• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.317-324
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• 1995

A need exists for the accurate identification of time series models having time varying parameters, as is important in the case of real time identification of nonstationary EMG signal. Thls paper describes real time identification and muscle fatigue monitoring method of nonstationary EMG signal. The method is composed of the efficient identifier which estimates the autoregressive parameters of nonstationary EMG signal model, and its real time implementation by using T805 parallel processing computer. The method is verified through experiment with real EMG signals which are obtained from surface electrode. As a result, the proposed method provides a new approach for real time Implementation of muscle fatigue monitoring and the execution time is 0.894ms/sample for 1024Hz EMG signal.

• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.1-6
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• 2011

Vibration-based Structural Health Monitoring (SHM) systems use field measurements of operational signals, which are distorted by noise from many sources. Reducing this noise allows a more accurate assessment of the original "clean" signal and improves analysis results. The implementation of a noise reduction methodology for the Modal Distribution Method (MDM) is reported here. The spectral subtraction method is a popular broadband noise reduction technique used in speech signal processing. Its basic principle is to subtract the magnitude of the noise from the total noisy signal in the frequency domain. The underlying assumption of the method is that noise is additive and uncorrelated with the signal. In speech signal processing, noise can be measured when there is no signal. In the MDM, however, the magnitude of the noise profile can be estimated only from the magnitude of the Power Spectral Density (PSD) at higher frequencies than the frequency range of the true signal associated with structural vibrations under the additional assumption of white noise. The implementation of the spectral subtraction method to MDM may decrease the energy of the individual mode. In this work, a modification of the spectral subtraction method is introduced that enables the conservation of the energies of individual modes. The main difference is that any (negative) bars with a height below zero after subtraction are set to the absolute value of their height. Both noise reduction methods are implemented in the MDM, and an application example is presented that demonstrates its effectiveness when used with a signal corrupted by noise.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.359-364
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• 2004

This paper deals with the comparison of signal processing techniques of ultrasonic data. The goal of signal processing is the ultrasonic speckle suppression and the visibility enhancement of flaw-reflected ultrasonic echo. The performance of conventional SSP(split spectrum processing) method and the wavelet denoising method are compared and discussed for tested ultrasonic data. Tested ultrasonic data obtained from the weld area of centrifugal-casted stainless steel material and safe-ending material with holes and notch of variable depths are presented. In experimental results, the outputs of wavelet-based denoising method show the clear and sharp peaks at the positions of flaw-reflected echos comparing with those of SSP method.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.7
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• pp.110-116
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• 1994

In this paper a Duty Effective Overlap Scanning method (DEOS) for the improvement of driving efficiency of LC displays which have the RMS voltage responding characteristics is proposed and new processing method of data signals for optimum application of this method is also proposed. Proposed method has a few advantages such as the increment of duty ratio the increment of driving power loaded on LC cell and the decrement of RMS voltage error rate caused by signal attenuation on electrodes composing of display when compared with the conventional method which is called as optimum voltage amplitude selection method. And also by adopting new data signal processing method which has 3 kinds of voltage levels additional advantage much improving crosstalk phenomenon which is the most serious problems of simple matrix structured display is obtained. For the characteristic estimation new mathematical representation for new overlap scanning method and data signal processing method are induced and defined. And by the defined formula and simulation the characteristics of the proposed method and the conventional method are compared and analyzed. As a result of estimation this new method being able to optimize the overlap rate of scan signal and using 3 levels of data signals has the characteristic which can improve the driving efficiency of LC displays.

• Journal of Advanced Navigation Technology
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• v.23 no.2
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• pp.142-150
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• 2019

Muzzle Velocity is one of the most important measurement items for evaluation of ammunition. The muzzle velocity is defined as the velocity when the projectile leaves the muzzle. Particularly, since the muzzle velocity is closely related to the performance of the propellant, precise measurement of muzzle velocity is required. Doppler radar is used to measure the muzzle velocity, but the quality of Doppler radar signal depends on the test site environment. In this paper, a method to remove the clutter that degrades the signal quality of Doppler radar by improving the structure of the test site and the signal processing method is suggested. For the application of the improved signal processing method, a program for acquiring Doppler radar's raw Doppler data was created. Statistical verification of the velocity data obtained through the improvement of the test site structure and signal processing method proved that the proposed method is effective for the removal of clutter as compared with the existing method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.119-127
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• 1996

The proposed method uses a signal of the smae frequency as the input modulating carrier frequency and of a different phase. This signal is generated in the digital automatic frequency control part to decide the input color demodulated signal. And the phase error from the burst signal is calculated. The calculated phase error is utilized to rmove the phase error contained inthe demodulated color signal. In this paper, digital signal processing of automatic color control is proposed for VCR system campatible with both NTSC and PAL TV systems.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.19-25
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• 2023

In this paper, we propose a method for improving raw images captured in a low light condition based on deep learning considering the image signal processing. In the case of a smart phone camera, compared to a DSLR camera, the size of a lens or sensor is limited, so the noise increases and the reduces the quality of images in low light conditions. Existing deep learning-based low-light image processing methods create unnatural images in some cases since they do not consider the lens shading effect and white balance, which are major factors in the image signal processing. In this paper, pixel distances from the image center and channel average values are used to consider the lens shading effect and white balance with a deep learning model. Experiments with low-light images taken with a smart phone demonstrate that the proposed method achieves a higher peak signal to noise ratio and structural similarity index measure than the existing method by creating high-quality low-light images.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.79-84
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• 2021

The most important device of the AGV is the guide sensor, and the typical function of this sensor is high accuracy and extraction of the road. If the accuracy of the guide sensor is low or the sensor device is extracted the wrong track, this causes the problems such as the AGV collision, track-out, the load falling due to AGV swing. In order to improve these problems, this study is proposed a signal processing method of the guide sensor based on multi-maskings and the center of gravity method, and evaluated its performance. As a result, the proposed method showed that the mean error of absolute value is 2.32[mm] and it showed performance improvement of 27[%] than the center of gravity method of existence. Therefore, when the proposed signal processing method is applied, It is thought that the posture control and driving stability of the AGV will be improved.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.54-61
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• 2010

In this paper, we design the signal processing unit to effectively support the proposed algorithm for an automotive Frequency Modulation Continuous Wave(FMCW) radar. In the proposed method, we can obtain the distance and velocity with improved error depending on each range(long, middle, and short) of the target. Since a high computational capacity is required to obtain more accurate distance and velocity for target in near range, the proposed signal processing unit employs the time de-interleaving and the frequency interpolation method to overcome the limitation. Moreover, for real-time signal processing, the parallel architecture is used to extract simultaneously the distance and velocity in each range.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.