• Communications for Statistical Applications and Methods
• /
• v.19 no.2
• /
• pp.287-292
• /
• 2012

Sample periodogram is widely known as an inconsistent estimator for true spectral density. We show that it becomes consistent when the true spectrum at the zero frequency (often known as long-run variance) equals zero. Asymptotic results for consistency of the periodogram as well as the rate of convergence are formally derived.

• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.377-390
• /
• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• The Korean Journal of Applied Statistics
• /
• v.21 no.3
• /
• pp.547-560
• /
• 2008

It is well known that spectral density function determines auto-covariance function of stationary time-series data and smoothed periodogram is a consistent estimator of spectral density function. Recently, Kim and Park (2007) showed that smoothed- periodogram based distances performs very well for the classification. In this paper, we introduce classification methods with smoothed periodogram and apply the approaches to the monthly precipitation measurements obtained from January, 1987 through December, 2007 at 22 locations in South Korea.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.1
• /
• pp.25-32
• /
• 2022

IR-UWB radar has been regarded as the most promising technology for non-contact respiration and heartbeat monitoring because of its ability of detecting slight motion even in submillimeter range. Measuring heart rate is most challenging since the chest movement by heartbeat is quite subtle and easily interfered with by a random body motion or background noise. Additionally, periodic sampling can be limited by the performance of computer that handles the radar signals. In this paper, we deploy Lomb-Scargle periodogram method that estimates heart rate even with irregularly sampled data and uneven signal amplitude. Lomb-Scargle periodogram is known as a method for finding periodicity in irregularly-sampled and noisy data set. We also implement a motion detection scheme in order to make the heart rate estimation pause when a random motion is detected. Our scheme is implemented using Novelda's X4M03 radar development kit and its corresponding drivers and Python packages. Experimental results show that the estimation with Lomb-Scargle periodogram yield more accurate heart rate than the method of measuring peak-to-peak distance.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.1047-1056
• /
• 2003

In this paper, we consider a modified bootstrap scheme for inference of the GPH estimator and establish the sup-norm consistency of the proposed bootstrapping.

• The Korean Journal of Ecology
• /
• v.13 no.2
• /
• pp.83-92
• /
• 1990

Two-dimensional analysis provides a comprehensive description of the structure, scales of pattern and directional components in a spatial data set. In spectral analysisi, four functions are illustrated,; the autocorrelation, the periodogram, the R-spectrum and the $\theta$ -spectrum. The R-spectrum and $\theta$ -spectrum function respectively summarize the periodogram in term of scale of pattern and directional components. Sampling is measured in the Naejang National Park area where the Daphniphyllum trees grow. 320 contiguous (15$\times$15)m plots are located along the transect and density of all trees over DBH 3 cm recorded respectively. 12 species of vascular plant are recorded in this survey area. The trend surface of density of all plant are estimated using polynomial regression and are exhibited in 3-dimensional graph and density contour map. Transformation to the corresponding polar spectrum from the periodogram emphasized the directional components and the scales to pattern. R-spectrum corresponding to the scale of pattern of periodogram showed a large peak 15.47 in the interval 9$\theta$-spectrum corresponding to directional components have two peaks 8.28 and 11.05 in the interval $35^{\circ}\theta <45^{\circ}and 125^{\circ}\theta< <135^{\circ}, respectively. Programs to compute all the analyses described in this study was obtained from Dr. Ranshow and was translated to BASIC by the author.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.4C
• /
• pp.406-412
• /
• 2003

A fast multiple object motion detection method using periodogram has been proposed. Each frame is projected to vertical and horizontal directions, and then temporal FFT is applied. A line in two dimensional frequency domain indicates velocity. To estimate the velocity of an object, we intergrate along a line which passes through the origin in frequency domain. If a frame contains multiple moving objects, multiple peaks are detected corresponding to the velocity of each object. After pairing these peaks, we can determine the velocities of an object. In the proposal method we can easily pair horizontal and vertical velocity components efficiently with simple computation by combining projections in 45$^{\circ}$ and 135 $^{\circ}$ directions in addition to the vertical and horizontal direction.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.10C
• /
• pp.635-640
• /
• 2011

This paper proposes a frequency offset estimation scheme with low-complexity for orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems. The proposed scheme is applicable to any type of training symbol and has a lower complexity than the scheme in [9] by using the partial periodogram. Simulation results show that the estimation performance of the proposed scheme is almost the same as that of the scheme in [9].

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.3
• /
• pp.63-69
• /
• 2017

In order to observe the noise property of the flat-panel digital radiography detector, measuring the normalized noise power spectrum (NNPS) from acquired x-ray images is conducted. However, the conventional NNPS measurement has an unstable property depending on the acquired image. Averaging the sample periodograms of the input image is usually performed to estimate the NNPS values and increasing the number of samples can provide a reliable NNPS measurement. In this paper, for a finite number of images, two measurement methods, which are based on averaging spectra, such as the image periodogram, are proposed and their performances are analyzed. Using x-ray images acquired from two types of radiography detectors, the two spectrum averaging methods are compared and it is shown that averaging spectra based on the maximal number of combinations of the image pairs provides the best performance in measuring NNPS.

• ETRI Journal
• /
• v.35 no.1
• /
• pp.162-165
• /
• 2013

Recently, a new method called the local polynomial periodogram-Hough transform (LHT) was proposed for linear frequency modulated (LFM) signal detection. In this letter, a closed-form expression of the output signal-to-noise ratio is derived for the LHT, showing that the method exhibits a threshold effect for LFM signal detection. Comparisons with the pseudo-Wigner-Hough transform (PWHT) show that the threshold of the LHT is lower (better) than that of the PWHT.