEEMD- based time-frequency analysis methods used as highlight volumes technology are studied. The differences between these methods in hydrocarbon detection will be discussed. The question of getting a meaningful instantaneous frequency by HT and mode-mixing issues in EMD will be analysed. The work was supported by NSFC under grant Nos. 41430323, 41404102 and 41274128.
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.
Simler, B. Robert; Levy, Yaakov; Onuchic, José N.; Matthews, C. Robert
2007-01-01
Enhanced structural insights into the folding energy landscape of the N-terminal dimerization domain of E. coli tryptophan repressor, [2-66]2 TR, were obtained from a combined experimental and theoretical analysis of its equilibrium folding reaction. Previous studies have shown that the three intertwined helices in [2-66]2 TR are sufficient to drive the formation of a stable dimer for the full-length protein, [2-107]2 TR. The monomeric and dimeric folding intermediates that appear during the folding reactions of [2-66]2 TR have counterparts in the folding mechanism of the full-length protein. The equilibrium unfolding energy surface on which the folding and dimerization reactions occur for [2-66]2 TR was examined with a combination of native-state hydrogen exchange analysis, pepsin digestion and MALDI mass spectrometry performed at several protein and denaturant concentrations. Peptides corresponding to all three helices in [2-66]2 TR show multi-layered protection patterns consistent with the relative stabilities of the dimeric and monomeric folding intermediates. The observation of protection exceeding that offered by the dimeric intermediate in segments from all three helices implies that a segment-swapping mechanism may be operative in the monomeric intermediate. Protection greater than that expected from the global stability for a single amide hydrogen in a peptide from the A-helix and another from the C-helix may reflect non-random structure, possibly a pre-cursor for segment swapping, in the urea-denatured state. Native topology-based model simulations that correspond to a funnel energy landscape capture both the monomeric and dimeric intermediates suggested by the HX-MS data and provide a rationale for the progressive acquisition of secondary structure in their conformational ensembles. PMID:16956620
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...
Time-frequency analysis of the Surge Onset in the Centrifugal Blower
NASA Astrophysics Data System (ADS)
Liskiewicz, Grzegorz; Horodko, Longin
2015-09-01
Time frequency analysis of the surge onset was performed in the centrifugal blower. A pressure signal was registered at the blower inlet, outlet and three locations at the impeller shroud. The time-frequency scalograms were obtained by means of the Continuous Wavelet Transform (CWT). The blower was found to successively operate in four different conditions: stable working condition, inlet recirculation, transient phase and deep surge. Scalograms revealed different spectral structures of aforementioned phases and suggest possible ways of detecting the surge predecessors.
Time-frequency decomposition of click evoked otoacoustic emissions in children.
Mishra, Srikanta K; Biswal, Milan
2016-05-01
Determining the time-frequency distributions of click-evoked otoacoustic emissions (CEOAEs) are scientifically and clinically relevant because of their relationship with cochlear mechanisms. This study investigated the time-frequency properties of CEOAEs in 5-10 year old children. In the first part, we examined the feasibility of the S transform to characterize the time-frequency features of CEOAEs. A synthetic signal with known gammatones was analyzed using the S transform, as well as a wavelet transform with the basis function used traditionally for CEOAE analysis. The S and wavelet transforms provided similar representations of the gammatones of the synthetic signal in the mid and high frequencies. However, the S transform yielded a slightly more precise time-frequency representation at low frequencies (500 and 707 Hz). In the second part, we applied the S transform to compare the time-frequency distribution of CEOAEs between adults and children. Several confounding variables, such as spontaneous emissions and potential efferent effects from the use of higher click rates, were considered for obtaining reliable CEOAE recordings. The results revealed that the emission level, level versus frequency plot, latency, and latency versus frequency plot in 5-10 year old children are adult-like. The time-frequency characteristics of CEOAEs in 5-10 year old children are consistent with the maturation of various aspects of cochlear mechanics, including the basal to apical transition. In sum, the description of the time-frequency features in children and the use of the S transform to decompose CEOAEs, are novel aspects of this study. The S transform can be used as an alternative approach to characterize the time-frequency distribution of CEOAEs. PMID:26976693
Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis
NASA Astrophysics Data System (ADS)
He, Qingbo
2013-02-01
Time-frequency feature is beneficial to representation of non-stationary signals for effective machinery fault diagnosis. The time-frequency distribution (TFD) is a major tool to reveal the synthetic time-frequency pattern. However, the TFD will also face noise corruption and dimensionality reduction issues in engineering applications. This paper proposes a novel nonlinear time-frequency feature based on a time-frequency manifold (TFM) technique. The new TFM feature is generated by mainly addressing manifold learning on the TFDs in a reconstructed phase space. It combines the non-stationary information and the nonlinear information of analyzed signals, and hence exhibits valuable properties. Specifically, the new feature is a quantitative low-dimensional representation, and reveals the intrinsic time-frequency pattern related to machinery health, which can effectively overcome the effects of noise and condition variance issues in sampling signals. The effectiveness and the merits of the proposed TFM feature are confirmed by case study on gear wear diagnosis, bearing defect identification and defect severity evaluation. Results show the value and potential of the new feature in machinery fault pattern representation and classification.
Wang, Yazhou; Cui, Hongyan; Pu, Jiangbo; Luk, K D K; Hu, Yong
2015-08-31
Somatosensory evoked potentials (SEPs) were found to exhibit different time-frequency patterns after acute spinal cord injury (SCI) at different levels, which implies that changes of these patterns may be associated with the location of SCI. Based on this finding, we propose the hypothesis that there are information regarding the location of SCI contained in the time-frequency patterns of SEPs. Purpose of the present study is to verify this hypothesis by comparing the time-frequency patterns of SEPs after acute and chronic SCI at the same level. The study examined the distribution patterns of the time-frequency components (TFCs) of SEPs before and after acute and chronic injury at C5 level in the spinal cord. Experimental results of SEP recordings from 24 adult rats show that there are common areas in the time-frequency distributions of SEPs. The TFCs from both the acute injury group and the chronic injury group are located in these areas with no TFCs from the normal group. Findings suggest that these areas are likely to possess information concerning the site of neurological deficits in spinal cord while independent of the modality of injury. This study provides basis for identification of stable time-frequency patterns of SEPs after different types and locations of SCI, which will guide the development of SEP-based SCI location detection. PMID:26170248
NASA Astrophysics Data System (ADS)
Yang, Yang; Li, Xiukun
2016-04-01
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.
Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis
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
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.
Time-frequency data fusion technique with application to vibration signal analysis
NASA Astrophysics Data System (ADS)
Peng, Z. K.; Zhang, W. M.; Lang, Z. Q.; Meng, G.; Chu, F. L.
2012-05-01
To overcome the inherent deficiencies of conventional time-frequency analysis (TFA) methods, i.e., different TFA methods or the same TFA method with different control parameters will present different results for the same target signal, a novel scheme named as the time-frequency data fusion (TFDF) is developed in this study by extending the idea of data fusion technique. By combining the results produced by two or more different TFA methods, the TFDF technique can present a more accurate time-frequency presentation for the target signal than what can be achieved by any individual TFA method. Therefore, the TFDF has potential to render a significantly improved time-frequency representation and greatly facilitates extracting time-frequency features of target signals. This will promote the applications of TFA in engineering practices and make TFA methods more acceptable to field engineers. The effectiveness of the TFDF technique is validated by three numerical case studies and the analysis of a rubbing-impact signal collected from a rotor test rig.
2013-01-01
Introduction Cyclophilin A (CypA) is implicated in rheumatoid arthritis (RA) pathogenesis. We studied whether a novel anti-CypA single domain antibody (sdAb) treatment would modulate the severity of the disease in two different animal models of RA. Methods A novel sdAb, named sdAbA1, was screened from an immunized camel sdAb library and found to have a high binding affinity (KD = 6.9 × 10-9 M) for CypA. The SCID-HuRAg model and the collagen-induced arthritis (CIA) in mice were used to evaluate the effects of sdAbA1 treatment on inflammation and joint destruction. For in vitro analysis, monocytes/macrophages were purified from synovial fluid and peripheral blood of patients with RA and were tested for the effect of anti-CypA sdAb on metalloproteinase (MMP) production. Human monocyte cell line THP-1 cells were selected and western blot analyses were performed to examine the potential signaling pathways. Results In the CIA model of RA, the sdAbA1 treatment resulted in a significant decrease in clinical symptoms as well as of joint damage (P <0.05). In the SCID-HuRAg model, treatment with anti-CypA antibody sdAbA1 significantly reduced cartilage erosion, inflammatory cell numbers and MMP-9 production in the implanted tissues (P <0.05). It also significantly reduced the levels of human inflammatory cytokines IL-6 and IL-8 in mouse serum (P <0.05). No toxic effects were observed in the two animal models. In vitro results showed that sdAbA1 could counteract CypA-dependent MMP-9 secretion and IL-8 production by interfering with the ERK-NF-κB pathway. Conclusions Blockade of CypA significantly inhibited synovitis and cartilage/bone erosion in the two tested animal models of RA. Our findings provide evidence that sdAbA1 may be a potential therapeutic agent for RA. PMID:24314202
NASA Astrophysics Data System (ADS)
Lin, Yih-Hwang; Wu, Hsien-Chang; Wu, Chung-Yung
2006-12-01
The purpose of this study is to develop an automated system for condition classification of a reciprocating compressor. Various time-frequency analysis techniques will be examined for decomposition of the vibration signals. Because a time-frequency distribution is a 3D data map, data reduction is indispensable for subsequent analysis. The extraction of the system characteristics using three indices, namely the time index, frequency index, and amplitude index, will be presented and examined for their applicability. The probability neural network is applied for automated condition classification using a combination of the three indices. The study reveals that a proper choice of the index combination and the time-frequency band can provide excellent classification accuracy for the machinery conditions examined in this work.
From wavelets to adaptive approximations: time-frequency parametrization of EEG.
Durka, Piotr J
2003-01-01
This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals. PMID:12605721
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. PMID:20136233
van 't Klooster, Maryse A; Zijlmans, Maeike; Leijten, Frans S S; Ferrier, Cyrille H; van Putten, Michel J A M; Huiskamp, Geertjan J M
2011-10-01
Epilepsy surgery depends on reliable pre-surgical markers of epileptogenic tissue. The current gold standard is the seizure onset zone in ictal, i.e. chronic, electrocorticography recordings. Single pulse electrical stimulation can evoke epileptic, spike-like responses in areas of seizure onset also recorded by electrocorticography. Recently, spontaneous pathological high-frequency oscillations (80-520 Hz) have been observed in the electrocorticogram that are related to epileptic spikes, but seem more specific for epileptogenic cortex. We wanted to see whether a quantitative electroencephalography analysis using time-frequency information including the higher frequency range could be applied to evoked responses by single pulse electrical stimulation, to enhance its specificity and clinical use. Electrocorticography data were recorded at a 2048-Hz sampling rate from 13 patients. Single pulse electrical stimulation (10 stimuli, 1 ms, 8 mA, 0.2 Hz) was performed stimulating pairs of adjacent electrodes. A time-frequency analysis based on Morlet wavelet transformation was performed in a [-1 s : 1 s] time interval around the stimulus and a frequency range of 10-520 Hz. Significant (P = 0.05) changes in power spectra averaged for 10 epochs were computed, resulting in event-related spectral perturbation images. In these images, time-frequency analysis of single pulse-evoked responses, in the range of 10-80 Hz for spikes, 80-250 Hz for ripples and 250-520 Hz for fast ripples, were scored by two observers independently. Sensitivity, specificity and predictive value of time-frequency single pulse-evoked responses in the three frequency ranges were compared with seizure onset zone and post-surgical outcome. In all patients, evoked responses included spikes, ripples and fast ripples. For the seizure onset zone, the median sensitivity of time-frequency single pulse-evoked responses decreased from 100% for spikes to 67% for fast ripples and the median specificity increased from
NASA Astrophysics Data System (ADS)
Arroucau, Pierre; Lebedev, Sergei
2016-04-01
Ireland is located on the European North Atlantic margin, at the northwesternmost edge of the Eurasian continent, several hundred kilometers away from the closest plate boundaries, namely the North Atlantic ridge and the Nubia-Eurasia convergence front. Its low level of seismicity, according to the number of events and magnitudes given by the existing catalogs, is thus expected. However, it still appears surprisingly low compared to neighboring domains, including Great Britain and, more generally, the rest of the Atlantic margin. One explanation might be that the events reported in those catalogs do not reflect the actual seismic activity of Ireland due to a lack, until recently, of permanent seismological stations on the Irish territory. Although the Irish National seismic Network (INSN) now consists of 6 stations, and despite a good station coverage of Britain, to the east, by the British Geological survey (BGS) stations, most of the earthquakes occurring in Ireland may still be missed because of their low magnitude. Here, we combine the waveform data recorded at permanent (INSN, BGS) stations with that from dense temporary array deployed in the past 5 years by the Dublin Institute for Advanced Studies (DIAS) and the University College Dublin (UCD). In addition to new arrival time data and new locations for already known catalog events, our analysis reveals newly detected earthquakes in Ireland, and sheds new light on the seismotectonics of this intraplate continental region. This sets the stage for joint earthquake relocation and 3D velocity model determination, which should lead to a better understanding of the relationships between the current seismic activity and the geological structure of the Irish lithosphere.
Important trait and application of time-frequency to traceable source link
NASA Astrophysics Data System (ADS)
Ni, Guang-Ren; Xu, Lu-Ping; He, Kang-Yuan
2006-03-01
Taking the traceable source link system of the time-frequency as the typical one from the National Intelligence Standard Research Institute (NIST) of the United States, the article provides its block-diagram and technical indicators reviews its aim and its task. The article also gives the important characters of the traceable source link of the time-frequency, which includes: having the high-accuracy cesium fountain primitive frequency benchmark (accuracy and steady degrees all reach to 1×E-15), keeping ahead in the aspect of time-frequency transform method technology research and development, then reaching the advanced level in the frequency measurement and the analytical system of long-range calibration (FMAS), so as in the quality of the high integration, automatization, intelligent, lightweight of top-level equipment in the traceabel source link system. At last, the article describes the importance and the key technical indicator achieved at present to the high-accuracy synchronous time-frequency system in the field of the astronomy measurement, the guided missile launch, navigation and orientation, etc.
[Evoked Potential Blind Extraction Based on Fractional Lower Order Spatial Time-Frequency Matrix].
Long, Junbo; Wang, Haibin; Zha, Daifeng
2015-04-01
The impulsive electroencephalograph (EEG) noises in evoked potential (EP) signals is very strong, usually with a heavy tail and infinite variance characteristics like the acceleration noise impact, hypoxia and etc., as shown in other special tests. The noises can be described by a stable distribution model. In this paper, Wigner-Ville distribution (WVD) and pseudo Wigner-Ville distribution (PWVD) time-frequency distribution based on the fractional lower order moment are presented to be improved. We got fractional lower order WVD (FLO-WVD) and fractional lower order PWVD (FLO-PWVD) time-frequency distribution which could be suitable for a stable distribution process. We also proposed the fractional lower order spatial time-frequency distribution matrix (FLO-STFM) concept. Therefore, combining with time-frequency underdetermined blind source separation (TF-UBSS), we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation (FLO-TF-UBSS) which can work in a stable distribution environment. We used the FLO-TF-UBSS algorithm to extract EPs. Simulations showed that the proposed method could effectively extract EPs in EEG noises, and the separated EPs and EEG signals based on FLO-TF-UBSS were almost the same as the original signal, but blind separation based on TF-UBSS had certain deviation. The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio (GSNR) changed from 10 dB to 30 dB and a varied from 1. 06 to 1. 94, and was approximately e- qual to 1. Hence, the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment. PMID:26211238
NASA Astrophysics Data System (ADS)
Le Bras, Ronan; Victor, Sucic; Damir, Malnar; Götz, Bokelmann
2014-05-01
In order to enrich the set of attributes in setting up a large database of whale signals, as envisioned in the Baleakanta project, we investigate methods of time-frequency analysis. The purpose of establishing the database is to increase and refine knowledge of the emitted signal and of its propagation characteristics, leading to a better understanding of the animal migrations in a non-invasive manner and to characterize acoustic propagation in oceanic media. The higher resolution for signal extraction and a better separation from other signals and noise will be used for various purposes, including improved signal detection and individual animal identification. The quadratic class of time-frequency distributions (TFDs) is the most popular set of time-frequency tools for analysis and processing of non-stationary signals. Two best known and most studied members of this class are the spectrogram and the Wigner-Ville distribution. However, to be used efficiently, i.e. to have highly concentrated signal components while significantly suppressing interference and noise simultaneously, TFDs need to be optimized first. The optimization method used in this paper is based on the Cross-Wigner-Ville distribution, and unlike similar approaches it does not require prior information on the analysed signal. The method is applied to whale signals, which, just like the majority of other real-life signals, can generally be classified as multicomponent non-stationary signals, and hence time-frequency techniques are a natural choice for their representation, analysis, and processing. We present processed data from a set containing hundreds of individual calls. The TFD optimization method results into a high resolution time-frequency representation of the signals. It allows for a simple extraction of signal components from the TFD's dominant ridges. The local peaks of those ridges can then be used for the signal components instantaneous frequency estimation, which in turn can be used as
Inverse synthetic aperture radar processing using parametric time-frequency estimators Phase I
Candy, J.V., LLNL
1997-12-31
This report summarizes the work performed for the Office of the Chief of Naval Research (ONR) during the period of 1 September 1997 through 31 December 1997. The primary objective of this research was aimed at developing an alternative time-frequency approach which is recursive-in-time to be applied to the Inverse Synthethic Aperture Radar (ISAR) imaging problem discussed subsequently. Our short term (Phase I) goals were to: 1. Develop an ISAR stepped-frequency waveform (SFWF) radar simulator based on a point scatterer vehicular target model incorporating both translational and rotational motion; 2. Develop a parametric, recursive-in-time approach to the ISAR target imaging problem; 3. Apply the standard time-frequency short-term Fourier transform (STFT) estimator, initially to a synthesized data set; and 4. Initiate the development of the recursive algorithm. We have achieved all of these goals during the Phase I of the project and plan to complete the overall development, application and comparison of the parametric approach to other time-frequency estimators (STFT, etc.) on our synthesized vehicular data sets during the next phase of funding. It should also be noted that we developed a batch minimum variance translational motion compensation (TMC) algorithm to estimate the radial components of target motion (see Section IV). This algorithm is easily extended to recursive solution and will probably become part of the overall recursive processing approach to solve the ISAR imaging problem. Our goals for the continued effort are to: 1. Develop and extend a complex, recursive-in-time, time- frequency parameter estimator based on the recursive prediction error method (RPEM) using the underlying Gauss- Newton algorithms. 2. Apply the complex RPEM algorithm to synthesized ISAR data using the above simulator. 3. Compare the performance of the proposed algorithm to standard time-frequency estimators applied to the same data sets.
NASA Astrophysics Data System (ADS)
Wang, Peng; Xu, Jin-yu; Liu, Shi
2015-01-01
Evaluation of the residual properties of thermally damaged rocks is of vital importance for rock engineering. For this study, uniaxial compression experiments and ultrasonic tests were conducted on sandstone specimens which experienced temperature treatments of different levels, including 25, 100, 200, 400, 600, 800 and 1000°C. Time-frequency analysis methods were applied to evaluate the deformation and strength properties of sandstone after being exposed to high temperature, confirming the effectiveness of the ultrasonic evaluation method. Linear correlations between the peak stress, deformation modulus and the longitudinal wave velocity confirm the effectiveness of ultrasonic time-domain properties in estimating the deformation behaviour of the thermally damaged sandstone. Synchronisation in the change of the peak stress and the kurtosis of frequency spectrum as temperature rises, defined in this paper to describe the spectrum distribution, as well as the centroid frequency, demonstrates the feasibility of ultrasonic frequency-domain properties in estimating the residual strength of the thermally damaged sandstone. The results have certain guiding significance for rock engineering in a high-temperature environment.
Swelling of a joint ... Joint swelling may occur along with joint pain . The swelling may cause the joint to appear larger or abnormally shaped. Joint swelling can cause pain or stiffness. After an ...
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
Considering the influence of artificial environmental noise to study cough time-frequency features
NASA Astrophysics Data System (ADS)
Van Hirtum, A.; Berckmans, D.
2003-09-01
In general the study of the cough mechanism and sound in both animal and human is performed by eliciting coughing in a reproducible way by nebulization of an irritating substance. Due to ventilation the controlled evaporation-protocol causes artificial noises from a mechanical origin. The resulting environmental low-frequency noises complicate cough time-frequency features. In order to optimize the study of the cough-sound the research described in this paper attempts on the one hand to characterize and model the environmental noises and on the other hand to evaluate the influence of the noise on the time-frequency representation for the intended cough sounds by comparing different de-noising approaches. Free field acoustic sound is continuously registered during 30 min citric acid cough-challenges on individual Belgian Landrace piglets and during respiratory infection experiments, with a duration of about 10 days, where room-ventilation was present.
Kolmogorov-Smirnov like test for time-frequency Fourier spectrogram analysis in LISA Pathfinder
NASA Astrophysics Data System (ADS)
Ferraioli, Luigi; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Gibert, Ferran; Hewitson, Martin; Hueller, Mauro; Karnesis, Nikolaos; Korsakova, Natalia; Nofrarias, Miquel; Plagnol, Eric; Vitale, Stefano
2015-03-01
A statistical procedure for the analysis of time-frequency noise maps is presented and applied to LISA Pathfinder mission synthetic data. The procedure is based on the Kolmogorov-Smirnov like test that is applied to the analysis of time-frequency noise maps produced with the spectrogram technique. The influence of the finite size windowing on the statistic of the test is calculated with a Monte Carlo simulation for 4 different windows type. Such calculation demonstrate that the test statistic is modified by the correlations introduced in the spectrum by the finite size of the window and by the correlations between different time bins originated by overlapping between windowed segments. The application of the test procedure to LISA Pathfinder data demonstrates the test capability of detecting non-stationary features in a noise time series that is simulating low frequency non-stationary noise in the system.
Nagaraj, S B; Stevenson, N J; Marnane, W P; Boylan, G B; Lightbody, G
2014-01-01
In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93. PMID:25570580
Single-shot time-frequency imaging spectroscopy using an echelon mirror.
Sakaibara, Hiroyuki; Ikegaya, Yuki; Katayama, Ikufumi; Takeda, Jun
2012-03-15
We demonstrate single-shot time-frequency imaging spectroscopy with an echelon mirror for measuring ultrashort laser pulses as well as ultrafast responses of materials using the same optical setup. The echelon mirror produces a spatially encoded time delay for the probe pulse whereby both the probe and pump pulses are focused on samples with small spot size. Using the optical Kerr gate apparatus, we successfully mapped the time-frequency images of ultrashort laser pulses and subsequently evaluated the chirp characteristics with the phase-retrieval procedure on a single-shot basis. By simply replacing the Kerr medium with samples, we could also visualize the phonon-polariton oscillations in ferroelectric LiNbO3. PMID:22446244
NASA Astrophysics Data System (ADS)
Barkat, B.; Abed-Meraim, K.
2004-12-01
We propose novel algorithms to select and extract separately all the components, using the time-frequency distribution (TFD), of a given multicomponent frequency-modulated (FM) signal. These algorithms do not use any a priori information about the various components. However, their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms, we applied them for the estimation of the instantaneous frequency coefficients of a multicomponent signal and the results are compared with those of the higher-order ambiguity function (HAF) algorithm. Monte Carlo simulation results show the superiority of the proposed algorithms over the HAF.
Ecological prediction with nonlinear multivariate time-frequency functional data models
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.
He, Muyi; Guo, Dan; Chen, Yu; Xiong, Xingchuang; Fang, Xiang; Xu, Wei
2014-12-01
In this study, a method for measuring ion collision crosssections (CCSs) was proposed through time-frequency analysis of ion trajectories in quadrupole ion traps. A linear ion trap with added high-order electric fields was designed and simulated. With the presence of high-order electric fields and ion-neutral collisions, ion secular motion frequency within the quadrupole ion trap will be a function of ion motion amplitude, thus a function of time and ion CCS. A direct relationship was then established between ion CCS and ion motion frequency with respect to time, which could be obtained through time-frequency analysis of ion trajectories (or ion motion induced image currents). To confirm the proposed theory, realistic ion trajectory simulations were performed, where the CCSs of bradykinin, angiotensin I and II, and ubiquitin ions were calculated from simulated ion trajectories. As an example, differentiation of isomeric ubiquitin ions was also demonstrated in the simulations. PMID:25319271
NASA Technical Reports Server (NTRS)
Hellwig, H.; Stein, S. R.; Walls, F. L.; Kahan, A.
1978-01-01
The relationship between system performance and clock or oscillator performance is discussed. Tradeoffs discussed include: short term stability versus bandwidth requirements; frequency accuracy versus signal acquisition time; flicker of frequency and drift versus resynchronization time; frequency precision versus communications traffic volume; spectral purity versus bit error rate, and frequency standard stability versus frequency selection and adjustability. The benefits and tradeoffs of using precise frequency and time signals are various levels of precision and accuracy are emphasized.
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.
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.
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. PMID:25933878
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 the 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.
NASA Astrophysics Data System (ADS)
Vulli, S.; Dunne, J. F.; Potenza, R.; Richardson, D.; King, P.
2009-04-01
The short-term-Fourier-transform (STFT) is used to identify different sources of IC engine-block vibration from single-point acceleration measurements taken with a commercial knock sensor. Interest is focused on using the STFT to distinguish normal combustion from other sources of excitation including valve impact, injector pulses, and abnormal combustion, such as knocking. Positive identification of these other events using a single method can be useful for pre-processing of measured knock-sensor data for neural-network-based reconstruction of cylinder pressure. It can also be useful separately as part of a fast knock detection system. A series of experiments is discussed to create the data to isolate these different events on a 3-cylinder gasoline engine. In each case, the measured data is processed using the STFT to attempt to isolate the occurrence of particular events in the time domain. Four classes of experiments are undertaken: (i) an un-fired (motored) engine, driven by a dynamometer, with spark plugs fitted, and then removed, to isolate valve impact; (ii) a fired engine running under idle conditions, to contrast no-load combustion with no combustion; (iii) a part-loaded engine running normally, and then running with one injector switched-off, and (iv) a fully-loaded engine running normally, and then running with knock-control switched-off. The paper shows that a single Time-frequency analysis method, applied to knock sensor data in the form of an appropriately-tuned STFT, can effectively identify the occurrence of these events in the time domain if responses are adequately separated and strong enough.
Time-frequency analysis of the event-related potentials associated with the Stroop test.
Ergen, Mehmet; Saban, Sara; Kirmizi-Alsan, Elif; Uslu, Atilla; Keskin-Ergen, Yasemin; Demiralp, Tamer
2014-12-01
Multiple executive processes are suggested to be engaged at Stroop test, and time-frequency analysis is acknowledged to improve the informative utility of EEG in cognitive brain research. We aimed to investigate event-related oscillations associated with the Stroop test. EEG data was collected from 23 healthy volunteers while they performed a computer version of Stroop test. Both evoked (phase-locked) and total (phase-locked+non-phase-locked) oscillatory responses in the EEG were analyzed by wavelet transform. Data from the congruent (color-word matching) and incongruent stimuli (color-word non-matching) conditions are compared. In the incongruent condition, N450 wave was more negative and amplitude of the late slow wave was more positive. In the time-frequency plane, the fronto-central total theta amplitude (300-700 ms) was larger in the incongruent condition. The evoked delta (250-600 ms) was larger in the congruent condition particularly over parieto-occipital regions. The larger frontal theta response in the incongruent condition was associated with the detection of interference and inhibition of the response to task-irrelevant features, while the larger evoked delta in the congruent condition was suggestive of the easier decision process owing to congruency between the physical attribute and the verbal meaning of the stimuli. Furthermore, in the incongruent condition, amplitude of the occipital total alpha in the very late phase (700-900 ms) was smaller. This prolonged desynchronization in the alpha band could be reflecting augmentation of attentional filters in visual modality for the next stimulus. These multiple findings on EEG time-frequency plane provide improved description of the overlapping processes in Stroop test. PMID:25135670
Missing data outside the detector range. II. Application to time-frequency entanglement
NASA Astrophysics Data System (ADS)
Ray, Megan R.; van Enk, S. J.
2013-12-01
In a previous paper, we pointed out the problem of missing data outside the detector range for continuous-variable entanglement verification and quantum key distribution, and we provided a straightforward solution based on entropic separability criteria (as those work better than variance-based criteria). We apply that solution here to the verification of time-frequency entanglement of photon pairs, particularly to the quantum key distribution scheme proposed by Nunn [Opt. ExpressOPEXFF1094-408710.1364/OE.21.015959 21, 15959 (2013)]. We find that the scheme does lead to verifiable entanglement, but that transmission noise quickly destroys the ability to verify the entanglement.
Reassigned time-frequency peak filtering for seismic random noise attenuation
NASA Astrophysics Data System (ADS)
Lin, H.; Li, Y.; Ma, H.
2012-12-01
Seismic noise attenuation for the aim of improving signal-to-noise ratio (S/N) plays an important role in seismic data processing for detailed description of oil and gas reservoirs. In particular, strong seismic random noise, which is unpredictable and incoherent in space and time, always degrades the qualities of seismic exploration and much more difficult to be suppressed than coherent noise, since only its statistical properties can be used. It is a common problem in random noise attenuation to keep the signal with minimized distortion. Multi-direction, multi-scale and time-varying methods can be considered as appropriate for tracking the signal characteristics varying in time. In particular, time-frequency based methods might better recover the local characteristics of the non-stationary seismic signal, which is important to produce a satisfactory random noise attenuation result. Time-frequency peak filtering(TFPF), which has already proved to be a powerful tool for Gaussian random noise attenuation in linear signal, can be alternative tool for seismic random noise attenuation. Indeed, seismic noise sometimes may have an asymmetric Wigner-Ville spectrum(WVS) and the seismic signal is nonlinear in time, which might induce amplitude attenuation and residual random noise in the results. This work reports the preliminary results from an improved TFPF method planned to obtain more accurate estimation of the seismic signal by increasing the signal concentration of the time-frequency distribution(TFD) during TFPF. At the beginning the improved reassignment TFPF(RTFPF) encoded the seismic trace as an instantaneous frequency (IF) of the analytic signal generated by frequency modulation. After that the smooth pseudo Wigner-Ville distribution(SPWVD) of the coded analytic signal was computed. The separate frequency window of the SPWVD helps to smooth away the random oscillations introduced by the WVS of seismic noise and nonlinear signal component in the pseudo Wigner
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. PMID:21061480
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 Heart Rate Variability for Neonatal Seizure Detection
NASA Astrophysics Data System (ADS)
Malarvili, M. B.; Mesbah, Mostefa; Boashash, Boualem
2007-12-01
There are a number of automatic techniques available for detecting epileptic seizures using solely electroencephalogram (EEG), which has been the primary diagnosis tool in newborns. The electrocardiogram (ECG) has been much neglected in automatic seizure detection. Changes in heart rate and ECG rhythm were previously linked to seizure in case of adult humans and animals. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. In this paper, we assess the suitability of HRV as a tool for seizure detection in newborns. The features of HRV in the low-frequency band (LF: 0.03-0.07 Hz), mid-frequency band (MF: 0.07-0.15 Hz), and high-frequency band (HF: 0.15-0.6 Hz) have been obtained by means of the time-frequency distribution (TFD). Results of ongoing time-frequency (TF) research are presented. Based on our preliminary results, the first conditional moment of HRV which is the mean/central frequency in the LF band and the variance in the HF band can be used as a good feature to discriminate the newborn seizure from the nonseizure.
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.
Using time-frequency and wavelet analysis to assess turbulence/rotor interactions
Kelley, N.D.; Osgood, R.M.; Bialasiewicz, J.T.; Jakubowski, A.
2000-01-05
Large loading events on wind turbine rotor blades are often associated with transient bursts of coherent turbulent energy in the turbine inflow. These coherent turbulent structures are identified as peaks in the three-dimensional, instantaneous, turbulent shearing stress field. Such organized inflow structures and the accompanying rotor aeroelastic responses typically have time scales of only a few seconds and therefore do not lend themselves for analysis by conventional Fourier spectral techniques. Time-frequency analysis (and wavelet analysis in particular) offers the ability to more closely study the spectral decomposition of short period events such as the interaction of coherent turbulence with a moving rotor blade. In this paper, the authors discuss the initial progress in the application of time-frequency analysis techniques to the decomposition and interpretation of turbulence/rotor interaction. The authors discuss the results of applying both the continuous and discrete wavelet transforms for their application. Several examples are given of the techniques applied to both observed turbulence and turbine responses and those generated using numerical simulations. They found that the presence of coherent turbulent structures, as revealed by the inflow Reynolds stress field, is a major contributor to large load excursions. These bursts of coherent turbulent energy induce a broadband aeroelastic response in the turbine rotor as it passes through them.
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.
Wang, Yazhou; Zhang, Zhiguo; Li, Xiang; Cui, Hongyan; Xie, Xiaobo; Luk, Keith Dip-Kei; Hu, Yong
2015-08-01
Somatosensory evoked potentials (SEPs) have been widely used to monitor the neurological integrity of the spinal cord during spinal surgery. However, the location of neurologic impairment cannot be determined from SEPs. Previous studies imply that the time-frequency characteristics of SEPs may reflect the location of the spinal cord injury. To validate the hypothesis that time-frequency patterns of SEPs are associated with the location of neurologic deficits in the spinal cord, we studied the time-frequency distributions of SEPs at different injury levels. Twenty-four rats were equally divided into one normal group and three injury groups, in which weight-drop contusions were delivered to the spinal cord of the rats at C4, C5, or C6 level, respectively. By comparing the time-frequency patterns of SEPs across groups, we found significant differences in several time-frequency regions of interest in the time-frequency distributions of the normal group and the injury groups. Importantly, the regions of interest were different across injury groups, suggesting that these regions of interest could be specific to injury locations. The results imply that changes of the time-frequency patterns of SEPs may be related to the location of the spinal cord injury. PMID:25626775
NASA Astrophysics Data System (ADS)
Frank Pai, P.; Deng, Haoguang; Sundaresan, Mannur J.
2015-10-01
Guided wave-based technique is one major approach for damage inspection of structures. To detect a small damage, an elastic wave's wavelength needs to be in the order of the damage size and hence the frequency needs to be high. Unfortunately, high-frequency wave dynamics always involves complicated wave reflection, refraction and diffraction, and it is difficult to separate them in order to perform detailed examination and system identification. This paper investigates dynamic characteristics of Lamb waves in plates in order to be used for material evaluation and damage inspection of thin-walled structures. A one-dimensional finite-element modeling and analysis technique is developed for computing dispersion curves and all symmetric and antisymmetric modes of Lamb waves in isotropic and multi-layer plates. Moreover, the conjugate-pair decomposition (CPD) method is introduced for time-frequency analysis of propagating Lamb waves. Results show that, under a k-cycle sine-burst excitation at a plate's edge, the time-varying frequency of a surface point's response can reveal the Lamb wave propagating inside the plate being a symmetric or an antisymmetric mode. The frequency of the measured wave packet increases from the wave front to the trailing edge if it is a symmetric mode, and the frequency decreases from the wave front to the trailing edge if it is an antisymmetric mode. Moreover, interaction of two different wave packets results in a peak in the time-frequency curve. These characteristics can be used for accurate separation of wave packets and identification of different wave speeds to enable fast and accurate material evaluation and damage inspection. Transient finite-element analysis of Lamb waves in finite plates with crack/delamination show that k-cycle sine-burst probing waves are good agents for guided wave-based damage inspection of structures. Although crack and delamination introduce different waves into and complicate the probing wave packet, time-frequency
NASA Astrophysics Data System (ADS)
Shi, Juanjuan; Liang, Ming; Necsulescu, Dan-Sorin; Guan, Yunpeng
2016-04-01
The energy concentration level is an important indicator for time-frequency analysis (TFA). Weak energy concentration would result in time-frequency representation (TFR) diffusion and thus leading to ambiguous results or even misleading signal analysis results, particularly for nonstationary multicomponent signals. To improve the energy concentration level, this paper proposes a generalized stepwise demodulation transform (GSDT). The rationale of the proposed method is that (1) the generalized demodulation (GD) can map the original signal into an analytic signal with constant instantaneous frequency (IF) and improve the energy concentration level on time-frequency plane, and (2) focusing on a short window around the time instant of interest, a backward demodulation operation can recover the original frequency at the time instant without affecting the improved energy concentration level. By repeating the backward demodulation at every time instant of interest, the TFR of the entire signal can be attained with enhanced energy concentration level. With the GSDT, an iterative GSDT (IGSDT) is developed to analyze multicomponent signal that is subjected to different modulating sources for their constituent components. The IGSDT iteratively demodulates each constituent component to attain its TFR and the TFR of the whole signal is derived from superposing all the resulting TFRs of constituent components. The cross-term free and more energy concentrated TFR of the signal is, therefore, obtained, and the diffusion in the TFR can be reduced. The GSDT-based synchrosqueezing transform is also elaborated to further enhance the GSDT(IGSDT) yielded TFR. The effectiveness of the proposed method in TFA is tested using both simulated monocomponent and multicomponent signals. The application of the proposed method to bearing fault detection is explored. Bearing condition and fault pattern can be revealed by the proposed method resulting TFR. The main advantages of the proposed method
Improving resolution of crosswell seismic section based on time-frequency analysis
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 which 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).
Time-frequency processing of track irregularities in high-speed train
NASA Astrophysics Data System (ADS)
Ning, Jing; Lin, Jianhui; Zhang, Bing
2016-01-01
Track irregularities are the main source of vehicle vibration. With the increase in the speed, the track irregularities have become a more significant issue of concerned. The axle box acceleration signals can be obtained for analyzing the track irregularities, but the signals are usually non-stationary and signal processing results are not normally satisfied with the ordinary way. Thus, time-frequency distribution analysis is proposed to use in this study. To minimize the cross-terms, a new method based on Empirical Mode Decomposition (EMD) and Cohen's class distribution has been developed and advanced. This approach has been tested with three typical simulation signals and then applied to analyze the track irregularities. The result is consistent with the result from track inspection cars. This indicates this new algorithm is suitable for analyzing the track irregularities. It can be applied in rail irregularity measurement to compensate some shortages of the track inspection cars.
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.
Gear-box fault detection using time-frequency based methods
Odgaard, Peter F.; Stoustrup, Jakob
2015-12-31
Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected. Two different time-frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen-Loeve basis. Both of them detects the gear-box fault with an acceptable detection delay.
Michalopoulou, Zoi-Heleni; Pole, Andrew
2016-07-01
The dispersion pattern of a received signal is critical for understanding physical properties of the propagation medium. The objective of this work is to estimate accurately sediment sound speed using modal arrival times obtained from dispersion curves extracted via time-frequency analysis of acoustic signals. A particle filter is used that estimates probability density functions of modal frequencies arriving at specific times. Employing this information, probability density functions of arrival times for modal frequencies are constructed. Samples of arrival time differences are then obtained and are propagated backwards through an inverse acoustic model. As a result, probability density functions of sediment sound speed are estimated. Maximum a posteriori estimates indicate that inversion is successful. It is also demonstrated that multiple frequency processing offers an advantage over inversion at a single frequency, producing results with reduced variance. PMID:27475202
Correlated Electron-Nuclear Motion Visualized Using a Wavelet Time-Frequency Analysis
NASA Astrophysics Data System (ADS)
Bandrauk, André D.; Chelkowski, Szczepan; Lu, Huizhong
We have solved numerically the time-dependent Schrödinger equation (TDSE) describing dissociative-ionization of a H2 molecule exposed to intense short-pulse laser light in one dimension. From the time dependent wave function we calculated the total average acceleration of the two electrons and the relative proton acceleration. We find that the general shape of the power spectra of electrons and protons is very similar except that the for the electrons the peaks occur at odd harmonics whereas for protons the peaks occur at even harmonics. The wavelet time-frequency analysis shows that, surprisingly, time profiles of electron and proton accelerations are nearly identical for high harmonics. The wavelet time profiles confirm predictions of the three-step quasi-classical model of harmonic generation by identifying several (up to three) electron return times with high precision.
Aesthetic appreciation: event-related field and time-frequency analyses.
Munar, Enric; Nadal, Marcos; Castellanos, Nazareth P; Flexas, Albert; Maestú, Fernando; Mirasso, Claudio; Cela-Conde, Camilo J
2011-01-01
Improvements in neuroimaging methods have afforded significant advances in our knowledge of the cognitive and neural foundations of aesthetic appreciation. We used magnetoencephalography (MEG) to register brain activity while participants decided about the beauty of visual stimuli. The data were analyzed with event-related field (ERF) and Time-Frequency (TF) procedures. ERFs revealed no significant differences between brain activity related with stimuli rated as "beautiful" and "not beautiful." TF analysis showed clear differences between both conditions 400 ms after stimulus onset. Oscillatory power was greater for stimuli rated as "beautiful" than those regarded as "not beautiful" in the four frequency bands (theta, alpha, beta, and gamma). These results are interpreted in the frame of synchronization studies. PMID:22287948
Aesthetic appreciation: event-related field and time-frequency analyses
Munar, Enric; Nadal, Marcos; Castellanos, Nazareth P.; Flexas, Albert; Maestú, Fernando; Mirasso, Claudio; Cela-Conde, Camilo J.
2012-01-01
Improvements in neuroimaging methods have afforded significant advances in our knowledge of the cognitive and neural foundations of aesthetic appreciation. We used magnetoencephalography (MEG) to register brain activity while participants decided about the beauty of visual stimuli. The data were analyzed with event-related field (ERF) and Time-Frequency (TF) procedures. ERFs revealed no significant differences between brain activity related with stimuli rated as “beautiful” and “not beautiful.” TF analysis showed clear differences between both conditions 400 ms after stimulus onset. Oscillatory power was greater for stimuli rated as “beautiful” than those regarded as “not beautiful” in the four frequency bands (theta, alpha, beta, and gamma). These results are interpreted in the frame of synchronization studies. PMID:22287948
Identification of nuclear components degradation by time-frequency ridge pattern
Park, G. Y.; Lee, C. K.; Kim, J. T.; Ryu, J. S.; Jung, H. S.
2006-07-01
A time-frequency analysis (TFA) was applies to the identification of operational status of various components of nuclear power plants, and, in this paper, the TFA is especially applied to the analysis of vibration signals from a pipe where some chemical corrosion is likely to occur by an acidic material being mixed in the coolant of nuclear power plants. A spalling out of the internal material pieces by the so-called flow-accelerated corrosion (FAC) is expected to change the structural vibration of a local point in the pipe, but this effect is too tiny to be recognized from the result of the Fourier transform [1], From the analysis by TFA, it is identified that the TFA can provide important information such as the amplitude fluctuations in the instantaneous frequency of each characteristic frequency. The analysis results show that the peak or ridge pattern of the TFA varied according to the status of the chemical corrosion within the pipe. (authors)
Enhanced sonar array target localization using time-frequency interference phenomena
NASA Astrophysics Data System (ADS)
Shibley, Jordan Almon
The ability of traditional active sonar processing methods to detect targets is often limited by clutter and reverberation from ocean environments. Similarly, multipath arrivals from radiating sources such as ships and submarines are received at sensors in passive sonar systems. Reverberation and multipath signals introduce constructive and destructive interference patterns in received spectrograms in both active and passive sonar applications that vary with target range and frequency. The characterization and use of interference phenomena can provide insights into environmental parameters and target movement in conjunction with standard processing methods including spectrograms and array beamforming. This thesis focuses on utilizing the time-frequency interference structure of moving targets captured on sonar arrays to enhance the resolution and abilities of conventional sonar methods to detect and localize targets. Physics-based methods for interference-based beamforming and target depth separation are presented with application of these methods shown using broadband simulated array data.
Sheu, Yae-Lin; Wu, Hau-Tieng; Hsu, Liang-Yan
2015-11-16
Time-frequency (TF) analysis is a powerful tool for exploring ultrafast dynamics in atoms and molecules. While some TF methods have demonstrated their usefulness and potential in several quantum systems, a systematic comparison among them is still lacking. To this end, we compare a series of classical and contemporary TF methods by taking hydrogen atom in a strong laser field as a benchmark. In addition, several TF methods such as Cohen class distribution other than the Wigner-Ville distribution, reassignment methods, and the empirical mode decomposition method are first introduced to exploration of ultrafast dynamics. Among these TF methods, the synchrosqueezing transform successfully illustrates the physical mechanisms in the multiphoton ionization regime and in the tunneling ionization regime. Furthermore, an empirical procedure to analyze an unknown complicated quantum system is provided, suggesting the versatility of TF analysis as a new viable venue for exploring quantum dynamics. PMID:26698525
Looking for activity cycles in late-type Kepler stars using time-frequency analysis
NASA Astrophysics Data System (ADS)
Vida, K.; Oláh, K.; Szabó, R.
2014-07-01
We analyse light curves covering four years of 39 fast-rotating (Prot ≲ 1 d) late-type active stars from the Kepler data base. Using time-frequency analysis (short-term Fourier transform), we find hints for activity cycles of 300-900 d at nine targets from the changing typical latitude of the starspots, which with the differential rotation of the stellar surface change the observed rotation period over the activity cycle. We also give a lowest estimation for the shear parameter of the differential rotation, which is ≈0.001 for the cycling targets. These results populate the less studied, short-period end of the rotation-cycle length relation.
Dynamic response and time-frequency analysis for gear tooth crack detection
NASA Astrophysics Data System (ADS)
Mohammed, Omar D.; Rantatalo, Matti
2016-01-01
Vibration health monitoring is a non-destructive technique which can be applied to detect cracks propagating in gear teeth. This paper studies gear tooth crack detection by investigating the natural frequencies and by performing time-frequency analysis of a 6 DOF dynamic gear model. The gear mesh stiffness used in the model was calculated analytically for different cases of crack sizes. The frequency response functions (FRFs) of the model were derived for healthy and faulty cases and dynamic simulation was performed to obtain the time signal responses. A new approach involving a short-time Fourier transform (STFT) was applied where a fast Fourier transform (FFT) was calculated for successive blocks with different sizes corresponding to the time segments of the varying gear mesh stiffness. The relationship between the different crack sizes and the mesh-stiffness-dependent eigenfrequencies was studied in order to detect the tooth crack and to estimate its size.
NASA Astrophysics Data System (ADS)
Pai, P. Frank
2011-10-01
Presented here is a new time-frequency signal processing methodology based on Hilbert-Huang transform (HHT) and a new conjugate-pair decomposition (CPD) method for characterization of nonlinear normal modes and parametric identification of nonlinear multiple-degree-of-freedom dynamical systems. Different from short-time Fourier transform and wavelet transform, HHT uses the apparent time scales revealed by the signal's local maxima and minima to sequentially sift components of different time scales. Because HHT does not use pre-determined basis functions and function orthogonality for component extraction, it provides more accurate time-varying amplitudes and frequencies of extracted components for accurate estimation of system characteristics and nonlinearities. CPD uses adaptive local harmonics and function orthogonality to extract and track time-localized nonlinearity-distorted harmonics without the end effect that destroys the accuracy of HHT at the two data ends. For parametric identification, the method only needs to process one steady-state response (a free undamped modal vibration or a steady-state response to a harmonic excitation) and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. A nonlinear two-degree-of-freedom system is used to illustrate the concepts and characterization of nonlinear normal modes, vibration localization, and nonlinear modal coupling. Numerical simulations show that the proposed method can provide accurate time-frequency characterization of nonlinear normal modes and parametric identification of nonlinear dynamical systems. Moreover, results show that nonlinear modal coupling makes it impossible to decompose a general nonlinear response of a highly nonlinear system into nonlinear normal modes even if nonlinear normal modes exist in the system.
ERIC Educational Resources Information Center
Magee, William; St-Arnaud, Sebastien
2012-01-01
Research on subjective wellbeing includes studies of both domain-related and global distress. The mental health literature, though, focuses almost exclusively on global distress. This seems to be partly due to a common belief that psychological distress, and the moods that comprise distress, necessarily lack referential content. However, if that…
NASA Astrophysics Data System (ADS)
Obuchowski, Jakub; Wyłomańska, Agnieszka; Zimroz, Radosław
2014-06-01
In this paper a new method of fault detection in rotating machinery is presented. It is based on a vibration time series analysis in time-frequency domain. A raw vibration signal is decomposed via the short-time Fourier transform (STFT). The time-frequency map is considered as matrix (M×N) with N sub-signals with length M. Each sub-signal is considered as a time series and might be interpreted as energy variation for narrow frequency bins. Each sub-signal is processed using a novel approach called the local maxima method. Basically, we search for local maxima because they should appear in the signal if local damage in bearings or gearbox exists. Finally, information for all sub-signals is combined in order to validate impulsive behavior of energy. Due to random character of the obtained time series, each maximum occurrence has to be checked for its significance. If there are time points for which the average number of local maxima for all sub-signals is significantly higher than for the other time instances, then location of these maxima is “weighted” as more important (at this time instance local maxima create for a set of Δf a pattern on the time-frequency map). This information, called vector of weights, is used for enhancement of spectrogram. When vector of weights is applied for spectrogram, non-informative energy is suppressed while informative features on spectrogram are enhanced. If the distribution of local maxima on spectrogram creates a pattern of wide-band cyclic energy growth, the machine is suspected of being damaged. For healthy condition, the vector of the average number of maxima for each time point should not have outliers, aggregation of information from all sub-signals is rather random and does not create any pattern. The method is illustrated by analysis of very noisy both real and simulated signals.
System identification through nonstationary data using Time-Frequency Blind Source Separation
NASA Astrophysics Data System (ADS)
Guo, Yanlin; Kareem, Ahsan
2016-06-01
Classical output-only system identification (SI) methods are based on the assumption of stationarity of the system response. However, measured response of buildings and bridges is usually non-stationary due to strong winds (e.g. typhoon, and thunder storm etc.), earthquakes and time-varying vehicle motions. Accordingly, the response data may have time-varying frequency contents and/or overlapping of modal frequencies due to non-stationary colored excitation. This renders traditional methods problematic for modal separation and identification. To address these challenges, a new SI technique based on Time-Frequency Blind Source Separation (TFBSS) is proposed. By selectively utilizing "effective" information in local regions of the time-frequency plane, where only one mode contributes to energy, the proposed technique can successfully identify mode shapes and recover modal responses from the non-stationary response where the traditional SI methods often encounter difficulties. This technique can also handle response with closely spaced modes which is a well-known challenge for the identification of large-scale structures. Based on the separated modal responses, frequency and damping can be easily identified using SI methods based on a single degree of freedom (SDOF) system. In addition to the exclusive advantage of handling non-stationary data and closely spaced modes, the proposed technique also benefits from the absence of the end effects and low sensitivity to noise in modal separation. The efficacy of the proposed technique is demonstrated using several simulation based studies, and compared to the popular Second-Order Blind Identification (SOBI) scheme. It is also noted that even some non-stationary response data can be analyzed by the stationary method SOBI. This paper also delineates non-stationary cases where SOBI and the proposed scheme perform comparably and highlights cases where the proposed approach is more advantageous. Finally, the performance of the
Time-frequency analysis of transient pressure signals for a mechanical heart valve cavitation study.
Yu, A A; White, J A; Hwang, N H
1998-01-01
A series of transient pressure signals (TPSs) can be measured using a miniature pressure transducer mounted near the tip of the inflow side of a mechanical heart valve (MHV) occluder during closure. A relationship appears to exist between the intensity and pattern of the TPS and the cavitation potential of a MHV. To study the relationship between MHV cavitation and the TPSs, we installed an MHV in a valve testing chamber of a digitally controlled burst test loop. A charge coupled device (CCD) camera and a personal computer based image grabbing program was used to visualize cavitation bubbles appearing on or near the occluder surface. One bileaflet MHV was used as the model for this study. Cavitation bubbles were observed within 300 microsec of the leaflet/housing impact. The valve was tested at various driving pressures between 100 and 1,300 mmHg. MHV cavitation bubble intensities were qualitatively classified into three categories: 1) strong, 2) weak, and 3) none. Digital images of the MHV occluder inflow surface were recorded simultaneously with the TPSs. TPSs were studied by the time-frequency analysis method (spectrogram) and correlated to MHV cavitation potential. The intensity of the cavitation bubbles was found to be associated with burst test loop driving pressures during leaflet closure. PMID:9804476
Phase retrieval and time-frequency methods in the measurement of ultrashort laser pulses
DeLong, K.W.; Fittinghoff, D.N.; Ladera, C.L.; Trebino, R.
1995-02-01
Recently several techniques have become available to measure the time- (or frequency-) dependent intensity and phase of ultrashort laser pulses. One of these, Frequency-Resolved Optical Gating (FROG), is rigorous and has achieved single-laser-shot operation. FROG combines the concepts of time-frequency analysis in the form of spectrogram generation (in order to create a two-dimensional problem), and uses a phase-retrieval-based algorithm to invert the experimental data to yield the intensity and phase of the laboratory laser pulse. In FROG it is easy to generate a spectrogram of the unknown signal, and inversion of the spectrogram to recover the signal is the main goal. Because the temporal width of a femtosecond laser pulse is much shorter than anything achievable by electronics, FROG uses the pulse to measure itself. In FROG, the laser pulse is split into two replicas of itself by a partially reflecting beamsplitter, and the two replicas interact with each other in a medium with an instantaneous nonlinear-optical response. This interaction generates a signal field that is then frequency-resolved using a spectrometer. The spectrum of the signal field is measured for all relevant values of the temporal delay between the two pulses. Here, the authors employ FROG and FROG related techniques to measure the time-dependent intensity and phase of an ultrashort laser pulse.
Time-Frequency Analysis of Rocket Nozzle Wall Pressures during Start-up Transients
NASA Astrophysics Data System (ADS)
Baars, Woutijn J.; Tinney, Charles E.; Ruf, Joseph H.
2011-12-01
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 Characteristics of Tsunami Magnetic Signals from Four Pacific Ocean Events
NASA Astrophysics Data System (ADS)
Schnepf, N. R.; Manoj, C.; An, C.; Sugioka, H.; Toh, H.
2016-07-01
The recent deployment of highly sensitive seafloor magnetometers coinciding with the deep solar minimum has provided excellent opportunities for observing tsunami electromagnetic signals. These fluctuating signals (periods ranging from 10-20 min) are generally found to be within ± ˜ 1 nT and coincide with the arrival of the tsunami waves. Previous studies focused on tsunami electromagnetic characteristics, as well as modeling the signal for individual events. This study instead aims to provide the time-frequency characteristics for a range of tsunami signals and a method to separate the data's noise using additional data from a remote observatory. We focus on four Pacific Ocean events of varying tsunami signal amplitude: (1) the 2011 Tohoku, Japan event (M9.0), (2) the 2010 Chile event (M8.8), (3) the 2009 Samoa event (M8.0) and, (4) the 2007 Kuril Islands event (M8.1). We find possible tsunami signals in high-pass filtered data and successfully isolate the signals from noise using a cross-wavelet analysis. The cross-wavelet analysis reveals that the longer period signals precede the stronger, shorter period signals. Our results are very encouraging for using tsunami magnetic signals in warning systems.
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.
Qazi, Obaid ur Rehman; van Dijk, Bas; Moonen, Marc; Wouters, Jan
2012-05-01
Cochlear implant (CI) recipients report severe degradation of speech understanding under noisy conditions. Most CI recipients typically can require about 10-25 dB higher signal-to-noise ratio than normal hearing (NH) listeners in order to achieve similar speech understanding performance. In recent years, significant emphasis has been put on binaural algorithms, which not only make use of the head shadow effect, but also have two or more microphone signals at their disposal to generate binaural inputs. Most of the CI recipients today are unilaterally implanted but they can still benefit from the binaural processing utilizing a contralateral microphone. The phase error filtering (PEF) algorithm tries to minimize the phase error variance utilizing a time-frequency mask for noise reduction. Potential improvement in speech intelligibility offered by the algorithm is evaluated with four different kinds of mask functions. The study reveals that the PEF algorithm which uses a contralateral microphone but unilateral presentation provides considerable improvement in intelligibility for both NH and CI subjects. Further, preference rating test suggests that CI subjects can tolerate higher levels of distortions than NH subjects, and therefore, more aggressive noise reduction for CI recipients is possible. PMID:22345522
Dynamics characterization and health monitoring of membrane structures by time-frequency analysis
NASA Astrophysics Data System (ADS)
Qian, Xin; Du, Xingwen; Pai, P. Frank
2010-03-01
Membrane dynamics is often nonlinear and nonstationary because of geometric nonlinearity induced by high local flexibility, non-uniform pre-tension, light weight, dynamic coupling with surrounding air, wave propagation, supportinduced nonlinearity, and others. Hence, dynamics characterization and health monitoring of membrane structures require advanced time-frequency analysis, and the focus is on how to obtain accurate time-varying frequency and amplitude of a nonlinear nonstationary signal. Here we propose the use of a conjugate-pair decomposition (CPD) method with the empirical mode decomposition (EMD) for characterization of membrane dynamics. First, EMD with signal conditioning techniques is used to separate a compound membrane response into well-behaved intrinsic mode functions (IMFs) without assuming the signal to be harmonic. Then, a pair of sliding conjugate functions is used to accurately extract the time-varying frequency and amplitude of each IMF by using only three neighboring data points for each time instant. Because the variations of frequencies and amplitudes of IMFs contain system characteristics, they can be used for system identification and damage detection. Experimental nonlinear responses of a horizontally tensioned Kapton membrane subjected to a transverse harmonic excitation provided by a shaker at one end are used to validate the proposed methodology. Results show that the clamped-clamped supports and pre-tension cause the first-mode vibration to have a hardening cubic nonlinearity, and several other nonlinear phenomena are identified.
Evaluation of the importance of time-frequency contributions to speech intelligibility in noise.
Yu, Chengzhu; Wójcicki, Kamil K; Loizou, Philipos C; Hansen, John H L; Johnson, Michael T
2014-05-01
Recent studies on binary masking techniques make the assumption that each time-frequency (T-F) unit contributes an equal amount to the overall intelligibility of speech. The present study demonstrated that the importance of each T-F unit to speech intelligibility varies in accordance with speech content. Specifically, T-F units are categorized into two classes, speech-present T-F units and speech-absent T-F units. Results indicate that the importance of each speech-present T-F unit to speech intelligibility is highly related to the loudness of its target component, while the importance of each speech-absent T-F unit varies according to the loudness of its masker component. Two types of mask errors are also considered, which include miss and false alarm errors. Consistent with previous work, false alarm errors are shown to be more harmful to speech intelligibility than miss errors when the mixture signal-to-noise ratio (SNR) is below 0 dB. However, the relative importance between the two types of error is conditioned on the SNR level of the input speech signal. Based on these observations, a mask-based objective measure, the loudness weighted hit-false, is proposed for predicting speech intelligibility. The proposed objective measure shows significantly higher correlation with intelligibility compared to two existing mask-based objective measures. PMID:24815280
Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis
NASA Astrophysics Data System (ADS)
Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli; Wang, Shibin; Chen, Xuefeng
2016-06-01
Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.
Cardiorespiratory Dynamic Response to Mental Stress: A Multivariate Time-Frequency Analysis
Orini, Michele; 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. PMID:24386006
Five-dimensional neuroimaging: Localization of the time-frequency dynamics of cortical activity
Dalal, Sarang S.; Guggisberg, Adrian G.; Edwards, Erik; Sekihara, Kensuke; Findlay, Anne M.; Canolty, Ryan T.; Berger, Mitchel S.; Knight, Robert T.; Barbaro, Nicholas M.; Kirsch, Heidi E.; Nagarajan, Srikantan S.
2008-01-01
The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12–30 Hz) and high gamma band (65–90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30–300 Hz) in the cerebellum, though with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition. PMID:18356081
Long-term polar motion prediction using normal time-frequency transform
NASA Astrophysics Data System (ADS)
Su, Xiaoqing; Liu, Lintao; Houtse, Hsu; Wang, Guocheng
2014-02-01
This paper presents normal time-frequency transform (NTFT) application in harmonic/quasi-harmonic signal prediction. Particularly, we use the normal wavelet transform (a special NTFT) to make long-term polar motion prediction. Instantaneous frequency, phase and amplitude of Chandler wobble, prograde and retrograde annual wobbles of Earth's polar motion are analyzed via the NTFT. Results show that the three main wobbles can be treated as quasi-harmonic processes. Current instantaneous harmonic information of the three wobbles can be acquired by the NTFT that has a kernel function constructed with a normal half-window function. Based on this information, we make the polar motion predictions with lead times of 1 year and 5 years. Results show that our prediction skills are very good with long lead time. An abnormality in the predictions occurs during the second half of 2005 and first half of 2006. Finally, we provide the future (starting from 2013) polar motion predictions with 1- and 5-year leads. These predictions will be used to verify the effectiveness of the method proposed in this paper.
Solving the EEG inverse problem based on space-time-frequency structured sparsity constraints.
Castaño-Candamil, Sebastián; Höhne, Johannes; Martínez-Vargas, Juan-David; An, Xing-Wei; Castellanos-Domínguez, German; Haufe, Stefan
2015-09-01
We introduce STOUT (spatio-temporal unifying tomography), a novel method for the source analysis of electroencephalograpic (EEG) recordings, which is based on a physiologically-motivated source representation. Our method assumes that only a small number of brain sources are active throughout a measurement, where each of the sources exhibits focal (smooth but localized) characteristics in space, time and frequency. This structure is enforced through an expansion of the source current density into appropriate spatio-temporal basis functions in combination with sparsity constraints. This approach combines the main strengths of two existing methods, namely Sparse Basis Field Expansions (Haufe et al., 2011) and Time-Frequency Mixed-Norm Estimates (Gramfort et al., 2013). By adjusting the ratio between two regularization terms, STOUT is capable of trading temporal for spatial reconstruction accuracy and vice versa, depending on the requirements of specific analyses and the provided data. Due to allowing for non-stationary source activations, STOUT is particularly suited for the localization of event-related potentials (ERP) and other evoked brain activity. We demonstrate its performance on simulated ERP data for varying signal-to-noise ratios and numbers of active sources. Our analysis of the generators of visual and auditory evoked N200 potentials reveals that the most active sources originate in the temporal and occipital lobes, in line with the literature on sensory processing. PMID:26048621
NASA Astrophysics Data System (ADS)
shiuan, C. W.; Chang, L.
2013-12-01
Due to global warming, climate change, and economic development, the stability of water supply is challenged using only surface water resources. Hence, groundwater becomes an important water resource for increasing water supply reliability. However, groundwater extraction many introduce damages such as land subsidence and seawater intrusion. To accurately evaluate the response of groundwater aquifers, correct hydrogeological structure is a key factor. In the past, the evaluation of the hydrogeological structure relies on subjective judgment which is arbitrarily made based on available information of core sampling record, fossils, geological dating, etc. This study develops a quantitative method to provide objective information for improving the judgment. This method uses observed groundwater water level and time-frequency analysis. Precisely, the signal strength of the groundwater level is evaluated using Fast Fourier Transform (FFT) which is done by a commercially available software named Visual Signal. Two signal frequencies, daily and annual frequency, are studied. This method is applied to Lanyang Plain in Taiwan. The groundwater level record of shallow wells is selected for the signal processing. Therefore, higher signal strength of an annual signal indicates higher recharge which is an indicator of unconfined aquifer. In the case of Lanyang Plain, the low signal strength area includes fan top area and scatter areas at fan central and fantail areas. This signal information along with core sampling information can provide a complete picture of the hydrogeological structure and characteristics for the studied area Ilan shallow water wells in different frequencies
Variability and Mode Lifetimes in K Giants: Time-frequency Analysis
NASA Astrophysics Data System (ADS)
Preston, Heather L.; Buzasi, D. L.
2010-01-01
During the period 1999 - 2007, the WIRE spacecraft was the first space-based asteroseismology mission (Buzasi 2002, ASP Conf. Proc. 259, 616). Here we report on the final processing and analysis of WIRE observations of 23 K giant stars. Processing was extended beyond the basic pipeline (Bruntt & Buzasi 2006, Mem. Soc. Ast. Italiana 77, 278) to include filtering the time series based on a granulation and activity model fit in Fourier space. The resulting power spectra were analyzed in a consistent manner to locate the region of peak power and to determine the large separation, as well as to identify major peaks. In addition, we have introduced the use of time-frequency analysis to examine mode lifetimes and variability. Current space missions such as COROT and Kepler are making this a golden age for asteroseismology, and these would seem to have rendered the WIRE data set largely obsolete. However, these data can still serve as a testbed for analysis techniques. Even more significantly, since all WIRE targets were brighter than m = 5, ground-based followup can be performed in a way which is impossible for the much fainter targets of the newer missions.
Detection of sudden structural damage using blind source separation and time-frequency approaches
NASA Astrophysics Data System (ADS)
Morovati, V.; Kazemi, M. T.
2016-05-01
Seismic signal processing is one of the most reliable methods of detecting the structural damage during earthquakes. In this paper, the use of the hybrid method of blind source separation (BSS) and time-frequency analysis (TFA) is explored to detect the changes in the structural response data. The combination of the BSS and TFA is applied to the seismic signals due to the non-stationary nature of them. Firstly, the second-order blind identification technique is used to decompose the response signal of structural vibration into modal coordinate signals which will be mono-components for TFA. Then each mono-component signal is analyzed to extract instantaneous frequency of structure. Numerical simulations and a real-world seismic-excited structure with time-varying frequencies show the accuracy and robustness of the developed algorithm. TFA of extracted sources shows that used method can be successfully applied to structural damage detection. The results also demonstrate that the combined method can be used to identify the time instant of structural damage occurrence more sharply and effectively than by the use of TFA alone.
Time-frequency composition of mosquito flight tones obtained using Hilbert spectral analysis.
Aldersley, Andrew; Champneys, Alan; Homer, Martin; Robert, Daniel
2014-10-01
Techniques for estimating temporal variation in the frequency content of acoustic tones based on short-time fast Fourier transforms are fundamentally limited by an inherent time-frequency trade-off. This paper presents an alternative methodology, based on Hilbert spectral analysis, which is not affected by this weakness, and applies it to the accurate estimation of mosquito wing beat frequencies. Mosquitoes are known to communicate with one another via the sounds generated by their flapping wings. Active frequency modulation between pairs of mosquitoes is thought to take place as a precursor to courtship. Studying the acoustically-based interactions of mosquitoes therefore relies on an accurate representation of flight frequency as a time-evolving property, yet conventional Fourier spectrograms are unable to capture the rapid modulations in frequency that mosquito flight tones exhibit. The algorithms introduced in this paper are able to automatically detect and extract fully temporally resolved frequency information from audio recordings. Application of the technique to experimental recordings of single tethered mosquitoes in flight reveals corroboration with previous reported findings. The advantages of the method for animal communication studies are discussed, with particular attention given to its potential utility for studying pairwise mosquito interactions. PMID:25324097
NASA Astrophysics Data System (ADS)
Chan, Chun-Kai; Loh, Chin-Hsiung; Wu, Tzu-Hsiu
2015-04-01
In civil engineering, health monitoring and damage detection are typically carry out by using a large amount of sensors. Typically, most methods require global measurements to extract the properties of the structure. However, some sensors, like LVDT, cannot be used due to in situ limitation so that the global deformation remains unknown. An experiment is used to demonstrate the proposed algorithms: a one-story 2-bay reinforce concrete frame under weak and strong seismic excitation. In this paper signal processing techniques and nonlinear identification are used and applied to the response measurements of seismic response of reinforced concrete structures subject to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of RC frame using both input and output response data or output only measurement. From the signal-based damage identification method, which include the enhancement of time-frequency analysis of acceleration responses and the estimation of permanent deformation using directly from acceleration response data. Finally, local deformation measurement from dense optical tractor is also use to quantify the damage of the RC frame structure.
NASA Astrophysics Data System (ADS)
Martini, S.; Nerini, D.; Tamburini, C.
2014-09-01
We consider the statistical analysis of a 1.7-year high-frequency sampled time series, between 2009 and 2010, recorded at the ANTARES observatory in the deep NW Mediterranean Sea (2475 m depth). The objective was to estimate relationships between bioluminescence and environmental time series (temperature, salinity and current speed). As this entire dataset is characterized by non-linearity and non-stationarity, two time-frequency decomposition methods (wavelet and Hilbert-Huang) were used. These mathematical methods are dedicated to the analysis of a signal at various time and frequencies scales. This work propose some statistical tools dedicated to the study of relationships between two time series. Our study highlights three events of high bioluminescence activity in March 2009, December 2009 and March 2010. We demonstrate that the two events occurring in March 2009 and 2010 are correlated to the arrival of newly formed deep water masses at frequencies of approximately 4.8×10-7 (period of 24.1 days). In contrast, the event in December 2009 is only correlated with current speed at frequencies of approximately 1.9×10-6 (period of 6.0 days). The use of both wavelet and Hilbert-Huang transformations has proven to be successful for the analysis of multivariate time series. These methods are well-suited in a context of the increasing number of long time series recorded in oceanography.
The time-frequency characteristics of violin vibrato: modal distribution analysis and synthesis
Mellody; Wakefield
2000-01-01
A high-resolution time-frequency distribution, the modal distribution, is applied to the study of violin vibrato. The analysis indicates that the frequency modulation induced by the motion of the stopped finger on the string is accompanied by a significant amplitude variation in each partial of that note. Amplitude and frequency estimates for each partial are extracted from the modal distribution of ten pitches that span the range of the violin instrument. The frequency modulation is well-represented by a single sinusoid with a mean rate of 5.9 Hz and a mean excursion of +/- 15.2 cents. A spectral decomposition of the amplitude envelopes of the partials shows that the peaks lie primarily at integer multiples of the vibrato rate. These amplitude and frequency estimates are used in an additive synthesis model to generate synthetic replicates of violin vibrato. Simple approximations to these estimates are created, and synthesized sounds using these are evaluated perceptually by seven subjects using discrimination, nonmetric multidimensional scaling (MDS), and sound quality scoring tasks. It is found that the absence of frequency modulation has little effect on the perceptual response to violin vibrato, while the absence of amplitude modulation causes marked changes in both sound quality and MDS results. Low-order spectral decompositions of the amplitude and frequency estimates also occupy the same perceptual space as the original recording for a subset of the pitches studied. PMID:10641668
NASA Astrophysics Data System (ADS)
Galiana-Merino, J. J.; Rosa-Cintas, S.; Rosa-Herranz, J. L.; Molina-Palacios, S.; Martinez-Espla, J. J.
2011-12-01
Microzonation studies using ambient noise measurements constitute an extended and useful procedure for determine the local soil characteristics and its response due to an earthquake. Several methods exist for analyzing the noise measurements, being the most popular the horizontal-to-vertical spectral ratio (H/V) and the array techniques, i.e. the frequency-wavenumber (F-K) transform. Many works exist about this topic and it stills being an ongoing debate about ambient noise composition, whether body or surface waves constitute most of it, showing the importance of identifying the different kinds of waves presents in a seismic record. In this work we utilize a new method of time-frequency polarization analysis, based on the stationary wavelet packet transform, to investigate how the polarization characteristics of the wavefield influence in the application of ambient noise techniques. The signals are divided in different bands, according to their reciprocal ellipticity values and then the H/V method and the F-K array analysis are computed for each band. The qualitative and quantitative comparison between the original curve and the obtained for the analyzed intervals provide information about the signals composition, showing that the major components of the seismic noise present reciprocal ellipticity values lower than 0.5. The efficient application of the studied techniques by using just the main a part of the entire signal, [0 - 0.5], is also evaluated, showing favorable results.
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders.
Tsinalis, Orestis; Matthews, Paul M; Guo, Yike
2016-05-01
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the sleep stages. We used class-balanced random sampling across sleep stages for each model in the ensemble to avoid skewed performance in favor of the most represented sleep stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75-80%), and high mean [Formula: see text]-score (84%, range 82-86%) and mean accuracy across individual sleep stages (86%, range 84-88%) over all subjects. The performance of our method appears to be uncorrelated with the sleep efficiency and percentage of transitional epochs in each recording. PMID:26464268
Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns
NASA Astrophysics Data System (ADS)
Kalkan, Habil; Ince, Nuri Firat; Tewfik, Ahmed H.; Yardimci, Yasemin; Pearson, Tom
2007-12-01
Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds ( Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods.
Seismic random noise attenuation based on adaptive time-frequency peak filtering
NASA Astrophysics Data System (ADS)
Deng, Xinhuan; Ma, Haitao; Li, Yue; Zeng, Qian
2015-02-01
Time-frequency peak filtering (TFPF) method uses a specific window with fixed length to recover band-limited signal in stationary random noise. However, the derivatives of signal such as seismic wavelets may change rapidly in some short time intervals. In this case, TFPF equipped with fixed window length will not provide an optimal solution. In this letter, we present an adaptive version of TFPF for seismic random noise attenuation. In our version, the improved intersection of confidence intervals combined with short-time energy criterion is used to preprocess the noisy signal. And then, we choose an appropriate threshold to divide the noisy signal into signal, buffer and noise. Different optimal window lengths are used in each type of segments. We test the proposed method on both synthetic and field seismic data. The experimental results illustrate that the proposed method makes the degree of amplitude preservation raise more than 10% and signal-to-noise (SNR) improve 2-4 dB compared with the original algorithm.
Automated extraction and classification of time-frequency contours in humpback vocalizations.
Ou, Hui; Au, Whitlow W L; Zurk, Lisa M; Lammers, Marc O
2013-01-01
A time-frequency contour extraction and classification algorithm was created to analyze humpback whale vocalizations. The algorithm automatically extracted contours of whale vocalization units by searching for gray-level discontinuities in the spectrogram images. The unit-to-unit similarity was quantified by cross-correlating the contour lines. A library of distinctive humpback units was then generated by applying an unsupervised, cluster-based learning algorithm. The purpose of this study was to provide a fast and automated feature selection tool to describe the vocal signatures of animal groups. This approach could benefit a variety of applications such as species description, identification, and evolution of song structures. The algorithm was tested on humpback whale song data recorded at various locations in Hawaii from 2002 to 2003. Results presented in this paper showed low probability of false alarm (0%-4%) under noisy environments with small boat vessels and snapping shrimp. The classification algorithm was tested on a controlled set of 30 units forming six unit types, and all the units were correctly classified. In a case study on humpback data collected in the Auau Chanel, Hawaii, in 2002, the algorithm extracted 951 units, which were classified into 12 distinctive types. PMID:23297903
NASA Astrophysics Data System (ADS)
Zhang, Chao; Li, Yue; Lin, Hongbo; Yang, Baojun
2015-11-01
Attenuating random noise is of great significance in seismic data processing. In recent years, time-frequency peak filtering (TFPF) has been successfully applied to seismic random noise attenuation field. However, a fixed window length (WL) is used in the conventional TFPF. Since a short WL in the TFPF is used to preserve signals while a long WL can eliminate random noise effectively, signal preserving and noise attenuation cannot be balanced by a fixed WL especially when the signal-to-noise ratio of the noisy seismic record is low. Thus, we need to divide a noisy signal into signal and noise segments before the filtering. Then a short WL is used to the signal segments to preserve signals and a long WL is chosen for noise segments to eliminate random noise. In this paper, we test the smoothness of signals and random noise in time using the Hurst exponent which is a statistic for representing smoothness characteristics of signals. The time-series of signals with higher smoothness which lead to larger Hurst exponent values, however random noise is a random series in time without fixed waveforms and thus its smoothness is low, so the signal and noise segments can be divided by the Hurst exponent values. After the segmentation, we can adopt different filtering WLs in the TFPF for different segments to make a trade-off between signal preserving and random noise attenuation. Synthetic and real data experiments demonstrate that the proposed method can remove random noise from seismic record and preserve reflection events effectively.
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
Barma, Shovan; Chen, Bo-Wei; Ji, Wen; Rho, Seungmin; Chou, Chih-Hung; Wang, Jhing-Fa
2016-08-01
This study presents a precise way to detect the third ( S3 ) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time-frequency localization. The detection of the S3 is obscured due to its significantly low energy and frequency. Even more, the detected S3 may be misunderstood as an abnormal second heart sound with a fixed split, which was not addressed in the literature. To detect such S3, the Hilbert vibration decomposition method is applied to decompose the heart sound into a certain number of subcomponents while intactly preserving the phase information. Thus, the time information of all of the decomposed components are unchanged, which further expedites the identification and localization of any module/section of a signal properly. Next, the proposed localization step is applied to the decomposed subcomponents by using smoothed pseudo Wigner-Ville distribution followed by the reassignment method. Finally, based on the positional information, the S3 is distinguished and confirmed by measuring time delays between the S2 and S3. In total, 82 sets of cardiac cycles collected from different databases including Texas Heart Institute database are examined for evaluation of the proposed method. The result analysis shows that the proposed method can detect the S3 correctly, even when the normalized temporal energy of S3 is larger than 0.16, and the frequency of those is larger than 34 Hz. In a performance analysis, the proposed method demonstrates that the accuracy rate of S3 detection is as high as 93.9%, which is significantly higher compared with the other methods. Such findings prove the robustness of the proposed idea for detecting substantially low-energized S3 . PMID:26584485
4D time-frequency representation for binaural speech signal processing
NASA Astrophysics Data System (ADS)
Mikhael, Raed; Szu, Harold H.
2006-04-01
Hearing is the ability to detect and process auditory information produced by the vibrating hair cilia residing in the corti of the ears to the auditory cortex of the brain via the auditory nerve. The primary and secondary corti of the brain interact with one another to distinguish and correlate the received information by distinguishing the varying spectrum of arriving frequencies. Binaural hearing is nature's way of employing the power inherent in working in pairs to process information, enhance sound perception, and reduce undesired noise. One ear might play a prominent role in sound recognition, while the other reinforces their perceived mutual information. Developing binaural hearing aid devices can be crucial in emulating the working powers of two ears and may be a step closer to significantly alleviating hearing loss of the inner ear. This can be accomplished by combining current speech research to already existing technologies such as RF communication between PDAs and Bluetooth. Ear Level Instrument (ELI) developed by Micro-tech Hearing Instruments and Starkey Laboratories is a good example of a digital bi-directional signal communicating between a PDA/mobile phone and Bluetooth. The agreement and disagreement of arriving auditory information to the Bluetooth device can be classified as sound and noise, respectively. Finding common features of arriving sound using a four coordinate system for sound analysis (four dimensional time-frequency representation), noise can be greatly reduced and hearing aids would become more efficient. Techniques developed by Szu within an Artificial Neural Network (ANN), Blind Source Separation (BSS), Adaptive Wavelets Transform (AWT), and Independent Component Analysis (ICA) hold many possibilities to the improvement of acoustic segmentation of phoneme, all of which will be discussed in this paper. Transmitted and perceived acoustic speech signal will improve, as the binaural hearing aid will emulate two ears in sound
The benefits of using time-frequency analysis with synthetic aperture focusing technique
Albright, Austin E-mail: claytonda@ornl.gov; Clayton, Dwight 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 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
The Benefits of Using Time-Frequency Analysis with Synthetic Aperture Focusing Technique
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 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 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 on
NASA Astrophysics Data System (ADS)
Mehrkanoon, Saeid; Breakspear, Michael; Daffertshofer, Andreas; Boonstra, Tjeerd W.
2013-12-01
Synchronization of neural activity from distant parts of the brain is crucial for the coordination of cognitive activities. Because neural synchronization varies both in time and frequency, time-frequency (T-F) coherence is commonly employed to assess interdependences in electrophysiological recordings. T-F coherence entails smoothing the cross and power spectra to ensure statistical consistency of the estimate, which reduces its T-F resolution. This trade-off has been described in detail when the cross and power spectra are smoothed using identical smoothing operators, which may yield spurious coherent frequencies. In this article, we examine the use of non-identical smoothing operators for the estimation of T-F interdependence, i.e., phase synchronization is characterized by phase locking between signals captured by the cross spectrum and we may hence improve the trade-off by selectively smoothing the auto spectra. We first show that the frequency marginal density of the present estimate is bound within [0,1] when using non-identical smoothing operators. An analytic calculation of the bias and variance of present estimators is performed and compared with the bias and variance of standard T-F coherence using Monte Carlo simulations. We then test the use of non-identical smoothing operators on simulated data, whose T-F properties are known through construction. Finally, we analyze empirical data from eyes-closed surface electroencephalography recorded in human subjects to investigate alpha-band synchronization. These analyses show that selectively smoothing the auto spectra reduces the bias of the estimator and may improve the detection of T-F interdependence in electrophysiological data at high temporal resolution.