• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.769-772
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• 2001

Waish-Hadamard Transform은 압축, 필터링, 코드 디자인 등 다양한 이미지처리 분야에 응용되어왔다. 이러한 Hadamard Transform을 기본으로 확장한 Jacket Transform은 행렬의 원소에 가중치를 부여함으로써 Weighted Hadamard Matrix라고 한다. Jacket Matrix의 cocyclic한 특성은 암호화, 정보이론, TCM 등 더욱 다양한 응용분야를 가질 수 있고, Space Time Code에서 대역효율, 전력면에서도 효율적인 특성을 나타낸다 [6],[7]. 본 논문에서는 Distributed Arithmetic(DA) 구조를 이용하여 Fast Jacket Transform(FJT)을 구현한다. Distributed Arithmetic은 ROM과 어큐뮬레이터를 이용하고, Jacket Watrix의 행렬을 분할하고 간략화하여 구현함으로써 하드웨어의 복잡도를 줄이고 기존의 시스톨릭한 구조보다 면적의 이득을 얻을 수 있다. 이 방법은 수학적으로 간단할 뿐 만 아니라 행렬의 곱의 형태를 단지 덧셈과 뺄셈의 형태로 나타냄으로써 하드웨어로 쉽게 구현할 수 있다. 이 구조는 입력데이타의 워드길이가 n일 때, O(2n)의 계산 복잡도를 가지므로 기존의 시스톨릭한 구조와 비교하여 더 적은 면적을 필요로 하고 FPGA로의 구현에도 적절하다.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.92-98
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• 2017

Generally, image contains geometrical and radiometric errors. Census transform can solve the stereo mismatching problem caused by the radiometric distortion. Since the general census transform compares center of window pixel value with neighbor pixel value, it is hard to obtain an accurate matching result when the difference of pixel value is not large. To solve that problem, we propose a census transform method that applies different 4-step weight for each pixel value difference by applying an assistance window inside the window kernel. If the current pixel value is larger than the average of assistance window pixel value, a high weight value is given. Otherwise, a low weight value is assigned to perform a differential census transform. After generating an initial disparity map using a weighted census transform and input images, the gradient information is additionally used to model a cost function for generating a final disparity map. In order to find an optimal cost value, we use guided filtering. Since the filtering is performed using the input image and the disparity image, the object boundary region can be preserved. From the experimental results, we confirm that the performance of the proposed stereo matching method is improved compare to the conventional method.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.1-10
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• 2019

In this paper, we propose the track initiation algorithm based on the weighted score for TWS radar tracking. This algorithm utilizes radar velocity information to calculate the probabilistic track score and applies the Non-Maximum-Suppression(NMS) to confirm the targets to track. This approach is understood as a modification of a conventional track initiation algorithm in a probabilistic manner. Also, we additionally apply the weighted Hough transform to compensate a measurement error, and it helps to improve the track detection probability. We designed the simulator in order to demonstrate the performance of the proposed track initiation algorithm. The simulation result show that the proposed algorithm, which reduces about 40 % of a false track probability, is better than the conventional algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.143-147
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• 2008

이 논문에서는 곤충 발자국의 패턴을 인식하기 위해, Trace 변환을 이용하여 발자국의 인식에 필요한 특징을 추출하는 기법을 제안한다. Trace 변환을 이용하면 패턴의 이동, 회전, 반사에 불변하는 특징값을 얻을 수 있다. 이러한 특징값들은 곤충 발자국과 같이 다양한 변형이 존재하는 패턴을 인식하는 데에 적합하다. 이 방법은 특징값을 추출하기 위해서 병렬로 표현되는 trace-line을 따라 특징들을 일차적으로 도출하고, 또 다시 도출된 특징들은 diametric, circus 단계의 함수를 거치면서 새로운 특징값으로 재구성된다. 곤충의 발자국 패턴을 이용하여 실험한 결과 곤충 발자국의 이동, 회전 반사에 관계없이 동일한 특징값이 추출됨을 확인할 수 있고, 곤충발자국의 고유한 패턴을 찾아 인식하기 위해서 추출된 특징값들은 퍼지 가중치 평균을 이용하여 인식 실험을 수행하고 그 결과를 제시하였다.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.154-158
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• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.127-133
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• 2009

This paper presents an approach to classify normal and epilepsy from electroencephalogram(EEG) using a neural network with weighted fuzzy membership functions(NEWFM). To extract input features used in NEWFM, wavelet transform is used in the first step. In the second step, the frequency distribution of signal and the amount of changes in frequency distribution are used for extracting twenty-four numbers of input features from coefficients and approximations produced by wavelet transform in the previous step. NEWFM classifies normal and epilepsy using twenty four numbers of input features, and then the accuracy rate is 98%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.98-105
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• 1989

Various models incorporating Human Visual System (HVS) with the Hadamard transform (HT) represented by Walsh functions are considered. Using the exact frequency components of HT basis functions, the optimum modulation transfer function (MTF) which has a higher peak frequency than DCT schemes is obtimum modulation transfer function (MTF) which has a higher peak frequency than DCT schemes is obtained analytically and visually. The main criterion, for error measurement, is errors at the block boundaries which is an important factor in transform coding. The scheme which has no inverse HVS is proposed. It causes some degradation of image data but it is insignigicant. Crossing area of 4 blocks is equalized by the HVS weighting coefficients. The HVS weighted coding results in perceptually higher quality images compared with the unweighted scheme.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.179-186
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• 2009

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.517-529
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• 2016

This study compares the inverse transform and the composition methods for generating data from the Lindley distribution. The expression for the inverse of the distribution function for the Lindley distribution does not exist in closed form. Hence, authors of many empirical studies on the Lindley distribution used methods for generating Lindley variates other than the inverse transform. We generated data from the Lindley distribution using the inverse transform approach by obtaining the Lindley variates numerically; we also generated data from this distribution using the composition approach. Following the generation of the Lindley variates using these two methods, we compare some statistical properties of the estimates of the Lindley model parameters based on the generated data. We conclude that the two methods produce similar results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1294-1304
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• 1995

In this paper, a method has been proposed for the fully automatic detection of left ventricular endocardial boundary in B-mode short axis echocardiography without manual intervention by human operator. The proposed method makes use of the weighted model that approximates to endocardium and incomplete edge information for echocardiography. Therefore, this method is more effective than boundary detection by only edge information. The implementation of this method is as follows. First, the proposed algorithms are used in order to detect the approximate boundary, then a weighted model with the approximate boundary is constructed. Finally, the cavity center of the left ventricle performing the Hough transform with the weighted model and edge image can be found automatically, and then the endocardial boundary using detected center, original image, weighted model, and edge image can be detected. validations of this method with experimental results on echo image of dog's heart and clinical echocardiography is verified.