• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.238-239
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• 2008

In this paper, active noise control (ANC) of a Volterra system with a nonlinear secondary path is proposed in the presence of a linear acoustic feedback, whereby the conventional ANC of a linear system with online acoustic feedback-path modeling is further extended to ANC of a Volterra system with a linear acoustic feedback path. In particular, the proposed ANC system consists of two adaptive Volterra filters (for nonlinear noise control and nonlinear adaptive noise cancellation) and one feedback-path modeling filter. Simulation results show that the proposed approach yields more effective reduction of disturbances arising from the acoustic feedback, in addition to high nonlinear ANC performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.89-101
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• 1997

A technical method of noise modeling and adaptive filtering reducing of speckle noise in ultrasonic medical images is presented. By adjusting the characteristics of the filer according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in diagnosis. Homogeneous factor(HF) from the noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the diagnostic systems with various inner statistical properties is proposed. We show by the experimented that the performance of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3E
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• pp.93-98
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• 2009

In this paper, we propose a new VSS-NLMS algorithm through a simple modification of the conventional NLMS algorithm, which leads to a low complexity algorithm with enhanced performance. The step size of the proposed algorithm becomes smaller as the error signal is getting orthogonal to the input vector. We also show that the proposed algorithm is an approximated normalized version of the KZ-algorithm and requires less computation than the KZ-algorithm. We carried out a performance comparison of the proposed algorithm with the conventional NLMS and other VSS algorithms using an adaptive channel equalization model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.27-35
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• 1993

In this paper, a new algorithm for high level security and quality of speech is proposed. The algorithm is based on the rearrangement of the fast fourier transform (FFT) coefficients with pre and post filter process, hamming window and adaptive pseudo spectrum insertion. Then, the pre and post filters are used for the whitening of speech spectrum and the adaptive pseudo spectrum is inserted for the unclassification of silence/speech. Also, the hamming window technique is applied for the robustness to the syncronization error in the telephone line. According to the simulation results, it can be seen that the security of scrambled signal and the quality of descrambled signal have been improved fairly in both subjective and objective performance test and the new FFT scrambler is robust to the synchronization error.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.91-94
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• 2001

Recently a constant-norm constraint equation-error method was proposed to solve the bias problem in adaptive IIR filtering. However, the method adopts a fixed step-size and thus results in slow convergence for a small step-size and significant misadjustment error for a largestep-size. In this paper, we propose a variable step-size (VSS) algorithm that greatly improves convergence properties of the constant-norm constraint equation-error method. The analysis and the simulation results show that the proposed method indeed achieves both fast convergence and small misadjustment error.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.125-130
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• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.719-726
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• 1987

In this paper, an adaptive IIR (infinite impulse response) notch filter of lattice type is constructed and its adaptation algorithm is proposed for the detection and retrieval of a sine wave signal embedded in noise. A modified method which adapts only one coefficient of the filter is also suggested. All these methods adapt the coefficients while keepting the poles of the filter inside the unit circle on z-plane, and thus they satisfy the condition on the stability of the IIR filter after it has converged. To investigate the convergence characteristics of these methods such as convergence speed and output S/N ratio, intensive computer simulation has been performed by varying the frequency of the sine wave and the input S/N ratio. And the results of the simulation have been compared to those of Rao and Kung's which shows relatively fast convergence speed. The methods proposed here, especially the second one. shows faster convergence speed and higher output S/N ratio than the Rao and Kung's.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.91-98
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• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

• Proceedings of the IEEK Conference
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• summer
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• pp.159-164
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• 2004

This paper aims towards designing and implementation of a new adaptive Peer to Peer (P2P) network that cluster itself on the basis of semantic proximity. We also developed an algorithm to classify the nodes to form the semantic groups and to direct the queries to appropriate groups without any human intervention. This is done using Bloom filters to summarise keywords of the documents shared by a peer. The queries are directed towards the appropriate clusters instead of flooding them. The proposed topology supports a system for maintaining a global, omnipresent trust value for each peer in an efficient manner both in terms of decision time and network load.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.433-434
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• 2007

In this paper, active control of noise in a HVAC duct is considered. Most adaptive control filters have used FIR structures based on filtered-x LMS algorithms. But, the IIR structures are more desirable for the active control of duct noise in order to remove the poles introduced by the acoustic feedback and presented an algorithm to adjust the coefficients of an IIR filter using the recursive least mean square (RLMS) algorithm. A smoothed LMS algorithm is proposed to improve a convergent speed of filter parameters when the noise is wide band and power of input is time varying. And computer simulations have performed to show the effectiveness of the proposed algorithm.