scholarly journals Real-time discrimination of spectra by time-domain adaptive filtering in a Fourier transform interferometer

2001 ◽  
Author(s):  
S. R. Bhalotra ◽  
H. L. Kung ◽  
D. A. B. Miller
Keyword(s):  
Fourier Transform ◽  
Real Time ◽  
Adaptive Filtering ◽  
Time Domain ◽  
Time Discrimination

Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network

1999 ◽  
Vol 46 (2) ◽  
pp. 179-185 ◽  
Author(s):  
K. Minami ◽  
H. Nakajima ◽  
T. Toyoshima
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Real Time ◽  
Ventricular Tachyarrhythmia ◽  
Time Discrimination

FPGA Implementation and Evaluation of a Real-Time Image-Based Vibration Detection System with Adaptive Filtering

2020 ◽  
Vol E103.A (12) ◽  
pp. 1472-1480
Author(s):  
Taito MANABE ◽  
Kazuya UETSUHARA ◽  
Akane TAHARA ◽  
Yuichiro SHIBATA
Keyword(s):  
Real Time ◽  
Adaptive Filtering ◽  
Detection System ◽  
Fpga Implementation ◽  
Vibration Detection ◽  
Real Time Image ◽  
Time Image

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

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.

A Practical Real-Time Fourier Transform Infrared Detector for Liquid Chromatography

1978 ◽  
Vol 32 (5) ◽  
pp. 502-506 ◽  
Author(s):  
D. Warren Vidrine ◽  
David R. Mattson
Keyword(s):  
Fourier Transform ◽  
Liquid Chromatography ◽  
Real Time ◽  
Infrared Spectra ◽  
Infrared Detector ◽  
Fourier Transform Infrared ◽  
Paraffin Oil ◽  
Chromatographic Detection ◽  
Liquid Chromatographic ◽  
System Capability

A practical Fourier transform infrared system for real-time liquid chromatographic detection is described. Simple flowcell detection with automatic solvent subtraction is used, and detection of 500 ng of injected paraffin oil is demonstrated. Results from several chromatographic runs illustrate the system capability to serve as a real time infrared detector and simultaneously acquire infrared spectra.

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