AN IMPLICIT METHOD FOR DATA PREDICTION AND IMPULSE NOISE REMOVAL FROM CORRUPTED SIGNALS

2002 ◽  
Vol 13 (04) ◽  
pp. 565-583
Author(s):  
DE S. ZHANG ◽  
HAIXIANG WANG ◽  
DONALD J. KOURI ◽  
DAVID K. HOFFMAN
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Implicit Method ◽  
Frequency Noise ◽  
Analytic Approximation ◽  
Algebraic Equations ◽  
Data Prediction ◽  
Linear Algebraic Equations ◽  
Low Pass ◽  
Low Pass Filters

A robust and reliable implicit method is proposed for application in data interpolation. The algorithm is based on a recently developed analytic approximation method, namely the distributed approximating functionals (DAFs), which is known to have the "well-tempered" property of UNIFORMLY approximating a function and its derivatives. In comparison with the conventionally used local explicit interpolation algorithms, the implicit method achieves much more accurate interpolation results because it couples all sample values (both known and unknown) in the domain of interest using a set of simultaneous linear algebraic equations. Due to the fact that the well-tempered DAFs also are very good low-pass filters, the performance of the DAF-based implicit method is not affected very much by the high frequency noise in the input signal. As an application, the proposed algorithm is applied to signals that are corrupted with impulse noise.

scholarly journals Butterworth filters as tracking differentiators

2021 ◽  
Author(s):  
philip olivier
Keyword(s):  
High Frequency ◽  
Integral Form ◽  
Frequency Noise ◽  
Output Measurement ◽  
Tracking Differentiator ◽  
Noise Rejection ◽  
Output Noise ◽  
Low Pass ◽  
Butterworth Filters ◽  
Low Pass Filters

<div> <div> <div> <p>This letter describes how traditional Butterworth low pass filters can enhance the performance of the tracking differentiator introduced by Han by mitigating the effect of additive high frequency noise that corrupts the output measurement. The tracking differentiator obtains much of its utility from its realization in cascaded integral form. By combining the cascaded integral form realization of Butterworth low pass filters with its the noise rejection features one can design a tracking differentiator that is efficiently tuned to reject high frequency output noise. </p> </div> </div> </div>

Required Data Collection Times for Steady and Quasi-steady Experiments

2017 ◽  
Author(s):  
Robert F. Roddy ◽  
David E. Hess
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Frequency Noise ◽  
Surface Ship ◽  
System A ◽  
Low Pass ◽  
Collection Period ◽  
Almost All ◽  
Low Pass Filters

One of the requirements in performing steady or quasi-steady experiments is the determination of adequate collection times so that the data will not be biased due to low frequency energy in the data stream. Since virtually all steady experiments run at DTMB have low pass filters in line with the signal conditioning, high frequency noise is not a consideration in determining the required collection times. At both EMB and DTMB almost all of the surface ship drag measurements were made using gravity type balances until about 1970. These balances used both springs and dampers to modify the natural frequency of the system so that a good average model drag could be determined in a 5-6 sec collection period. Submarine model experiments began using block gages to measure drag beginning in the late 1950's. For these experiments crude methods were used to damp the output data but, to the author’s knowledge, no methods were ever put into place that was analogous to the springs and damper system. A method for determining the required collection times for any steady or quasi-steady experiment is presented along with sample cases showing the necessity for, and the utility of, using such a method.

scholarly journals Butterworth filters as tracking differentiators

2021 ◽  
Author(s):  
philip olivier
Keyword(s):  
High Frequency ◽  
Integral Form ◽  
Frequency Noise ◽  
Output Measurement ◽  
Tracking Differentiator ◽  
Noise Rejection ◽  
Output Noise ◽  
Low Pass ◽  
Butterworth Filters ◽  
Low Pass Filters

<div> <div> <div> <p>This letter describes how traditional Butterworth low pass filters can enhance the performance of the tracking differentiator introduced by Han by mitigating the effect of additive high frequency noise that corrupts the output measurement. The tracking differentiator obtains much of its utility from its realization in cascaded integral form. By combining the cascaded integral form realization of Butterworth low pass filters with its the noise rejection features one can design a tracking differentiator that is efficiently tuned to reject high frequency output noise. </p> </div> </div> </div>

scholarly journals Frequency domain analysis of core inflation measures for Brazil

2005 ◽  
Vol 35 (1) ◽  
pp. 5-36
Author(s):  
Eurilton Araújo ◽  
Antonio Fiorencio
Keyword(s):  
Frequency Domain ◽  
Frequency Domain Analysis ◽  
Frequency Noise ◽  
Low Frequencies ◽  
Trimmed Mean ◽  
Core Inflation ◽  
Trend Inflation ◽  
Low Pass ◽  
Low Pass Filters ◽  
Frequency Domain Properties

This paper analyses the frequency domain properties of two well-known measures of core inflation: the trimmed mean estimator and the SVAR estimator. It also investigates whether a small modification of the trimmed mean estimator enhances its capacity of filtering high‑frequency noise. We find that the two versions of the trimmed estimator are rather similar. They work as imperfect approximations for low pass filters. Therefore, they are capturing very well trend inflation. The SVAR estimator, however, is quite different from both of them. It emphasizes intermediate frequencies rather than low frequencies, indicating that cyclical movements associated with excess demand pressures are very important in the medium run.

Ku/Ka-band Compact Orthomode Junction with Low Pass Filters for High Power Applications

2015 ◽  
Vol E98.C (2) ◽  
pp. 156-161
Author(s):  
Hidenori YUKAWA ◽  
Koji YOSHIDA ◽  
Tomohiro MIZUNO ◽  
Tetsu OWADA ◽  
Moriyasu MIYAZAKI
Keyword(s):  
High Power ◽  
Ka Band ◽  
Low Pass ◽  
Low Pass Filters

On Improved Approximate Solution of the Fredholm Integral Equation

2006 ◽  
Vol 6 (3) ◽  
pp. 264-268
Author(s):  
G. Berikelashvili ◽  
G. Karkarashvili
Keyword(s):  
Integral Equation ◽  
Approximate Solution ◽  
Fredholm Integral Equation ◽  
Algebraic Equations ◽  
Order Convergence ◽  
One Dimensional ◽  
Averaging Operator ◽  
Linear Algebraic Equations ◽  
Convergence Solution

AbstractA method of approximate solution of the linear one-dimensional Fredholm integral equation of the second kind is constructed. With the help of the Steklov averaging operator the integral equation is approximated by a system of linear algebraic equations. On the basis of the approximation used an increased order convergence solution has been obtained.

Mathematical Model of Low-Pass Filters

2011 ◽  
Vol 5 (2) ◽  
pp. 155-162
Author(s):  
Jose de Jesus Rubio ◽  
Diana M. Vazquez ◽  
Jaime Pacheco ◽  
Vicente Garcia
Keyword(s):  
Mathematical Model ◽  
Low Pass ◽  
Low Pass Filters
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