• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1274-1282
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• 1996

This paper deals with an inverse modeling of nonminimum phase communication channel utilizing the HOS (High Order Statistics) of the received signal. After the communication channel is separated into the minimum phase and maximum phase components, the inverse modeling is performed independently. The performance superiority is confirmed by monte-carlo computer simulation in comparison with the traditional CMA (Constant Modulus Algorithm) method. By utilizing the proposed algorithm employing the HOS of the received signal, the inverse frequency characteristics of the channel can be obtained withoug transmitted signal in digital communication. This algorithm is required in preprocessing or postprocessing in order to remove the channel effect, and effective in the self adaptive equalizer which can minimize the bit error rate or symbol error rate in the recovry of received signal.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.62-68
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• 2006

In this paper, we propose an adaptation strategy for blind equalizers, which combines a blind algorithm based on high order statistics and the decision directed LMS algorithm. In contrast to 'Stop-and-Go' algorithm, where adaptation is stopped for unreliable signals, the proposed algorithm applies high order statistics (HOS) blind algorithm to the unreliable signals and applies DD-LMS for the reliable signals. The proposed algorithm, named 'Run-and-Go' algorithm, inherits minimum MSE performance of DD-LMS and acquisition ability of blind algorithms. Furthermore, by updating the reliable signal region according to signal quality in each iteration, the convergence speed and acquisition ability is further improved.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1139-1149
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• 2006

This paper describes a novel and efficient algorithm, which extracts focused objects from still images with low depth-of-field (DOF). The algorithm unfolds into four modules. In the first module, a HOS map, in which the spatial distribution of the high-frequency components is represented, is obtained from an input low DOF image [1]. The second module finds OOI candidate by using characteristics of the HOS. Since it is possible to contain some holes in the region, the third module detects and fills them. In order to obtain an OOI, the last module gets rid of background pixels in the OOI candidate. The experimental results show that the proposed method is highly useful in various applications, such as image indexing for content-based retrieval from huge amounts of image database, image analysis for digital cameras, and video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.3
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• pp.53-60
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• 2008

Recently, various recording technologies are studied for optical data storage. After standardization of BD (Blue-ray Disc) and HD-DVD (High-Definition Digital Versatile Disc), the industry is looking for a suitable technology for next generation optical data storage. Super-RENS (Super-resolution near field structure) technique, which is capable of compatibility with other systems, is one of next optical data storage. In this paper, we analyze the nonlinearity of Super-RENS read-out signal through the bicoherence test, which uses HOS (Higher-Order Statistics) and apply neural networks for nonlinear modeling. The model structure considered in this paper is the NARX (Nonlinear AutoRegressive eXogenous) model. The experiment results indicate that the read-out signals have nonlinear characteristics. In addition, it verified the possibility that neural networks can be utilized for nonlinear modeling of Super-RENS systems.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.369-374
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• 2006

This paper related with the performance improvement of adaptive equalizer that is a based on the tricepstrum eqalization algorithm by using the received signal. Adaptive equalizer used for the improvement of communication performance, like as high speed, maintain of synchronization, BER, at the receive side in the environment of communication channel of the presence of the aditive noise, phase distortion and frequency selective fading, mainly. It's characteristics are nearly same as the inverse characterstics of the communication channel. In this paper, the TEA algorithm using the HOS and the 16-QAM which is 2-dimensional signaling method for being considered signal was used. For the precoding of 16-QAM singnal in the assignment of the signal costellation, Gray code was used, and the improvement of performance was gained by computer simulation in the residual intersymbol interence and mean squared error which is representive measurement of adaptive equalizer.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.187-192
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• 2015

If the software is developed to analyze the speech disorder, the application of various converged areas will be very high. This paper implements the user-friendly program based on CART(Classification and regression trees) analysis to distinguish between normal and pathological voices utilizing combination of the acoustical and HOS(Higher-order statistics) parameters. It means convergence between medical information and signal processing. Then the acoustical parameters are Jitter(%) and Shimmer(%). The proposed HOS parameters are means and variances of skewness(MOS and VOS) and kurtosis(MOK and VOK). Database consist of 53 normal and 173 pathological voices distributed by Kay Elemetrics. When the acoustical and proposed parameters together are used to generate the decision tree, the average accuracy is 83.11%. Finally, we developed a program with more user-friendly interface and frameworks.