• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1475-1478
• /
• 2002

This paper presents a performance analysis of an improved adaptive algorithm proposed by the authors recently. It is based on the normalized least mean square (NLMS) algorithm, which Is one of the major techniques to adapt the cofficients of a transversal filter. Generally, the performance of an adaptive algorithm is often discussed by investigating the mis-adjustment. In this paper, unlike these approaches, a novel analytical method is considered. letting the parameters so that the residual mean square error (MSE) after the convergence of the algorithm is equal to that of the NLMS algorithm, the MSE level is compared. It is shown that the theoretical analysis is agreed with the simulation results.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.2
• /
• pp.431-434
• /
• 2006

현재 사용되는 디지털 신호처리 방식의 GPS(Global Positioning System) 수신기는 최소의 RF 블록만을 이용하여 모든 디지털 신호 처리를 소프트웨어로 수행하고 있다. 소프트웨어 GPS수신기의 장점으로는 디지털 신호처리 과정이 모두 소프트웨어로 구현되어 있기 때문에 새로운 구현 방식이나 알고리즘을 적용할 시, 하드웨어 변경은 필요가 없게 되고, 소프트웨어 재설계만으로 가능하다. GPS 수신기의 Tracking module 성능은 DLL discriminator function과 FLL discriminator function의 Algorithm에 의해서 영향을 받게 된다. DLL discriminator function algorithm에는 dot-product 와 normalized dot-product 방법 등이 있으며, FLL discriminator function algorithm에는 sign(dot)(cross)-product 와 cross-product 그리고 ATAN 방법이 있다. 이러한 discriminator function들은 algorithm에 따라 input error에 대한 discriminator의 출력 값을 일정한 범위에서의 차이를 나타내게 되면서 ranging accuracy에서 차이가 나타나게 된다. 본 논문에서는 Nordnav 소프트웨어 GPS 수신기를 이용하여 DLL discriminator algorithm과 FLL discriminator algorithm의 성능을 비교, 분석을 하고 Tracking accuracy, sensibility, 그리고 multipath rejection에 대해서 알아보고자 한다.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2004.11a
• /
• pp.134-134
• /
• 2004

• The Journal of the Acoustical Society of Korea
• /
• v.31 no.7
• /
• pp.427-434
• /
• 2012

The overlap-add technique based on waveform similarity (WSOLA) method is known to be an efficient high-quality algorithm for time scaling of speech signal. The computational load of WSOLA is concentrated on the repeated normalized cross-correlation (NCC) calculation to evaluate the similarity between two signal waveforms. To reduce the computational complexity of WSOLA, this paper proposes a fast NCC computation method, in which NCC is obtained through pre-calculated sum tables to eliminate redundancy of repeated NCC calculations in the adjacent regions. While the denominator part of NCC has much redundancy irrespective of the time-scale factor, the numerator part of NCC has less redundancy and the amount of redundancy is dependent on both the time-scale factor and optimal shift value, thereby requiring more sophisticated algorithm for fast computation. The simulation results show that the proposed method reduces about 40%, 47% and 52% of the WSOLA execution time for the time-scale compression, 2 and 3 times time-scale expansions, respectively, while maintaining exactly the same speech quality of the conventional WSOLA.

• Journal of Korea Multimedia Society
• /
• v.13 no.11
• /
• pp.1698-1705
• /
• 2010

In this paper, we propose a fast liver segmentation method from magnetic resonance(MR) images. Our method efficiently divides a MR image into a set of discrete objects, and boundaries based on the normalized gradient magnitude information. Then, the objects belonging to the liver are detected by using 2D seeded region growing with seed points, which are extracted from the segmented liver region of the slice immediately above or below the current slice. Finally, rolling ball algorithm, and connected component analysis minimizes false positive error near the liver boundaries. Our method was validated by twenty data sets and the results were compared with the manually segmented result. The average volumetric overlap error was 5.2%, and average absolute volumetric measurement error was 1.9%. The average processing time for segmenting one data set was about three seconds. Our method could be used for computer-aided liver diagnosis, which requires a fast and accurate segmentation of liver.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.333-337
• /
• 2004

This paper describe a design of 5GHz OFDM baseband chip for IEEE 802.11a wireless LAN. The proposed device is consists of transmitter and receiver within a single FPGA chip. We applied single tap equalizer that use Normalized LMS algorithm to remove ISI that happen at high speed data transmission. And also, we used carrier wave frequency offset algorithm that use training symbol to remove ICI. The simulation results show the correct transmission without errors the between transmitter and receiver And we can remarkably reduce the number of register through the synthesized circuits by using DSP block and EMB(Embedded Memory Block). The target device for implementation of the synthesized circuits is Altera Stratix EPIS25FC672 FPGA and design platform is VHDL.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.5C
• /
• pp.478-486
• /
• 2007

Double-talk detection errors can result in either large residual echo or distorting the near-end talker's input speech. Thus accurate double-talk detection is an important problem in the acoustic echo canceller to improve the speech quality. In the double-talk detection algorithm using a cross-correlation coefficient, double-talk detection errors can occur in the initial convergence period of an adaptive filter or in noisy environment since the cross-correlation coefficient becomes large in such situations. In this paper, we propose a new double-talk detection algorithm based on the cross-correlation method using a normalized error signal power to reduce the double-talk detection errors. The experimental results have shown the performance improvement of an acoustic echo canceller as well as the noise-robustness of the proposed double-talk detector.

• Phonetics and Speech Sciences
• /
• v.4 no.3
• /
• pp.103-110
• /
• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Speech Sciences
• /
• v.12 no.3
• /
• pp.79-89
• /
• 2005

Acoustic feedback between microphone and receiver can be effectively cancelled adaptive feedback cancellation algorithm. Although many speech sounds have non-Gaussian distribution, most algorithms were tested with speech like sounds whose distribution were Guassian type. In this paper, we proposed an adaptive feedback cancellation algorithm based on independent component analysis (ICA) for digital hearing aid. The algorithm was tested with not only Gaussian distribution but also Laplacian distribution. We verified that the proposed algorithm has better acoustic feedback cancelling performance than conventional normalized root mean square (NLMS) algorithm, especially speech like sounds with Laplacian distribution.

• Journal of Information Processing Systems
• /
• v.7 no.3
• /
• pp.425-434
• /
• 2011

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.