• The Journal of the Acoustical Society of Korea
• /
• v.18 no.7
• /
• pp.45-55
• /
• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• International Journal of Aeronautical and Space Sciences
• /
• v.17 no.2
• /
• pp.214-221
• /
• 2016

An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.1C
• /
• pp.1-8
• /
• 2007

LMS(Least Mean Square) algorithm using steepest descent way in adaptive signal processing requires simple equation and is used widely because of the less complexity. But eigenvalues change by width of input signals in time domain, so the rate of convergence becomes low. In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and the same performance as the existing fast wavelet transform algorithm with less computational complexity. The proposed filter structure is applied to wavelet based adaptive algorithm. Simulation results show a better performance than the existing one.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.21 no.2
• /
• pp.29-35
• /
• 1984

LMS algorithm is used widely in adaptive filtering because of its simplicity. In this paper it is shown that the one dimensional LMS adaptive filter can be extended in the two dimensional adaptive filter and the methods for improving the convergence rate and the several problems inherent in the two dimensional adaptive filter are discussed.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.1
• /
• pp.25-35
• /
• 2022

The SAP(salt-and-pepper) noise changes the pixel value to the maximum and minimum values of the dynamic region of the pixel. For this reason, unlike white Gaussian noise, SAP noise can predict the ratio of noise relatively easily. Because the condition of the neighboring pixels that can be referenced changes according to the noise ratio, it is necessary to apply different noise reduction methods according to the noise ratio. This paper proposes an adaptive switching filtering algorithm which can eliminates the SAP noise. It consists of two phases. It first detects the location of the SAP noise and calculates the noise ratio. After that, the image is reconstructed using different methods depending on which of the three sections the calculated noise ratio belongs to. As a result of the experiment, the proposed method showed superior objective and subjective image quality compared to the previous methods such as MF, AFSWMF, NAMF and RWMF.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.4
• /
• pp.295-303
• /
• 2008

In general, transform domain adaptive filters show faster convergence speed than the time domain adaptive filters, but the amount of calculation increases dramatically as the filter order increases. This problem can be solved by making use of the subband structure in transform domain adaptive filters. In this paper, to increase the convergence speed on the generalized subband decomposition FIR adaptive filters, a structure of the adaptive filter with subfilter of dyadic sparsity factor in wavelet transform domain is designed. And, in this adaptive filter, the equivalent input in transform domain is derived and, by using the input, the convergence properties for the LMS algorithm is analyzed and evaluated. By using this sub band adaptive filter, the inverse system modeling and the periodic noise canceller were designed, and, by computer simulation, the convergence speeds of the systems on LMS algorithm were compared with that of the subband adaptive filter using DFT(discrete Fourier transform).

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.495-498
• /
• 1999

In this paper, we propose the adaptive Odd/Even Multi-shell Median Filter(adaptive O/E MMF) to improve the defect that Modified Multi-shell Median Filter(MMMF) can not recover missing lines of vertical and cross direction. This filter uses odd/even multi-shells and new proposed threshold strategy The performance of the proposed filter is evaluated over image 'airfield 'by using MATLAB. As the proposed threshold strategy eliminate the number of redundant replacement, it suppresses impulse noise and recovers missing lines.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.171-174
• /
• 2000

This paper proposes a pipelined adaptive filter based on affine projection algorithm with order 2. This filter gives a better convergence performance than that of LMS or NLMS pipeline algorithm and has same latency with the pipeline algorithm based on equivalent transformation. Compared to the critical path of the pipeline NLMS implementation, only 2 additions are increased in that of the proposed implementation.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.3
• /
• pp.231-238
• /
• 2004

This paper presents the high speed noise reduction processing system using the modified discrete fourier transform(MDFT) on the frequency domain. The proposed filter uses the linear prediction coefficients of the adaptive line enhance(ALE) method based on the Sign algorithm The signals with a random noise tracking performance are examined through computer simulations. It is confirmed that the fast adaptive digital filter is realized by the high speed adaptive noise reduction(HANR) algorithm with rapid convergence on the frequency domain(FD).

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.6
• /
• pp.518-524
• /
• 2013

In this paper, we propose a variable step size algorithm to enhance the normalized subband adaptive filter which has been proposed to improve the convergence characteristics of the conventional full band adaptive filter. The well-known Kwong's variable step size algorithm is simple, but shows better performance than that of the fixed step size algorithm. However, in case that large additive noise is present, the performance of Kwong's algorithm is getting deteriorated in proportion to the amount of the additive noise. We devised a variable step size algorithm which does not depend on the amount of additive noise by exploiting a normalized adaptation error which is the error subtracted and normalized by the estimated additive noise. We carried out a performance comparison of the proposed algorithm with other algorithms using a system identification model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments.