• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1074-1081
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• 2000

In this paper, in order to speed up the convergence process and improve the steady mean square error simultaneously, we propose the Stop-and-Go Dual Mode Modified Constant Modulus Algorithm(SAG DM MCMA) for adaptive blind channel equalization of high order QAM. The proposed algorithm is a hybrid scheme of the Modified CMA that treat error signals with real and imaginary components of the equalizer output, the concept of dual mode CMA, and Stop-and-Go algorithm. As a result it can prevent blind equalization from converging to incorrect direction and simultaneously operates reliably for tap weight adaptation. We demonstrate via simulation that the proposed algorithm achieves lower steady state mean square error and residual ISI than the conventional algorithms under high order QAM signals and severe channel environment.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2992-2994
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• 1999

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the weight adjustment is developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Also, the tracking errors between the system outputs and the desired outputs converge to zero asymptotically. To evaluate the performance of the controller, the proposed method is applied to the trajectory control of the two-link manipulator.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.84-87
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• 2008

본 논문에서는 Q.Wang & R.K.Ward 가 제안한 WTHE(weighted and thresholded histogram equalization)방법의 enhancement parameters를 주어진 영상의 히스토그램 분포에 따라 적응적으로 제공하는 방법을 제안한다. WTHE는 영상의 히스토그램을 weight와 threshold를 이용하여 변형한 후 히스토그램 평활화(histogram equalization : HE)방법을 수행 함으로써 화질을 개선하는 방법이다. 이 방법은 두 가지 parameters 제어로 기존의 히스토그램 평활화 방법의 단점인 과도한 밝기 변화와 불필요한 artifacts를 줄일 수 있다. 본 논문에서는 WTHE 방법을 좀 더 간편하면서 다양한 분야에 적용하기 위해서 입력 영상에 따라 달라지는 parameters 값을 자동으로 제공하는 적응형 WTHE(Adaptive WTHE : AWTHE) 방법을 제안하고, 제안된 방법의 성능을 실험으로 제시한다.

• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.658-665
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• 2015

In the production of medical images, noise reduction and contrast enhancement are important methods to increase qualities of processing results. By using the edge-based denoising and adaptive nonlinear histogram stretching, a novel medical image enhancement algorithm is proposed. First, a medical image is decomposed by wavelet transform, and then all high frequency sub-images are decomposed by Haar transform. At the same time, edge detection with Sobel operator is performed. Second, noises in all high frequency sub-images are reduced by edge-based soft-threshold method. Third, high frequency coefficients are further enhanced by adaptive weight values in different sub-images. Finally, an adaptive nonlinear histogram stretching method is applied to increase the contrast of resultant image. Experimental results show that the proposed algorithm can enhance a low contrast medical image while preserving edges effectively without blurring the details.

• Journal of KIISE
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• v.44 no.6
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• pp.628-636
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• 2017

HTTP adaptive streaming is a technique that improves the quality of experience by storing various quality videos on the server and requesting files of the appropriate quality based on network bandwidth. However, it is difficult to measure the actual bandwidth in wireless networks with frequent bandwidth changes and high loss rate. Frequent quality changes and playback interruptions due to bandwidth measurement errors degrade the quality of experience. We propose a technique to estimate the available bandwidth by measuring the jitter, which is the derivation of delay, on a packet basis and assigning a weight according to jitter. The proposed scheme reduces the number of quality changes and mitigates the buffer underflow by reflecting less bandwidth change when high jitter occurs due to rapid bandwidth change. The experimental results show that the proposed scheme improves the quality of experience by mitigating buffer underflow and reducing the number of quality changes in wireless networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4A
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• pp.269-277
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• 2012

When there are mismatches between the beamforming steering vector and the array response vector for the desired signal, the performance can be severely degraded as the adaptive array attempts to suppress the desired signal as well as interferences. In this paper, an robust method is proposed for the adaptive array in the presence of both direction errors and random errors in the steering vector. The proposed method first finds a signal-plus-interference subspace (SIS) from the correlation matrix, which in turn is exploited to extract an interference subspace based on the structure of a uniform linear array (ULA), the effect of the desired signal direction vector being reduced as much as possible. Then, the weight vector is attained to be orthogonal to the interference subspace. Simulation shows that the proposed method, in terms of signal-to-interference plus noise ratio (SINR), outperforms existing ones such as the doubly constrained robust Capon beamformer (DCRCB).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.285-294
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• 2004

Median Filters in the Signal Processing have been most widely used and have demonstrated the most strongest effects. This paper proposes the Adaptive Median Filters by using noise detection. The basic algorithm of the proposed filters is to determine whether noise or not by the each noise judgement standards, and then take the Median Filter if it satisfies the conditions as a result of judgement and returns to the original image(No Filters) if not. This paper presented Noise Detection by Local Variance and Local Central Variance for noise judgement, compared and analyzed the features and performance of existing [5]∼[10] Filters. Filter improved on the result of executing the existing filters at the same condition and showed the effects over that when it was judged with naked eyes. Accordingly, the Adaptive Median Filters by Local Variance and Local Central Variance was proven to have reinforced edge preservation ability and have the strong features for removing the Impulse Noise of the Median Filter.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.145-156
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• 2005

In application for acoustic echo cancellation and adaptive equalization, input signal is highly correlated and the long length of adaptive filter is needed. Affine projection algorithms, in these applications, can produce a good convergence performance. However, they have a drawback that is a complex hardware implementation. In this paper, we propose a new subband affine projection algorithm with improved convergence and reduced computational complexity. In addition, we suggest a good approach to implement the proposed method. In this method by applying polyphase decomposition, noble identity and critical decimation to the anne projection algorithm the number of input vectors for decorrelation can be reduced. The weight-updating formula of the proposed method is derived as a simple form that compared with the NLMS(normalized least mean square) algorithm by the reduced projection order The efficiency of the proposed algorithm for a colored input signal was evaluated by using computer simulations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.1
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• pp.45-50
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• 2015

In this paper, we confirm the effective adaptive algorithm for tha active noise contorl (ANC) though the performance comparison between adaptive algorithms. Generally, the normalized least mean square (NLMS) algorithm has been widely used for an adaptive algorithm thanks to its simplicity and having a fast convergence speed. However, the convergence performance of the NLMS algorithms is often deteriorated by colored input signals. To overcome this problem, the affine pojection (AP) algorithm that updates the weight vector based on a number of recent input vectors can be used for allowing a higher convergence speed than the NLMS algorithm, but it is computationally complex. Thus, the proper algorithm were determined by the comparison between NLMS and AP algorithms regarding as the convergence performance and complexity. Simulation results confirmed that the noise reduction performance of NLMS algorithm was comparable to AP algorithm with low complexity. Therefore the NLMS algorithm is more effective for ANC system.