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Automated diagnosis of bipolar depression through Welch periodogram and machine learning techniques
Bipolar depression is a chronic mood disorder that causes severe shifts in mood, behaviors and energy levels. Very few studies to date have utilized...
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Hybrid Method of Speech Signals Spectral Analysis Based on the Autoregressive Model and Schuster Periodogram
The task of measuring the spectral density of power of a speech signal in sliding observation window mode is examined. A parametric approach to...
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Method for Autoregression Modeling of a Speech Signal Using the Envelope of the Schuster Periodogram as a Reference Spectral Sample
AbstractThe problem of autoregressive modeling of a speech signal based on the data of the discrete Fourier transform in the mode of a sliding window...
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Determining chromosomal arms 1p/19q co-deletion status in low graded glioma by cross correlation-periodogram pattern analysis
Prediction of mutational status of different graded glioma is extremely crucial for its diagnosis and treatment planning. Currently FISH and the...
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Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology
In this study, monthly particulate matter (PM 2.5 ) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a...
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On a Chirp-Like Model and Its Parameter Estimation Using Periodogram-Type Estimators
Parametric modelling of physical phenomena has received a great deal of attention in the signal processing literature. Different models like ARMA...
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Filling the gap of seismic ambient noise taken from the earth by modification of the frequency content of the existing time series
Seismic ambient noise is increasingly important in estimating velocity and attenuation structures, as well as understanding parameter variations....
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Mathematical Modelling of the Lomb–Scargle Method in Astrophysics
In astrophysics, the Lomb–Scargle method is widely used to analyse time series observations of stellar objects. The method allows us to detect...
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Log-periodogram regression of two-dimensional intrinsically stationary random fields
We propose a new semiparametric model for two-dimensional intrinsically stationary random fields and an estimator for the long memory parameter of...
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Fundamental Frequency and its Harmonics Model: A Robust Method of Estimation
In this paper we have proposed a novel robust method of estimation of the unknown parameters of a fundamental frequency and its harmonics model....
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Asymptotic Properties of One Class of Periodic Estimates
The authors consider a class of periodogram estimates of unknown parameters of the nonlinear regression model “signal plus noise” and prove...
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Robust local bootstrap for weakly stationary time series in the presence of additive outliers
This paper proposes a generalization of the local bootstrap for periodogram statistics when weakly stationary time series are contaminated by...
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Determination of Some Elastic Constants of Materials Using Impact Analysis
Materials with increased stiffness and lesser weight are widely used in aerospace, automotive and other manufacturing industries. Nondestructive...
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LRD spectral analysis of multifractional functional time series on manifolds
This paper addresses the estimation of the second-order structure of a manifold cross-time random field (RF) displaying spatially varying Long Range...
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Bayesian Estimation of Laser Linewidth From Delayed Self-Heterodyne Measurements
We present a statistical inference approach to estimate the frequency noise characteristics of ultra-narrow linewidth lasers from delayed...
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Nonparametric spectral density estimation under local differential privacy
We consider nonparametric estimation of the spectral density of a stationary time series under local differential privacy. In this framework only an...
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A New Blockage Detection Approach for 6G THz Systems
Blockage detection is a critical functionality for the air interface in modern 5G and future 6G systems operating in millimeter wave (mmWave, 30–300...
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A fragmented-periodogram approach for clustering big data time series
We propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the...
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Time Series Analysis
Analysis of epidemic time series is a large endeavor because of the richness of dynamical patterns and a plentitude of historical data (Rohani &...