• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.210-212
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• 2021

In modern society, various digital equipment are being distributed due to the influence of the 4th industrial revolution, and they are used in a wide range of fields such as automated processes, intelligent CCTV, medical industry, robots, and drones. Accordingly, the importance of the preprocessing process in a system operating based on an image is increasing, and an algorithm for effectively reconstructing an image is drawing attention. In this paper, we propose a filter algorithm based on a combined weight value to reconstruct an image in a complex noise environment. The proposed algorithm calculates the weight according to the spatial distance and the weight according to the difference between the pixel values for the input image and the pixel values inside the filtering mask, respectively. The final output was filtered by applying the join weights calculated based on the two weights to the mask. In order to verify the performance of the proposed algorithm, we simulated it by comparing it with the existing filter algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2029-2036
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• 2017

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.351-359
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• 2003

In this paper, we proposed a method for acoustic echo cancellation based on independent component analysis. When the large acoustic noise is picked up by the microphone, the performance of echo cancellation decreased. We used two microphones that received echo signal which is linearly mixed with the noise, then separated the echo signals from the received signals with independent component analysis algorithm. The separated echo signal is used for the reference signal of adaptive algorithm which leads to better performance of the echo cancellation. Computer simulation results show the validity of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.798-801
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• 2014

Nowadays, the demand for multimedia services has grown with the rapid evolution in the digital era. But due to external causes in the process of processing, transmitting and storing image data, the images are damaged. One of the major causes of such damage is known to be noise. Some of the most commonly used methods for removing noise are CWMF(center weighted median filter), A-TMF(alpha-trimmed mean filter) and AWMF(adaptive weighted median filter). However, these filters all leave a bit to be desired in removing noise in a complex noise environment. Therefore this paper suggest an image restoration filter algorithm that first judges the noise and sets a adjustment weight based on the median value and distance of the mask to remove the complex noise. For an objective analysis, the results were compared against existing methods and the PSNR(peak signal to noise ratio) was used as a reference.

• Journal of Astronomy and Space Sciences
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• v.36 no.4
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• pp.283-292
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• 2019

The purpose of this study was to develop a period analysis algorithm for detecting new variable stars in the time-series data observed by charge coupled device (CCD). We used the data from a variable star monitoring program of the CBNUO. The R filter data of some magnetic cataclysmic variables observed for more than 20 days were chosen to achieve good statistical results. World Coordinate System (WCS) Tools was used to correct the rotation of the observed images and assign the same IDs to the stars included in the analyzed areas. The developed algorithm was applied to the data of DO Dra, TT Ari, RXSJ1803, and MU Cam. In these fields, we found 13 variable stars, five of which were new variable stars not previously reported. Our period analysis algorithm were tested in the case of observation data mixed with various fields of view because the observations were carried with 2K CCD as well as 4K CCD at the CBNUO. Our results show that variable stars can be detected using our algorithm even with observational data for which the field of view has changed. Our algorithm is useful to detect new variable stars and analyze them based on existing time-series data. The developed algorithm can play an important role as a recycling technique for used data

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.141-147
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• 2010

In this paper, we propose a new speech separation algorithm to extract and enhance the target speech signals from mixed speech signals by utilizing both magnitude and phase information. Since the previous statistical modeling algorithms assume that the log power spectrum values of the mixed speech signals are independent in the temporal and frequency domain, discontinuities occur in the resultant separated speech signals. To reduce the discontinuities, we apply a smoothing filter in the time-frequency domain. To further improve speech separation performance, we propose a statistical model based on both magnitude and phase information of speech signals. Experimental results show that the proposed algorithm improve signal-to-interference ratio (SIR) by 1.5 dB compared with the previous magnitude-only algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.434-436
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• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.647-653
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• 2016

Recently, the data image processing has been applied to a variety of fields including broadcasting, communication, computer graphics, medicine, and so on. Generally, the image data may develop the noise during their transmission. Therefore, the studies have been actively conducted to remove the noise on the image. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by histogram and spatial weighted values after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. Regarding the processed results by applying Lena images which were corrupted by salt and pepper noise(P=50%) and AWGN(${\sigma}=10$), suggested algorithm showed the improvement by 7.06[dB], 10.90[dB], 5.97[dB] respectively compared with the existing CWMF, A-TMF, AWMF.