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Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately ascertaining the S-wave arrival times of microseismic signals is crucial for improving the accuracy with which microseismic events can be located. However, the first arrival of an S-wave is often overlapped by the P-wave which can make it difficult to accurately determine the arrival time of the S-wave in tunnels. Therefore, an accurate method for determining the S-wave arrival time of microseismic signals in tunnels is urgently needed. In this paper, a novel method is proposed based on the instantaneous phase difference intensity (IPDI) of the S-wave between two microseismic sensors. The peaks in the IPDI directly indicate the first breaking of an S-wave thus allowing the arrival time of the S-wave to be identified. Tests using synthetic signals verify that the IPDI method can correctly determine the arrival time of an S-wave when the first break of the S-wave is overlapped by the P-wave. The IPDI method was further applied to microseismic monitoring in a deeply buried tunnel in the Jinping II hydropower station in China. The IPDI peaks were found to successfully highlight the arrival of S-waves that could not be determined manually. The location accuracy of rockburst and microseismic events was thus significantly improved using the new IPDI method. Highlights: A novel index (IPDI) is introduced to highlight the first break of microseismic S-wave based on the instantaneous phase of sensors. The IPDI method can identify the S-wave arrival time even in near-field MS monitoring cases where S- and P waves overlap. The IPDI-picked S-wave arrival time substantially improves the MS location accuracy. [ABSTRACT FROM AUTHOR]

To address the shortage of damage identification methods for modal bridge expansion joints (MBEJs). Two damage indexes, DI(IPH) and DI(HS) were constructed using the instantaneous phase (IP) and Hilbert spectrum of Hilbert Huang transform (HHT). Three specimens were designed and fabricated to identify the damage to the concrete in the center beam, support bar and anchorage zone of MBEJs. The impact vibration of MBEJs was excited by the hammer impulses and acceleration sensors were used to obtain the vibration response. The damage of the center beam, support bar and concrete in anchorage zone was all classified into five levels (level 1–level 5). DI(IPH) and DI(HS) increase gradually with increasing damage levels. After the center beam was completely broken, the DI(IPH) and DI(HS) improved by 40.31 and 40.61, respectively. The DI(IPH) and DI(HS) increased by 25.25 and 55.91 after the complete breakage of the support bar. The DI(IPH) and DI(HS) of the concrete in the anchorage zone at Level 5 showed more minor numerical changes than the center beam and support bar, with an increase of 7.49 and 35.45, respectively. The experimental results showed that HHT could effectively identify the damage in various parts of MBEJs with high accuracy. There is a clear indication of the degree of damage, which is suitable for the identification of damage in MBEJs. [ABSTRACT FROM AUTHOR]

Several studies in the field of fMRI have reported the synchrony between the brain regions using instantaneous phase (IP) representation (derived from analytic representation of BOLD time series). We hypothesized that instantaneous amplitude (IA) representation from different brain regions might give additional information to the functional brain networks. To validate this, we explored this representation of resting state BOLD fMRI signal for deriving resting state networks (RSNs) and compared it with the IP representation based RSNs. Resting state fMRI data of 100 healthy adults (age=20–35 years, 54 females) from the population of 500 Subjects of HCP dataset were studied. Data was acquired using a 3T scanner in four runs (15-min each) with the phase encoding directions: Left to Right (LR), Right to Left (RL). These four runs were acquired in two sessions, and subjects were asked to keep their eyes open with a fixation on a white cross. The IA and IP representations were derived from a narrow-band filtered BOLD time series using Hilbert transforms and a seed-based approach is used to compute the RSNs in the brain. The experimental results demonstrate that within the frequency range 0.01–0.1 Hz, IA representation based RSNs have the highest similarity score between the two sessions for the motor network. Whereas for fronto-parietal network, IP based activation maps have the highest similarity score for all the frequency bands. For higher frequency band (0.198–0.25 Hz) consistency of the obtained RSNs across two sessions reduced for both IA and IP representations. Fusion of IA and IP representations based RSNs in comparison to those of IP based representation, leads to 3–10% improvement in the similarity scores between the default mode network obtained for the two sessions. In addition, the same comparison demonstrates 15–20% improvement for the motor network in the frequency bands: 0.01–0.04 Hz, 0.04–0.07 Hz, slow5 (0.01–0.027 Hz) and slow-4 (0.027–0.073 Hz). It is also observed that the similarity score between two sessions using instantaneous frequency (IF) (derivative of unwrapped IP) representation in exploring functional connectivity (FC) networks is comparable with those obtained using IP representation. Our findings suggest that IA-representation based measures can estimate RSNs with the reproducibility between the sessions comparable to that of the IP representation-based methods. This study demonstrates that IA and IP representations contain the complementary information of BOLD signal, and their fusion improves the results of FC. • Instantaneous amplitude (IA), Instantaneous frequency (IF), and Instantaneous phase (IP) of brain regions can be explored using a narrowband-filtered BOLD signal. • In this study, we hypothesized that incorporating IA, IF, and IP representations could enhance BOLD fMRI signal-based RSNs analysis. • The experimental results demonstrate that the fusion of the IA and IP functional connectivity maps improves the consistency of the obtained RSNs. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymers (CFRP) are extensively used in aerospace applications. Out-of-plane wrinkles frequently occur in aerospace CFRP parts that are commonly large and complex. Wrinkles acting as failure initiators severely damage the mechanical performance of CFRP parts. Wrinkles have no significant acoustic impedance mismatch, reflecting weak echoes. The total focusing method (TFM) using weak reflection signals is vulnerable to noise, so our primary work is to design discrete-time filters to relieve the noise interference. Wrinkles in CFRP composites are geometric defects, and their direct detection requires high spatial precision. The TFM method is a time-domain delay-and-sum algorithm, and it requires that the time information of filtered signals has no change or can be corrected. A linear phase filter can avoid phase distortion, and its filtered signal can be corrected by shifting a constant time. We first propose a wrinkle detection method using linear phase FIR-filtered ultrasonic array data. Linear phase filters almost do not affect the wrinkle geometry of detection results and can relieve noise-induced dislocation. Four filters with different bandwidths have been designed and applied for wrinkle detection. The 2 MHz bandwidth filter is recommended as an optimum choice. [ABSTRACT FROM AUTHOR]

Abstract The accumulation of shallow gas in the seabed reduces the strength of strata or forms a high-pressure air sac, endangering ocean engineering construction. Therefore, it is important to identify the distribution of shallow gas in the seabed within the study area. Shallow gas increases the soil mass porosity and reduces the acoustic wave velocity, causing attenuation by absorbing to high-frequency components in the acoustic waves. Based on the geological drilling data in the area surrounding an oil platform in Bohai Bay, a stratigraphic model was established for forward analysis, and the results suggest the presence of the phase inversion of reflective waves at the interface between shallow gas and strata and sunken events for the lower shallow gas. According to a survey of stratigraphic profiles surrounding the platform, a seismic attribute analysis of acoustic stratigraphic profile data concerning amplitude, instantaneous phase, and instantaneous frequency was carried out, and characteristics such as disordered weak amplitude reflection, phase inversion, sunken events and indicators, including high-frequency loss and shallow gas reflection, were identified. Given that the shallow gas reflection is columnar and ended at the top clay strata of the seabed, the shallow gas was probably produced from deep depths.

The phase of signals representing cyclic behavioural patterns provides valuable information for understanding the mechanisms driving the observed behaviours. Methods usually adopted to estimate the phase, which are based on projecting the signal onto the complex plane, have strict requirements on its frequency content, which limits their application. To overcome these limitations, input signals can be processed using band-pass filters or decomposition techniques. In this paper, we briefly review these approaches and propose a new one. Our approach is based on the principles of Empirical Mode Decomposition (EMD), but unlike EMD, it does not aim to decompose the input signal. This avoids the many problems that can occur when extracting a signal's components one by one. The proposed approach estimates the phase of experimental signals that have one main oscillatory component modulated by slower activity and perturbed by weak, sparse, or random activity at faster time scales. We illustrate how our approach works by estimating the phase dynamics of synthetic signals and real-world signals representing knee angles during flexion/extension activity, heel height during gait, and the activity of different organs involved in speech production. [ABSTRACT FROM AUTHOR]

Accurate extraction of instantaneous phase information is an essential step in condition monitoring and intelligent diagnosis of rotating machinery under variable speed conditions, which will directly affect the reliability of the tacho-less order tracking (TLOT) results. At present, instantaneous phase information extraction techniques are mainly divided into time–frequency analysis and signal decomposition. These methods are constrained because they are not available to extract harmonic components adaptively. Therefore, it is important to develop an intelligent model to accurately separate harmonic component with strict physical meaning for TLOT. In order to address the aforementioned issues, a deep binary mask (BM) signal separation model is presented for rotating machinery TLOT. The deep BM signal separation method can adaptively separate the fundamental harmonic component of vibration signals without prior knowledge. The outcomes indicate that the deep BM signal separation model is more productive and flexible in terms of accuracy and self-adaptation compared to some advanced TLOT algorithms. [ABSTRACT FROM AUTHOR]

The accumulation of shallow gas in the seabed reduces the strength of strata or forms a high-pressure air sac, endangering ocean engineering construction. Therefore, it is important to identify the distribution of shallow gas in the seabed within the study area. Shallow gas increases the soil mass porosity and reduces the acoustic wave velocity, causing attenuation by absorbing to high-frequency components in the acoustic waves. Based on the geological drilling data in the area surrounding an oil platform in Bohai Bay, a stratigraphic model was established for forward analysis, and the results suggest the presence of the phase inversion of reflective waves at the interface between shallow gas and strata and sunken events for the lower shallow gas. According to a survey of stratigraphic profiles surrounding the platform, a seismic attribute analysis of acoustic stratigraphic profile data concerning amplitude, instantaneous phase, and instantaneous frequency was carried out, and characteristics such as disordered weak amplitude reflection, phase inversion, sunken events and indicators, including high-frequency loss and shallow gas reflection, were identified. Given that the shallow gas reflection is columnar and ended at the top clay strata of the seabed, the shallow gas was probably produced from deep depths. [ABSTRACT FROM AUTHOR]

Oscillatory dynamics pervades the universe, appearing in systems of all scales. Whilst autonomous oscillatory dynamics has been extensively studied and is well understood, the very important problem of non-autonomous oscillatory dynamics is less well understood. Here, we provide a framework for non-autonomous oscillatory dynamics, within which we can define intermittent phenomena such as intermittent phase synchronisation. Moreover, we demonstrate this framework with a coupled pair of non-autonomous phase oscillators as well as a higher-dimensional system comprising of two interacting phase-oscillator networks. • A new framework for phase-dynamics phenomena in non-autonomous oscillatory systems. • Interactions between pairs of non-autonomous oscillators and networks are considered. • Intermittent synchronisation is formulated mathematically. • Relevant for many real-world problems. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymers (CFRP) are extensively used in aerospace applications. Out-of-plane wrinkles frequently occur in aerospace CFRP parts that are commonly large and complex. Wrinkles acting as failure initiators severely damage the mechanical performance of CFRP parts. Wrinkles have no significant acoustic impedance mismatch, reflecting weak echoes. The total focusing method (TFM) using weak reflection signals is vulnerable to noise, so our primary work is to design discrete-time filters to relieve the noise interference. Wrinkles in CFRP composites are geometric defects, and their direct detection requires high spatial precision. The TFM method is a time-domain delay-and-sum algorithm, and it requires that the time information of filtered signals has no change or can be corrected. A linear phase filter can avoid phase distortion, and its filtered signal can be corrected by shifting a constant time. We first propose a wrinkle detection method using linear phase FIR-filtered ultrasonic array data. Linear phase filters almost do not affect the wrinkle geometry of detection results and can relieve noise-induced dislocation. Four filters with different bandwidths have been designed and applied for wrinkle detection. The 2 MHz bandwidth filter is recommended as an optimum choice.

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The processing of electroencephalogram (EEG) signals is important in brain-computer interface applications. Many different spectral analysis or time–frequency analysis methods have been used for this process. However, most of the methods used in previous studies were based on the features obtained from the power or energy of EEG signals. To date, a sufficiently high level of accuracy in classifying four-class motor imagery (MI) states with these features has not been achieved. Therefore, studies are still being carried out on this subject to increase accuracy. Since different parts of the brain interact in MI applications, it may also be important to examine phase information in MI classifications. Based on this idea, the empirical mode decomposition method was applied to EEG signals from 22 channels in this study. Thus, the intrinsic mode functions (IMFs) of each channel were obtained. The instantaneous phase values were calculated by applying the Hilbert transform to these IMFs. Twenty-two eigenvalues were obtained for each window by sliding a 22 × 22 window with as many rows and columns as the number of channels on the instantaneous phase values. A feature vector was obtained by adding the eigenvalues of all windows, one under the other. Higher accuracy (89.89%) was achieved compared to state-of-the-art studies when feature vectors were applied to the long short-term memory (LSTM) deep network. This method increased the accuracy by 4.3%. This showed that the features obtained from the phase information are highly representative in MI classification applications and may be important for MI applications. [ABSTRACT FROM AUTHOR]

Synchrosqueezing transform (SST) can effectively improve time-frequency precision and resolution by squeezing time-frequency spectra via instantaneous frequencies, and it has been applied in many diverse disciplines; however, the precision of estimated instantaneous frequencies during SST is usually affected by the time-sample interval of the inputted signal; this usually leads to low-precision or inaccurate SST results and limits its further application. To obtain high-precision and high-resolution SST results with high efficiency, we propose a high-precision and high-resolution SST via time-frequency instantaneous phases (HSST); in HSST, time-frequency instantaneous phases with period-jumps removal are used for high-precision instantaneous frequencies estimation and SST. Two synthetic signal examples show that HSST can minimize the impact of the time-sample interval to achieve high-precision and high-resolution SST results with high efficiency. A real 3D seismic data application demonstrates that HSST has fantastic performance in time-frequency precision and resolution enhancement, and it can be widely used in digital signals processing and interpretation fields. [ABSTRACT FROM AUTHOR]

The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu- Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km². The horizon was calibrated and defined on the seismic section with well logs data (well tops, check shot, sonic logs, and density logs) in the interpretation process to identify the upper and lower boundaries of the Formation. Seismic attributes were used to study the formation, including instantaneous phase attributes and relative acoustic impedance on time slice of 3D seismic data . Also, relative acoustic impedance was utilized to study the top of the Mishrif Formation. Based on these seismic attributes, karst features of the formation were identified. In addition, the nature of the lithology in the study area and the change in porosity were determined through the relative acoustic impedance The overlap of the top of the Mishrif Formation with the bottom of the Khasib Formation was determined because the Mishrif Formation is considered as an unconformity surface. [ABSTRACT FROM AUTHOR]

Deep wells drilling in many parts of the world, especially in fractured cavernous reservoirs, faces a number of difficulties, both of technical and geological nature such as, the manifestations of abnormally high formation pressure. With the increase in the number of wells withabnormally high formation pressure, and the number of cases of emergency situations, it became absolutely necessary to study this problem in order to prevent negative consequences. The proper study of abnormally high formation pressure started in the late 70-s to the 90-s of the last century.The geological features, patterns, mechanisms for formation of abnormally high formation pressure were studied and various techniques and methods for assessment of reservoir pressures both before and during well drilling were developed. This article analyzes geological information on the development and distribution of abnormal pressures. It is shown that zones of abnormally high formation pressures can be associated with significant changes in the seismic field, that is, seismic attributes. The generalized analysis of presented data allows determining that the zones of abnormally high formation pressure are quite widespread in the sedimentary cover, including in the area of the study. The existing problem of the connection of abnormally high formation pressure with seismic attributes is far from the final solution. A correct forecast of formation pressure assessment makes it possible to conduct drilling in optimal conditions. The task of the prediction of abnormally high formation pressure zones draining reservoir strata in this field is relevant and urgent, since at present it is being drilled through a network of production wells. The aim of the research is the use of seismic attributes to control the development and refinement of a dynamic tank model;identification of abnormally high formation pressurezones in the sections of the hydrocarbon field according to seismic data (seismic attributes). Objects: rocks with a complex structure of the void space due to intense cavernosity and fracturing formed at the expense of tectonic dislocations in the sedimentary cover. Methodology: theoretical and experimental justification of the connection of abnormally high formation pressurezones and seismic data. Results.The authors have constructed the maps of reservoir pressure distribution across the area in the interwell space for gas condensate field under study.

Synchrosqueezing transform (SST) can effectively improve time-frequency precision and resolution by squeezing time-frequency spectra via instantaneous frequencies, and it has been applied in many diverse disciplines; however, the precision of estimated instantaneous frequencies during SST is usually affected by the time-sample interval of the inputted signal; this usually leads to low-precision or inaccurate SST results and limits its further application. To obtain high-precision and high-resolution SST results with high efficiency, we propose a high-precision and high-resolution SST via time-frequency instantaneous phases (HSST); in HSST, time-frequency instantaneous phases with period-jumps removal are used for high-precision instantaneous frequencies estimation and SST. Two synthetic signal examples show that HSST can minimize the impact of the time-sample interval to achieve high-precision and high-resolution SST results with high efficiency. A real 3D seismic data application demonstrates that HSST has fantastic performance in time-frequency precision and resolution enhancement, and it can be widely used in digital signals processing and interpretation fields.

In the active Lamb wave based monitoring of plate structural health, it is difficult to extract damage information from active Lamb waves based on single sensor collection. Based on the Hilbert–Huang transform (HHT) instantaneous processing method, this paper proposes to study the damage information carried by a single sensor monitoring signal from the instantaneous parameter characteristics of the signal. The instantaneous phase change caused by the phase difference between the damage scattering wave and the direct wave is studied. The change of the marginal spectrum amplitude in the effective range caused by the damage scattering wave is studied in continuous multiple frequency bands. Finally, the damage information extraction based on a single sensor monitoring signal is realized. From the model analysis and experimental results, it is reliable and feasible to realize the active Lamb wave based monitoring of plate structural health according to the instantaneous parameter change characteristics from a single sensor signal. [ABSTRACT FROM AUTHOR]

Neural oscillations are vital for the functioning of a central nervous system because they assist in brain communication across a huge network of neurons. Alpha frequency oscillations are believed to depict idling or inhibition of task-irrelevant cortical activities. However, recent studies on alpha oscillations (particularly alpha phase) hypothesize that they have an active and direct role in the mechanisms of attention and working memory. To understand the role of alpha oscillations in several cognitive processes, accurate estimations of phase, amplitude, and frequency are required. Herein, we propose an approach for time-series forward prediction by comparing an autoregressive (AR) model and an adaptive method (least mean square (LMS)-based AR model). This study tested both methods for two prediction lengths of data. Our results indicate that for shorter data segments (prediction of 128 ms), the AR model outperforms the LMS-based AR model, while for longer prediction lengths (256 ms), the LMS- based AR model surpasses the AR model. LMS with low computational cost can aid in electroencephalography (EEG) phase prediction (alpha oscillations) in basic research to reveal the functional role of the oscillatory phase as well as for applications for brain-computer interfaces. [ABSTRACT FROM AUTHOR]

Instantaneous phase is a commonly used attribute for structural and stratigraphic feature characterization. The conventional calculation method is to construct the complex-valued seismic trace, then get the ratio of the imaginary part to the real part and finally compute the antitangent of the ratio as the instantaneous phase attribute. In this way, the phase result at one time sample point is the total phase rotation from the beginning of the trace to this point, which means the traditional instantaneous phase is cumulative. Furthermore, the phase obtained by arctangent is usually entangled, which makes it more difficult to apply to seismic interpretation. To address the two issues above, we proposed a new way to calculate the improved local phase variation attributes. Firstly, we calculate traditional instantaneous phase and unwrap it. Then we set a time window on the unwrapped phase to compute the local phase variation by using some difference methods. Finally, we slide the time window on the whole trace to obtain the final phase variation attributes. This strategy turns the whole cumulative value into local variational value, which makes the obtained local phase variation nearly zero in the continuous region but changed greatly at the interface or the abnormal structure areas. Tested by the numerical model and the real data, the proposed attributes have a good application effect in channel detection, which provides a train of thought to seismic structure interpretation with phase attributes. [ABSTRACT FROM AUTHOR]

The full-waveform inversion method is a high-precision inversion method based on the minimization of the misfit between the synthetic seismograms and the observed data. However, this method suffers from cycle skipping in the time domain or phase wrapping in the frequency because of the inaccurate initial velocity or the lack of low-frequency information. furthermore, the object scale of inversion is affected by the observation system and wavelet bandwidth, the inversion for large-scale structures is a strongly nonlinear problem that is considerably difficult to solve. In this study, we modify the unwrapping algorithm to obtain accurate unwrapped instantaneous phase, then using this phase conducts the inversion for reducing the strong nonlinearity. The normal instantaneous phases are measured as modulo 2π, leading the loss of true phase information. The path integral algorithm can be used to unwrap the instantaneous phase of the seismograms having time series and one-dimensional (1D) signal characteristics. However, the unwrapped phase is easily affected by the numerical simulation and phase calculations, resulting in the low resolution of inversion parameters. To increase the noise resistance and ensure the inversion accuracy, we present an improved unwrapping method by adding an envelope into the path integral unwrapping algorithm for restricting the phase mutation points, getting accurate instantaneous phase. The objective function constructed by unwrapping instantaneous phase is less affected by the local minimum, thereby making it suitable for full-waveform inversion. Further, the corresponding instantaneous phase inversion formulas are provided. Using the improved algorithm, we can invert the low-wavenumber components of the underneath structure and ensure the accuracy of the inverted velocity. Finally, the numerical tests of the 2D Marmousi model and 3D SEG/EAGE salt model prove the accuracy of the proposed algorithm and the ability to restore large-scale low-wavenumber structures, respectively. [ABSTRACT FROM AUTHOR]

Ferroresonance is a nonlinear phenomenon that damages the undesired, destructive system for energy transmission lines. The formation and development of this overvoltage ferroresonance phenomenon is an important research topic as a mysterious event in energy power systems. In this study, Seyit Omer-Iklar energy transmission line from Turkey's electrical transmission networks was modelled using real parameters. Ferroresonance scenario was created on the model. The energy transmission system with the scenario is set to a simulation time of 4 seconds. The first two seconds are set to normal. By cutting off the transmission line in the 2nd sec., the system is provided with ferroresonance drift. According to the data obtained therefrom, spectrograms, instantaneous amplitude, instantaneous frequency and instantaneous phase were conducted. The analyses have made clear that the instantaneous frequency amplitude defined the system's ferroresonance moment very well. [ABSTRACT FROM AUTHOR]

Automatic detection of horizons and other geological structures of interest is often employed as a preliminary step in seismic interpretation. Although necessary, the identification and extraction of horizons can be a laborious and time-consuming step for the interpreter. Therefore, we present a semi-automatic horizon extraction algorithm based on the absolute phase of seismic data obtained through phase unwrapping techniques. We assess the algorithm accuracy and robustness in several examples, including noisy data sets. In addition, we present comparative results between two phase unwrapping methods, showing their robustness when associated with seismic horizon extraction. We compare the results obtained by our methods with the results obtained by seismic horizon extraction algorithms available in the literature. • Seismic horizon extraction tool using absolute seismic phase. • Comparison of our method with other algorithms for seismic horizon extraction. • Robustness evaluation of two classes of phase unwrapping algorithms in seismic data. [ABSTRACT FROM AUTHOR]

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses.

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations.

Time of flight diffraction (TOFD) is considered a reliable non-destructive testing method for the inspection of welds using a pair of single-element probes. On the other hand, ultrasonic phased array imaging has been continuously developed over the last couple of decades, and now features powerful algorithms, such as the total focusing method (TFM) and its multi-view approach to rendering detailed images of inspected parts. This article focuses on a different implementation of the TFM algorithm, relying on the coherent summation of the instantaneous signal phase. This approach presents a wide range of benefits, such as removing the need for calibration, and is highly sensitive to defect tips. This study compares the sizing and localization capabilities of the proposed method with the well-known TOFD. Both instantaneous phase algorithm and TOFD do not take advantage of the signal amplitude. Experimental tests were performed on a ¾″-thick steel sample with crack-like defects at different angles. Phase-based imaging techniques showed similar characterization capabilities as the standard TOFD method. However, the proposed method adds the benefit of generating an easy-to-interpret image that can help in localizing the defect. These results pave the way for a new characterization approach, especially in the field of automated ultrasonic testing (AUT).

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses. [ABSTRACT FROM AUTHOR]

Abstract The recurrence rate of atrial fibrillation (AF) following catheter ablation is high. Successful non-recurrence prediction before ablation is financially and physically beneficial. Atrial fibrillation cycle length (AFCL) has been used for such predictions. The study explored a nonlinear and non-stationary signal analysis technique, ensemble empirical model decomposition (EEMD), to capture intrinsic AFCL pattern. Twenty-eight AF patients underwent regular catheter ablation protocols and follow-up treatments. Surface ECG were recorded before ablation procedures, and preprocessed with QRS-T cancellation method to extract intrinsic AF signals, which were then analyzed with EEMD to calculate AFCL adaptively. Conventional Fourier-based dominant frequency (DF) analysis was conducted for comparison. During 12 months follow-up, 17 patients maintained sinus rhythm while 11 patients had AF recurrence. Patients who maintained sinus rhythm had longer AFCL (152.2 (145.0, 157.6) ms) than those with AF recurrence (138.8 (135.0, 145.2) ms). Patients with AFCL >152.1 ms had no recurrence (Kaplan–Meier curve analysis, P=0.006, hazard ratio: 5.68). With AFCL as a predictor of AF non-recurrence at 12 months, the area under ROC curve is 0.80, while with DF analysis, the area is 0.66. EEMD-based intrinsic AFCL measured from surface ECG may serve as an effective non-invasive catheter ablation screening tool for AF non-recurrence. Highlights • A novel atrial fibrillation cycle length measurement from lead V1 of surface ECG. • The proposed method performed better than Fourier-based dominant frequency analysis. • Parameter measured before catheter ablation was associated with success of ablation. • Parameter > 152.1 ms may serve as a predictor of non-recurrence at 1 year. [ABSTRACT FROM AUTHOR]

In the event of a single line to ground (SLG) fault in a non-effectively grounded distribution network, the faulted current is weak (only a few amperes or less) and the existing devices cannot accurately judge the faulted feeder. In this paper, we proposed algorithms that combine complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert transform to construct a multi-criteria comprehensive voting method. First, CEEMDAN algorithm is used to decompose the zero-sequence current to obtain the IMF1(the first intrinsic mode function) component and the Hilbert transform is used to calculate the instantaneous amplitude and instantaneous phase. Then, according to the three largest instantaneous amplitudes information, we constructed the characteristic instantaneous phase, characteristic instantaneous energy relative entropy and characteristic instantaneous zero sequence current polarity criteria from the phase, energy and polarity, respectively. Finally, we proposed a comprehensive voting method, which is specifically shown as follows: when two or more criteria show that one feeder or the bus has an SLG fault, it is voted that the feeder or the bus has an SLG fault. In contrast, if the judgment results of the three criteria are inconsistent, then we would return to recalculation and then vote. Compared with existing method, simulation tests and field experiments show that the method proposed in this paper has higher accuracy and a faster calculation speed. [ABSTRACT FROM AUTHOR]

The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness. The importance of the topic and its vast exploration in different domains of the neuroscience presses the need for appropriate tools and methods for measuring the level of phase synchronization of neuronal activities. Measuring the level of instantaneous phase (IP) synchronization has been used extensively in numerous studies of ERPs as well as oscillatory activity for a better understanding of the underlying cognitive binding with regard to different set of stimulations such as auditory and visual. However, the reliability of results can be challenged as a result of noise artifact in IP. Phase distortion due to environmental noise artifacts as well as different pre-processing steps on signals can lead to generation of artificial phase jumps. One of such effects presented recently is the effect of low envelope on the IP of signal. It has been shown that as the instantaneous envelope of the analytic signal approaches zero, the variations in the phase increase, effectively leading to abrupt transitions in the phase. These abrupt transitions can distort the phase synchronization results as they are not related to any neurophysiological effect. These transitions are called spurious phase variation. In this study, we present a model to remove generated artificial phase variations due to the effect of low envelope. The proposed method is based on a simplified form of a Kalman smoother, that is able to model the IP behavior in narrow-bandpassed oscillatory signals. In this work we first explain the details of the proposed Kalman smoother for modeling the dynamics of the phase variations in narrow-bandpassed signals and then evaluate it on a set of synthetic signals. Finally, we apply the model on ongoing-EEG signals to assess the removal of spurious phase variations. [ABSTRACT FROM AUTHOR]