scholarly journals An extensible complex fast Fourier transform processor chip for real-time spectrum analysis and measurement

1998 ◽  
Vol 47 (1) ◽  
pp. 95-99 ◽  
Author(s):  
E. Cetin ◽  
R.C.S. Morling ◽  
I. Kale
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Spectrum Analysis ◽  
Time Spectrum

A Robust Spectrum Sensing Method Using Filter Bank

2011 ◽  
Vol 58-60 ◽  
pp. 54-59
Author(s):  
Dong Feng Zhao ◽  
Xiao Ma ◽  
Xian Wei Zhou
Keyword(s):  
Fourier Transform ◽  
Cognitive Radio ◽  
Fast Fourier Transform ◽  
Real Time ◽  
Spectrum Sensing ◽  
Filter Bank ◽  
Gaussian Function ◽  
Time Spectrum ◽  
Radio System ◽  
Wireless Environment

Robust spectrum sensing is one of the essential issues for building and commercializing the cognitive radio system. With the challenging wireless environment, a reliable and computation feasible real-time spectrum sensing method using filter bank has been proposed in this letter. Extended Gaussian Function and Fast Fourier Transform are effectively combined together to implement a non-parameter and thus robust spectrum sensing. Numerical results are provided to compare the performance of the proposed method with that of the existing spectrum sensing.

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.

Speed Synchronization System of Master-Slave Motor Based on Repetitive Control

2014 ◽  
Vol 644-650 ◽  
pp. 523-526
Author(s):  
Yun Ling ◽  
Huan Chen ◽  
Fei Li
Keyword(s):  
Real Time ◽  
Spectrum Analysis ◽  
Repetitive Control ◽  
Speed Control ◽  
Time Spectrum ◽  
Mechanical Resonance ◽  
Synchronization System ◽  
Speed Controller ◽  
Control Scheme ◽  
Magnet Wire

In the wrapping process of magnet wire, as the haulage speed of master motor varies periodically, it is difficult for slave wrapping motor to track master motor due to the mechanical resonance, which destabilizes the wrapping pitch. In the proposed system, the synchronization speed control scheme of master-slave motor based on repetitive control compensation is employed. In the process of control, real-time spectrum analysis of the haulage speed is given, which can be used to adjust the parameters of wrapping speed controller adaptively with the acquired characteristic information of the mechanical resonant. Simulation shows that wrapping speed can track haulage speed well in the proposed system, and the maximum tracking synchronous deviation can be reduced to 56% of that in the system without repetitive control.

Real time spectrum analysis

1983 ◽  
Vol 216 (1-2) ◽  
pp. 191-203 ◽  
Author(s):  
A. Blunden ◽  
D.G. O'Prey ◽  
W.H. Tait
Keyword(s):  
Real Time ◽  
Spectrum Analysis ◽  
Time Spectrum
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