Waveform Extraction Research Articles

A Novel Approach to Automatically Digitize Analog Seismograms

Abstract Before the widespread adoption of the digital seismographs, seismic records were stored in analog form on paper and manually read by analysts. These analog seismograms contained various useful information and were crucial for seismic research. To meet the demands of the modern computational analysis, researchers must digitize historical analog seismograms and extract information. In this article, we present a novel approach to automatically digitize analog seismograms. Initially, Otsu threshold segmentation was applied to the analog seismograms to remove underlying noise and improve their clarity. Subsequently, a novel dynamic distributed seismic waveform onset-point-search algorithm was implemented, which automatically locates the onset point of each seismic waveform baseline in analog seismograms and accurately determines the total number of seismic waveform curves. To address the complexity and diversity of seismic waveforms, we implemented an innovative seismic waveform classification algorithm that can distinguish between complex waveforms and smooth waveforms, and further implemented a new smooth waveform removal method to eliminate interference from smooth waveforms during complex waveform extraction. Then, we used a YOLOv9s-based model to identify time markers within the seismic waveforms for removal. In addition, in the seismic waveform digitization extraction and reconstruction phase, we implemented a novel method for extracting significant seismic waveform features and geometric restoration for peak and trough feature extraction and geometric restoration, as well as vertical feature extraction of seismic waveforms. Finally, we implemented a new waveform sequence integration and time mapping model, which can effectively reconstruct seismic waveform data based on the extracted features and map arrival times to each waveform point. Experiments have verified the significant superiority and stability of the methods implemented in this article for digitizing analog seismograms.

A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.

Code generation for AMReX with applications to numerical relativity

We present a new python/SymPy based code generator for producing executable numerical expressions for partial differential equations in AMReX-based applications. We demonstrate the code generator capabilities for the case of ADM formulations of numerical relativity for the constraint damped, conformal Z4 formulations (Z4c and CCZ4). The generated spacetime solvers are examined for stability and accuracy using a selection of checks from the standard Apples with Apples testbeds for numerical relativity applications. We also explore physically interesting vacuum spacetimes including head-on and inspiraling black hole binary collisions, and investigate the simulated gravitational waveforms from such events with the Newman–Penrose formulation of waveform extraction.

Jugular Venous Pulse Waveform Extraction From a Wearable Radio Frequency Sensor.

Many prevalent heart diseases can be indicated by the features of the jugular venous pulse (JVP), an efficacious indicator of right heart health. However, JVP dynamics are not widely utilized in clinical settings as its observation and sensing remain cumbersome. Non-invasive measures of cardiac behavior, including the JVP, are of growing interest to enable continuous and at-home monitoring of cardiac disorders. In this work, we propose a wearable near-field radio-frequency (RF) sensor affixed with a neck collar on the clavicle over the internal jugular vein to enable non-invasive JVP sensing. We employed a complex vector injection signal processing method to extract repeatable JVP waveform features in multiple postures. With a 21-subject human study, we demonstrated morphologically consistent JVP sensing with consistent a-, c-, and v-wave feature timings, benchmarked by synchronous electrocardiogram and phonocardiogram. Further, inter-postural experiments demonstrated the capability of the proposed system to quantify morphological changes to the JVP which are present in many cardiac disorders. The results of this work suggest the proposed near-field RF sensor is capable of non-invasive JVP monitoring, potentially enabling improved sensing in both clinical and ambulatory environments.

Effect of longitudinal lift distribution on sonic boom of a canard-wing-stabilator-body configuration

After the last flight of the Concorde in 2003, sonic boom has been one of the obstacles to the return of a supersonic transport aircraft to service. To reduce the sonic boom intensity to an acceptable level, it is of great significance to study the effect of lift distribution on far-field sonic boom, since lift is one of the most important contributors to an intense sonic boom. Existing studies on the longitudinal lift distribution used low-fidelity methods, such as Whitham theory, and in turn, only preliminary conclusions were obtained, such as that extending the lift distribution can reduce sonic boom. This paper uses a newly developed high-fidelity prediction method to quantitatively study the effect of longitudinal lift distribution on the sonic boom of a Canard-Wing-Stabilator-Body (CWSB) configuration. This high-fidelity prediction method combines near-field CFD simulation with far-field propagation by solving the augmented Burgers equation. A multipole analysis method is employed for the extraction of near-field waveform in order to reduce computational cost. Seven configurations with the same total lift but different distributions are studied, and the quantitative relationship between the longitudinal lift distribution and far-field sonic boom intensity is investigated. It is observed that a small lift generated by the stabilator can prevent aft-stabilator and aft-fuselage shocks from merging, while the balanced lift generated by the canard and wing can effectively keep the corresponding shocks further apart, which is beneficial for reducing both the on-track and off-track sonic boom. In turn, the acoustic level perceived at the ground can be reduced by 5.9 PLdB on-track and 5.4 PLdB off-track, on average.

Intranasal Pressure Recording for Monitoring Mouse Respiration.

Respiration is a highly dynamic signal that influences voluntary behaviors including odor sampling and entrains rhythmic activity in the brain. Many techniques exist to record respiration with each exhibiting strengths and drawbacks given the ultimate goals of the respiration recording. Intranasal cannula implantation, coupled with pressure sensor recording, allows for temporal precision and detailed feature extraction of the respiratory waveform. Here we describe the implantation process and necessary recording equipment to effectively conduct intranasal pressure recording of respiration. This is an ideal method for understanding the dynamics of odor sampling in conjunction with olfactory sensory transmission.

A robust local pulse wave imaging method based on digital image processing techniques.

The original diameter velocity loop method (ln(D)U-loop) cannot accurately extract the blood vessel diameter waveform when the quality of ultrasound image data is not high (such as obesity, age, and the operation of the ultrasound doctor), so it is unable to measure the pulse wave velocity (PWV) of the ascending aorta. This study proposes a diameter waveform extraction method combining threshold, gradient filtering, and the center of gravity method. At the same time, the linear regression method of searching for the rising point of the systolic period is replaced by the optimal average of two linear regression methods. This method can also extract the diameter waveform with poor-quality images and obtain a more accurate PWV. In vivo experimental data from 17 (age 60.5 ± 9.2) elderly patients with cerebral infarction and 12 (age 32.5 ± 5.6) healthy young adults were used for processing, and the results showed that the mean PWV using the ln(D)U-loop method was 12.56 (SD = 3.47) ms-1 for patients with cerebral infarction and 6.81 (SD = 1.73) ms-1 for healthy young adults. The PWV results based on the Wilcoxon rank-sum test and calculated based on the improved ln(D)U-loop method were both statistically significant (p < 0.01). The agreement analysis (Bland-Altman analysis) between the QA-loop and ln(D)U-loop methods showed that the mean deviation of the measured PWV was 0.07 m/s and the standard deviation of the deviation was 1.18 m/s. The experimental results demonstrated the effectiveness of the improved ln(D)U-loop method proposed in this paper on poor-quality images. This study can improve the possibility of the ln(D)U-loop method being widely used in the clinical measurement of ascending aortic PWV.

A prediction method for transport stress in meat sheep based on GA-BPNN

Transportation of live sheep is a relatively important part of the meat sheep industry, and the generation of transportation stress seriously threatens the organism health of live sheep as well as the quality of lamb meat. In this study, we selected Lake sheep, one of the edible sheep breeds, as the meat sheep research object, analyzed the actual needs of meat sheep transportation bio-sensing and modeling evaluation, and built a systematic bio-signal detection, processing and modeling method from bio-signal sensing, core waveform extraction to feature parameter estimation using wearable meat sheep physiological dynamic sensing and continuous monitoring of transportation environment sensors, and used modern spectrum estimation methods to judge and strip the target signal waveform from it to achieve accurate sensing and acquisition of transport key parameters. We also use modern spectral estimation methods to determine and extract the target signal waveforms from them, and achieve accurate sensing and acquisition of transport key parameters. Based on this, a qualitative stress assessment method based on external performance and a graded prediction model for transportation stress of meat sheep are constructed, and a predictive analysis and estimation management mechanism of stress level based on genetic algorithm optimized reverse artificial neural network is realized. Using a qualitative stress assessment method based on external manifestations, we established stage-specific classification rules for monitoring data in the transportation environment of meat sheep.The artificial neural network with optimized parameters based on the classification rules applying genetic algorithm is designed and implemented for real-time and efficient stress level prediction management mechanism for meat sheep transportation. The graded prediction model of meat sheep transportation stress was established by GA-BPNN and compared with the BP neural network optimized by particle swarm algorithm, simulated annealing algorithm and ant colony algorithm. The best optimization effect of GA was obtained, and the average prediction accuracy of the model could reach 89.81%. Finally, it achieves to improve the transportation reliability, reduce the transportation risk, and solve the problems of inefficient meat sheep transportation supervision and quality control.

Assessing Hand Perfusion With Eulerian Video Magnification and Waveform Extraction

Timely and accurate triage of upper extremity injuries is critical, but current perfusion monitoring technologies have shortcomings. These limitations are especially pronounced in patients with darker skin tones. This pilot study evaluates a Eulerian Video Magnification (EVM) algorithm combined with color channel waveform extraction to enable video-based measurement of hand and finger perfusion. Videos of 10 volunteer study participants with Fitzpatrick skin types III-VI were taken in a controlled environment during normal perfusion and tourniquet-induced ischemia. Videos were EVM processed, and red/green/blue color channel characteristics were extracted to produce waveforms. These videos were assessed by surgeons with a range of expertise in hand injuries. The videos were randomized and presented in 1 of 3 ways: unprocessed, EVM processed, and EVM with waveform output (EVM+waveform). Survey respondents indicated whether the video showed an ischemic or perfused hand or if they were unable to tell. We used group comparisons to evaluate response accuracy across video types, skin tones, and respondent groups. Of the 51 providers to whom the surveys were sent, 25 (49%) completed them. Using the Pearson χ2 test, the frequencies of correct responses were significantly higher in the EVM+waveform category than in the unprocessed or EVM videos. Additionally, the agreement was higher among responses to the EVM+waveform questions than among responses to the unprocessed or EVM processed. The accuracy and agreement from the EVM+waveform group were consistent across all skin pigmentations evaluated. Video-based EVM processing combined with waveform extraction from color channels improved the surgeon's ability to identify tourniquet-induced finger ischemia via video across all skin types tested. Eulerian Video Magnification with waveform extraction improved the assessment of perfusion in the distal upper extremity and has potential future applications, including triage, postsurgery vascular assessment, and telemedicine.

Non-intrusive power waveform modeling and identification of air conditioning load

As a typical flexible load, air conditioner (AC) can play a crucial role in improving energy efficiency and optimizing power grid operation. However, due to its continuously variable load characteristics, AC faces difficulties in feature extraction and unsupervised modeling for non-intrusive load monitoring. In coping with these problems, a novel fully unsupervised non-intrusive AC monitoring scheme is designed. Firstly, an autonomous AC waveform modeling method is introduced. According to the general electrical characteristics, the candidate AC (start and stop, etc.) transient waveform templates are captured from the aggregated data. On this basis, the transient waveform samples similar to candidate template are extracted and verified based on the common usage habit characteristics. Then AC model consisting of waveform template and feature vector is subsequently established by multi-dimensional clustering of the waveform samples. Secondly, an online AC state identification and power disaggregation method is proposed. Based on the dynamic time warping algorithm and guided filtering algorithm, an AC transient waveform extraction method via template matching is presented, which can extract complete and pure transient AC waveforms from the multi-appliance mixed operation scenarios. According to the extracted AC waveforms, the state identification and energy consumption estimation can be realized. In addition, the incremental clustering is carried on the online identification results to further update the established AC model. Finally, the comparison experiments on the REDD dataset and the real-world data measured from multiple users in China show that, the proposed method can construct AC templates in unseen scenarios and update the established AC models automatically, thus outperform the benchmarks in both operating state identification and power disaggregation.

Separation of cardiogenic oscillations from airflow waveforms using singular spectrum analysis

Background and ObjectiveAirflow fluctuations caused by cardiac contraction can trigger inappropriate ventilator pressure support in anesthesia machines and intensive care unit mechanical ventilators. Removal of this cardiogenic artifact from the airflow signal would improve ventilator function. The application of singular spectrum analysis (SSA) to remove cardiogenic oscillations from ventilator airflow signals recorded from intubated, mechanically ventilated patients under general anesthesia was evaluated in this study. MethodsAirflow (liters/minute) and CO2 (mmHg) data were collected at a sampling rate of 125 Hz from the intraoperative monitoring systems using special-purpose software. Simultaneous electrocardiogram signals (mV) were also collected at a sampling rate of 250 Hz. One-dimensional SSA was performed offline on normalized airflow signals using a window length sufficient to span one period of typical respiratory variation. The main components of the airflow waveform are respiratory excursions and cardiogenic oscillations, with respiratory excursions more slowly varying and of higher magnitude. The smooth respiratory waveform was formed from elementary reconstructed series corresponding to the highest singular values obtained with SSA analysis. The quality of respiratory waveform extraction with SSA was determined by calculating the weighted correlation between the selected elementary reconstructed series. ResultsAirflow data was recorded from 6 patients. The respiratory component of the airflow signal without cardiogenic oscillations was reconstructed from elementary series corresponding to singular values of highest magnitude. The weighted correlations obtained were greater than 0.96 in the majority of patients studied. Cardiogenic oscillations were reconstructed from elementary reconstructed series corresponding to singular values of lower magnitude. ConclusionsSSA is effective in extracting higher amplitude respiratory excursions while excluding lower amplitude cardiogenic oscillations and noise from the airflow signal. This study demonstrates that suppression of the cardiogenic artefact with SSA is computationally feasible to augment ventilator performance.

Stimulation and Acquisition’ System to Obtain the Visual Evoked Potential Related to the Optic Nerve Pathologies

The development and integration of an electroencephalographic signal acquisition and stimulation system is proposed to support the detection of pathologies related to the optic nerve. It is based on the extraction and analysis of a characteristic waveform called Visual Evoked Potential immersed in the electroencephalographic register. Some parameters of this potential, as latency and amplitude, are affected by the optic nerve pathologies, and measurement of changes from its normal states can be used as indicators of abnormal visual functionality of a patient. The proposed stimulation system can generate Visual Evoked Potential and multifocal Visual Evoked Potential which can help to locate pathologies in different areas of the visual field.

Tremor Waveform Extraction and Automatic Location With Neural Network Interpretation

Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often associated with tectonic tremor, nonimpulsive signals that can easily be buried in seismic noise and go undetected. We present a new methodology aimed at improving the detection and location of tremors hidden within seismic noise. After identifying tremors with a classic convolutional neural network (CNN), we rely on neural network attribution to extract core tremor signatures. We observe that the signals resulting from the neural network attribution analysis correspond to a waveform traveling in the Earth&#x2019;s crust and mantle at wavespeeds consistent with seismological estimates. We then use these waveforms signatures to locate the source of tremors with standard array-based techniques. We apply this method to the Cascadia subduction zone, where we identify tremor patches consistent with existing catalogs. This approach allows us to extract small signals hidden within the noise, and to locate more tremors than in existing catalogs.

Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

In order to improve the feature extraction effect of digital music and improve the efficiency of music retrieval, this paper combines digital technology to analyze music waveforms, extract music features, and realize digital processing of music features. Taking the extraction of waveform music file features as the starting point, this paper combines the digital music feature extraction algorithm to build a music feature extraction model and conducts an in-depth analysis of the digital music waveform extraction process. In addition, by setting the threshold, the linear difference between the sampling points on both sides of the threshold on the leading edge of the waveform is used to obtain the overthreshold time. From the experimental research results, it can be seen that the music feature extraction model based on digital music waveform analysis proposed in this paper has good results.

A template-free approach for waveform extraction of gravitational wave events

We develop a general data-driven and template-free method for the extraction of event waveforms in the presence of background noise. Recent gravitational-wave observations provide one of the significant scientific areas requiring data analysis and waveform extraction capability. We use our method to find the waveforms for the reported events from the first, second, and third LIGO observation runs (O1, O2, and O3). Using the instantaneous frequencies derived by the Hilbert transform of the extracted waveforms, we provide the physical time delays between the arrivals of gravitational waves to the detectors.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh–Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain–computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio. Approach. Using the principle of nonlinear aperiodic FitzHugh–Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times. Results: A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise. Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

Extraction of binary black hole gravitational wave signals from detector data using deep learning

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.

Extraction of apex beat waveform from acoustic pulse wave by sound sensing system using stochastic resonance

With a sound sensing system using stochastic resonance (4SR), it became possible to obtain an acoustic pulse wave (APW)—a waveform created via a mixture of apex beat and heart sound. We examined 50 subjects who were healthy, with no underlying cardiovascular diseases. We could determine boundary frequency (BF) using APW and phonocardiogram signals. APW data was divided into two bands, one from 0.5 Hz to BF, and a second one from BF to 50 Hz. This permitted the extraction of cardiac apex beat (CAB) and cardiac acoustic sound (CAS), respectively. BF could be expressed by a quadratic function of heart rate, and made it possible to collect CAB and CAS in real time. According to heart rate variability analysis, the fluctuation was 1/f, which indicated an efficient cardiac movement when heart rate was 70 to 80/min. In the frequency band between 0.5 Hz and BF, CAB readings collected from the precordial region resembled apex cardiogram data. The waveforms were classified into five types. Therefore, the new 4SR sensing system can be used as a physical diagnostic tool to obtain biological pulse wave data non-invasively and repeatedly over a long period, and it shows promise for broader applications, including AI analysis.

Measurement algorithm for wafer alignment based on principal component analysis.

We propose a novel measurement algorithm for wafer alignment technology based on principal component analysis (PCA) of a mark image. The waveform of the mark is extracted from the enlarged mark image, which is collected by CCD. The position of the mark center on the CCD can be calculated based on the extracted waveform. By applying PCA to the mark image, the first principal component containing position information of the mark can be obtained. Therefore PCA can be used to extract the waveform from the mark image. Compared with the typical waveform extraction method (the summed projection (SP) method), the proposed PCA method can use the position information contained in the mark image more effectively. Through simulation and experiment, it is proved that the proposed PCA method can improve the contrast of the normalized waveform, and then improve the alignment accuracy.

An integrated framework for evaluation on typical ECG-derived respiration waveform extraction and respiration

ObjectiveECG-derived respiration (EDR) methods have been developed during the past decades to obtain respiration-relevant information. However, it is still necessary to compare the performance of these methods under uniform conditions for reasonable application. Approach: In this paper, the performance of 10 feature-based EDR methods was evaluated comprehensively on three aspects: sampling rate, noise, and window length. The Fantasia database was used in this study, as it contained ECG signals and simultaneously measured respiration signals. The performance was quantified by two parameters: waveform correlation and breathing rate (BR) errors. Main results: The BR errors of AMarea, AMQR, AMR were all below 2 beats per minute (bpm) when the sampling rate was above 150 Hz, while they decreased sharply by about 60% when the sampling rate was below 150 Hz. FMRR presented stable performance with an error below 2 bpm at different sampling rates. The effect of noise was obviously found in amplitude-based EDR methods, with the maximum decreased by about 40% in waveform correlation. For all EDR methods, significant increase of BR errors occurred with the window shorting from 32 s to 16 s in the frequency-based technique. In addition, about 30%–40% of the window cannot obtain the BR error, calculated based on the time-based technique, within an 8 s window. Significance: We proposed a comprehensive and integrated evaluation on typical ECG-derived respiration waveform extraction and respiration rate calculation, providing references for algorithm selection based on different requirements.