Real‐time fast‐Fourier‐transform analysis ofM‐Hhysteresis loops

1993 ◽  
Vol 73 (10) ◽  
pp. 6849-6851 ◽  
Author(s):  
John L. Wallace
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Fast Fourier Transform Analysis

scholarly journals A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 234 ◽  
Author(s):  
Hyun Yoo ◽  
Soyoung Han ◽  
Kyungyong Chung
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Normal Control ◽  
Input Data ◽  
Deep Neural Network ◽  
Pattern Mining ◽  
F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

Acoustic Analysis of Cylindrical, Conical and Worm Gears

2019 ◽  
Vol 957 ◽  
pp. 211-220
Author(s):  
Cornel Cristian Enciu ◽  
Cristian Tarba ◽  
Cristian Barbulescu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Acoustic Analysis ◽  
Single Channel ◽  
Intensity Level ◽  
Analog Filter ◽  
Transform Method ◽  
Worm Gears ◽  
Electric Drill

The paper aims to determine the characteristic frequencies of an electric drill, by measuring and analyzing comparatively the variations of the acoustic intensity level, using the Fast Fourier Transform method (FFT). This was applied using a stand which had been specifically developed for the presented work. In two channel and multichannel systems, digital methods have been used for the calculation of cross properties as they were the only practical methods. Using digital techniques has gained considerable ground, being nowadays applied to problems once solved by resorting to analog methods. The increasing use of Fast Fourier Transform methods is found in single channel real time narrow band measurements and the Digital Filtering is replacing the Analog Filter bank which was used as the basis for real time analyzers operating with constant percentage bandwidth.

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