• Journal of Astronomy and Space Sciences
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• v.28 no.4
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• pp.267-271
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• 2011

I developed an enhanced correction method for Ricean bias which occurs in linear polarization measurement. Two known methods for Ricean bias correction are reviewed. In low signal-to-noise area, the method based on the mode of the equation gives better representation of the fractional polarization. But a caution should be given that the accurate estimation of noise level, i.e. ${\sigma}$ of the polarized flux, is important. The maximum likelihood method is better choice for high signal-to-noise area. I suggest a hybrid method which uses the mode of the equation at the low signal-to-noise area and takes the maximum likelihood method at the high signal-to-noise area. A modified correction coefficient for the mode solution is proposed. The impact on the depolarization measure analysis is discussed.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.378-381
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• 1991

In this paper, the active noise controll system in duct is analyzed with real time implementation. The primary noise signal detected by microphone is modeled using adaptive algorithm and the secondary signal which has the same amplitude and $180^{\circ}$ phase shift with the primary noise signal is generated in the controller. The signal processor DSP56001 is used to implement the real-time controller and the experimental results shows that our system can reduce the noise level in duct to $20{\sim}40$ [db].

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.652-657
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• 2009

This paper proposes an adaptive noise suppression system based on human auditory model to enhance speech signal that is degraded by various background noises. The proposed system detects voiced, unvoiced and silence sections for each frame and implements an adaptive auditory process, then reduces the noise speech signal using a neural network including amplitude component and phase component. Based on measuring signal-to-noise ratios, experiments confirm that the proposed system is effective for speech signal that is degraded by various noises.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.1-6
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• 2007

In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2783-2785
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• 2003

In multi-dimensional magnetic resonance imaging, data is obtained in the spatial frequency domain. Since the signal variation in the spatial frequency domain is much larger than that in the spatial domain, analog-to-digital converts with wide conversion bits are required. In this paper, the quantization noise in magnetic resonance imaging is analyzed. The signal-to-quantization noise ratio(SQNR) in the reconstructed image is derived from the level of quantization in the data acquisition. Since the quantization noise is proportional to the signal amplitude, it becomes more dominant in high field imaging. Using the derived formula the SQNR for several MRI systems are evaluated, and it is shown that the quantization noise can be a limiting factor in high field imaging, especially in three dimensional imaging in magnetic resonance imaging.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2247-2252
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• 2009

Since the dimensions of MEMS gyroscope are very small compared to those of conventional gyroscope, MEMS gyroscope should be able to measure charge of pico-coulomb caused by very small change of electrodes gap. However, feed-through signal from driving electrodes to the sensing electrodes due to the electromagnetic coupling is much greater than the sensing signal, which degrades the sensitivity of MEMS gyroscope. This paper introduces the feed-through noise canceling technique using dummy port and confirms the feasibility of feed-through noise canceling experimentally. Experimental results shows that, when driving signal is 6 Vpp, 30 kHz, feed-through signal of vacuum packaged Si Gyroscope decreases from -53.2 dBm to -77.1 dBm by using feed-through reduction technique. Q-factor that could not be measured without noise reduction is measured to be about 2500 and resonance frequency to be 7.018 kHz.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.423-426
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• 2003

Adaptive algorithm used in Acoustic Echo Canceller (AEC) needs fast convergence algorithm when reference signal is colored speech signal. Set-Membership Affine Projection (SMAP) algorithm is derived from the constraint, which is the minimum value adaptive filter coefficient error. In this paper, we test the characteristic about noise of the SMAP algorithm and proposed modified version of SMAP algorithm fur using at AEC. As the projection order increase, the convergence characteristic of the SMAP algorithm is improved where no noise space. But if the noise uncorrelated with input signal exists, the AEC shows bad performance. In this paper, we propose normalized version of adaptive constants using estimated error signal for robust to noise and show the good performance through AEC simulation.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.5
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• pp.38-43
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• 1996

Windowing routines have as their purpose the reduction of the sidelobes of a spectral output of the FFT or DFT routines. Windowing routines accomplish this by forcing the beginning and end of any sequence to approach each other in value. Since they must work with any sequence they force the beginning and ending samples near zero. To make up for this reduction in power, windowing routines give extra weight to the values near the middle of the sequence. The difference between windows is the way in which they transition from the low weights near the edges to the higher weights neqr the middle of the sequence. Signal-to-noise ratio(SNR) can be determined by the ratio of the output noisy signal variance to the input noisy signal variance of a window. Standard deviation of noise is reduced by windowing. Thus, the windowing operation improved the SNR of the noisy signal. This paper shows a performance estimation of windowing on a single tone with added Gaussian noise and uniform noise.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.102-105
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• 2002

A noise reduction technique to reduce background noise in corrupted voice is proposed. The proposed method is based on linear prediction and takes advantages of periodicity of voiced speech. A voiced sound is regarded as a periodic stationary signal in short time interval. Therefore, the current voice signal is correlated with the voice signal delayed by a pitch period. A linear predictor can estimate only the current signal correlated with the delayed signal. Therefore, the enhanced voice can be obtained as output of the linear predictor. Simulation results show that the proposed method is able to reduce the background noise.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.249-257
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• 2001

The voluntary contracted EMG signal of spinal cord injured patients is very small because the information from central nervous system is not sufficiently transmitted to $\alpha$ motor neuron or muscle fiber. Therefore the acquisited EMG signal from needle or surface electrodes can not be identified obvious voluntary contraction pattern by muscle movement. In this paper we propose the extraction technique of voluntary muscle contraction and relaxation pattern from EMG signal of spinal cord injured patient whose EMG signal is composed of the linear sum of mo색 unit action potentials with two noise sources, additive noise assumed to be white Gaussian noise and high frequency discharge assumed to be not motor unit action potential but impulsive noise. In order to eliminate impulsive noise and additive noise from voluntary contracted EMG signal, we use the FatBear filter which is a nonarithmetic piecewise constant filter, and multiscale nonlinear wavelet denoising processing, respectively. The proposed technique is applied to the EMG signal acquisited from transverse myelitis patients to extract voluntary muscle contraction pattern.