The Applications and Simulation of Adaptive Filter in Noise Canceling

He, Ying; He, Hong; Li, Li; Wu, Yi; Pan, Hongyan

doi:10.1109/csse.2008.370

Cited by 30 publications

(18 citation statements)

References 1 publication

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“…The main advantage of EMD is it does not depend on the filter orders as like linear and adaptive filters [5,6] and also does not require any basic or prior function as in the case like Wavelets [7]. EMD also exhibits stable performance along with moderate speed and less complexity.…”

Section: Introductionmentioning

confidence: 99%

AdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application

Cheepurupalli¹,

Konduri²

2013

IJCA

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Estimation of time delay between signals received at two spatially separated sensors has considerable practical importance in the applications like source localization, direction finding etc., in RADAR, SONAR and other communication systems. In this paper cross correlation (CC) generalized cross correlation with phase transform (GCC-PHAT) and maximum likelihood (ML) estimation methods are used as the time delay estimation methods. Prior to the delay estimation, the received signals are de-noised by AdaBoost based EMD technique. The performance of the delay estimation is significantly degraded by the signal-tonoise ratio (SNR) level and hence this factor has been considered as a principal factor. The simulation results of the proposed method are compared with the basic EMD as a denoising technique at various SNR levels. The results show that the proposed method improves the resolution in the delay estimation in the noisy environment.

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Section: Introductionmentioning

confidence: 99%

AdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application

Cheepurupalli¹,

Konduri²

2013

IJCA

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show abstract

“…Adding the carried 1 gives 25 so we put 5 and carry 2 ( 2 5). Finally we multiply vertically on the left, get 3 × 4=12 and add the carried 2 to get 14 which we put down [5].…”

Section: Vedic Multiplication Algorithmmentioning

confidence: 99%

Implementation of LMS-ALE Filter using Vedic Algorithm

U¹

2015

J Appl Computat Math

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“…Inputs to the adaptive filter or predictor unit is units behind the original input signal. Therefore, in order to time align the enhanced signal, ŝ(n) with the input signal, x(n) the adaptive filter must be able to 'predict ahead' in time by optimizing its filter coefficients in a least squares sense, hence the instantaneous squared error is minimized [5].…”

Section: Ale Filtermentioning

confidence: 99%

“…In this work we use Vedic algorithm for doing the multiplication. Compared to conventional booth algorithm Vedic multipliers requires less partial product adders, hence it improves the performance in terms of delay, resource utilization and power requirement [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%