• Current Optics and Photonics
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• v.7 no.1
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• pp.73-82
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• 2023

In this study, we demonstrate ultrahigh-resolution spectral-domain optical coherence tomography with a 200-kHz line rate using a superluminescent diode with a -3-dB bandwidth of 100 nm at 849 nm. To increase the line rate, a subset of the total number of camera pixels is used. In addition, a partial-spectrum detection method is used to obtain OCT images within an imaging depth of 2.1 mm while maintaining ultrahigh axial resolution. The partially detected spectrum has a flat-topped intensity profile, and side lobes occur after fast Fourier transformation. Consequently, we propose and apply the super-Gaussian window function as a new window function, to reduce the side lobes and obtain a result that is close to that of the axial-resolution condition with no window function applied. Upon application of the super-Gaussian window function, the result is close to the ultrahigh axial resolution of 4.2 ㎛ in air, corresponding to 3.1 ㎛ in tissue (n = 1.35).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.80-85
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• 1995

Too see the effects of finite record on the estimation of WVD in practice, a window which has time varying length is examined. Its length increases linearly with time in the first half of the record, and decreases from the center of the record. The bias error due to this window decreases inversely proportionally to the window length as time increases in the first half. In the second half, the bias error increases and the resolution decreases as time increases. The bias error due to the smoothing of WVD, which is obtained by two-dimensional convolution of the true WVD and the smoothing window, which has fixed lengths along time and frequency axes, is derived for arbitrary smoothing window function. In the case of using a Gaussian window as a smoothing window, the bias error is found to be expressed as an infinite summation of differential operators. It is demonstrated that the derived formula is well applicable to the continuous WVD, but when WVD has some discontinuities, it shows the trend of the error. This is a consequence of the assumption of the derivation, that is the continuity of WVD. For windows other than Gaussian window, the derived equation is shown to be well applicable for the prediction of the bias error.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.123-128
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• 2011

In this paper, we propose the improved noise power estimation in speech enhancement under various noise environments. The previous MS algorithm tracking the minimum value of finite search window uses the optimal power spectrum of signal for smoothing and adopts minimum probability. From the investigation of the previous MS-based methods it can be seen that a fixed size of the minimum search window is assumed regardless of the various environment. To achieve the different search window size, we use the noise classification algorithm based on the Gaussian mixture model (GMM). Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 perceptual evaluation of speech quality (PESQ) under various noise environments. Based on this, we show that the proposed algorithm yields better result compared to the conventional MS method.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.189-200
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• 1996

In this paper, we compared the performance of the Kaiser window with those of others as a weight function of well known anodization technique for regression of side lobe in a field pattern resulted from focusing of transducer array. The Kaiser window is an window providing many types of curve with several variables. In order to compare performance of the Kaiser window as the weight function, anodization results of the previously used Hamming window function and the Matched Gaussian function are compared Result of computer simulation, the pertormance of Kaiser window with $\delta$=0.0025 in side lobe regression was better than that of Hamming window or Matched Gausian function.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1271-1284
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• 2011

The entropy-based test of fit for the inverse Gaussian distribution presented by Mudholkar and Tian(2002) can only be applied to the composite hypothesis that a sample is drawn from an inverse Gaussian distribution with both the location and scale parameters unknown. In application, however, a researcher may want a test of fit either for an inverse Gaussian distribution with one parameter known or for an inverse Gaussian distribution with both the two partameters known. In this paper, we introduce tests of fit for the inverse Gaussian distribution based on the Kullback-Leibler information as an extension of the entropy-based test. A window size should be chosen to implement the proposed tests. By means of Monte Carlo simulations, window sizes are determined for a wide range of sample sizes and the corresponding critical values of the test statistics are estimated. The results of power analysis for various alternatives report that the Kullback-Leibler information-based goodness-of-fit tests have good power.

• Journal of the Korean Society of Safety
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• v.12 no.4
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• pp.3-8
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• 1997

This paper shows a performance estimation of windowing a single tone with added Gaussian noise and uniform noise. Signal-to-noise ratio 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 Signal-to-noise ratio of the noisy signal is reduced by the windowing operation. Thus, performance of window function can be determined by this filtering operation that improved the signal-to-noise ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.183-190
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• 2013

In this paper, we propose a spatially adaptive noise detection and removal algorithm for a single degraded image. Under the assumption that an observed image is Gaussian-distributed, the noise information is estimated by local statistics of degraded image, and the degree of the additive noise is detected by the local statistics of the estimated noise. In addition, we describe a noise removal method taking a modified Gaussian filter which is adaptively determined by filter parameters and window size. The experimental results demonstrate the capability of the proposed algorithm.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.758-762
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• 2011

In this paper an efficient algorithm is proposed to remove additive white Gaussian noise(AWGN) with edge preservation. A function is used to separate the filtering mask to two sets according to the direction information. Then, we calculate the mean and standard deviation of the pixels in each set. In order to preserve the details, we also compare standard deviations between the two sets to find out smaller one. Corrupted pixel is replaced by the mean of the filtering window's median value and the smaller set's mean value that the rate of change is faster than the other one. Experiment results show that the proposed algorithm outperforms with significant improvement in image quality than the conventional algorithms. The proposed method removes the Gaussian noise very effectively.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.69-72
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• 2000

In CDMA systems, the performance of the typical multipath searcher degrades much according as the signal to noise ratio becomes low. In this paper, multipath searcher algorithm is proposed based on sliding window to overcome this drawback. In searcher systems, correlation values between incoming and local PN sequences are used to acquire multipath components. Therefore more accurate distributions of correlation values obtained through this proposed algorithm enables to get higher detection probability. In computer simulations, it is verified that proposed algorithm has better performances in Rayleigh fading channel and Gaussian channel.

• Journal of the Korea Society of Computer and Information
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• v.4 no.2
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• pp.120-134
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• 1999

Image processing steps consist of image acquisition, pre-processing, region segmentation and recognition, and the images are easily corrupted by noise during the data transmission, data capture, and data processing. Impulse noise and gaussian noise are major noises, which can occur during the process. Many filters such as mean filter, median filter, weighted median filter, Cheikh filter, and Kyu-cheol Lee filter were proposed as spatial noise reduction filters so far. Many researches have been focused on the reduction of impulse noise, but comparatively the research in the reduction of gaussian noise has been neglected. For the reduction of gaussian noise, subset averaged median filter, using median information and subset average information of pixels in a window. was proposed. At this time, consider of the window size as 3$^{*}$3 pixel. The window is divided to 4 subsets consisted of 4 pixels. First of all, we calculate the average value of each subset, and then find the median value by sorting the average values and center pixel's value. In this paper, a better reduction of gaussian noise was proved. The proposed algorithms were implemented by ANSI C language on a Sun Ultra 2 for testing purposes and the effects and results of the filter in the various levels of noise and images were proposed by comparing the values of PSNR, MSE, and RMSE with the value of the other existing filtering methods.thods.