• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1097-1106
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• 1997

Most works on importance sampling (IS) as an efficient evaluation technique havd been done in an additibe white gaussian noise channel (Awgn). In this paper we propose a CQ(conventional importance sampling and quasi-translantion) IS technique for the mobile radio channel modeled as Rician fading, and analyze the IS estimator's variance to determine optimum IS parameters and the minimum number of run times. Reference showed that CIS technique has a poor performance for systems with meories, but it is shown that the CIS technique can be improved by combining with quasi-translation technique even for systems with memories. Here the CQ IS technique modifies the variance of additive noise and also performs quasi-translation for the fading distribution. We determine the optimum IS parameters of the proposed CQ IS estimator and whow that the simulation gains are about 10$^{3}$~10$^{6}$ for the mobile communication systems with memories in case of the expected BERs 10$^{-5}$ ~10$^{-8}$ .

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.147-157
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• 2018

In this study, we analyzed an outdoor visible light communication system and implemented it through a simulation. We designed a Reed-Solomon encoder, a variable interleaver structure, and set it to the Institute of Electrical and Electronics Engineers (IEEE) 802.15.7 PHY I specification mode. We also analyzed the performance of an additive white gaussian noise (AWGN) channel environment using a root-raised-cosine (RRC) filter, implemented a MATLAB simulation and analyzed its performance. The results showed a requirement for an additional signal-to-noise ratio (SNR) of approximately 1.5 dB in a 3-ray multipath visible light channel environment than in an AWGN environment.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.545-557
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• 2018

The Global Positioning System (GPS) has become popular and widely used in many fields from military to civilian applications. However, GPS signals are suffered from interference due to its weak signal over wireless channel. There are many types of interference, such as jamming, blocking multipath, and spoofing, which can mislead the operation of GPS receiver. In this paper, vector-based tracking loop model with integrity check is proposed to detect and mitigate the harmful effect of interference on GPS receiver operation. The suggested methods are implemented in the tracking loop of GPS receiver. As a first method, integrity check with carrier-to-noise ratio (C/No) monitoring technique is applied to detect the presence of interference and prevent contaminated channels out of tracking channels to calculate position. As a second method, a vector-based tracking loop using Extended Kalman Filter with adaptive noise covariance according to C/No monitoring results. The proposed methods have been implemented on simulated dataset. The results demonstrates that the suggested methods significantly mitigate interference of Additive White Gaussian Noise (AWGN) and improve position calculation by 44%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.756-763
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• 2009

The Least Mean Square (LMS) algorithm is often used to restore signal corrupted by additive noise. A major defect of this algorithm is that the excess Mean Square Error (EMSE) increases linearly according to speech signal power. This result reduces the efficiency of performance significantly due to the large EMSE around the optimum value. Choosing a small step size solves this defect but causes a slow rate of convergence. The step size must be optimized to satisfy a fast rate of convergence and minimize EMSE. In this paper, the Instantaneous Gain Control (IGC) algorithm is proposed to deal with the situation as it exists in speech signals. Simulations were carried out using a real speech signal combined with Gaussian white noise. Results demonstrate the superiority of the proposed IGC algorithm over the LMS algorithm in rate of convergence, noise reduction and EMSE.

• ETRI Journal
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• v.33 no.4
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• pp.528-536
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• 2011

In this paper, we analyze the location accuracy of real-time locating systems (RTLSs) in multipath environments in which the RTLSs comply with the ISO/IEC 24730-2 international standard. To analyze the location error of RTLS in multipath environments, we consider a direct path and indirect path, in which time and phase are delayed, and also white Gaussian noise is added. The location error depends strongly on both the noise level and phase difference under a low signal-to-noise ratio (SNR) regime, but only on the noise level under a high SNR regime. The phase difference effect can be minimized by matching it to the time delay difference at a ratio of 180 degrees per 1 chip time delay (Tc). At a relatively high SNR of 10 dB, a location error of less than 3 m is expected at any phase and time delay value of an indirect signal. At a low SNR regime, the location error range increases to 8.1 m at a 0.5 Tc, and to 7.3 m at a 1.5 Tc. However, if the correlation energy is accumulated for an 8-bit period, the location error can be reduced to 3.9 m and 2.5 m, respectively.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.333-338
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• 2006

This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.74-79
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• 2017

In this paper, we propose a system model that predicts the original signal transmitted from the transmitter using the received signal in the power line communication system based on the multi - layer perceptron which is one of the machine learning algorithms. Power line communication system using communication system using power network has more noise than communication system using general communication line. It causes a problem that the performance of the power line communication system is degraded. In order to solve this problem, the communication system model proposed in this paper minimizes the influence of noise through original signal prediction and mitigates the performance degradation of the power line communication system. In this paper, we prove that the original signal is predicted by applying the proposed communication system model to the white noise environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.28-38
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• 2010

In order to remove the noise contained in recorded analog video, it is important to recognize the real characteristics and strength of the noise. This paper presents an efficient training-based noise reduction method for recorded analog video after analyzing the noise characteristics of analog video captured in a real broadcasting system. First we show that there is non-negligible noise correlation in recorded analog video and describe the limitations of the traditional noise estimation and reduction methods based on additive white Gaussian noise (AWGN) model. In addition, we show that auto-regressive (AR) model considering noise correlation can be successfully utilized to estimate and synthesize the noise contained in the recorded analog video, and the estimated AR parameters are utilized in the training-based noise reduction scheme to reduce the video noise. Experiment results show that the proposed method can be efficiently applied for noise reduction of recorded analog video with non-negligible noise correlation.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.48-55
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• 2001

A novel method to reduce additive non-stationary noise is proposed. The method requires neither the information about noise nor the estimate of the noise statistics from any pause regions. The enhancement is performed on a band-by-band basis for each time frame. Based on both the decision on whether a particular band in a frame is speech or noise dominant and the masking property of the human auditory system, an appropriate amount of noise is reduced using spectral subtraction. The proposed method was tested on various noisy conditions (car noise, Fl6 noise, white Gaussian noise, pink noise, tank noise and babble noise) and on the basis of comparing segmental SNR with spectral subtraction method and visually inspecting the enhanced spectrograms and listening to the enhanced speech, the method was able to effectively reduce various noise while minimizing distortion to speech.