Researching on Clutch Rotational Speed Signal Processing of AMT Vehicle

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.

Download Full-text

Butterworth filters as tracking differentiators

10.36227/techrxiv.13646678.v1 ◽

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>

Download Full-text

Required Data Collection Times for Steady and Quasi-steady Experiments

10.5957/attc-2017-0012 ◽

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.

Download Full-text

SOFTENING THE NONLINEARITY IN CHUA'S CIRCUIT

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127496001922 ◽

1996 ◽

Vol 06 (01) ◽

pp. 179-183 ◽

Cited By ~ 2

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.

Download Full-text

Effective resolution concepts for lidar observations

Atmospheric Measurement Techniques ◽

10.5194/amt-8-5157-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 5157-5176 ◽

Cited By ~ 15

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.

Download Full-text

A stable and efficient approach of Q reverse time migration

Geophysics ◽

10.1190/geo2018-0022.1 ◽

2018 ◽

Vol 83 (6) ◽

pp. S557-S567 ◽

Cited By ~ 1

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.

Download Full-text

Butterworth filters as tracking differentiators

10.36227/techrxiv.13646678 ◽

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

Download Full-text

Frequency domain analysis of core inflation measures for Brazil

Estudos Econômicos (São Paulo) ◽

10.1590/s0101-41612005000100001 ◽

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.

Download Full-text

Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction

Scientific Reports ◽

10.1038/s41598-019-53460-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Mehdi Hasan Chowdhury ◽

Ray C. C. Cheung

Keyword(s):

Signal Processing ◽

Noise Reduction ◽

Real Time ◽

High Frequency ◽

Reconfigurable Architecture ◽

Fast Fourier Transformation ◽

Frequency Noise ◽

Compression Rate ◽

Processing Unit ◽

High Frequency Noise

AbstractElectrocardiogram (ECG) is a record of the heart’s electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq −7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system.

Download Full-text

A Simple and Versatile Fourier Domain Digital Filter

Applied Spectroscopy ◽

10.1366/000370276774456453 ◽

1976 ◽

Vol 30 (1) ◽

pp. 23-27 ◽

Cited By ~ 34

Author(s):

Keith R. Betty ◽

Gary Horlick

Keyword(s):

Signal Processing ◽

Digital Filter ◽

High Frequency ◽

Resolution Enhancement ◽

Quantization Noise ◽

Frequency Noise ◽

Fixed Frequency ◽

Spectral Signal ◽

Approximate Deconvolution ◽

The Fourier Transform

A number of signal processing operations can be carried out on spectra using a digital filter based on a simple trapezoid function. The filter is applied to the Fourier transform of the spectral signal. Several signal processing examples are presented to illustrate the capabilities of this filter. These examples include filtering and diagnosis of high frequency noise on a signal, removal of fixed frequency noise, minimization of quantization noise, and differentiation and approximate deconvolution for the purpose of resolution enhancement.

Download Full-text