Bibliographies: 'Raw waveform'

1

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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2

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. &lt;br&gt;&lt;br&gt; 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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3

Xu, D. C., B. D. Xu, E. J. Bao, et al. "Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments." Journal of Instrumentation 17, no. 06 (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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4

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 (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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5

Younis, Raneen, and Andreas Reinhardt. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures." Energies 13, no. 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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6

Li, Zheming, and Wei He. "A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model." Sensors 21, no. 21 (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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7

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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8

Moriya, Hirokazu. "Phase-only correlation of time-varying spectral representations of microseismic data for identification of similar seismic events." GEOPHYSICS 76, no. 6 (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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9

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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10

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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Academic literature on the topic 'Raw waveform'

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Journal articles on the topic "Raw waveform"