A linear frequency modulated continuous wave (LFMCW) radar is introduced to localize the impedance discontinuities on the instrument cable used in nuclear plants. The LFMCW reflectometry uses a phenomenon that electromagnetic pulses are reflected at the impedance discontinuities to localize impedance discontinuity points on the cable. For localizing impedance discontinuities, time delays betwee...

We develop a discrete-time stochastic volatility option pricing model, which exploits the information contained in high-frequency data. The Realized Volatility (RV) is used as a proxy of the unobservable log-returns volatility. We model its dynamics by a simple but effective (pseudo) long-memory process, the Leverage Heterogeneous Auto-Regressive Gamma (HARGL) process. Both the discrete-time sp...

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering app...

The purpose of the present study is to look into the characteristics of the mean monthly total ozone time series over Arosa (46.8N/ 9.68E), Switzerland using statistical methodologies. In this paper, the time series pertains to the data between 1932 and 1971. The intrinsic deterministic patterns of the time series have been investigated through autocorrelation analysis. A second order Auto Regr...

We develop a new class of Continuous-time Auto-Regressive Fractionally Integrated Moving-Average (CARFIMA) models which are useful for modelling regularly-spaced and irregularly-spaced discrete-time long-memory data. We derive the autocovariance function of a stationary CARFIMA model, and study maximum likelihood estimation of a regression model with CARFIMA errors, based on discrete-time data ...

In this paper the author proposes to use the Least Squares Lattice filter with forgetting factor to estimate time-varying parameters of the model for noise processes. We simulated an Auto-Regressive (AR) noise process in which we let the parameters of the AR vary in time. We investigate a new way of implementation of Least Squares Lattice filter in following the non stationary time series for s...

The pandemic caused by the SARS-CoV-2 virus (COVID-19) has significantly affected tourism industry. Tourist destinations have adopted emergency measures and restrictions that mobility of individuals around world. This study aims to analyze effects COVID-19 on industry in Malaysia its overall economic performance. research used an extensive set statistical tests, including a newly constructed Au...

In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change...

Wireless health monitoring schemes are innovative techniques, which effectively remove the disadvantages associated with current wire-based sensing systems, i.e., high installation and upkeep costs. However, recorded data sets may have relative time-delays due to the blockage of sensors or inherent internal clock errors. In this paper, two algorithms are proposed for the synchronization of the ...