A stable data adaptive method for matched‐field array processing in acoustic waveguides

1989 ◽  
Vol 85 (S1) ◽  
pp. S17-S17
Author(s):  
C. L. Byrne ◽  
R. I. Brent ◽  
C. Feuillade ◽  
D. R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

A stable data‐adaptive method for matched‐field array processing in acoustic waveguides

1990 ◽  
Vol 87 (6) ◽  
pp. 2493-2502 ◽  
Author(s):  
Charles L. Byrne ◽  
Ronald T. Brent ◽  
Christopher Feuillade ◽  
Donald R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

scholarly journals In situ measurements of directional wave spectra from an unmanned aerial system

2021 ◽  
Author(s):  
Clarence Collins ◽  
Katherine Brodie
Keyword(s):  
Single Point ◽  
Field Research ◽  
Ocean Waves ◽  
Adaptive Method ◽  
Technical Note ◽  
Sea Surface ◽  
Unmanned Aerial System ◽  
Frequency Spectra ◽  
Directional Spectrum ◽  
Data Adaptive

This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the ability to measure the directional-frequency spectrum of sea surface waves based on the motion of a floating unmanned aerial system (UAS). The UAS used in this effort was custom built and designed to land on and take off from the sea surface. It was deployed in the vicinity of an operational wave sensor, the 8 m* array, at the US Army Engineer Research and Development Center (ERDC), Field Research Facility (FRF) in Duck, NC. While on the sea surface, an inertial navigation system (INS) recorded the response of the UAS to the incoming ocean waves. Two different INS signals were used to calculate one-dimensional (1D) frequency spectra and compared against the 8 m array. Two-dimensional (2D) directional-frequency spectra were calculated from INS data using traditional single-point-triplet analysis and a data adaptive method. The directional spectrum compared favorably against the 8 m array.

Detecting local and regional seismic events using the data-adaptive method at the VAF seismograph station in Finland

1991 ◽  
Vol 81 (4) ◽  
pp. 1373-1379
Author(s):  
Matti Tarvainen
Keyword(s):  
Detection Threshold ◽  
Seismic Signal ◽  
Adaptive Method ◽  
Digital Data ◽  
Seismic Events ◽  
Visual Methods ◽  
Long Period ◽  
Detection Window ◽  
Seismograph Station ◽  
Data Adaptive

Abstract The data-adaptive autoregressive (hereafter DA) method was used to detect local and regional seismic events using digital data from the Vaasa (VAF) station with co-ordinates (62.3°N, 22.2°E) in western Finland. The seismic signal and the noise were assumed to have been normally distributed stochastic processes with a zero mean. The parameters of these processes were then adapted on the change of the registered signal as a function of time within a predefined detection window. The accuracy of the method presented is compared with the STA/LTA and visual methods. When the same detection threshold was used with the DA detector and the STA/LTA detector, it was found that the DA detector was more precise in detecting the onsets of seismic events. Bandpass (1.5 to 20 Hz) filtering was used in all the events discussed. This was done to reject the long-period microseismic noise. In one case, the detector was used on nonfiltered as well as filtered data, in order to show coinciding results.

scholarly journals Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

2014 ◽  
Vol 5 ◽  
pp. 2016-2025 ◽  
Author(s):  
Torsten Bölke ◽  
Lisa Krapf ◽  
Regina Orzekowsky-Schroeder ◽  
Tobias Vossmeyer ◽  
Jelena Dimitrijevic ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Ground Truth ◽  
Adaptive Method ◽  
Fluorescent Nanoparticles ◽  
Noise Characteristics ◽  
Mucosal Tissues ◽  
Noise Structure ◽  
Non Local ◽  
Fine Structures ◽  
Data Adaptive

Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.

Automatic seismogram analysis: Statistical phase picking and locating methods using one-station three-component data

1992 ◽  
Vol 82 (2) ◽  
pp. 860-869 ◽  
Author(s):  
Matti Tarvainen
Keyword(s):  
Maximum Likelihood Method ◽  
Statistical Data ◽  
Polarization State ◽  
Adaptive Method ◽  
Likelihood Method ◽  
Arrival Times ◽  
On Line ◽  
The Difference ◽  
Data Adaptive ◽  
Using Data

Abstract Seismic events at local and regional distances are located using data from the Vaasa (VAF 63.0 °N, 22.7 °E) three-component station in western Finland. The analysis is performed off-line after pseudo on-line detections. The continuous data are first recorded on long disk loops. These loops can cover 10 days of data. The events are picked using Murdock-Hutt detector and after that they are recorded as separate files for further analysis. The direction of approach, which estimates the polarization state of the signal, is determined by the well-established maximum likelihood method. The difference between the arrival times of P and S phases is used to estimate the distance. Phases observed up to 185 km distance from the source are assumed to be Pg and Sg and beyond that Pn and Sn. The onset times of the phases are estimated by a statistical data-adaptive method. The onsets calculated in this way are very accurate when compared with earlier methods like STA/LTA. Epicenter locations are similar to those of FINSA network reported in Helsinki bulletins. The median of the location differences is about 50 km.

scholarly journals Adaptive curvelet-domain primary-multiple separation

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. A17-A21 ◽  
Author(s):  
Felix J. Herrmann ◽  
Deli Wang ◽  
Dirk J. (Eric) Verschuur
Keyword(s):  
Least Squares ◽  
High Frequency ◽  
Real Data ◽  
Adaptive Method ◽  
Phase Errors ◽  
Successful Separation ◽  
Data Adaptive ◽  
Offset Time ◽  
Multiple Elimination

In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function of offset, time, and dip), they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band), and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

scholarly journals Empirical Mode Decomposition on the sphere: application to the spatial scales of surface temperature variations

2008 ◽  
Vol 12 (3) ◽  
pp. 933-941 ◽  
Author(s):  
N. Fauchereau ◽  
G. G. S. Pegram ◽  
S. Sinclair
Keyword(s):  
Surface Temperature ◽  
Empirical Mode Decomposition ◽  
Large Scale ◽  
Spatial Scales ◽  
Adaptive Method ◽  
Two Dimensions ◽  
Temperature Variations ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
Global Data

Abstract. Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-adaptive method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. EMD is first applied to an artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related, for example, to topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global data-adaptive filter, which will be useful in analysing geophysical phenomena that arise as the result of forcings at multiple spatial scales.

Estimation of data adaptive minimal clinically important difference with a nonconvex optimization procedure

2019 ◽  
Vol 29 (3) ◽  
pp. 879-893 ◽  
Author(s):  
Zehua Zhou ◽  
Jiwei Zhao ◽  
Leslie J. Bisson
Keyword(s):  
Clinical Significance ◽  
Statistical Significance ◽  
A Priori ◽  
Optimization Procedure ◽  
Adaptive Method ◽  
Individual Level ◽  
Gradient Based ◽  
Data Adaptive ◽  
The Individual ◽  
Important Significance

Understanding the limitation of solely relying on statistical significance, researchers have proposed methods to draw biomedical conclusions based on clinical significance. The minimal clinically important significance is one of the most fundamental concepts to study clinical significance. Based on an anchor question usually available in the patients' reported outcome, Hedayat et al. presented a method to estimate minimal clinically important significance using the classification technique. However, their method implicitly requires that the binary outcome of the anchor question is equally likely, i.e. the balanced outcome assumption. This assumption cannot be guaranteed a priori when one designs the study; hence, it cannot be satisfied in general. In this paper, we propose a data adaptive method, which can overcome this limitation. Compared to Hedayat et al., our method uses a faster gradient based algorithm and adopts a more flexible structure of the minimal clinically important significance at the individual level. We conduct comprehensive simulation studies and apply our method to the chondral lesions and meniscus procedure study to demonstrate its usefulness and also its outperformance.

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