Estimation of the ECG signal spectrum during ventricular fibrillation using the fast Fourier transform and maximum entropy methods

In this study, we propose a method to classify individuals under stress and those without stress using k-means clustering. After extracting the R and S peak values from the ECG signal, the heart rate variability is extracted using a fast Fourier transform. Then, a criterion for classifying the ECG signal for the stress state is set, and the stress state is classified through k-means clustering. In addition, the stress level is indicated using the 𝐑 − 𝐒𝐩𝐞𝐚𝐤 value. This method is expected to be applied to the U-healthcare field to help manage the mental health of people suffering from stress.

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The Systematic Study of Music Spectrum Display Based on FFT

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.1764 ◽

2014 ◽

Vol 945-949 ◽

pp. 1764-1767

Author(s):

Lin Jin ◽

Qiu Lei Du

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Spectrum Analysis ◽

Frequency Component ◽

Frequency Domain Analysis ◽

Point Of View ◽

Seismic Exploration ◽

Signal Spectrum ◽

Main Work ◽

Software Implementations

Spectrum analysis is the superposition signal intodifferent frequency component and the imaginary indexcomponent, the analysis of signal from the frequency point of view. It is widely used in voice processing, image processing, digital audio, seismic exploration. The FFT algorithm, provides an effective solution forreal-time processing of frequency domain analysis. Music display system based on FFT spectrum, the main work is as follows:First, the fast Fourier transform ideas a brief description, and combined with the specific hardware and software implementations of the algorithm of the system used by the fast Fourier transform is described in detail. Then, describes the main hardware devices used in the system, including STC12C5A60S2 chips, display principle Principle 32 * 64LED dot matrix screen, use 74LS595 and 74LS164's. The system is simple, easy to use, you can use STC12C5A60S2 SCM data processing functions of the sound signal spectrum analysis.

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Fast Jacket-Haar Transform with Any Size

Mathematical Problems in Engineering ◽

10.1155/2015/628642 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11

Author(s):

Guibo Liu ◽

Dazu Huang ◽

Dayong Luo ◽

Wang Lei ◽

Ying Guo ◽

...

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Fast Algorithm ◽

Ecg Signal ◽

Arbitrary Length ◽

Haar Transform ◽

Ecg Signal Processing ◽

Simulation Results ◽

Electrocardiogram Ecg

Jacket-Haar transform has been recently generalized from Haar transform and Jacket transform, but, unfortunately, it is not available in a case where the lengthNis not a power of 2. In this paper, we have proposed an arbitrary-length Jacket-Haar transform which can be conveniently constructed from the 2-point generalized Haar transforms with the fast algorithm, and thus it can be constructed with any sizes. Moreover, it can be further extended with elegant structures, which result in the fast algorithms for decomposing. We show that this approach can be practically applied for the electrocardiogram (ECG) signal processing. Simulation results show that it is more efficient than the conventional fast Fourier transform (FFT) in signal processing.

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Sparse Fourier transform and amplitude–frequency characteristics analysis of vortex street signal

Measurement and Control ◽

10.1177/0020294020919870 ◽

2020 ◽

pp. 002029402091987 ◽

Cited By ~ 1

Author(s):

Jie Chen ◽

Yun Cao ◽

Chengyi Wang ◽

Bin Li

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Flow Measurement ◽

Noise Signal ◽

Frequency Characteristics ◽

Vortex Street ◽

Signal Frequency ◽

Signal Spectrum ◽

Vortex Flowmeter ◽

Characteristics Analysis

Vortex flowmeter is a commonly used flow measurement device. It is almost not affected by the density and viscosity of the fluid, so the vortex flowmeter can be used for the detection of various medium, such as gas, liquid and steam. When dealing with vortex street signal, we usually use fast Fourier transform to calculate the signal frequency, but this traditional vortex street signal processing method is not only inefficient, but also difficult to filter out noise signals in the same frequency band as the vortex signal. The sparse Fourier transform utilizes the sparsity of the signal to efficiently calculate the signal spectrum, and the computational complexity is lower than that of the fast Fourier transform algorithm. In this paper, the amplitude and frequency of the vortex signal is analyzed by sparse Fourier transform and the noise signal is removed based on the amplitude–frequency characteristics of the vortex signal. Finally, by comparing with the other methods, we found that the time complexity of our algorithm is one-tenth of others’ methods. This means that our approach is 10 times faster than others.

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Toward an online strategy for mechanical failures diagnostics inside the wind turbine generators based on spectral analysis

Wind Engineering ◽

10.1177/0309524x211028759 ◽

2021 ◽

pp. 0309524X2110287

Author(s):

Sharaf Eddine Kramti ◽

Jaouher Ben Ali ◽

Eric Bechhoefer ◽

Karim Takrouni ◽

Abdelghani Darghouthi ◽

...

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Wind Turbine ◽

Signal Spectrum ◽

Data Set ◽

Vibration Signals ◽

Envelope Analysis ◽

Turbine Generators ◽

Wind Turbine Generators ◽

Mechanical Faults

Mechanical faults in wind turbine generators lead to a huge problems of breaking electricity production, increase the maintenance cost and can damage the wind turbine generator, which caught fire in the nacelle. Gearbox installed in the nacelle is the important component in wind turbine, it composed by shafts bearings and gearings. Always bearings and gearings failures are related together. Therefore, to control these rotating components we need to analysis their vibration signature. This work was done with Tunisian Electricity and Gas Company (STEG) it’s about mechanical failures diagnostics which are based on three temporal vibration signals which are made by three sensors installed also in three positions axial, vertical, and horizontal. In this work we present a Fast Fourier Transform which is applied on raw mechanical vibration signals by the vibration department of STEG, which are compared with a new strategy of envelope analysis is commonly used to obtain the mechanical faults harmonics from the envelope signal spectrum analysis and has shown a more suitable results of diagnostics which is applied on the same data sets from Fast Fourier Transform. In this work, we illustrate a squared envelope based on spectral kurtosis approach to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. This method proves a better diagnosis results using real vibration data set from vibration department of STEG.

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