• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.191-194
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• 2002

최근 음성 부호화기의 연구방향은 저전송률, 저복잡도와 더불어 가변전송률 음성부호화기에 대한 연구로 진행되고 있다. 지금까지 제안된 저전송률 음성부호화기로는 스펙트럼 모델링을 이용한 MBE 계열과 혼성부호화 방식의 CELP 계열이 있다. 그 중에서 가장 많은 연구가 이루어지고 있는 방식이 CELP 방식이다. 이 방식은 4.8kbps 내외의 전송율에서 양호한 음질을 얻을 수 있다. 본 논문에서는 평균자승오차값을 최소화하여 계산량을 줄이고 음질을 향상시킬 수 있는 새로운 알고리즘을 제안한다. 먼저 G.723.1 부호화기에서 인지가중필터를 거친 신호를 LSP를 이용하여 각 포만트의 위치를 검출하여 Pole점만 비교하여 Zero점의 영향을 최소화 하였고 평균자승오차값을 최소화 하여 문턱값에 가장 가까운 값을 대표 피치이득계수로 정하고 그때의 피치와 함께 부호화한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.381-387
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• 2002

In this paper, an efficient method to find shot boundaries in the MPEG video stream data is proposed. For this purpose, we first assume that the histogram difference value(HDV) and pixel difference value(PDV) as an one dimensional signal and apply the median filter to these signals. The output of the median filter is subtracted from the original signal to produce the median filtered difference(MFD). The MFD is a criterion of shot boundary. In addition a neural network is employed and trained to find exactly cut boundary. The proposed algorithm shows that the cut boundaries are well extracted, especially in a dynamic video.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.401-408
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• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• The Journal of the Acoustical Society of Korea
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• v.30 no.7
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• pp.383-389
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• 2011

In this paper, we propose a improved speech absence probability estimation algorithm by applying environmental noise classification for speech enhancement. The previous speech absence probability required to seek a priori probability of speech absence was derived by applying microphone input signal and the noise signal based on the estimated value of a posteriori SNR threshold. In this paper, the proposed algorithm estimates the speech absence probability using noise classification algorithm which is based on Gaussian mixture model in order to apply the optimal parameter each noise types, unlike the conventional fixed threshold and smoothing parameter. Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 PESQ (perceptual evaluation of speech quality) and composite measure under various noise environments. It is verified that the proposed algorithm yields better results compared to the conventional speech absence probability estimation algorithm.

• Journal of Broadcast Engineering
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• v.12 no.3
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• pp.278-288
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• 2007

We propose the fast full search algorithm that reduces the computational load of the block matching algorithm which is used for a motion estimation in the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion picture. However we proposed the efficient motion estimation method which is good for the fast and slow motion picture. Firstly, when finding the initial threshold value, we use the expanded predictor that can approximately calculate minimum threshold value. The proposed algorithm estimates the motion in the new search order after partitioning the search window and adapt the directional search order in the re-divided search window. At the result, we can check that the proposed algorithm reduces the computational load 94% in average compared to the conventional spiral full search algorithm without any loss of image quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.398-405
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• 2005

The multiuser detection (MUD) based wireless receiver requires more accurate channel estimation than the single user detection (SUD) schemes such as Rake receiver, and hence the post processing is required for MUD to clean up the estimated channel coefficients by eliminating the noise only coefficients. The adaptive post processing method is proposed in order to provide more accurate channel responses and the power level of the background noise and interferences at the cost of the negligible processing delay compared to the conventional method based on the threshold test with the threshold value relative to the noise variance. The simulations are performed in 3GPP-TDD mode environment. The results show that the noise estimation error of the proposed method is maximum $10\%$, which is much smaller than $50\%$ maximum error of the conventional method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.55-63
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• 2011

This paper describes new moving target detection technique combining two algorithms to detect targets and reject clutters in video frame images for surveillance system: One obtains the region of moving target using phase correlation method using $N{\times}M$ sub-block images in frequency domain. The other uses adaptive threshold using learning weight for extracting target candidates in subtracted image. The block region with moving target can be obtained using the characteristics that the highest value of phase correlation depends on the movement of largest image in block. This technique can be used in camera motion environment calculating and compensating camera movement using FFT phase correlation between input video frame images. The experimental results show that the proposed algorithm accurately detects target(s) with a low false alarm rate in variety environment using the receiver operating characteristics (ROC) curve.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.338-343
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• 2020

In this paper, it was shown that the decoding method of Hamming codes using soft values can be applied to molecular communication channels. A soft value criterion that can be used for decoding of Hamming codes for a molecular communication channel was proposed, and it has been shown through simulation that the decoding method using these values can improve reliability even in the molecular communication channel. A diffusion-based molecular communication channel was assumed, and information symbols were transmitted using BCSK modulation. After demodulating the number of molecules absorbed by the receiver at each symbol interval with an appropriate threshold, the number of molecules is no longer used. In this paper, the BER performance of the decoder was improved by utilizing information on the number of molecules that are no longer used as soft values in the decoding process. Simulation was performed to confirm the improvement in BER performance. When the number of molecules per bit is 600, the error rate of the Hamming code (15,11) was improved about 5.0×10^-3 to the error rate of the BCSK system without the Hamming code. It can be seen that the error rate of (15,11) Hamming code with the soft values was improved to the same extent. In the case of (7,4) Hamming code, the result is similar to that of (15,11) Hamming code. Therefore, it can be seen that the BER performance of the Hamming code can be greatly improved even in the molecular communication channel by using the difference between the number of molecules absorbed by the receiver and the threshold value as a soft value.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1171-1176
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• 2003

In this paper, we propose an effective bar code detection algorithm using the feature analysis and the labeling. After computing the direction of pixels using four line operators, we obtain the histogram about the direction of pixels by a block unit. We calculate the difference between the maximum value and the minimum value of the histogram and consider the block that have the largest difference value as the block of the bar code region. We get the line passing by the bar code region with the selected block but detect blocks of interest to get the more accurate line. The largest difference value is used to decide the threshold value to obtain the binary image. After obtaining a binary image, we do the labeling about the binary image. Therefore, we find blocks of interest in the bar code region. We calculate the gradient and the center of the bar code with blocks of interest, and then get the line passing by the bar code and detect the bar code. As we obtain the gray level of the line passing by the bar code, we grasp the information of the bar code.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.