• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.611-624
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• 2012

'Gesture' is the most intuitive means of communication except the voice. Therefore, there are many researches for method that controls computer using gesture input to replace the keyboard or mouse. In these researches, the method of user detection and main body parts estimation is one of the very important process. in this paper, we propose user objects detection and main body parts estimation method on inaccurate depth information for pose estimation. we present user detection method using 2D and 3D depth information, so this method robust to changes in lighting and noise and 2D signal processing 1D signals, so mainly suitable for real-time and using the previous object information, so more accurate and robust. Also, we present main body parts estimation method using 2D contour information, 3D depth information, and tracking. The result of an experiment, proposed user detection method is more robust than only using 2D information method and exactly detect object on inaccurate depth information. Also, proposed main body parts estimation method overcome the disadvantage that can't detect main body parts in occlusion area only using 2D contour information and sensitive to changes in illumination or environment using color information.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.383-388
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• 2006

This Paper presents a novel approach to single channel microphone speech enhancement in noisy environments. Widely used noise reduction techniques based on the spectral subtraction are generally expressed as a spectral gam depending on the signal-to-noise ratio (SNR). The well-known decision-directed(DD) estimator of Ephraim and Malah efficiently reduces musical noise under the background noise conditions, but generates the delay of the a prioiri SNR because the DD weights the speech spectrum component of the Previous frame in the speech signal. Therefore, the noise suppression gain which is affected by the delay of the a priori SNR, which is estimated by the DD matches the previous frame rather than the current one, so after noise suppression. this degrades the noise reduction performance during speech transient periods. We propose a computationally simple but effective speech enhancement technique based on the sigmoid type function for the weight Parameter of the DD. The proposed approach solves the delay problem about the main parameter, the a priori SNR of the DD while maintaining the benefits of the DD. Performances of the proposed enhancement algorithm are evaluated by ITU-T p.862 Perceptual Evaluation of Speech duality (PESQ). the Mean Opinion Score (MOS) and the speech spectrogram under various noise environments and yields better results compared with the fixed weight parameter of the DD.

• Journal of KSNVE
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• v.9 no.4
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• pp.849-855
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• 1999

Neural Networks have shown potential to become an attractive alternative to classic methods for identification and control of non-linear dynamic systems. The purpose of this paper is to present an application of neural networks, that is a neural reconstruction of the input signal of a non-linear unknown system. This basic methodology could be used for practical purpose in several engineering fields. Clearly applications of the proposed scheme can be of interest for physical systems where a complete network of sensors measuring system inputs is not available. It should also be emphasized that the application of the reconstruction scheme is of little or no interest when the analyzed system works and operates at nominal conditions. In fact, only when failures and/or system anomailes occur, leasing to performance degradation and/or shutdown, the application of this scheme is of interest. The paper presents the results of the methodology applied to unknown non-linear dynamic systems and the robustness of the scheme to white and colored system noise was evaluated.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.212-218
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• 2013

This paper presents an SVD(Singular Value Decomposition)-Pseudo Spectrum method for SAR (Synthetic Aperture Radar) imaging. The purpose of this work is to improve resolution and target separability of SAR images. This paper proposes SVD-Pseudo Spectrum method whose advantages are noise robustness, reduction of sidelobes and high resolution of spectral estimation. SVD-Pseudo Spectrum method uses Hankel Matrix of signal components and SVD (Singular Value Decomposition) method. In this paper, it is demonstrated that the SVD-Pseudo Spectrum method shows better performance than the matched filtering method and the conventional super-resolution based multiple signal classification (MUSIC) method in SAR image processing. The targets to be separated are modeled, and this modeled data is used to demonstrate the performance of algorithms.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.59-68
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• 2005

Object tracking in a real time image is one of interesting subjects in computer vision and many practical application fields Past couple of years. But sometimes existing systems cannot find object by recognize background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which does not influenced by illumination and remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up MBR(minimum bounding rectangle) using the internal point of detected otject, the system tracks otiect through this MBR. In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.986-994
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• 2005

This Paper proposes the likelihood-based nonlinear dimension reduction method of the speech feature parameters in order to construct the voice activity detecter adaptable in noisy environment. The proposed method uses the nonlinear values of the Gaussian probability density function with the new parameters for the speec/nonspeech class. We adapted Likelihood Ratio Test to find speech part and compared its performance with that of Linear Discriminant Analysis technique. In experiments we found that the proposed method has the similar results to that of Gaussian Mixture Models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.173-176
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• 2003

지문은 종생불변성, 만인부동성, 그리고 사용상의 편리함 때문에 신원인증을 위한 생체인식에 많이 사용되고 있다. 최근에는 기하학구조에 기반한 특이점 매칭방식이 제안되어 인식성능이 매우 높고 잡음에 강한 특성이 있으나 매칭 회수가 많아 인식속도가 느린 단점이 있다. 따라서 기존의 방식은 소수의 지문에 대한 1:다 매칭이나 1:1매칭에 주로 사용된다. 본 논문에서는 기존의 문제점들을 개선하기 위하여 생체 면역계의 자기-비자기 인식 능력에 주목하였다. 생체 면역계는 자기-비자기의 구별 능력을 바탕으로 바이러스나병원균 등의 낮선 외부침입자로부터 자신을 보호하고 침입자를 식별, 제거하는 시스템이다. 본 논문에서는 생체 면역계를 이루는 면역세포 중의 하나인 세포독성 T세포의 생성과정에서 자기, 비자기를 구별하기 위한 MHC 인식부를 형성하는 과정에 착안한 빠르고 신뢰성 있는 지문 인식 알고리즘을 제안한다. 제안한 방식은 지문에 존재하는 특이점(minutiae)인식을 통해 1단계로 global 패턴을 생성하고 2단계로 기하학적인 구조를 만들며, 인식시 global 패턴을 인식한 MHC 인식부에 대해서만 2차 local 매칭을 수행함으로써 매칭 속도가 매우 빠르며 지문의 비틀림이나 회전 등에 대하여 강인하게 인식된다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3115-3117
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• 2000

기존의 LMS 알고리듬을 이용한 적응필터에 비해 연산횟수를 줄이고 입력신호의 통계적 특성에 덜 민감한 적응필터를 제안한다. 입력 신호와 기준신호에 대한 고속 하다마드 변환을 수행한 후 하다마드 변환 영역에서 LMS 알고리듬을 적용한다 기존의 적응필터와 비교하여 필터의 입력신호 추정 성능은 유지하면서 고속 하다마드 변환으로 인해 적응과정에서의 곱셈연산이 크게 줄어드며 잡음의 분산값 변화와 같은 입력신호의 변화에 대한 필터의 안정도와 강인성이 크게 향상됨을 보인다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.50-59
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• 2011

In this paper, we present a spectrum sensing method by exploiting the relationship of previous and current detected data sets in frequency domain. Most of the traditional spectrum sensing methods only consider the current detected data sets of Primary User (PU). Previous state of PU is a kind of conditional probability that strengthens the reliability of the detector. By considering the relationship of the previous and current spectrum sensing, cross entropy-based spectrum sensing is proposed to detect PU signal more effectively, which has a strengthened performance and is robust. When previous detected signal is noise, the discriminating ability of cross entropy-based spectrum sensing is no better than conventional entropy-based spectrum sensing. To address this problem, we propose an improved cross entropy-based frequency-domain spectrum sensing. Regarding the spectrum sensing scheme, we have derived that the proposed method is superior to the cross entropy-based spectrum sensing. We proceed a comparison of the proposed method with the up-to-date entropy-based spectrum sensing in frequency-domain. The simulation results demonstrate the performance improvement of the proposed spectrum sensing method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.95-100
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• 2020

This paper related with the performance evaluation of mSE-MMA (modified Signed Error-Multi Modulus Algorithm) adaptive equalization algorithm which is possible to reduce the distortion that is occurs in nonlinear communication channel like as additive noise, intersymbol interference and fading. The SE-MMA algorithm are emerged in order to reducing the computational load compared to the presently MMA algorithm, it has the degraded equalization performance by this. In order to improve the performance degradation of SE-MMA, the mSE-MMA controls the step size according to the existence of arbitrary radius circle of equalizer output is centered at transmitted symbol point. The performance of proposed mSE-MMA algorithm were compared to present SE-MMA using the same channel and noise environment by computer simulation. For this, the recoverd signal constellation which is the output of equalizer, residual isi and MD (Maximum Distortion), MSE learning curve which is represents the convergence performance and SER which is represents the roburstness of noise were used as performance index. As a result of simulation, the mSE-MMA has more superior to the SE-MMA in every performance index, and was confirmed that mSE-MMA has roburstness to the noise in the SER performance than SE-MMA especially.