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Stelling, N., and K. Richter. "VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 755–62. http://dx.doi.org/10.5194/isprs-archives-xli-b8-755-2016.
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The advantages of using airborne full-waveform laser scanner data in forest applications, e.g. for the description of the vertical vegetation structure or accurate biomass estimation, have been emphasized in many publications. To exploit the full potential offered by airborne full-waveform laser scanning data, the development of voxel based methods for data analysis is essential. In contrast to existing approaches based on the extraction of discrete 3D points by a Gaussian decomposition, it is very promising to derive the voxel attributes from the digitised waveform directly. For this purpose, the waveform data have to be transferred into a 3D voxel representation. This requires a series of radiometric and geometric transformations of the raw full-waveform laser scanner data. Thus, the paper deals with the geometric aspects and describes a processing chain from the raw waveform data to an attenuationcorrected volumetric forest stand reconstruction. <br><br> The integration of attenuation-corrected waveform data into the voxel space is realised with an efficient parametric voxel traversal method operating on an octree data structure. The voxel attributes are derived from the amplitudes of the attenuation-corrected waveforms. Additionally, a new 3D filtering approach is presented to eliminate non-object voxel. Applying these methods to real full-waveform laser scanning data, a voxel based representation of a spruce was generated combining three flight strips from different viewing directions.
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Stelling, N., and K. Richter. "VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 755–62. http://dx.doi.org/10.5194/isprsarchives-xli-b8-755-2016.
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The advantages of using airborne full-waveform laser scanner data in forest applications, e.g. for the description of the vertical vegetation structure or accurate biomass estimation, have been emphasized in many publications. To exploit the full potential offered by airborne full-waveform laser scanning data, the development of voxel based methods for data analysis is essential. In contrast to existing approaches based on the extraction of discrete 3D points by a Gaussian decomposition, it is very promising to derive the voxel attributes from the digitised waveform directly. For this purpose, the waveform data have to be transferred into a 3D voxel representation. This requires a series of radiometric and geometric transformations of the raw full-waveform laser scanner data. Thus, the paper deals with the geometric aspects and describes a processing chain from the raw waveform data to an attenuationcorrected volumetric forest stand reconstruction. <br><br> The integration of attenuation-corrected waveform data into the voxel space is realised with an efficient parametric voxel traversal method operating on an octree data structure. The voxel attributes are derived from the amplitudes of the attenuation-corrected waveforms. Additionally, a new 3D filtering approach is presented to eliminate non-object voxel. Applying these methods to real full-waveform laser scanning data, a voxel based representation of a spruce was generated combining three flight strips from different viewing directions.
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Xu, D. C., B. D. Xu, E. J. Bao, Y. Y. Wu, A. Q. Zhang, Y. Y. Wang, G. L. Zhang, et al. "Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments." Journal of Instrumentation 17, no. 06 (June 1, 2022): P06040. http://dx.doi.org/10.1088/1748-0221/17/06/p06040.
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Abstract Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to × 2) and energy (up to × 1.07) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.
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Li, Shaobo, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, and Jianjun Hu. "An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition." Applied Sciences 8, no. 7 (July 15, 2018): 1152. http://dx.doi.org/10.3390/app8071152.
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Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.
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Younis, Raneen, and Andreas Reinhardt. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures." Energies 13, no. 12 (June 12, 2020): 3039. http://dx.doi.org/10.3390/en13123039.
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The number of globally deployed smart meters is rising, and so are the sampling rates at which they can meter electrical consumption data. As a consequence thereof, the technological foundation is established to track the power intake of buildings at sampling rates up to several k Hz . Processing raw signal waveforms at such rates, however, imposes a high resource demand on the metering devices and data processing algorithms alike. In fact, the ensuing resource demand often exceeds the capabilities of the embedded systems present in current-generation smart meters. Consequently, the majority of today’s energy data processing algorithms are confined to the use of RMS values of the data instead, reported once per second or even less frequently. This entirely eliminates the spectral characteristics of the signal waveform (i.e., waveform trajectories of electrical voltage, current, or power) from the data, despite the wealth of information they have been shown to contain about the operational states of the operative appliances. In order to overcome this limitation, we pursue a novel approach to handle the ensuing volume of load signature data and simultaneously facilitate their analysis. Our proposed method is based on approximating the current intake of electrical appliances by means of parametric models, the determination of whose parameters only requires little computational power. Through the identification of model parameters from raw measurements, smart meters not only need to transmit less data, but the identification of individual loads in aggregate load signature data is facilitated at the same time. We conduct an analysis of the fundamental waveform shapes prevalent in the electrical power consumption data of more than 50 electrical appliances, and assess the induced approximation errors when replacing raw current consumption data by parametric models. Our results show that the current consumption of many household appliances can be accurately modeled by a small number of parameterizable waveforms.
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Li, Zheming, and Wei He. "A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model." Sensors 21, no. 21 (October 29, 2021): 7207. http://dx.doi.org/10.3390/s21217207.
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Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
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Pashaei, Mohammad, Michael J. Starek, Craig L. Glennie, and Jacob Berryhill. "Terrestrial Lidar Data Classification Based on Raw Waveform Samples Versus Online Waveform Attributes." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–19. http://dx.doi.org/10.1109/tgrs.2021.3132356.
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Moriya, Hirokazu. "Phase-only correlation of time-varying spectral representations of microseismic data for identification of similar seismic events." GEOPHYSICS 76, no. 6 (November 2011): WC37—WC45. http://dx.doi.org/10.1190/geo2011-0021.1.
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Identification of similar seismic events is important for precise estimation of source locations and for evaluation of subsurface structure. Phase-only correlation is well known as a real-time image-matching method for fingerprint identification. I applied the phase-only correlation in a geophysical context to identify similar waveforms among microseismic events. The waveforms were first transformed into time-varying spectral representations to express frequency content in the time-frequency domain. The phase-only correlation function is calculated between two time-varying spectral representations and similarity is evaluated using the peak value of the phase-only correlation function. This method was applied to arbitrarily selected waveforms from aftershocks of an earthquake in Japan to assess its ability to identify similar waveforms perturbed by white noise. The detection of similarity of the proposed algorithm was compared to the similarity as detected by a 2D crosscorrelation function of the time-varying spectral representation and a 1D crosscorrelation of the raw waveform. This showed that the phase-only correlation function exhibits a sharp peak that quantifies similarity and dissimilarity over a wide range of signal-to-noise ratio (S/N) and remained unaffected by the length of the time window used to estimate time-varying spectral representations. Phase-only correlation may also have applications in other geophysical analyses and interpretations that are based on waveform and seismic image data.
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Cole, P. T., and M. Carlos. "Use of Advanced A.E. Analysis for Source Discrimination Using Captured Waveforms." Advanced Materials Research 13-14 (February 2006): 401–6. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.401.
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Conventional methods of acquiring and using acoustic emission (AE) discard the raw signal waveform after extracting signal features from it. The main reason for this is the number of bytes required to save hundreds of thousands of AE waveforms, using a modern high speed multichannel system the hard-drive may be quickly filled. One side effect of this “feature extraction” approach is that information is thrown away with the wave-form. The advent of systems capable of acquiring AE waveforms on all channels has opened up the opportunity to use this extra data to get more information about the source and the transmission path. This paper describes the use of acoustic emission waveforms to aid source discrimination, and presents data acquired during pressure testing of a slug-catcher.
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Zhang, Yu, Bei Wang, Jin Jing, Jian Zhang, Junzhong Zou, and Masatoshi Nakamura. "A Comparison Study on Multidomain EEG Features for Sleep Stage Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4574079.
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Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.
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Warner, Mike, Tenice Nangoo, Adrian Umpleby, Nikhil Shah, Chris Manuel, Dimitri Bevc, and Miguel Merino. "Automated salt model building: From compaction trend to final velocity model using waveform inversion." Leading Edge 42, no. 3 (March 2023): 196–206. http://dx.doi.org/10.1190/tle42030196.1.
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Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.
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Song, Sixuan, Tianxin Zhang, Zhongxing Wang, Renzhong Pei, Shichu Yan, and Kai Chen. "Full waveform vibration and shock measurement tool for measurement-while-drilling." AIP Advances 12, no. 8 (August 1, 2022): 085114. http://dx.doi.org/10.1063/5.0090505.
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The bottom hole assembly during the drilling process is prone to fatigue and damage under the influence of alternating stress, particularly the drill bit and bottom part of the bottom hole assembly. The vibration and shock data are normally used to estimate the working status of the drill collars and for data post-processing of a particular logging method. The recent developments in drilling technology have increased investigations into continuous vibration and shock information measurement. However, existing tools store only the results of signal processing and cannot determine the raw full waveform; thus, they cannot be used to extract comprehensive information. Therefore, we proposed a novel tool for measurement-while-drilling, equipped with triaxial vibration and shock sensors. The tool can record the full waveform of six channels and use a large-capacity NAND flash to store the recorded raw full waveform. We performed laboratory and field tests to verify the stability and reliability of the tool at temperatures up to 150 °C to support operations in deep downhole environments. Furthermore, the tool can aid in effectively analyzing actual vibration and shock data to simulate a downhole test environment.
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Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (February 17, 2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.
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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.
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Li, Weiqiang, Estel Cardellach, Serni Ribó, Santi Oliveras, and Antonio Rius. "Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications." Remote Sensing 14, no. 6 (March 10, 2022): 1344. http://dx.doi.org/10.3390/rs14061344.
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Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.
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Pirotti, F., A. Guarnieri, A. Masiero, A. Vettore, and E. Lingua. "Processing lidar waveform data for 3D visual assessment of forest environments." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 493–99. http://dx.doi.org/10.5194/isprsarchives-xl-5-493-2014.
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The objective of this report is to present and discuss a work-flow for extracting, from full-waveform (FW) lidar data, formats which are compatible with common information systems (GIS) and statistical software packages. Full-waveform, specifically for forestry, got attention from the scientific community because a more in-depth analysis can add valuable information for classification and modelling of related variables (e.g. biomass). In order to assess if this is feasible and if the results are useful, the end-user has to deal with raw datasets from lidar sensors. In this study case we propose and test a work-flow which is implemented through a selfdeveloped software integrating ad-hoc C++ libraries and a graphical user interface for an easier approach by end-users. This software allows the user to add raw FW data and produce several products which can successively be easily imported in GIS or statistical software. To achieve this we used some state-of-the-art methods which have been extensively reported in literature and we discuss results and future developments. Results show that this software package can effectively work as a tool for linking raw FW data with forest-related spatial processing by providing punctual information directly derived from the FW data or area-based aggregated information for a more generalized description of the earth surface.
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&NA;. "Practical Use of the Raw Electroencephalogram Waveform During General Anesthesia." Survey of Anesthesiology 54, no. 2 (April 2010): 91–92. http://dx.doi.org/10.1097/01.sa.0000367805.02007.f2.
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Tang, Junlei, Junyang Li, Hu Wang, Yingying Wang, and Geng Chen. "In-Situ Monitoring and Analysis of the Pitting Corrosion of Carbon Steel by Acoustic Emission." Applied Sciences 9, no. 4 (February 18, 2019): 706. http://dx.doi.org/10.3390/app9040706.
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The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO3 + NaCl solutions. The open circuit potential (OCP) measurement and corrosion morphology in-situ capturing using an optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with a scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After completion of the signal processing, including pre-treatment, shape preserving interpolation, and denoising, for raw AE waveforms, three types of AE signals were classified in the correlation diagrams of the new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.
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Zhang, Lilun, Dezhi Wang, Changchun Bao, Yongxian Wang, and Kele Xu. "Large-Scale Whale-Call Classification by Transfer Learning on Multi-Scale Waveforms and Time-Frequency Features." Applied Sciences 9, no. 5 (March 12, 2019): 1020. http://dx.doi.org/10.3390/app9051020.
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Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations.
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Ito, Satoru, Kenneth R. Lutchen, and Béla Suki. "Effects of heterogeneities on the partitioning of airway and tissue properties in normal mice." Journal of Applied Physiology 102, no. 3 (March 2007): 859–69. http://dx.doi.org/10.1152/japplphysiol.00884.2006.
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We measured the mechanical properties of the respiratory system of C57BL/6 mice using the optimal ventilation waveform method in closed- and open-chest conditions at different positive end-expiratory pressures. The tissue damping (G), tissue elastance (H), airway resistance (Raw), and hysteresivity were obtained by fitting the impedance data to three different models: a constant-phase model by Hantos et al. (Hantos Z, Daroczy B, Suki B, Nagy S, Fredberg JJ. J Appl Physiol 72: 168–178, 1992), a heterogeneous Raw model by Suki et al. (Suki B, Yuan H, Zhang Q, Lutchen KR. J Appl Physiol 82: 1349–1359, 1997), and a heterogeneous H model by Ito et al. (Ito S, Ingenito EP, Arold SP, Parameswaran H, Tgavalekos NT, Lutchen KR, Suki B. J Appl Physiol 97: 204–212, 2004). Both in the closed- and open-chest conditions, G and hysteresivity were the lowest and Raw the highest in the heterogeneous Raw model, and G and H were the largest in the heterogeneous H model. Values of G, Raw, and hysteresivity were significantly higher in the closed-chest than in the open-chest condition. However, H was not affected by the conditions. When the tidal volume of the optimal ventilation waveform was decreased from 8 to 4 ml/kg in the closed-chest condition, G and hysteresivity significantly increased, but there were smaller changes in H or Raw. In summary, values of the obtained mechanical properties varied among these models, primarily due to heterogeneity. Moreover, the mechanical parameters were significantly affected by the chest wall and tidal volume in mice. Contribution of the chest wall and heterogeneity to the mechanical properties should be carefully considered in physiological studies in which partitioning of airway and tissue properties are attempted.
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McMillan, Justin R., Jonathan Botts, and Jason E. Summers. "Deep reinforcement learning for cognitive active-sonar employment." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A101. http://dx.doi.org/10.1121/10.0010785.
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We introduce a framework to leverage deep reinforcement learning (RL) for active sonar employment, wherein we train an RL agent to select waveform parameters, which maximize the probability of single-target detection. We first simulate raw sonar returns of targets and clutter in reverberation and noise using a physics-based sonar-simulation model, the Sonar Simulation Toolkit (SST), then process the resulting signatures into network inputs via an in-house signal and information processing model of an archetypal antisubmarine warfare (ASW) processing chain. We demonstrate that the trained RL agent is able to appropriately select between continuous wave (CW) and hyperbolic frequency modulated (HFM) waveforms depending on target trajectory, as well as select an optimal bandwidth and pulse length trade-off (when constrained by a constant time-bandwidth product), when presented with sonar returns from a reverb-limited or noise-limited environment.
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Miryala, S., S. Mittal, Y. Ren, G. Carini, G. Deptuch, J. Fried, S. Yoo, and S. Zohar. "Waveform processing using neural network algorithms on the front-end electronics." Journal of Instrumentation 17, no. 01 (January 1, 2022): C01039. http://dx.doi.org/10.1088/1748-0221/17/01/c01039.
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Abstract In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. To better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.
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Jóśko, Adam, Bogdan Dziadak, Jacek Starzyński, and Jan Sroka. "Derivative Probes Signal Integration Techniques for High Energy Pulses Measurements." Energies 15, no. 6 (March 18, 2022): 2244. http://dx.doi.org/10.3390/en15062244.
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The paper presents problems related to the processing of signals recorded with differential field probes E and H. The fundamental problem to which special attention has been paid is the result of the integration operation. Due to the presence of constant/slowly-varying components in the raw signal, there is a drift present in the outcome of integration. This line wander can be enormous. This is particularly evident if the integration is performed in a standard manner, uniformly over the entire recorded waveform. The paper contains the Authors’ proposition to segment the signal and perform the integration independently in each of the sub-regions. This approach is based on the assumption of a local mean value instead of its global character for the recorded waveform. Although this leads to more complex signal processing, it gives significantly better results as it is suppressing the deterioration drift in the integrated signal more than 400 times. The results are presented on laboratory recordings and outdoor tests. In the first case, voltage pulses with durations of about 50 ns and rise times in the range of single ns were recorded. In the second case, high-energy electromagnetic pulse signals were used. It was formed by sinusoidal waveforms packets of 3 GHz frequency with a single packet duration of 5 μs and packet repetition frequency f ≤ 300 Hz.
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Liu, Chang-Le, Sze-Wei Fu, You-Jin Li, Jen-Wei Huang, Hsin-Min Wang, and Yu Tsao. "Multichannel Speech Enhancement by Raw Waveform-Mapping Using Fully Convolutional Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020): 1888–900. http://dx.doi.org/10.1109/taslp.2020.2976193.
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Sharan, Roneel V. "Cough sound detection from raw waveform using SincNet and bidirectional GRU." Biomedical Signal Processing and Control 82 (April 2023): 104580. http://dx.doi.org/10.1016/j.bspc.2023.104580.
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Sinclair, Jonathan, Paul John Taylor, and Sarah Jane Hobbs. "Digital Filtering of Three-Dimensional Lower Extremity Kinematics: an Assessment." Journal of Human Kinetics 39, no. 1 (December 1, 2013): 25–36. http://dx.doi.org/10.2478/hukin-2013-0065.
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Abstract Errors in kinematic data are referred to as noise and are an undesirable portion of any waveform. Noise is typically removed using a low-pass filter which removes the high frequency components of the signal. The selection of an optimal frequency cut-off is very important when processing kinematic information and a number of techniques exists for the determination of an optimal frequency cut-off. Despite the importance of cut-off frequency to the efficacy of kinematic analyses there is currently a paucity of research examining the influence of different cut-off frequencies on the resultant 3-D kinematic waveforms and discrete parameters. Twenty participants ran at 4.0 m•s-1 as lower extremity kinematics in the sagittal, coronal and transverse planes were measured using an eight camera motion analysis system. The data were filtered at a range of cut-off frequencies and the discrete kinematic parameters were examined using repeated measures ANOVA’s. The similarity between the raw and filtered waveforms were examined using intra-class correlations. The results show that the cut-off frequency has a significant influence on the discrete kinematic measure across displacement and derivative information in all three planes of rotation. Furthermore, it was also revealed that as the cut-off frequency decreased the attenuation of the kinematic waveforms became more pronounced, particularly in the coronal and transverse planes at the second derivative. In conclusion, this investigation provides new information regarding the influence of digital filtering on lower extremity kinematics and re-emphasizes the importance of selecting the correct cut-off frequency.
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Richter, K., N. Stelling, and H. G. Maas. "Correcting attenuation effects caused by interactions in the forest canopy in full-waveform airborne laser scanner data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 273–80. http://dx.doi.org/10.5194/isprsarchives-xl-3-273-2014.
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Full-waveform airborne laser scanning offers a great potential for various forestry applications. Especially applications requiring information on the vertical structure of the lower canopy parts benefit from the great amount of information contained in waveform data. To enable the derivation of vertical forest canopy structure, the development of suitable voxel based data analysis methods is straightforward. Beyond extracting additional 3D points, it is very promising to derive the voxel attributes from the digitized waveform directly. For this purpose, the differential backscatter cross sections have to be projected into a Cartesian voxel structure. Thereby the voxel entries represent amplitudes of the cross section and can be interpreted as a local measure for the amount of pulse reflecting matter. However, the "history" of each laser echo pulse is characterized by attenuation effects caused by reflections in higher regions of the crown. As a result, the received waveform signals within the canopy have a lower amplitude than it would be observed for an identical structure without the previous canopy structure interactions (Romanczyk et al., 2012). If the biophysical structure is determined from the raw waveform data, material in the lower parts of the canopy is thus under-represented. <br><br> To achieve a radiometrically correct voxel space representation the loss of signal strength caused by partial reflections on the path of a laser pulse through the canopy has to be compensated. In this paper, we present an integral approach correcting the waveform at each recorded sample. The basic idea of the procedure is to enhance the waveform intensity values in lower parts of the canopy for portions of the pulse intensity, which have been reflected (and thus blocked) in higher parts of the canopy. The paper will discuss the developed correction method and show results from a validation both with synthetic and real world data.
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Wei, Hui, Weiwei Shu, Longjun Dong, Zhongying Huang, and Daoyuan Sun. "A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines." Sensors 20, no. 15 (August 3, 2020): 4322. http://dx.doi.org/10.3390/s20154322.
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The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis.
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Zheng, Chunjun, Chunli Wang, and Ning Jia. "A two-channel speech emotion recognition model based on raw stacked waveform." Multimedia Tools and Applications 81, no. 8 (February 18, 2022): 11537–62. http://dx.doi.org/10.1007/s11042-022-12378-1.
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Shdefat, Ahmed Younes, Moon-Il Joo, Sung-Hoon Choi, and Hee-Cheol Kim. "Utilizing ECG Waveform Features as New Biometric Authentication Method." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (April 1, 2018): 658. http://dx.doi.org/10.11591/ijece.v8i2.pp658-665.
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<p>In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.</p>
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Dasios, Aristotelis, Clive McCann, and Timothy Astin. "Least‐squares inversion of in‐situ sonic Q measurements: Stability and resolution." GEOPHYSICS 69, no. 2 (March 2004): 378–85. http://dx.doi.org/10.1190/1.1707056.
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We minimize the effect of noise and increase both the reliability and the resolution of attenuation estimates obtained from multireceiver full‐waveform sonics. Multiple measurements of effective attenuation were generated from full‐waveform sonic data recorded by an eight‐receiver sonic tool in a gas‐bearing sandstone reservoir using two independent techniques: the logarithmic spectral ratio (LSR) and the instantaneous frequency (IF) method. After rejecting unstable estimates [receiver separation <2 ft (0.61 m)], least‐squares inversion was used to combine the multiple estimates into high‐resolution attenuation logs. The procedure was applied to raw attenuation data obtained with both the LSR and IF methods, and the resulting logs showed that the attenuation estimates obtained for the maximum receiver separation of 3.5 ft (1.07 m) provide a smoothed approximation of the high‐resolution measurements. The approximation is better for the IF method, with the normalized crosscorrelation factor between the low‐ and high‐resolution logs being 0.90 for the IF method and 0.88 for the LSR method.
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Sakai, Hiroaki, Edward P. Ingenito, Rene Mora, Senay Abbay, Francisco S. A. Cavalcante, Kenneth R. Lutchen, and Béla Suki. "Hysteresivity of the lung and tissue strip in the normal rat: effects of heterogeneities." Journal of Applied Physiology 91, no. 2 (August 1, 2001): 737–47. http://dx.doi.org/10.1152/jappl.2001.91.2.737.
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We measured lung impedance in rats in closed chest (CC), open chest (OC), and isolated lungs (IL) at four transpulmonary pressures with a optimal ventilator waveform. Data were analyzed with an homogeneous linear or an inhomogeneous linear model. Both models include tissue damping and elastance and airway inertance. The homogeneous linear model includes airway resistance (Raw), whereas the inhomogeneous linear model has a continuous distribution of Raw characterized by the mean Raw and the standard deviation of Raw (SDR). Lung mechanics were compared with tissue strip mechanics at frequencies and operating stresses comparable to those during lung impedance measurements. The hysteresivity (η) was calculated as tissue damping/elastance. We found that 1) airway and tissue parameters were different in the IL than in the CC and OC conditions; 2) SDR was lowest in the IL; and 3) η in IL at low transpulmonary pressure was similar to η in the tissue strip. We conclude that η is primarily determined by lung connective tissue, and its elevated estimates from impedance data in the CC and OC conditions are a consequence of compartment-like heterogeneity being greater in CC and OC conditions than in the IL.
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Kim, Gwantae, Bonhwa Ku, Jae-Kwang Ahn, and Hanseok Ko. "Graph Convolution Networks for Seismic Events Classification Using Raw Waveform Data From Multiple Stations." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2021.3127874.
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Juvela, Lauri, Bajibabu Bollepalli, Vassilis Tsiaras, and Paavo Alku. "GlotNet—A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 6 (June 2019): 1019–30. http://dx.doi.org/10.1109/taslp.2019.2906484.
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Bennett, Cambell, Logan J. Voss, John P. M. Barnard, and James W. Sleigh. "Practical Use of the Raw Electroencephalogram Waveform During General Anesthesia: The Art and Science." Anesthesia & Analgesia 109, no. 2 (August 2009): 539–50. http://dx.doi.org/10.1213/ane.0b013e3181a9fc38.
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Lanzano, Giovanni, Lucia Luzi, Carlo Cauzzi, Jarek Bienkowski, Dino Bindi, John Clinton, Massimo Cocco, et al. "Accessing European Strong-Motion Data: An Update on ORFEUS Coordinated Services." Seismological Research Letters 92, no. 3 (February 17, 2021): 1642–58. http://dx.doi.org/10.1785/0220200398.
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Abstract Strong ground motion records and free open access to strong-motion data repositories are fundamental inputs to seismology, engineering seismology, soil dynamics, and earthquake engineering science and practice. This article presents the current status and outlook of the Observatories and Research Facilities for European Seismology (ORFEUS) coordinated strong-motion seismology services, namely the rapid raw strong-motion (RRSM) and the engineering strong-motion (ESM) databases and associated web interfaces and webservices. We compare and discuss the role and use of these two systems using the Mw 6.5 Norcia (Central Italy) earthquake that occurred on 30 October 2016 as an example of a well-recorded earthquake that triggered major interest in the seismological and earthquake engineering communities. The RRSM is a fully automated system for rapid dissemination of earthquake shaking information, whereas the ESM provides quality-checked, manually processed waveforms and reviewed earthquake information. The RRSM uses only data from the European Integrated Waveform Data Archive, whereas the ESM also includes offline data from other sources, such as the ITalian ACcelerometric Archive (ITACA). Advanced software tools are also included in the ESM to allow users to process strong-motion data and to select ground-motion waveform sets for seismic structural analyses. The RRSM and ESM are complementary services designed for a variety of possible stakeholders, ranging from scientists to the educated general public. The RRSM and ESM are developed, organized, and reviewed by selected members of the seismological community in Europe, including strong-motion data providers and expert users. Global access and usage of the data is encouraged. The ESM is presently the reference database for harmonized seismic hazard and risk studies in Europe. ORFEUS strong-motion data are open, “Findable, Accessible, Interoperable, and Reusable,” and accompanied by licensing information. The users are encouraged to properly cite the data providers, using the digital object identifiers of the seismic networks.
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Vossen, Robbert van, Andrew Curtis, Andreas Laake, and Jeannot Trampert. "Surface-consistent deconvolution using reciprocity and waveform inversion." GEOPHYSICS 71, no. 2 (March 2006): V19—V30. http://dx.doi.org/10.1190/1.2187799.
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Source and receiver responses must be equalized when their behavior or coupling changes with location within a given survey. Existing surface-consistent deconvolution techniques that account for these effects assume that common-midpoint (CMP) gathering is valid — the seismic trace is decomposed into a source function, a receiver response, a normal-incidence reflectivity term, and an offset-related component that is laterally shift invariant. As a result, the performance of existing surface-consistent deconvolution techniques is best when applied to primary reflection data only, since the offset dependency of ground roll and multiples varies laterally in media with lateral variations. We have developed an alternative method for surface-consistent deconvolution that is applicable to the entire seismic trace and is therefore essentially a raw-data preprocessing step. The method is based on reciprocity of the medium response. Assuming that conditions for applicability of reciprocity are met, we can attribute differences between normal and reciprocal recordings to the source and receiver perturbations. Contrary to existing surface-consistent deconvolution methods, this approach uses the full description of the wavefield and is therefore ideally suited for prestack processing. We have applied this technique to single-sensor data acquired in Manistee County, Michigan. At this site, near-surface conditions vary, and this significantly affects data quality. The application of the new deconvolution procedure substantially improves S/N ratio on both prestack and poststack data, and these results compare favorably to those obtained using existing surface-consistent deconvolution techniques, since they require subjective data scaling to obtain acceptable results. The obtained source corrections are correlated to changes in near-surface conditions — in this case, to changes in water-saturation levels. We do not observe such a correlation for the receiver corrections, which vary rapidly along the spread. Finally, the receiver response does not agree with the generally accepted damped harmonic oscillator model. For frequencies below 100 Hz, the retrieved receiver variations are larger than predicted by this model, and we cannot explain the receiver response using a single resonant frequency for the geophone-ground coupling.
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Kaczka, David W., Edward P. Ingenito, Bela Suki, and Kenneth R. Lutchen. "Partitioning airway and lung tissue resistances in humans: effects of bronchoconstriction." Journal of Applied Physiology 82, no. 5 (May 1, 1997): 1531–41. http://dx.doi.org/10.1152/jappl.1997.82.5.1531.
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Kaczka, David W., Edward P. Ingenito, Bela Suki, and Kenneth R. Lutchen. Partitioning airway and lung tissue resistances in humans: effects of bronchoconstriction. J. Appl. Physiol. 82(5): 1531–1541, 1997.—The contribution of airway resistance (Raw) and tissue resistance (Rti) to total lung resistance (R l ) during breathing in humans is poorly understood. We have recently developed a method for separating Raw and Rti from measurements of Rland lung elastance (El) alone. In nine healthy, awake subjects, we applied a broad-band optimal ventilator waveform (OVW) with energy between 0.156 and 8.1 Hz that simultaneously provides tidal ventilation. In four of the subjects, data were acquired before and during a methacholine (MCh)-bronchoconstricted challenge. The Rland Eldata were first analyzed by using a model with a homogeneous airway compartment leading to a viscoelastic tissue compartment consisting of tissue damping and elastance parameters. Our OVW-based estimates of Raw correlated well with estimates obtained by using standard plethysmography and were responsive to MCh-induced bronchoconstriction. Our data suggest that Rti comprises ∼40% of total Rlat typical breathing frequencies, which corresponds to ∼60% of intrathoracic Rl. During mild MCh-induced bronchoconstriction, Raw accounts for most of the increase in Rl. At high doses of MCh, there was a substantial increase in Rlat all frequencies and in El at higher frequencies. Our analysis showed that both Raw and Rti increase, but most of the increase is due to Raw. The data also suggest that widespread peripheral constriction causes airway wall shunting to produce additional frequency dependence in El.
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Wang, Bote. "Deep Chnet 1-D CNN using raw audio waveform for recognizing traditional Chinese musical instrument." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 331–38. http://dx.doi.org/10.54254/2755-2721/6/20220802.
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The classification of music information using various deep learning models is increasingly popular in the field of Music Information Retrieval research. However, as most proposed works focus on western music and musical instruments, little attention is given to traditional Chinese music. This paper proposes a 1-D Convolutional Neural Network (1-D CNN) using only raw audio waveform as input, to undertake the task of traditional Chinese musical instruments classification. This paper starts with a review of the current state of research on the related field, then discuss the proposed model and its data in detail, followed by its performance metrics and then a conclusion on the experiment. The result shows that 1-D CNN provides competitive and even superior results when compared to its 2-D versions as well as when compared to traditional models.
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Manu, Daniel, Petro Mushidi Tshakwanda, Youzuo Lin, Weiwen Jiang, and Lei Yang. "Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices." Journal of Imaging 8, no. 12 (November 22, 2022): 312. http://dx.doi.org/10.3390/jimaging8120312.
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Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.
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Njirjak, Marko, Erik Otović, Dario Jozinović, Jonatan Lerga, Goran Mauša, Alberto Michelini, and Ivan Štajduhar. "The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data." Mathematics 10, no. 6 (March 17, 2022): 965. http://dx.doi.org/10.3390/math10060965.
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Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned hypothesis was evaluated by training three well-established convolutional neural networks using nine time–frequency representations. The results were compared to the base model, which was trained on the raw waveform data. The signals that were used in the experiment are three-component seismogram instances from the Local Earthquakes and Noise DataBase (LEN-DB). The results demonstrate that Pseudo Wigner–Ville and Wigner–Ville time–frequency representations yield significantly better results than the base model, while spectrogram and Margenau–Hill perform significantly worse (p < 0.01). Interestingly, the spectrogram, which is often used in signal analysis, had inferior performance when compared to the base model. The findings presented in this research could have notable impacts in the fields of geophysics and seismology as the phenomena that were previously hidden in the seismic noise are now more easily identified. Furthermore, the results indicate that applying Pseudo Wigner–Ville or Wigner–Ville time–frequency representations could result in a large increase in earthquakes in the catalogs and lessen the need to add new stations with an overall reduction in the costs. Finally, the proposed approach of extracting valuable information through time–frequency representations could be applied in other domains as well, such as electroencephalogram and electrocardiogram signal analysis, speech recognition, gravitational waves investigation, and so on.
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Yan, Zhengxiang, Guangmin Sun, Xiucheng Liu, Yu Li, Cunfu He, Zhixiang Xing, Xianxian Wang, Yangyang Zhang, and Mengshuai Ning. "FilterNet: A deep convolutional neural network for measuring plastic deformation from raw Barkhausen noise waveform." Journal of Magnetism and Magnetic Materials 555 (August 2022): 169330. http://dx.doi.org/10.1016/j.jmmm.2022.169330.
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Cătălin, Dumitrescu, Copaci Carmen, Iliescu Dan, Hangan Tony, Ionescu Ana-Maria, and Bobe Alexandru. "K - Complex Detection Using the Continuous Wavelet Transform." ARS Medica Tomitana 24, no. 4 (November 1, 2018): 144–52. http://dx.doi.org/10.2478/arsm-2018-0031.
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Abstract The wide variety of waveform in EEG signals and the high non-stationary nature of many of them is one of the main difficulties to develop automatic detection system for them. In sleep stage classification a relevant transient wave is the K-complex. This paper comprehend the developing of two algorithms in order to achieve an automatic K-complex detection from EEG raw data. These algorithms are based on a time-frequency analysis and two time-frequency techniques, the Short Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT), are tested in order to find out which one is the best for our purpose, being of two wavelet functions to measure the capability of them to detect K-complex and to choose one to be employed in the algorithms. The first algorithm is based on the energy distribution of the CWT detecting the spectral component of the K-complex. The second algorithm is focused on the morphology of the K-complex / sleep spindle waveform after the CWT. Evaluating the algorithms results reveals that a false K-complex detection is as important as real K-complex detection.
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Acciarri, R., B. Baller, V. Basque, C. Bromberg, F. Cavanna, D. Edmunds, R. S. Fitzpatrick, et al. "A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber." Journal of Instrumentation 17, no. 01 (January 1, 2022): P01018. http://dx.doi.org/10.1088/1748-0221/17/01/p01018.
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Abstract The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of ∼0.03–0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.
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Khalil, Ashraf A., Robert R. Stewart, and David C. Henley. "Full‐waveform processing and interpretation of kilohertz cross‐well seismic data." GEOPHYSICS 58, no. 9 (September 1993): 1248–56. http://dx.doi.org/10.1190/1.1443508.
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High‐frequency, cross‐well seismic data, from the Midale oil field of southeastern Saskatchewan, are analyzed for direct and reflected energy. The goal of the analysis is to produce interpretable sections to assist in enhanced oil recovery activities ([Formula: see text] injection) in this field. Direct arrivals are used for velocity information while reflected arrivals are processed into a reflection image. Raw field data show a complex assortment of wave types that includes direct compressional and shear waves and reflected shear waves. A traveltime inversion technique (layer stripping via ray tracing) is used to obtain P‐ and S‐wave interval velocities from the respective direct arrivals. The velocities from the cross‐well inversion and the sonic log are in reasonable agreement. The subsurface coverage of the cross‐well geometry is investigated; it covers zones extending from the source well to the receiver well and includes regions above and below the source/receiver depths. Upgoing and downgoing primary reflections are processed, in a manner similar to the vertical seismic profiling/common‐depth‐point (VSP/CDP) map, to construct the cross‐well images. A final section is produced by summing the individual reflection images from each receiver‐gather map. This section provides an image with evidence of strata thicknesses down to about 1 m. Synthetic seismograms are used to interpret the final sections. Correlations can be drawn between some of the events on the synthetic seismograms and the cross‐well image.
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Maklakov, Alexander S., Tao Jing, Andrey A. Radionov, Vadim R. Gasiyarov, and Tatyana A. Lisovskaya. "Finding the Best Programmable PWM Pattern for Three-Level Active Front-Ends at 18-Pulse Connection." Machines 9, no. 7 (June 23, 2021): 127. http://dx.doi.org/10.3390/machines9070127.
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The existing publications on the analysis of power quality indicators in modern electric power supply systems are void of a comprehensive approach to improving these indicators in power systems by implementing multipulse connections. To the authors’ knowledge, this paper is the first to analyze current harmonic distortions in an 18-pulse connection of three-level active front-ends (AFE) featuring a programmed PWM. Raw data were obtained from, and current quality was analyzed for the power circuit of the main electric drive actuating the rolls in the rolling stand of a plate mill. The key feature of such circuitry is that the synchronous motor of each work roll is connected to the grid with an 18-pulse connection that uses three phase-shift transformers, where the phase shifts are 0° (delta/delta), 20° (delta/polygon) and −20° (delta/polygon). The circuitry connects three frequency converters (FC) with the AFEs in parallel. Phase-shift transformers were found to periodically overheat in the process. When overheating occurred, a programmed PWM voltage waveform was applied where harmonics 17 and 19 were eliminated. The goal and objectives were to analyze why the transformer would overheat and to find out how the issue could be addressed. The authors developed a simulation model of the research object in order to assess power quality parameters. Simulation results obtained in Matlab/Simulink were used to estimate the total harmonic distortions (THD) and individual harmonic factors for up to the 50th secondary transformer winding and grid harmonic with four different programmed AFE PWM voltage waveforms. The results helped find the best such waveform to prevent phase-shift transformers from overheating; one with harmonics 5, 7, 17 and 19 eliminated. The experimental and mathematical modeling results in the paper were confirmed by positive effects after industrial implementation of the system. Research performed directly on the operating equipment has been classified by the company and is not publicly available. These results are highly versatile and could be used in similar research on other circuitries to ensure the electromagnetic compatibility of nonlinear power-consuming devices.
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Sujarittam, Krit, and James Choi. "Calibration of a focused passive cavitation detector using bubble shock waves." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A31. http://dx.doi.org/10.1121/10.0010558.
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In microbubble-mediated therapeutic ultrasound, a focused passive cavitation detector (PCD) is often used to measure the bubbles’ acoustic emissions, providing useful signals for treatment monitoring. However, calibrating a spherically focused PCD is challenging, due to the difficulty of generating a spherical wave that matches the PCD’s surface curvature. Here, a PCD was calibrated using broadband shock waves generated by inertial collapses of single microbubbles. Microbubbles were diluted to a very low concentration, flowed through a wall-less gel channel, and sonicated using single-cycle, 0.5-MHz-centre-frequency, 1-MPa acoustic pulses. The focused PCD to be calibrated and a reference needle hydrophone captured their emissions. The sensitivity and phase response of the PCD relative to the reference hydrophone was calculated from the single bubble signals. For comparison, the PCD was also calibrated using a focused emitter as a sound source (Rich and Mast, JASA , 2015). The nominal PCD sensitivities obtained using the two methods agreed within 1% ± 14% within the PCD’s bandwidth (2–10 MHz). The calibration data from the bubble method was then used to correct the PCD’s signal distortions. Our method recovered the impulse waveform of the bubble-generated shock wave from the raw PCD signal, where such a waveform was not previously observed.
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Arditty, P. C., F. Mathieu, and P. Staron. "EVALUATION OF FORMATION PROPERTIES FROM PROCESSING AND INTERPRETATION OF THE EVA TOOL LOGS." APPEA Journal 26, no. 1 (1986): 187. http://dx.doi.org/10.1071/aj85018.
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With the development of full waveform acoustic tools, geophysicists have tried to extract more and more information from acoustic parameters and to relate to the data to formation propertiesThe new tool, EVA (Evaluation of Velocity and Attenuation), is a 4-transmitter/12-receiver long spacing tool permitting recording of the complete waveform and processing of all information contained in the acoustic signal.The key point for optimum results is a robust and automatic processing which allows quantitative estimation of different parameters such as velocity, amplitude, and period of all the three main types of waves, i.e. compressional (P), shear (S), and Stoneley (ST) waves.In parallel with the processing phase, the analysis of the raw data leads to a qualitative and very rapid interpretation of the recordings.Once the processing is completed and all parameters obtained, these may be applied to a quantitative interpretation of the EVA data. Such interpretation can then be utilised for automatic lithology estimation, estimation of porosity and shale content, and to the detection of fractures and eventually the estimation of permeability.Further applications such as the detection of invaded zones, estimation behind casing, estimation of elastic moduli, and the reconstitution of a density log can provide significant data for exploration and production engineers.
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Wu, Yi-Chiao, Tomoki Hayashi, Patrick Lumban Tobing, Kazuhiro Kobayashi, and Tomoki Toda. "Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model With Pitch-Dependent Dilated Convolution Neural Network." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 1134–48. http://dx.doi.org/10.1109/taslp.2021.3061245.
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Rehm, Gregory B., Irene Cortés-Puch, Brooks T. Kuhn, Jimmy Nguyen, Sarina A. Fazio, Michael A. Johnson, Nicholas R. Anderson, Chen-Nee Chuah, and Jason Y. Adams. "Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data." Critical Care Explorations 3, no. 1 (January 2021): e0313. http://dx.doi.org/10.1097/cce.0000000000000313.
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Hono, Yukiya, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, and Keiichi Tokuda. "PeriodNet: A Non-Autoregressive Raw Waveform Generative Model With a Structure Separating Periodic and Aperiodic Components." IEEE Access 9 (2021): 137599–612. http://dx.doi.org/10.1109/access.2021.3118033.
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