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 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 EVALUATION OF INTEGRATED ANALOG RC FILTER STRUCTURES FOR MULTISTANDARD TRANSCEIVERS / INTEGRINIŲ ANALOGINIŲ RC FILTRŲ STRUKTŪRŲ DAUGIASTANDARČIAMS SIŲSTUVAMS-IMTUVAMS TYRIMAS

2016 ◽  
Vol 8 (3) ◽  
pp. 315-320
Author(s):  
Karolis Kiela ◽  
Aleksandr Mamajev ◽  
Romualdas Navickas
Keyword(s):  
Integrated Circuits ◽  
Low Noise ◽  
Filter Bandwidth ◽  
Output Noise ◽  
Low Pass ◽  
Simulation Results ◽  
Response Variation ◽  
Low Pass Filters ◽  
Filter Structures ◽  
Filter Frequency

Multistandard transceivers usually have high order low pass filters in their receiver chains. Different filter topologies may have various component variation tolerances and different output noise. In this work, three 6th order filter with different topologies are analyzed for use in multistandard transceivers. Filters are designed in 0.18 µm and 65 nm CMOS technologies and simu-lated using Cadence software. The results show, that the filter frequency response variation in integrated circuits does not de-pend on the filter topology. Simulation results also show that the Leapfrog filter topology has the smallest integrated output noise in the filter bandwidth and is most suited for low noise applications. Daugiaustandarčiuose siųstuvuose-imtuvuose naudojami aukštesnės eilės žemųjų dažnių filtrai. Skirtingos filtrų struktūros gali būti nevienodai jautrios jas sudarančių elementų nuokrypiams ir daryti skirtingą įtaką daugiastandarčių siųstuvų-imtuvų triukšmams. Darbe pateikti trijų šeštosios eilės skirtingų struktūrų aktyviųjų RC filtrų, skirtų daugiaustandarčiams siųstuvams-imtuvams, tyrimo rezultatai. Filtrų struktūros modeliuojamos naudojant 0,18 μm ir 65 nm KMOP integrinių grandynų gamybos technologijas ir Cadence Virtuoso programinę įrangą. Iš imitacijos rezultatų matyti, kad moderniose integrinių grandynų technologijose filtrų dažninių amplitudės charakteristikų (DACh) parametrų nuokrypis beveik nepriklauso nuo filtro struktūros. Tarp analizuotų filtrų struktūrų mažiausia praleidžiamųjų dažnių juostoje integruota išėjimo triukšmo vidutinė kvadratinė vertė gaunama naudojant šuolinių grįžtamųjų ryšių struktūros filtrus.

SOFTENING THE NONLINEARITY IN CHUA'S CIRCUIT

1996 ◽  
Vol 06 (01) ◽  
pp. 179-183 ◽  
Author(s):  
J. M. LIPTON ◽  
K. P. DABKE
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Frequency Noise ◽  
Chua’S Circuit ◽  
High Frequency Noise ◽  
Chua's Circuit ◽  
Low Pass

The effects of both hard and soft nonlinearities are examined in the frequency domain. Softening the hard nonlinearity in Chua's diode has a similar effect to low pass filtering or reducing the level of high frequency noise components.

scholarly journals Effective resolution concepts for lidar observations

2015 ◽  
Vol 8 (12) ◽  
pp. 5157-5176 ◽  
Author(s):  
M. Iarlori ◽  
F. Madonna ◽  
V. Rizi ◽  
T. Trickl ◽  
A. Amodeo
Keyword(s):  
High Frequency ◽  
Digital Signal ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Frequency Noise ◽  
Vertical Profiles ◽  
Range Resolution ◽  
Low Pass ◽  
The Impact ◽  
Aerosol Properties

Abstract. Since its establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has provided, through its database, quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or high-spectral-resolution lidars). These coefficients are stored in terms of vertical profiles, and the EARLINET database also includes the details of the range resolution of the vertical profiles. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly acting as low-pass filters to reduce the high-frequency noise. Data filtering is described by the digital signal processing (DSP) theory as a convolution sum: each filtered signal output at a given range is the result of a linear combination of several signal input data samples (relative to different ranges from the lidar receiver), and this could be seen as a loss of range resolution of the output signal. Low-pass filtering always introduces distortions in the lidar profile shape. Thus, both the removal of high frequency, i.e., the removal of details up to a certain spatial extension, and the spatial distortion produce a reduction of the range resolution. This paper discusses the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved from lidar data. Large attention has been dedicated to providing an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.

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.

A stable and efficient approach of Q reverse time migration

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. S557-S567 ◽  
Author(s):  
Yan Zhao ◽  
Ningbo Mao ◽  
Zhiming Ren
Keyword(s):  
Wave Equation ◽  
Computational Efficiency ◽  
High Frequency ◽  
Frequency Noise ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
High Frequency Noise ◽  
Time Migration ◽  
Low Pass ◽  
Wavefield Extrapolation

Amplitude energy attenuation and phase distortion of seismic waves caused by formation viscoelasticity reduce the resolution of reverse time migration (RTM) images. Q-RTM often is used to compensate the attenuation effects and improve the resolution of seismic imaging. However, serious high-frequency noise and tremendous amplitude will be produced during the wavefield extrapolation of Q-RTM, resulting in its inability to be imaged. Many Q-RTM algorithms solve the problem of instability through low-pass filtering in the wavenumber domain, but the method is less efficient in computation and has a truncation effect in the wavefield. We have developed a stable and efficient Q-RTM method, in which a regularization term was introduced into the viscoacoustic wave equation to suppress the high-frequency noise, and the finite-difference method was used to solve the viscoacoustic wave equation with a regularization term. We used the model example to visually demonstrate the instability of wavefield extrapolation in Q-RTM and compared the effect and computational efficiency of the two stabilization processing methods, low-pass filtering and regularization. Meanwhile, our method is not involved in solving the fractional derivatives by using the pseudo-spectral method, the computational efficiency also can be improved. We tested the Q-RTM approach on a simple layered model, Marmousi model, and real seismic data. The results of numerical examples demonstrated that the Q-RTM method can solve the problem of instability effectively and obtain a higher resolution image with lower computational cost.

On the estimation of the structural index from low-pass filtered magnetic data

Geophysics ◽  
2014 ◽  
Vol 79 (6) ◽  
pp. J67-J80 ◽  
Author(s):  
Giovanni Florio ◽  
Maurizio Fedi ◽  
Roman Pašteka
Keyword(s):  
Magnetic Data ◽  
Main Task ◽  
Euler Deconvolution ◽  
Structural Index ◽  
Potential Field Data ◽  
Upward Continuation ◽  
Physically Based ◽  
Low Pass ◽  
Butterworth Filters ◽  
Low Pass Filters

The estimation of the structural index and of the depth to the source is the main task of many popular methods used to analyze potential field data, such as Euler deconvolution. However, these estimates are unstable even in the presence of a weak amount of noise, and Euler deconvolution of noisy data leads to an underestimation of structural index and depth. We have studied how the structural index and depth estimates are affected by applying low-pass filtering to the data. Physically based low-pass filters, such as upward continuation and integration, have been shown to be the best choice over a range of altitudes (upward continuation) or orders (integration filters), mainly because their outputs have a well-defined physical meaning. In contrast, mathematical low-pass filters require that the filter parameters be tuned carefully by means of several trial tests to produce optimally smoothed fields. The C-norm criterion is a reliable strategy to produce a stabilized vertical derivative, and we discourage Butterworth filters because they tend to a vertical integral filter, for a high cutoff wavenumber, thus complicating the interpretation of the estimated structural index. We found that the estimated structural index and depth to source increase proportionally with the amount of smoothing, unless in the case of overfiltering. In that case, the severe distortion of the original field may cause a decrease of the estimated structural index and depth to source.

Researching on Clutch Rotational Speed Signal Processing of AMT Vehicle

2011 ◽  
Vol 80-81 ◽  
pp. 1278-1283
Author(s):  
Jian Xin Peng ◽  
Hai Ou Liu ◽  
Hui Yan Chen
Keyword(s):  
Signal Processing ◽  
Rotational Speed ◽  
High Frequency ◽  
Frequency Domain Analysis ◽  
Mathematical Statistic ◽  
Frequency Noise ◽  
Euler Formula ◽  
Low Pass ◽  
Clutch Control ◽  
Hardware Circuit

Aimed at clutch magnetoelectric tachometric transducer, rotational speed signal processing had achieved good results in hardware circuit and software algorithm two aspects. Firstly in hardware aspect RC Low-Pass Filter’s parameters are optimized by frequency-domain analysis of actual rotational speed signal according to vehicle travel features. As a result, useful information is reserved and high-frequency noise is shielded effectively. Secondly in software aspect, through analyzing the clutch control system’s characteristics and speed data’s structure, explicit Euler formula is adopted to predict the data’s structure. Maximum likelihood estimate of Mathematical Statistic Analysis is used to raise the data’s utilization and enhance the data’s robustness in order to provide the effective and stable parameters for clutch control.

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