Time Window Selection Algorithm for ISAR Ship Imaging Based on Instantaneous Doppler Frequency Estimations of Multiple Scatterers

This paper considers vital signs (VS) such as respiration movement detection of human subjects using an impulse ultra-wideband (UWB) through-wall radar with an improved sensing algorithm for random-noise de-noising and clutter elimination. One filter is used to improve the signal-to-noise ratio (SNR) of these VS signals. Using the wavelet packet decomposition, the standard deviation based spectral kurtosis is employed to analyze the signal characteristics to provide the distance estimate between the radar and human subject. The data size is reduced based on a defined region of interest (ROI), and this improves the system efficiency. The respiration frequency is estimated using a multiple time window selection algorithm. Experimental results are presented which illustrate the efficacy and reliability of this method. The proposed method is shown to provide better VS estimation than existing techniques in the literature.

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Cross-borehole tomography with correlation delay times

Geophysics ◽

10.1190/geo2013-0059.1 ◽

2014 ◽

Vol 79 (1) ◽

pp. R1-R12 ◽

Cited By ~ 7

Author(s):

E. Diego Mercerat ◽

Guust Nolet ◽

Christophe Zaroli

Keyword(s):

Time Window ◽

Body Wave ◽

Wave Dispersion ◽

Spectral Element ◽

Frequency Theory ◽

P Wave ◽

Checkerboard Pattern ◽

Finite Frequency ◽

Delay Times ◽

Window Selection

We evaluated a comprehensive numerical experiment of finite-frequency tomography with ray-based (“banana-doughnut”) kernels that tested all aspects of this method, starting from the generation of seismograms in a 3D model, the window selection, and the crosscorrelation with seismograms predicted for a background model, to the final regularized inversion. In particular, we tested if the quasilinearity of crosscorrelation delays allowed us to forego multiple (linearized) iterations in the case of strong reverberations characterizing multiple scattering and the gain in resolution that can be obtained by observing body-wave dispersion. Contrary to onset times, traveltimes observed by crosscorrelation allowed us to exploit energy arriving later in the time window centered in the P-wave or any other indentifiable ray arrival, either scattered from, or diffracted around, lateral heterogeneities. We tested using seismograms calculated by the spectral element method in a cross-borehole experiment conducted in a 3D checkerboard cube. The use of multiple frequency bands allowed us to estimate body-wave dispersion caused by diffraction effects. The large velocity contrast (10%) and the regularity of the checkerboard pattern caused severe reverberations that arrived late in the crosscorrelation windows. Nevertheless, the model resulting from the inversion with a data fit with reduced [Formula: see text] resulted in an excellent correspondence with the input model and allowed for a complete validation of the linearizations that lay at the basis of the theory. The use of multiple frequencies led to a significant increase in resolution. Moreover, we evaluated a case in which the sign of the anomalies in the checkerboard was systematically reversed in the ray-theoretical solution, a clear demonstration of the reality of the “doughnut-hole” effect. The experiment validated finite-frequency theory and disqualified ray-theoretical inversions of crosscorrelation delay times.

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Human Being Detection Utilizing Wavelet Transform: an Advanced Algorithm of Time Window Selection

2019 International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM) ◽

10.1109/iwem.2019.8887869 ◽

2019 ◽

Author(s):

Farnaz Mahmoudi Shikhsarmast ◽

Tingting Lyu ◽

Hao Zhang

Keyword(s):

Wavelet Transform ◽

Time Window ◽

Human Being ◽

Advanced Algorithm ◽

Window Selection

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Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search

Computers & Operations Research ◽

10.1016/j.cor.2013.09.018 ◽

2014 ◽

Vol 52 ◽

pp. 241-250 ◽

Cited By ~ 5

Author(s):

Yiyong Xiao ◽

Yun Tian ◽

Qiuhong Zhao

Keyword(s):

Association Rules ◽

Time Window ◽

Variable Neighbourhood Search ◽

Temporal Database ◽

Association Rules Mining ◽

Neighbourhood Search ◽

Selection For ◽

Window Selection

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Fish swarm window selection algorithm based on cell microscopic automatic focus

Cluster Computing ◽

10.1007/s10586-017-0752-4 ◽

2017 ◽

Vol 20 (1) ◽

pp. 485-495 ◽

Cited By ~ 5

Author(s):

Feng-shou Zhang ◽

Si-wen Li ◽

Zhi-gang Hu ◽

Zhe Du

Keyword(s):

Selection Algorithm ◽

Window Selection

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Principal component transforms of triaxial recordings by singular value decomposition

Geophysics ◽

10.1190/1.1443068 ◽

1991 ◽

Vol 56 (4) ◽

pp. 528-533 ◽

Cited By ~ 43

Author(s):

G. M. Jackson ◽

I. M. Mason ◽

S. A. Greenhalgh

Keyword(s):

Singular Value Decomposition ◽

Time Window ◽

Signal To Noise Ratio ◽

Random Noise ◽

Principal Component ◽

Singular Value ◽

Data Matrix ◽

Single Station ◽

Window Selection ◽

Value Decomposition

Polarization analysis can be achieved efficiently by treating a time window of a single‐station triaxial recording as a matrix and doing a singular value decomposition (SVD) of this seismic data matrix. SVD of the triaxial data matrix produces an eigenanalysis of the data covariance (cross‐energy) matrix and a rotation of the data onto the directions given by the eigenanalysis (Karhunen‐Loève transform), all in one step. SVD provides a complete principal components analysis of the data in the analysis time window. Selection of this time window is crucial to the success of the analysis and is governed by three considerations: the window should contain only one arrival; the window should be such that the signal‐to‐noise ratio is maximized; and the window should be long enough to be able to discriminate random noise from signal. The SVD analysis provides estimates of signal, signal polarization directions, and noise. An F‐test is proposed which gives the confidence level for the hypothesis of rectilinear polarization. This paper illustrates the analysis and interpretation of synthetic rectilinearly and elliptically polarized arrivals at a single triaxial station by SVD.

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Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

Collabra Psychology ◽

10.1525/collabra.25961 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Jonathan E. Peelle ◽

Kristin J. Van Engen

Keyword(s):

Eye Tracking ◽

Degrees Of Freedom ◽

Time Series Data ◽

Time Window ◽

Time Windows ◽

Basis Set ◽

Series Data ◽

Parameter Estimates ◽

Start Time ◽

Window Selection

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

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