• Title/Summary/Keyword: Least Square Filtering

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Motion Adaptive Temporal Noise Reduction Filtering Based on Iterative Least-Square Training (반복적 최적 자승 학습에 기반을 둔 움직임 적응적 시간영역 잡음 제거 필터링)

  • Kim, Sung-Deuk;Lim, Kyoung-Won
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.5
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    • pp.127-135
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    • 2010
  • In motion adaptive temporal noise reduction filtering used for reducing video noises, the strength of motion adaptive temporal filtering should be carefully controlled according to temporal movement. This paper presents a motion adaptive temporal filtering scheme based on least-square training. Each pixel is classified to a specific class code according to temporal movement, and then, an iterative least-square training method is applied for each class code to find optimal filtering coefficients. The iterative least-square training is an off-line procedure, and the trained filter coefficients are stored in a lookup table (LUT). In actual noise reduction filtering operation, after each pixel is classified by temporal movement, simple filtering operation is applied with the filter coefficients stored in the LUT according to the class code. Experiment results show that the proposed method efficiently reduces video noises without introducing blurring.

Ideal Phase map Extraction Method and Filtering of Electronic Speckle Pattern Interferometry (ESPI 에서의 이상적인 위상도 추출과 필터링 방법)

  • 유원재;이주성;강영준;채희창
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2001.04a
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    • pp.235-238
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    • 2001
  • Deformation phase can be obtained by using Least-Square Fitting. In extraction of phase values, Least-Square Fitting is superior to usual method like as 2, 3, 4-Bucket Algorithm. That can extract almost noise-free phase and retain 2$\pi$discontinuities. But more fringe in phase map, 2$\pi$ discontinuities is destroyed when that is filtered and reconstruction of deformation is not reliable. So, we adapted Least-Square Fitting using an isotropic window in dense fringe. using Sine-Cosine filter give us perfect 2$\pi$discontinuities information. We showed the process and result of extraction of phase map and filtering in this paper.

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Ideal Phase map Extraction Method and Filtering of Electronic Speckle Pattern Interferometry (전자 스페클 간섭법에서의 이상적인 위상도 추출과 필터링 방법)

  • 강영준;이주성;박낙규;권용기
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.12
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    • pp.20-26
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    • 2002
  • Deformation phase can be obtained by using Least-Square fitting. In extraction of phase values, Least-Square Fitting is superior to usual method such as 2, 3, 4-Bucket Algorithm. That can extract almost noise-free phase and retain 2 $\pi$ discontinuities. But more fringes in phase map, 2 $\pi$ discontinuities are destroyed when that are filtered and reconstruction of deformation is not reliable. So, we adapted Least-Square fitting using an isotropic window in dense fringe. Using Sine/cosine filter give us perfect 2 $\pi$ discontinuities information. We showed the process and result of extraction of phase map and filtering in this paper.

Reverse Filtering of Sound Field by Adaptive Filter and Neural Network (적응필터 및 신경회로망에 의한 음장의 역 필터링)

  • Choi, Jae-Seung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.5 no.2
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    • pp.145-151
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    • 2010
  • This paper proposes a reverse filtering system of sound field obtaining a state of sound field transmitted from two sounds, using an adaptive filter and neural network. The proposed system uses the reverse filtering method with calculating and renewing a coefficient of a filter, using least mean square. Based on training the neural network, experiments confirm that the proposed system is effective for a simple waveform with non-linear distortion, by using neural network and adaptive filtering method.

Reverse Filtering Method by Neural Network (신경회로망에 의한 역 필터링 기법)

  • Choi, Jae-seung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.10a
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    • pp.695-698
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    • 2009
  • 본 논문에서는 음원으로부터 나온 음과 동일한 음을 들을 수 있는 시스템을 구축하는 것을 목적으로 하여 이 두 개의 음으로부터 전달되어온 음장의 상태를 구하여 이 역 필터를 구성하는 방법을 연구한다. 본 논문에서는 최소 2승 평균법(Least Mean Square, LMS)을 사용하여 FIR 필터(Finite Impulse Response)의 계수를 계산하여 이를 갱신함으로써 역 필터법을 구축하는 방법을 사용한다. 또한 이 방법과는 별도로 LMS법의 부분을 신경회로망에 대처하는 알고리즘을 제안하였다. 시뮬레이션 실험으로부터 상당히 간단한 파형에 비선형인 왜곡이 있는 것을 본 논문에서 제안한 신경회로망에 의한 학습 가능한 것을 확인하였다.

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A Study on the to Shorten of Early Decay Time in the Reverberation Curve Using MINT (MINT법을 이용한 실내 잔향곡선의 초기감쇠시간 단축에 관한 연구)

  • 차경환
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.1
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    • pp.37-41
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    • 2002
  • In this paper, we made shorter EDT(early decay time) of room reverberation curve using multiple-channel. The speech signal was processed inverse filtering with full-band and sub-band in the basis MINT, and then the multiple-channel adaptive filters were used LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) algorithm. Experimental results, we could get 1/3 of time reduction at 20dB level in the reverberation curve using full-band NLMS when two microphones were used. Also, it is shown that the speech articulation was improved 80% from the test listeners with the speech, which was to shorten EDT by MINT in the subjective assessments using real room impulse response.

Performance Improvement of Stereo Acoustic Echo Canceller Using MINT Filtering (MINT 필터링에 의한 스테레오 음향 반향 제거기의 성능 향상)

  • 차경환
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.1
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    • pp.42-46
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    • 2002
  • In this paper, a new pre-processing algorithm is proposed to improve the performance of stereo acoustic echo canceller. The proposed algorithm has the improved performance by the estimation error reduction of filter coefficient using input signal which was reduced reverberation of room in the basis MINT (Mu1tip1e-input/output Inverse Theorem) filtering. For real stereo speech signal and real room impulse response the results of simulation, we showed that the proposed method could improved 3∼5 dB ERLE (Echo Return Loss Enhancement) regardless of NLMS (Normalized Least Mean Square) and Projection adaptive algorithm.

A Study on Error Reduction of Indoor Location Determination using triangulation Method and Least Square Method (삼각측량법과 최소자승법을 활용한 실내 위치 결정의 산포 감소 방안에 관한 연구)

  • Jang, Jung-Hwan;Lee, Doo-Yong;Zhang, Jing-Lun;Jho, Yong-Chul;Lee, Chang-Ho
    • Journal of the Korea Safety Management & Science
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    • v.14 no.1
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    • pp.217-224
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    • 2012
  • Location-Based Services(LBS) is a service that provide location information by using communication network or satellite signal. In order to provide LBS precisely and efficiently, we studied how we can reduce the error on location determination of objects such people and things. We focus on using the least square method and triangulation positioning method to improves the accuracy of the existing location determination method. Above two methods is useful if the distance between the AP and the tags can be find. Though there are a variety of ways to find the distance between the AP and tags, least squares and triangulation positioning method are wildely used. In this thesis, positioning method is composed of preprocessing and calculation of location coordinate and detail of methodology in each stage is explained. The distance between tag and AP is adjusted in the preprocessing stage then we utilize least square method and triangulation positioning method to calculate tag coordinate. In order to confirm the performance of suggested method, we developed the test program for location determination with Labview2010. According to test result, triangulation positioning method showed up loss error than least square method by 38% and also error reduction was obtained through adjustment process and filtering process. It is necessary to study how to reduce error by using additional filtering method and sensor addition in the future and also how to improve the accuracy of location determination at the boundary location between indoor and outdoor and mobile tag.

Research on Noise Reduction Algorithm Based on Combination of LMS Filter and Spectral Subtraction

  • Cao, Danyang;Chen, Zhixin;Gao, Xue
    • Journal of Information Processing Systems
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    • v.15 no.4
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    • pp.748-764
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    • 2019
  • In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

Block Adjustment and Orthorectification for Multi-Orbit Satellite Images

  • Chen, Liang-Chien;Liu, Chien-Liang;Teo, Tee-Ann
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.888-890
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    • 2003
  • The objective of this investigation is to establish a simple yet effective block adjustment procedure for the orthorectification of multi-orbit satellite images. The major works of the proposed scheme are: (1) adjustment of satellite‘s orbit accurately, (2) calculation of the error vectors for each tie point using digital terrain model and ray tracing technique, (3) refining the orbit using the Least Squares Filtering technique and (4) generation of the orthophotos. In the process of least squares filtering, we use the residual vectors on ground control points and tie points to collocate the orbit. In orthorectification, we use the indirect method to generate the orthoimage. Test areas cover northern Taiwan. Test images are from SPOT 5 satellite. Experimental results indicate that proposed method improves the relative accuracy significantly.

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