scholarly journals Optimized SNR-based ECAP threshold determination is comparable to the judgement of human evaluators

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259347
Author(s):  
Lutz Gärtner ◽  
Philipp Spitzer ◽  
Kathrin Lauss ◽  
Marko Takanen ◽  
Thomas Lenarz ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Time Windows ◽  
Subjective Assessment ◽  
Threshold Estimate ◽  
Clinical Specialist ◽  
Threshold Determination ◽  
Enhanced Resolution ◽  
Compound Action Potentials ◽  
Frequency Components ◽  
Stimulation Paradigm

In cochlear implant (CI) users, measurements of electrically evoked compound action potentials (ECAPs) prove the functionality of the neuron-electrode interface. Objective measures, e.g., the ECAP threshold, may serve as a basis for the clinical adjustment of the device for the optimal benefit of the CI user. As for many neural responses, the threshold determination often is based on the subjective assessment of the clinical specialist, whose decision-making process could be aided by autonomous computational algorithms. To that end, we extended the signal-to-noise ratio (SNR) approach for ECAP threshold determination to be applicable for FineGrain (FG) ECAP responses. The new approach takes advantage of two features: the FG stimulation paradigm with its enhanced resolution of recordings, and SNR-based ECAP threshold determination, which allows defining thresholds independently of morphology and with comparably low computational power. Pearson’s correlation coefficient r between the ECAP threshold determined by five experienced evaluators and the threshold determined with the FG-SNR algorithm was in the range of r = 0.78–0.93. Between evaluators, r was in a comparable range of 0.84–0.93. A subset of the parameters of the algorithm was varied to identify the parameters with the highest potential to improve the FG-SNR formalism in the future. The two steps with the strongest influence on the agreement between the threshold estimate of the evaluators and the algorithm were the removal of undesired frequency components (denoising of the response traces) and the exact determination of the two time windows (signal and noise and noise only).”The parameters were linked to the properties of an ECAP response, indicating how to adjust the algorithm for the automatic detection of other neurophysiological responses.

scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

scholarly journals Inverse- Q filtered migration

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. S1-S6 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Frequency Component ◽  
Attenuation Effect ◽  
The Earth ◽  
Enhanced Resolution ◽  
Seismic Image ◽  
Text Filtering ◽  
Frequency Components ◽  
Extrapolation Scheme ◽  
Phase Distortions ◽  
Wavefield Extrapolation

An inverse-[Formula: see text] filtered migration algorithm performs seismic migration and inverse-[Formula: see text] filtering simultaneously, in which the latter compensates for the amplitudes and corrects the phase distortions resulting from the earth attenuation effect. However, the amplitudes of high-frequency components grow rapidly in the extrapolation procedure, so numerical instability is a concern when including the inverse-[Formula: see text] filter in the migration. The instability for each frequency component is independent of data and is affected only by migration models. The stabilization problem may be treated separately from the wavefield-extrapolation scheme. The proposed strategy is to construct supersedent of attenuation coefficients, based on given velocity and [Formula: see text] models, before performing wavefield extrapolation in the space-frequency domain. This stabilized algorithm for inverse-[Formula: see text] filtered migration is applicable to subsurface media with vertical and lateral variations in velocity and [Formula: see text] functions. It produces a seismic image with enhanced resolution and corrected timing, comparable to an ideal image without the earth attenuation effect.

On the Feasibility of Long-term Seismic Monitoring Using Freight Train Signals

2021 ◽  
Author(s):  
Yixiao Sheng ◽  
Florent Brenguier ◽  
Pierre Boué ◽  
Aurélien Mordret ◽  
Yehuda Ben-Zion ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Body Wave ◽  
Time Windows ◽  
Seismic Monitoring ◽  
Body Waves ◽  
Time Shift ◽  
Seismic Signals ◽  
Noise Sources ◽  
Seismic Arrays

<div>Recent studies (Brenguier et al., 2019; Pinzon-Rincon et al., 2020) have successfully retrieved body waves between seismic arrays through the correlations of train-generated seismic signals. It remains uncertain whether these train-derived body waves are suitable for long-term seismic monitoring, which requires repeatable measurements over the years. This study tests the feasibility of obtaining stable body waves between individual broadband stations, using freight trains as noise sources. We use stations close to the railroad as markers to identify trains and pinpoint their potential locations. We select proper station pairs and perform seismic interferometry, focusing on the time windows when trains are detected. We test our workflow in southern California, with the freight trains running through the Coachella Valley. We successfully retrieve stable body-wave signals over ten years. We perform a weekly stacking to improve the signal-to-noise ratio and estimate the relative time shift. Our preliminary time-shift measurements reveal a systematic long-term increasing trend for station pairs locating on two sides of the San Jacinto fault. The next step is to examine the results statistically to reduce the bias introduced by moving sources. Despite that the long-term trend still needs further study, our experiment demonstrates that it is possible to perform long-term seismic monitoring using train generated seismic signals.</div>

Analyzing Steady-State Visual Evoked Potentials for Effective User Response Detection for Brain-Computer Interfaces

2013 ◽  
Author(s):  
Pouria Riyahi ◽  
Azim Eskandarian
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Time Windows ◽  
Time Step ◽  
Response Detection ◽  
Source Signals ◽  
Visual Evoked

This article evaluates an M-order Adaptive Kalman filter analysis on Steady-State Visual Evoked Potentials (SSVEPs). This model is based on finding the original brain source signals from their combined observed EEG signals. At each time step, observed brain signals are filtered according to their ideal reference signals measured from 10, 11, 12 and 13 Hz LED stimuli. SSVEP response detection is based on maximum Signal to Noise Ratio (SNR) of the brain source signals. In each test, the average system accuracy is calculated with and without overlapped time-windows along with system Information Transfer Rate (ITR). The overall system accuracy and ITR are showing promising level of SSVEP detection for future online BCI systems.

scholarly journals Learning-Assisted Rain Attenuation Prediction Models

2020 ◽  
Vol 10 (17) ◽  
pp. 6017
Author(s):  
Md Abdus Samad ◽  
Dong-You Choi
Keyword(s):  
Prediction Models ◽  
Signal To Noise Ratio ◽  
Rain Attenuation ◽  
Rain Event ◽  
Signal To Noise ◽  
Research Issues ◽  
The Earth ◽  
Open Research ◽  
Frequency Components ◽  
Mitigation Techniques

Rain attenuation becomes significant to degrade the earth-space or terrestrial radio link’s signal-to-noise ratio (SNR). So, to maintain the desired SNR level, with the help of fade mitigation techniques (FMTs), it needs to control transmitted signals power considering the expected rainfall. However, since the rain event is a random phenomenon, the rain attenuation that may be experienced by a specific link is difficult to estimate. Many empirical, physical, and compound nature-based models exist in the literature to predict the expected rain attenuation. Furthermore, many optimizations and decision-making functions have become simpler since the development of the learning-assisted (LA) technique. In this work, the LA rain attenuation (LARA) model was classified based on input parameters. Besides, for comparative analysis, each of the supported frequency components of LARA models were tabulated, and an accurate contribution of each model was identified. In contrast to all the currently available LARA models, the accuracy and correlation of input-output parameters are presented. Additionally, it summarizes and discusses open research issues and challenges.

Weak impulsive signals detection based on step-varying asymmetric stochastic resonance

2016 ◽  
Vol 231 (2) ◽  
pp. 242-262 ◽  
Author(s):  
Haibin Zhang ◽  
Yuan Zheng ◽  
Fanrang Kong
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Resonance System ◽  
Rotating Machine ◽  
Frequency Components ◽  
Optimal Dynamic ◽  
The Asymmetry ◽  
Fault Information

Rotating machinery response is often characterized by the presence of periodic impulses modulated by high-frequency components. The fault information is often hidden in its envelope signal which is unilateral when demodulated. Conventional stochastic resonance with a symmetric potential cannot always contain the signal’s original features especially the asymmetry. In this article, a step-varying asymmetric stochastic resonance system for impulsive signal denoising and recovery as well as the rotating machine fault diagnosis is proposed to further improve the impulsive signal-to-noise ratio. In the method, the asymmetry of step-varying asymmetric stochastic resonance can match the unilateral impulsive signal well to generate an optimal dynamic system by selecting proper system parameters and degree of asymmetry. Systems with different simulated or experimental signals are also studied to verify its effectiveness and availability. Results indicate that the step-varying asymmetric stochastic resonance performs much better in detection of impulsive signal than the conventional stochastic resonance with merits of good frequency response, anti-noise capability, adaptability to asymmetric signal and original waveform preserving.

scholarly journals A Versatile Analog Electronic Interface for Piezoelectric Sensors Used for Impacts Detection and Positioning in Structural Health Monitoring (SHM) Systems

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1047
Author(s):  
Lorenzo Capineri ◽  
Andrea Bulletti
Keyword(s):  
Lamb Waves ◽  
Signal To Noise Ratio ◽  
Piezoelectric Sensors ◽  
Signal Spectrum ◽  
Signal To Noise ◽  
Front End ◽  
Analog Front End ◽  
Frequency Components ◽  
Noise Ratio ◽  
The Impact

Continuous monitoring of mechanical impacts is one of the goals of modern SHM systems using a sensor network installed on a structure. For the evaluation of the impact position, there are generally applied triangulation techniques based on the estimation of the differential time of arrival (DToA). The signals generated by impacts are multimodal, dispersive Lamb waves propagating in the plate-like structure. Symmetrical S0 and antisymmetrical A0 Lamb waves are both generated by impact events with different velocities and energies. The discrimination of these two modes is an advantage for impact positioning and characterization. The faster S0 is less influenced by multiple path signal overlapping and is also less dispersive, but its amplitude is generally 40–80 dB lower than the amplitude of the A0 mode. The latter has an amplitude related to the impact energy, while S0 amplitude is related to the impact velocity and has higher frequency spectral content. For these reasons, the analog front-end (AFE) design is crucial to preserve the information of the impact event, and at the same time, the overall signal chain must be optimized. Large dynamic range ADCs with high resolution (at least 12-bit) are generally required for processing these signals to retrieve the DToA information found in the full signal spectrum, typically from 20 kHz to 500 kHz. A solution explored in this work is the design of a versatile analog front-end capable of matching the different types of piezoelectric sensors used for impact monitoring (piezoceramic, piezocomposite or piezopolymer) in a sensor node. The analog front-end interface has a programmable attenuator and three selectable configurations with different gain and bandwidth to optimize the signal-to-noise ratio and distortion of the selected Lamb wave mode. This interface is realized as a module compatible with the I/O of a 16 channels real-time electronic system for SHM previously developed by the authors. High-frequency components up to 270 kHz and lower-frequency components of the received signals are separated by different channels and generate high signal-to-noise ratio signals that can be easily treated by digital signal processing using a single central unit board with ADC and FPGA.

scholarly journals Crust and upper mantle structure of the Ligurian Sea revealed by ambient noise tomography using ocean bottom seismometer data

2020 ◽  
Author(s):  
Felix Noah Wolf ◽  
Dietrich Lange ◽  
Heidrun Kopp ◽  
Anke Dannowski ◽  
Ingo Grevemeyer ◽  
...  
Keyword(s):  
Phase Velocity ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Velocity Structure ◽  
Signal To Noise Ratio ◽  
Time Windows ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Ligurian Sea ◽  
Crust And Upper Mantle

<p>The Liguro-Provencal-basin was formed as a back-arc basin of the retreating Calabrian-Apennines subduction zone during the Oligocene and Miocene. The resulting rotation of the Corsica-Sardinia block at roughly 32–24 Ma is associated with rifting, shaping the Ligurian Sea. It is highly debated though, whether oceanic or atypical oceanic crust was formed or if the crust is continental and experienced extreme thinning during the opening of the basin.</p><p>To investigate the velocity structure of the Ligurian Sea a network (LOBSTER) of 29 broadband Ocean Bottom Seismometer (OBS) was installed jointly by GEOMAR (Germany) and ISTerre (France). The LOBSTER array measured continuously for eight months between June 2017 and February 2018 and is part of the AlpArray seismic network. AlpArray is a European initiative to further reveal the geophysical and geological properties of the greater Alpine area.</p><p>We contribute to the debate by surveying the type of crust and lithosphere flooring the Ligurian Sea.<br>Because of additional noise sources in the ocean, causing instrument tilt or seafloor compliance, OBS data are rarely used for ambient noise studies. However, we extensively pre-process the data to improve the signal-to-noise ratio. Then, we calculate daily cross-correlation functions for the LOBSTER array and surrounding land stations. Additionally, we correlate short time windows that include strong events. The cross-correlations of these are dominated by earthquake signals and allow us to derive surface wave group velocities for longer periods than using ambient noise only. Finally, phase velocity maps are obtained by inverting Green’s functions derived from cross-correlation of ambient noise and teleseismic events, respectively. The phase velocity maps show strong heterogeneities for short periods (5-15 s, corresponding to shallow depths). Causes for these include varying sediment thickness, fault zones and magmatism. For longer periods (20-80 s) the velocity structure smoothens and reveals mantle velocities north-northwest of the basin centre. This might hint on an asymmetric opening of the basin. We do not see strong indications for an oceanic spreading centre in the Ligurian basin.</p>

scholarly journals Methodological Advances for Detecting Physiological Synchrony During Dyadic Interactions

Methodology ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 41-53 ◽  
Author(s):  
Michael P. McAssey ◽  
Jonathan Helm ◽  
Fushing Hsieh ◽  
David A. Sbarra ◽  
Emilio Ferrer
Keyword(s):  
Cross Correlation ◽  
Time Windows ◽  
Low Frequency ◽  
Physiological Measures ◽  
Dyadic Interactions ◽  
Reciprocal Influence ◽  
Mode Decomposition ◽  
Physiological Synchrony ◽  
Important Challenge ◽  
Frequency Components

A defining feature of many physiological systems is their synchrony and reciprocal influence. An important challenge, however, is how to measure such features. This paper presents two new approaches for identifying synchrony between the physiological signals of individuals in dyads. The approaches are adaptations of two recently-developed techniques, depending on the nature of the physiological time series. For respiration and thoracic impedance, signals that are measured continuously, we use Empirical Mode Decomposition to extract the low-frequency components of a nonstationary signal, which carry the signal’s trend. We then compute the maximum cross-correlation between the trends of two signals within consecutive overlapping time windows of fixed width throughout each of a number of experimental tasks, and identify the proportion of large values of this measure occurring during each task. For heart rate, which is output discretely, we use a structural linear model that takes into account heteroscedastic measurement error on both series. The results of this study indicate that these methods are effective in detecting synchrony between physiological measures and can be used to examine emotional coherence in dyadic interactions.

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