• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.4-12
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• 1996

In this study, we examine the applicability of an artificial neural network(ANN) for filtering underwater random noise and for identifying underlying signals taken from noisy environment. The approach is to find a way of compressing the input data and then decompressing it using an ANN as in image compressing process. It is well known that random signal is hard to compress while ordered information is not. The use of a limited number of processing elements(PEs) in the hidden layer of an Ann ensures that some of the noise would be removed in the reconstruction process. Two types of the signals, synthesized and measured, are used to examine the effectiveness of the ANN-based filter. After training process is completed, the ANN successfully extracts the underlying signals form the synthesized or measured noisy signals. In particular, compared with the results form without filtering or moving averaged, the ANN-based filter gives much better spectrograms to identify underlying signals from the measured noisy data. This filtering process is achieved without using and kind of highly accurate signal processing technique. More experimentation needs to be followed to develop the ANN-based filtering technique to the level of complete understanding.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.553-558
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• 1999

The ECG signal is very important information for diagnosis of patient and a cardiac disorder That signal is hard to filter the noise because that is mixed with a lot of noise, and the error of the filtering will distort the ECG signal. The existing method for the filtering of the ECG signal has structure that has many steps for filtering, so that structure is complex and the processing speed is slow. For the improvement of that problem, we propose the method of filtering that has simple structure using the RBF neural networks and have good results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.173-180
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• 2003

The modified Wiener filtering method is proposed for effective noise suppression in edge region of images corrupted by additive white gaussian noise. Although the pixels classified as a edge region in the conventional Wiener filter have lots of noise components, the conventional Wiener filler cannot remove noise effectively due to the preserving of edges. To reduce noise well in edge region, we modify filter coefficients of the conventional Wiener filter The modified filter coefficients increase in noise suppression effect In edge region, while they preserve edges for strong edge region. From simulation $(256{\time}256$ size, 256 graylevel images) filtered images by the proposed method show much improved subjective image quality with some improved peak signal-to-noise ratio compared to those by the conventional Wiener filtering.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.118-126
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• 2004

The modified Wiener filtering method is proposed for effective noise suppression in edge region of images corrupted by additive white gaussian noise. Although the pixels classified as a edge region in the conventional Wiener filter have lots of noise components, the conventional Wiener filter cannot remove noise effectively due to the preserving of edges. To reduce noise well in edge region, we modify filter coefficients of the conventional Wiener filter. The modified filter coefficients increase in noise suppression effect in edge region, while they preserve edges for strong edge region. From simulation (256${\times}$256 size, 256 graylevel images) filtered images by the proposed method show much improved subjective image quality with higher peak signal-to-noise ratio compared to those by the conventional Wiener filtering.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.3
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• pp.342-348
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• 2015

This paper presents a 1.9-GHz digital ${{\Delta}{\Sigma}}$ fractional-N PLL with a finite impulse response (FIR) filter embedded for noise suppression. The proposed digital implementation of FIR provides a simple method of increasing the number of taps without complicated calculation for gain matching. This work demonstrates 32 tap FIR filtering for the first time and successfully filtered the in-band phase noise generated from delta-sigma modulator (DSM). Design considerations are also addressed to find the optimum number of taps when the resolution of time-to-digital converter (TDC) is given. The PLL, fabricated in $0.11-{\mu}m$ CMOS, achieves a well-regulated in-band phase noise of less than -100 dBc/Hz for the entire range inside the bandwidth of 3 MHz. Compared with the conventional dual-modulus division, the proposed PLL shows an overall noise suppression of about 15dB both at in-band and out-of-band region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.434-436
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• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.344.2-344
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• 2002

In this presentation, a power-based spatial filtering procedure with application to spherical acoustical holography is presented. Planar and cylindrical holography are the most widely used amongst the various nearfield acoustical holography techniques. However, when the geometry of a source is similar to a sphere. spherical holography may yield better results than other types of holography since there are no errors due to truncation of the sound field in the spherical case. (omitted)

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.568-579
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• 2015

Recently, mobile devices with touch display panel are widely used. Accuracy and reaction speed of touch signal are very important in touch devices. Therefore, we need to develop an effective algorithm to reduce touch noise quickly and accurately. This paper proposes a touch noise reduction algorithm using Kalman filtering in consideration of signal motion. First, a specific pre-emphasis processing is applied to an input signal so as to maximize the effect of Kalman filtering. In other words, a pure signal in the touch signal increases but noise in the touch signal decreases. Next, motion of the signal is detected. Motion estimation is performed only if motion is detected. If we detect motion by using the only neighborhood of the signal, we can reduce about 75% of the computation in comparison with examining the entire area. Finally, Kalman filtering using the previous state of current signal is performed. Experimental results show that the proposed algorithm suppresses touch noise sufficiently without degradation of the pure signal

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.347-352
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• 2013

In impulse noise filtering techniques window size play an important role. Usually, an appropriate window is determined according to the noise density. A small window may not be able to suppress noise properly whereas a large window may remove edges and fine image details. Moreover, the value of the central pixel is estimated by considering all pixels within the window. In this work, contrary to the previous approaches, we propose an iterative impulse noise removal scheme that emphasizes on noise-free pixels within a small neighborhood. The iterative process continues until all noisy pixels are replaced with the estimated pixels. In order to estimate the optimal value for a noisy pixel, a genetic programming (GP) based estimator is evolved that takes few noise-free pixels as input. The estimator is constituent of noise-free pixels, arithmetic operators and random constants. Experimental results show that theproposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.3
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• pp.55-62
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• 1993

Autoregressive moving average(ARMA) model which is a time domain parametric modeling method is implemented for modeling and reproducing characteristics of exhaust noise of an automobile in various RPM range. Experiments have been carried out using 9 set of exhaust noise signals measured at 1,000-3,000 RPM range. Characteristics of sampled signals were estimated using ARMA modeling and Akaike's FPE(final prediction error) criterion to define exact model structure and for model validation. The digital filter consisted of the esitmated ARMA(70,1) model parameters was programed to reproduce exhaust noise. The spectral analysis of reproduced noise is very close to original. The results show that our approaching technique for reproducing acoustic characteristics is valid and feasible to apply in the field of noise quality control.