• Journal of information and communication convergence engineering
• /
• v.9 no.1
• /
• pp.59-63
• /
• 2011

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.190-190
• /
• 2000

This paper presents a multirate state feedback control (MRSFC) method for systems sensitive to disturbance and noise based on the multirate estimator design using the current estimator. MRSFC updates the controller output slower than the measurement sampling frequency of system output by a lifting factor R=T$\sub$c//T$\sub$s/. The closed-loop MRSFC system is less sensitive to disturbance and noise due to filtering effect than the conventional single-rate control system. The multirate estimator gain is obtained from solving a conventional pole placement problem such that MRSFC has the same spectrum of eigenvalues in the s-plane as the single-rate control. We applied the proposed multirate state feedback controller to a galvanometer servo system. Simulation and experimental results show that settling and tracking performances are improved compared with a conventional single-rate pole placement control (PPC).

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.2E
• /
• pp.86-99
• /
• 2010

This paper analyzes the performance of various single channel speech enhancement algorithms when they are applied to automatic speech recognition (ASR) systems as a preprocessor. The functional modules of speech enhancement systems are first divided into four major modules such as a gain estimator, a noise power spectrum estimator, a priori signal to noise ratio (SNR) estimator, and a speech absence probability (SAP) estimator. We investigate the relationship between speech recognition accuracy and the roles of each module. Simulation results show that the Wiener filter outperforms other gain functions such as minimum mean square error-short time spectral amplitude (MMSE-STSA) and minimum mean square error-log spectral amplitude (MMSE-LSA) estimators when a perfect noise estimator is applied. When the performance of the noise estimator degrades, however, MMSE methods including the decision directed module to estimate a priori SNR and the SAP estimation module helps to improve the performance of the enhancement algorithm for speech recognition systems.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.18-20
• /
• 2006

The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to remove additive Gaussian noise contaminating images. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber's minimax norm. Our estimator is also optimal in the respect of maximizing the efficacy under the above noise environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1004-1013
• /
• 2002

A multirate state feedback control (MRSFC) method is proposed for systems sensitive to disturbance and noise based on the multirate estimator design using current estimator. MRSFC updates the controller output slower than the measurement sampling fiequency of system output by a lifting factor $R=T_c/T_s$ The closed-loop MRSFC system is less sensitive to disturbance and noise due to filtering effect than the conventional single-rate control system The multirate estimator gain can be obtained by solving a conventional pole placement problem such that MRSFC has the same spectrum of eigenvalues in the s-plane as the single-rate control. We applied the proposed multirate state feedback controller to a galvanometer servo system Simulation and experimental results show that settling and tracking performances are improved compared with a conventional single-rate pole placement control (PPC).

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.6
• /
• pp.93-99
• /
• 2007

Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.

• Journal of Digital Convergence
• /
• v.10 no.10
• /
• pp.277-282
• /
• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.4
• /
• pp.365-370
• /
• 2013

In this paper, we propose robust blind estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, a simpler estimator based on the ML estimator is proposed. From numerical results, we confirm that the proposed estimators are robust to the non-Gaussian noise and have a better estimation performance over the conventional estimator in non-Gaussian noise environments.

• Journal of Digital Convergence
• /
• v.10 no.11
• /
• pp.377-382
• /
• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.859-866
• /
• 2001

The problem of wavelet density estimation is studied when the sample observations are contaminated with random noise. In this paper a linear wavelet estimator based on Meyer-type wavelets is shown to be L$_1$ strongly consistent for f(x) with bounded support when Fourier transform of random noise has polynomial descent or exponential descent.