Opening Instants Research Articles

Fatigue crack closure assessment by wavelet transform of infrared thermography signals

The occurrence of crack closure significantly impacts the fatigue life of materials and structural components. Whether it is induced by the nature of the loading, the fabrication process or the geometry of the structure, its magnitude and effect should be considered to further improve predictive models of fatigue crack propagation. However, the definition of reliable experimental methods for the observation and assessment of fatigue crack closure, and in particular suited to structure testing, remains a challenge. The present study aims to provide a novel approach for the assessment of fatigue crack closure via the continuous wavelet transform of infrared thermography data. The processing of the temperature signal close to the crack in a coherent time–frequency space allows for the identification of crack closing and opening instants associated with high-frequency components. The method is meant to be suited to any testing configuration (conventional compact tension specimen or full-scale structures) with minimum operator-dependent parameters.

A generalized ASIP with arrivals to all sites and particle movements in all directions

AbstractA generalized n-site Asymmetric Simple Inclusion Process (ASIP) network is studied, where gate-opening instants are determined by a renewal process and arrivals occur to all sites. Various types of batch particle movements between sites are analyzed: (i) unidirectional probabilistic forward movements; (ii) probabilistic forward movements combined with feedback to the first site; and (iii) general probabilistic multidirectional movements. In contrast to the tedious successive substitution method used in previous ASIP studies, an efficient matrix approach is applied to derive the multidimensional probability generating function (PGF) of site occupancies right after gate opening instants. The complexity of the ASIP processes allows us to obtain explicit PGF results for small-size networks only, while for larger networks, a formula to calculate the mean site occupancies is derived for all types of movements. In movement case (i) the means are directly and explicitly calculated. For movement case (ii), where the network is homogeneous with equal probabilities of forward movements from site i to downstream sites $$j \ge i$$ j ≥ i , we show that the ratio between the mean occupancies of consecutive sites approaches a constant when the network becomes large, and calculate this ratio. Finally, we investigate an n-site network where at gate opening instants all gates open simultaneously, and particles move in all directions. Numerical examples are presented.

Near-infrared photoglottography for measuring multiple glottal events.

Near-infrared (NIR) photoglottography (PGG) is a non-invasive method for monitoring glottal activities which retains functionality of conventional PGG using visible light with more convenient accessibility. This paper is to investigate its performance in comparison with simultaneously recorded electroglottography (EGG) signals. Results showed that NIR PGG detects continuous transillumination for glottal aperture and vocal-fold contact. Glottal timing markers known as glottal closure and opening instants are detectable agreeing to the corresponding EGG-based instants. Further, it was inferred that variations of glottal waveforms based on NIR PGG reflect vertical vocal-fold edge motions.

Development of Improved Suppression Measures Against Reignition Overvoltages Caused by Vacuum Circuit Breakers in Offshore Wind Farms

In offshore wind farms, reignition overvoltages are usually caused by switching-off operations of vacuum circuit breakers, which degrade the insulation of power equipment, especially transformers. This paper proposes a new quantitative evaluation method of overvoltage suppression measures comprehensively considering three main indicators, which include the amplitude, steepness and reignition number. Based on the quantitative evaluation results, an improved suppression measure against high-steepness reignition overvoltages is developed. Firstly, high-frequency transient models of main components of a typical wind farm are developed in PSCAD/EMTDC. Then the stochasticity of opening instants is considered, and three main indicators are extracted for comprehensively analyzing the transient characteristics of reignition overvoltages. Subsequently, a quantitative evaluation is performed to analyze the effectiveness of different suppression measures, which involves surge arresters, RC snubbers and smart chokes with different properties and installation positions. Results show that a new combination of a surge arrester and a smart choke is capable of providing sufficient protection against a high-amplitude, high-steepness and severe-reignitions overvoltage. This research can provide a new overvoltage suppression solution for the insulation coordination of medium-voltage systems in offshore wind farms.

Analyzing seismocardiographic approach for heart rate variability measurement

As a vital risk stratification tool, heart rate variability (HRV) has the ability to provide early warning signs for many life-threatening diseases. This paper presents a study on reliable cardiac cycle extraction and HRV measurement with a seismocardiographic (SCG) method. Like R-peaks in an ECG, the proposed method relies on peaks corresponding to aortic valve opening (AO) instants in an SCG signal. Due to better reliability and accessibility, the SCG signal is selected for the study. Initially, the prominent AO peaks in an SCG signal are estimated using our previously proposed modified variational mode decomposition (MVMD) based approach. In the present method, the detection performance of AO peaks is improved by employing a decision-rule-based post-processing scheme. Subsequently, tachogram of AO–AO intervals is used for the estimation of HRV parameters. A set of real-time signals collected in various physiological conditions and the SCG signals taken from a publicly available standard database are used to test and validate the proposed method. Experimental results clearly tell that the cardiac intervals obtained from the SCG signal using the proposed method can be used for HRV analysis. Also, the resulted parameters of HRV analysis on ECG and SCG exhibit strong correlation and agreement that shows the effectiveness of the proposed method.

Orthogonal subspace projection based framework to extract heart cycles from SCG signal

Early diagnosis and prediction of heart diseases are essential to reduce the cardiac risks. Change in heart cycle morphologies is a vital diagnostic feature for cardiac clinical systems. A seismocardiogram (SCG) signal provides more detailed information of different cardiac phases in a heart cycle compared to other cardiac signals. Hence, heart cycle extraction using SCG is very important to examine cardiac activities. In this manuscript, an orthogonal subspace projection based framework is proposed to extract heart cycles from a SCG signal. The heart cycle is estimated by calculating intervals between consecutive aortic valve opening (AO) instants, and post aortic valve closing (postAC) instants. Orthogonal subspace projection is applied to the SCG signal on ECG subspace for AO peak detection. The signal generated from projection gives the locations of AO peaks in the SCG signal. The postAC peaks are determined on intervals between consecutive AO peaks using segmentation, FIR based smoothing, Butterworth high pass filtering, and finding maxima point. The performance of the proposed method is evaluated using SCG signals from CEBS database, publicly available at Physionet archive. The performance results show that the proposed method produces an acceptable detection rate with a minimal detection error. The evaluation results of the proposed method show its extendibility in heart rate variability analysis and hemodynamic parameter extraction.

Glottal Inverse Filtering Using Probabilistic Weighted Linear Prediction

Glottal inverse filtering is a noninvasive method for getting the glottal flow estimate from the speech. In this paper, we propose a method for glottal inverse filtering based on probabilistic weighted linear prediction PWLP in which the speech is assumed to be the output of an all-pole filter with glottal flow as an excitation. First, we introduce a probabilistic interpretation of the WLP, and we propose a probabilistic temporal weighting as convolution of a binary vector and a fixed window. We construct the posterior distribution based on the PWLP likelihood and a Gaussian prior on the filter coefficients. The parameters are estimated using the Gibbs sampling. The experiments are performed using the Lijencrants–Fant LF model based synthetic data, a physical model based synthetic data of different vowels and real speech data. Results demonstrate that the proposed method outperforms the best of the existing state-of-the-art methods in terms of the normalized amplitude quotient by 0.035 and 0.12 for the LF model and physical model based synthetic data, respectively. The results based on real speech data show that the glottal flow estimated by the proposed method in the closed phase is flatter and has less formant ripple compared to existing state-of-the-art methods. We also show two key features of the proposed method: first, the proposed method does not need prior detection of glottal closure or opening instants. The temporal weights are learnt in a data-driven manner, which is often found to be high near the closed phase of the glottal cycle, second, the Gaussian prior helps in estimating the filter coefficients when the closed phase duration is small.

Development of a time-of-flight low-energy neutral particle analyzer for EAST tokamak

To understand the erosion effect of neutral particles on the first wall, a low energy neutral particle analyzer (LENPA), based on the time-of-flight method, has been developed for the Experimental Advanced Superconducting Tokamak (EAST). The LENPA to be installed in the EAST mid-plane mainly consists of a chopper sub-system, a 3 m long flight tube, two sets of detector assemblies, and data acquisition and processing, vacuum, power supply, and control sub-systems. The neutral outflux is gated in bunches of 1 μs time scale by a slotted rotating disc which is driven by a vacuum compatible motor modified from a turbomolecular pump. A He-Ne laser beam is projected through the disc slit to record the instants of chopper slits opening with an avalanche photodiode module. An on-axis electron multiplier detects chopped neutrals, and a central perforated Cu-Be plate is employed to channel the emitted secondary electrons into an off-axis electron multiplier. The radiation peaks of on-axis electron multiplier caused by UV photons projected through the central hole of the Cu-Be plate provide an alternative way to record the chopper slits' opening time. With an additional 4 TB fast memory card, 1 GS/s sampling rate has been realized by using a GaGe acquisition card continuously. The LENPA data from the EAST together with neutral particle material erosion experiments will be used to benchmark the simulation results for better predictions on future fusion reactors, such as ITER and China Fusion Engineering Test Reactor (CFETR).

Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals.

Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.

Двухканальный драйвер силовых ключей с параллельным резонансом в цепи затвора

The well-known scheme of a one-channel power switch driver with parallel resonance in gate circuit (PRD) is considered and its merits and demerits in comparison with the standard drivers are given. A new type of PRD drivers for control of FETs, combined and composite transistors is proposed and described. The proposed driver performs opposite-phase control of two MOSFETs with induced channels and can be used in advanced static converters of a megahertz frequency range. The two-channel PRD driver has a number of advantages over the previously proposed driver schemes. In comparison with traditional drivers it allows significantly smaller gating losses and a higher switching frequency to be achieved. In comparison with quasi-resonant circuits, the two-channel PRD opens the possibility to have a considerably lower power and cost of the semiconductor amplifier. In comparison with one- channel drivers better performance characteristics and more stable operation were obtained owing to the use of a resonant-circuit three-winding coil and mutual compensation of the nonlinearity of the transistors’ input capacitances. The above-mentioned nonlinearity is compensated due to the transistor input capacities being connected in coupled resonant circuits in an opposite-phase manner. As a result, a close to a sine wave shape of the gate voltage is obtained. The duty cycle for each switch is a little bit smaller than 0.5. The use of the proposed scheme guarantees that a dead interval of time is secured between the opening instants of switches controlled by a single driver and that the system will have high noise immunity. On the other hand, the two-channel PRD driver has certain shortcomings: it features a fixed duty cycle, a comparatively long switching time, and a noticeable influence of the power circuit parameters on the system operation. In view of these circumstances, it is recommended to use PRD drivers in megahertz frequency range resonance converters with phase and discrete control modes. The functional diagram of a bridge converter with phase control and two-channel PRD drivers is presented, and recommendations on selecting its elements are given. The proposed drivers are supposed to be used mainly for constructing compact converters with a high specific power for portable devices and for electric vehicles.

Accurate Estimation of Glottal Closure Instants and Glottal Opening Instants from Electroglottographic Signal Using Variational Mode Decomposition

The objective of the proposed work is to accurately estimate the glottal closure instants (GCIs) and glottal opening instant (GOIs) from electroglottographic (EGG) signals. This work also addresses the issues with existing EGG-based GCI/GOI detection methods. GCIs are the instants at which excitation to the vocal tract is maximum and GOIs, on the other hand, have minimum excitation compared to GCIs. Both these instants occur instantaneously with a fundamental frequency defined for each glottal cycle in a given EGG signal. Accurate detection of these instants from the EGG signal is essential for the performance evaluation of GCIs and GOIs estimated from the speech signal directly. This work proposes a new method for accurate detection of GCIs and GOIs from the EGG signal using variational mode decomposition (VMD) algorithm. The EGG signal has been decomposed into sub-signals using the VMD algorithm. It is shown that VMD captures the center frequency close to the fundamental frequency of the EGG signal through one of its modes. This property of the corresponding mode helps to estimate GCIs and GOIs from the same. Besides, instantaneous pitch frequency is estimated from the obtained GCIs. The proposed method has been evaluated on the CMU-arctic database for GCI/GOI estimation and the Keele pitch extraction reference database for instantaneous pitch frequency estimation. The effectiveness of the proposed method is confirmed by comparison with state-of-the-art methods. Experimental results show that the proposed method has better accuracy and identification rate compared to state-of-the-art methods.

Effective Glottal Instant Detection and Electroglottographic Parameter Extraction for Automated Voice Pathology Assessment.

Accurate determination of glottal instants and electroglottographic (EGG) parameters is most important in voice pathology analysis including multiple voice disorders: neurological, functional, and laryngeal diseases. In this paper, we present a new effective method for reliable detection of glottal instants and EGG parameters from an EGG signal composed of voiced and nonvoice segments. In the first stage, we present an adaptive variational mode decomposition based algorithm for suppressing low-frequency artifacts and additive high-frequency noises. Based upon mode center frequency criterion, the proposed method first constructs a candidate EGG feature signal for determination of glottal closure and opening instants. In the second stage, the candidate glottal instants are determined by detecting the positive and negative zerocrossings in normalized candidate EGG feature signal, respectively. Finally, an autocorrelation features based postprocessing algorithm is presented to reject nonglottal instants from the nonspeech production segments. The accuracy and robustness of the method is tested using noise-free and noisy EGG signals. Evaluation results show that the proposed method achieves an average overall accuracy of 95.06%, identification rate of 95.34%, missed rate of 3.60%, and false alarm rate of 0.06% with average absolute identification error of 0.71 ± 0.66ms for an SNR of 15dB. Results demonstrate that the proposed method significantly outperforms the other existing methods under both noise-free and noisy EGG signals.

Glottal opening instants detection using zero frequency resonator

Zero frequency resonator (ZFR) was proposed earlier for the extraction of glottal closure instants (GCIs) (Murty and Yegnanarayana 2008). The output of ZFR is an exponentially growing/decaying signal. The trend of this signal can be removed to get the required resolution for detecting relevant information. By considering a window size of typical 1---2 pitch periods, the trend removed signal mainly exhibits information related to GCIs. This work proposes two methods for the detection of glottal opening instants (GOIs) using ZFR. In the first method, the window size for trend removing is reduced to a lower level (say, 0.33 $$\times $$× pitch period), and the possibility of hypothesizing GOIs is demonstrated. In the second method, window size remains in the range of 1---2 pitch periods, but the input to ZFR is modified to remove GCIs information. The proposed methods are evaluated using CMU-Arctic database and compared with existing methods for GOI detection. The performance for the detection of GOIs is comparable to that of GCIs and also existing methods.

Segmentation of the period of the fundamental tone of a voice source

The extrema of the logarithmic derivative of the mean energy of a voice signal in the frequency range of 1000–3000 Hz are used to determine the instants of opening and closure of the glottis. The inaccuracy of analysis is estimated with the Arctic CMU database, which contains synchronous recordings of speech signals and electro-glottograms. The estimates of the instants of opening and closure of the glottis, found by the developed algorithm, are compared with the instants of the maximum and minimum of the derivative from electro-glottogram signals, which are taken as the “true” instants. The mean square deviation of the glottal opening instant from the extrema of the derivative from the electro-glottogram signals for different speakers is in the range of 1.03–1.64 ms. The error rate of a false estimate of the glottal opening instant is from 0.01 to 0.14%, and the error rate of omission is from 0.42 to 2.38%. An error-detection algorithm is developed. The mean square deviation with an relative—to the period of the fundamental tone—error in detecting the glottal opening instant is in the range of 13–18% for the most probable error from 0 to +5%.

A Fast Method for High-Resolution Voiced/Unvoiced Detection and Glottal Closure/Opening Instant Estimation of Speech

We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.

Smoothness of an equation for the glottal flow rate versus the glottal area.

This letter proposes a correction to an equation by Titze [J. Acoust. Soc. Am. 75, 570-580 (1984)] for the volume velocity of the glottal air flow given the glottal area and other laryngeal parameters. It shows that the equation produces non-differentiable waveforms at the instants of glottal closure and opening, if the glottal area is also not differentiable at those instants. By adding an air viscosity term to the equation, twice-differentiability is obtained. Also, the letter corrects a sign error in the original formulation.

A comparative study of explicit and implicit modelling of subsegmental speaker-specific excitation source information

In this paper, the explicit and implicit modelling of the subsegmental excitation information are experimentally compared. For explicit modelling, the static and dynamic values of the standard Liljencrants–Fant (LF) parameters that model the glottal flow derivative (GFD) are used. A simplified approximation method is proposed to compute these LF parameters by locating the glottal closing and opening instants. The proposed approach significantly reduces the computation needed to implement the LF model. For implicit modelling, linear prediction (LP) residual samples considered in blocks of 5 ms with shift of 2.5 ms are used. Different speaker recognition studies are performed using NIST-99 and NIST-03 databases. In case of speaker identification, the implicit modelling provides significantly better performance compared to explicit modelling. Alternatively, the explicit modelling seem to be providing better performance in case of speaker verification. This indicates that explicit modelling seem to have relatively less intra and inter-speaker variability. The implicit modelling on the other hand, has more intra and inter-speaker variability. What is desirable is less intra and more inter-speaker variability. Therefore, for speaker verification task explicit modelling may be used and for speaker identification task implicit modelling may be used. Further, for both speaker identification and verification tasks the explicit modelling provides relatively more complimentary information to the state-of-the-art vocal tract features. The contribution of the explicit features is relatively more robust against noise. We suggest that the explicit approach can be used to model the subsegmental excitation information for speaker recognition.

Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -best dynamic programming. A novel GOI detector based upon the consistency of the candidates' closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closed-phase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms.

Modeling of Human Voice Box in VLSI for Low Power Biomedical Applications

Communication is heavily dependent on ideas expressed through speech. The ideas and tonal qualities during vocal expression give voice an idea about person characteristics and personality. The estimation of vocal tract configuration from speech sound to represent speech events with robust and compact signals that describe the salient features of speech is an important area of speech communication. The Human Voice Box (Glottis) is the source of voice production and an important part of human body. Recent techniques of speech processing, such as speech recognition and speech synthesis, use the glottal closure and opening instants. Current models of the glottal waves derive their shape from approximate information rather than from exactly measured data. In this work, an electrical model of Human Voice Box is presented by exploiting fluid volume velocity to current and fluid pressure to voltage. The glottis modeled as current source includes linear and nonlinear impedances to represent laminar and turbulent flow, respectively, in the vocal tract. The complete model of glottis runs at power supply of 1.8 volt and the design is verified in a standard TSMC 0.18 micrometer technology on BSIM 3v3 model using ELDO Simulator.

Improved Multi-***scale Product Method for Glottal Opening Instants From Speech Signal

In this paper, we improve the glottal opening instant (GOI) detection from speech signal by a new approach based on Multi-***scale Product Method (MPM). We compute wavelet transform coefficients products at three scales using the quadratic spline wavelet. Glottal closure and opening instants appear respectively as strong maximum and weak minimum on the product signal. The GOI localisation is enhanced by refining the time interval containing this instant and based on the open quotient value. The proposed method is applied and evaluated on the Keele University database. Evaluation results show that the MPM detects GOIs with a performance rate equals to 96% for the entire database.