• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.295-302
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• 2004

On the field of speech processing the treatment of noise is still important problems for speech research. Especially, it has been noticed that the background noise causes remarkable reduction of speech recognition ratio. As the examples of the background noise, there are such various non-stationary noises existing in the real environment as driving noise of automobiles on the road or typing noise of printer. The treatment for these kinds of noises is not so simple as could be eliminated by the former Wiener filter, but needs more skillful techniques. In this paper as one of these trials, we show an algorithm which is a speech enhancement method using a model of mutual inhibition for noise reduction in speech which is contaminated by white noise or background noise mentioned above. It is confirmed that the proposed algorithm is effective for the speech degraded not only by white noise but also by colored noise, judging from the spectral distortion measurement.

• ETRI Journal
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• v.34 no.4
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• pp.641-644
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• 2012

The problem of sinusoidal parameter estimation at two channels with common frequency in white Gaussian noise is addressed. By making use of the linear prediction property, an iterative linear least squares (LLS) algorithm for accurate frequency estimation is devised. The remaining parameters are then determined according to the LLS fit with the use of the frequency estimate. It is proven that the variance of the frequency estimate achieves Cram$\acute{e}$r-Rao lower bound at sufficiently small noise conditions.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.559-562
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• 2008

In this paper we propose a lock-in detection method for portable sensor systems and demonstrate with the portable detection module that has high sensitivity and robustness against the noise. The simple portable sensor module is manufactured using MCU(Micro Contorl Unit), OPAMP, MOSFET and a pair of infrared sensor. Manufactured sensor module is testified in the noisy environment caused by an external light and an white noise source. Without any type of band pass filters, we recover a signal of 33 $mV_{p-p}$ in 80 $mV_{p-p}$ white noise and get the DR(Dynamic Reserve) of 14 dB.

• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.3
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• pp.37-45
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• 1982

This paper is a sequel of the Part I paper[1] on the modified digital Costas loop. In this Part II we analyze the performance of the system in the presence of noise. It is shown that, when the input signal is corrupted by additive white Gaussian noise, the noise process in the loop becomes Rician as a result of the tan-1 (.) function of the phase error detector. Steady state probability density functions of phase errors of the first-and second-order loops have been obtained by solving the Chapman-Kolmogorov equation numerically. Also, the mean and variance of phase error in the steady state have been obtained analytically, and are compared with the results obtained by computer simulation.

• Progress in Superconductivity
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• v.11 no.1
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• pp.30-35
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• 2009

We have investigated the characteristics of a superconductive Pb shield for hemispherical shape and plate to improving signal-to-noise ratio(SNR) of biomagnetism. We measured the shielding factor for the position of helmet shape Pb and for changing the distance from Pb surface. To make a uniform magnetic field, a $1.5m{\times}1.5m$ set of the helmholtz coils activated at several frequencies. The shielding factor of hemispherical shape Pb was from 20 to 57 dB and of Pb plate was about $6{\sim}26dB$ as a function of distance from the lead surface. The shielding factor was rapidly reduced as increasing the distance from Pb surface. The white noise of superconductive quantum interference device(SQUID) with a superconductive shield was about $12fT/Hz^{1/2}$ at 1 Hz, $7fT/Hz^{1/2}$ at 100 Hz. The white noise was more increased about two times than conventional SQUID system without Pb shielding. An auditory signal was measured by first order gradiometer and magnetometer with Pb superconductive shield and compared the SNR. The SQUID system with Pb shield had better performance at low frequency noise level.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.1
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• pp.19-24
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• 1999

This paper is second part on the auditory thresholds and fish behaviors to the underwater sounds for luring of target species at the set-net in the coast of Cheju. In order to obtain the critical ratio of yellow tails(Seriola quinqueradiata) and the emission level of underwater sound for luring of them, we make experiments to measure the auditory threshold of them using conditioning with electric shock. In state that the white noise with 10dB higher sound pressure level than ambient noise is emitted, the auditory thresholds of yellow tails are measured with 100~116.5dB and they are higher than those in state of no emission of white noise by the masking effects of it. Although sound pressure level of background noise go down, the auditory thresholds go up with frequency above than 300Hz.The critical ratio of yellow-tails in frequency of 80Hz, 100Hz, 200Hz, 500Hz, 800Hz are 46dB, 40dB, 50dB, 52dB, 60dB, 70dB respectively. The sound pressure level of which the signal sound is recognized by yellow tails under the ambient noise is above 100dB and the critical ratio of them is above 40dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1773-1778
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• 2012

Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.936-939
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• 2015

Recently, the image processing is utilized in various fields and many studies on the image restoration have been carried out in order to remove the noise occurring in the process of data transmission, processing and storage. There are many types of noises added to the image according to the cause and shape, and AWGN(additive white Gaussian noise) is one of typical noises. This paper proposed an algorithm which applies the weighting of filter differently according to the standard deviation in order to alleviate AWGN added to the image, and compared this algorithm with the current methods using PSNR(peak signal to noise ratio) as a criterion of judgment.

• ETRI Journal
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• v.39 no.1
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• pp.87-96
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• 2017

Ultrasonic surgical devices are routinely used for surgical procedures. The incision and coagulation of tissue generate a temperature of $40^{\circ}C-150^{\circ}C$ and depend on the controllable output power level of the surgical device. Recently, research on the classification of grasped tissues to automatically control the power level was published. However, this research did not consider the specific characteristics of the surgical device, tissue denaturalization, and so on. Therefore, this research proposes a robust algorithm that simulates noise to resemble real situations and classifies tissue using conventional classifier algorithms. In this research, the bioimpedance spectrum for six tissues (liver, large intestine, kidney, lung, muscle, and fat) is measured, and five classifier algorithms are used. A signal-to-noise ratio of additive white Gaussian noise diversifies the testing sets, and as a result, each classifier's performance exhibits a difference. The k-nearest neighbors algorithm shows the highest classification rate of 92.09% (p < 0.01) and a standard deviation of 1.92%, which confirms high reproducibility.