• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3497-3515
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• 2018

Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual's sensitive information. However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random. To this end, we proposed an adaptive Gaussian mechanism based on expected data utility under conditional filtering noise. Firstly, this paper made conditional filtering for Gaussian mechanism noise. Secondly, we defined the expected data utility according to the absolute value of relative error. Finally, we presented an adaptive Gaussian mechanism by combining expected data utility with conditional filtering noise. Through comparative analysis, the adaptive Gaussian mechanism satisfies differential privacy and achieves expected data utility for giving any privacy budget. Furthermore, our scheme is easy extend to engineering implementation.

• 디지털산업정보학회논문지
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• 제8권4호
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• pp.13-18
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• 2012

In this paper, we propose a noise estimation method for noise reduction. It is based on block and pixel-based noise estimation. We assume that an input image is contaminated by the additive white Gaussian noise. Thus, we use an adaptive Gaussian filter and estimate the amount of noise. It computes the standard deviation of each block and estimation is performed on pixel-based operation. The proposed algorithm divides an input image into blocks. This method calculates the standard deviation of each block and finds the minimum standard deviation block. The block in flat region shows well noise and filtering effects. Blocks which have similar standard deviation are selected as test blocks. These pixels are filtered by adaptive Gaussian filtering. Then, the amount of noise is calculated by the standard deviation of the differences between noisy and filtered blocks. Experimental results show that our proposed estimation method has better results than those by existing estimation methods.

• 전자공학회논문지B
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• 제30B권9호
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• pp.76-83
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• 1993

A morphological filtering algorithm using directional information is presented. Directional filtering technique is effective in reducing noises and preserving edges. The proposed directional filtering is composed of two stage filtering processes. The opening and closing operations in the lst stage are performed for the pixels is aligned to the vertical, horizontal, and two diagonal directions, respectively. The opening operation supresses the positive impulse noises, while the closing operation the negative ones. Then, each directional result and their average value are filtered by the opening or closing operations in the 2nd stage. The averaging operation diminishes the effects of Gaussian noises in the homogeneous regions. Thus, the morphological operation in the 1 st stageremoves the impulse noises and in 2nd stage reduces. Gaussian ones. The experimental results show that the proposed filtering is superior to the existing nonlinear filtering in the aspects of the subjective quality. Also, the morphological filtering method reduces the computational loads.

• 한국통신학회논문지
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• 제34권12A호
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• pp.982-990
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• 2009

IEEE 802.15.4 무선 센서 네트워크는 물리적 또는 환경적 조건을 모니터링하고 수집 하기 위해 센서를 사용하는 독자적인 디바이스로 구성된 무선 네트워크 이다. 최근 센서기술과 정보통신 인프라의 발전으로 환경 모니터링 기술의 하나인 위치추적 기술에 대한 관심이 증가되고 있다. 센서네트워크에서의 일반적인 수신신호 세기 RSSI(Received Signal Strength Indication)를 활용한 위치인식 시스템은 장애물이나 RF의 전파지연 및 멀티패스에 의해 정확한 위치 추적이 어렵다. 따라서 본 논문에서는 RSSI 기반의 위치 추적 시스템이 가지고 있는 이러한 문제를 해결하기 위해 Gaussian Filter algorithm을 적용하여 위치 인식 성능을 개선한다. 이에 RSSI 값에 따른 전파 감쇠 특성을 논의한 후, 노드마다 개별 RSSI 값에 따른 확률적 거리 테이블을 작성한 후 생성된 모델을 통해, 센서 노드로부터 추출된 데이터를 본 논문에서 제안한 Gaussian Filter Algorithm을 적용하여 오차개선을 하였다.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.539-551
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• 2018

Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

• Journal of information and communication convergence engineering
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• 제9권6호
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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.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1396-1403
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• 2019

The radar wind profiler data contaminates with various non-atmospheric components that produce errors in moments and wind velocity estimations. This study implemented an adaptive Gaussian model to detect and remove the clutter from the radar return. This model includes DC filtering, ground clutter recognition, Gaussian fitting, and cost function to mitigate the clutter component. The adaptive model tested for the various types of clutter components and found that it is effective in clutter removal process. It is also applied for the both time series and spectrum datasets. The moments estimated using this method are compared with those derived using conventional DC-filtering clutter removal method. The comparisons show that the proposed method effectively removes the clutter and produce reliable moments.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.297-298
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• 2008

A new equalization method for perpendicular magnetic recording channels is proposed. The proposed equalizer incorporates the Gaussian sum approximation into a Kalman filtering framework to mitigate inter-symbol interference in perpendicular magnetic recording systems. The proposed equalizer consists of a bank of linear equalizers using the Kalman filtering algorithm and its output is obtained by combining the outputs of linear equalizers through the Gaussian sum approximation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.176-179
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• 2010

In this paper, we propose a fast implementation of Bilateral filter for tone mapping algorithm. Bilateral filter is able to preserve detail while at the same time prevent halo-ing artifacts because of improper scale selection by ensuring image smoothed that not only depend on pixel closeness, but also similarity. We accelerate Bilateral filter by using a piecewise linear approximation and recursive Gaussian filter as its domain filter. Recursive Gaussian filter is scale independent filter that combines low cost 1D filter which makes this filter much faster than conventional convolution filter and filtering in frequency domain. The experiment results show that proposed method is simpler and faster than previous method without mortgaging the quality.

• 한국지능시스템학회논문지
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• 제23권2호
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• pp.184-189
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• 2013

본 논문에서는 인터넷 및 인터넷 저장 공간에 제한없이 유통되고 있는 유해동영상을 필터링하기 위해 유해동영상에 포함된 특정 소리를 이용한 유해 동영상 필터링 시스템을 제안한다. 이를 위하여 소리의 특성을 잘 표현할 수 있는 Gaussian Mixture Model을 이용하였으며, 필터링 대상 데이터와 소리모델과의 유사도를 계산하기위해 프레임단위 유사도 추정을 이용하였다. 또, 실시간 처리를 위하여 비교대상 데이터의 수를 줄임으로서 실시간 처리가 가능한 프루닝 방법을 적용하였으며, 고정도의 구별 성능을 위하여 기존 화자식별에서 우수한 성능을 보였던 MWMR 방법을 적용하였다. 식별실험결과, 일반 영상과 유해 영상의 기준인 전체프레임 대비 유사도 높은 프레임의 비를 50%로 설정한 경우, 판별 오류율은 6.06%였으며, 프레임 비의 기준이 60%인 경우, 오류율은 3.03%를 나타내어 소리를 이용한 유해동영상 필터링 시스템이 효과적으로 일반영상과 유해영상을 구별할 수 있는 것을 확인하였다.