scholarly journals Combining nonlinear Fourier transform and neural network-based processing in optical communications

2020 ◽  
Vol 45 (13) ◽  
pp. 3462 ◽  
Author(s):  
Oleksandr Kotlyar ◽  
Maryna Pankratova ◽  
Morteza Kamalian-Kopae ◽  
Anastasiia Vasylchenkova ◽  
Jaroslaw E. Prilepsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Optical Communications ◽  
Nonlinear Fourier Transform

Nonlinear Compensation in Optical Communications Systems With Normal Dispersion Fibers Using the Nonlinear Fourier Transform

2017 ◽  
Vol 35 (23) ◽  
pp. 5056-5068 ◽  
Author(s):  
Ivan T. Lima ◽  
Thiago D. S. DeMenezes ◽  
Vladimir S. Grigoryan ◽  
Maurice O'Sullivan ◽  
Curtis R. Menyuk
Keyword(s):  
Fourier Transform ◽  
Optical Communications ◽  
Normal Dispersion ◽  
Communications Systems ◽  
Nonlinear Compensation ◽  
Nonlinear Fourier Transform

Neural network for calculating direct and inverse nonlinear Fourier transform

2021 ◽  
Vol 51 (12) ◽  
pp. 1118-1121
Author(s):  
E V Sedov ◽  
I S Chekhovskoy ◽  
J E Prilepsky
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Nonlinear Fourier Transform

Alternative Decoding Methods for Optical Communications Based on Nonlinear Fourier Transform

2017 ◽  
Vol 35 (9) ◽  
pp. 1542-1550 ◽  
Author(s):  
Tao Gui ◽  
Terence H. Chan ◽  
Chao Lu ◽  
Alan Pak Tao Lau ◽  
Ping-Kong Alexander Wai
Keyword(s):  
Fourier Transform ◽  
Optical Communications ◽  
Nonlinear Fourier Transform

scholarly journals Convolutional long short-term memory neural network equalizer for nonlinear Fourier transform-based optical transmission systems

2021 ◽  
Vol 29 (7) ◽  
pp. 11254
Author(s):  
Oleksandr Kotlyar ◽  
Morteza Kamalian-Kopae ◽  
Maryna Pankratova ◽  
Anastasiia Vasylchenkova ◽  
Jaroslaw E. Prilepsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Optical Transmission ◽  
Short Term Memory ◽  
Short Term ◽  
Transmission Systems ◽  
Term Memory ◽  
Optical Transmission Systems ◽  
Long Short Term Memory ◽  
Nonlinear Fourier Transform

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.

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