Simultaneous Monitoring of Chromatic Dispersion and Optical Signal to Noise Ratio in Optical Network Using Asynchronous Delay Tap Sampling and Convolutional Neural Network (Deep Learning)

2018 ◽  
Author(s):  
Tomasz Mrozek
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Chromatic Dispersion ◽  
Optical Network ◽  
Optical Signal ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Simultaneous monitoring of chromatic dispersion and optical signal to noise ratio in optical links using convolutional neural network and asynchronous delay-tap sampling

2020 ◽  
Vol 50 (3) ◽  
Author(s):  
Tomasz Mrozek ◽  
Krzysztof Perlicki ◽  
Andrzej Jakubiak
Keyword(s):  
Signal To Noise Ratio ◽  
Chromatic Dispersion ◽  
Optical Network ◽  
Optical Signal ◽  
Phase Portraits ◽  
Signal To Noise ◽  
Optical Links ◽  
Binary Images ◽  
Noise Ratio ◽  
Amplified Spontaneous Emission Noise

The article presents a method for image analysis using asynchronous delay-tap sampling (ADTS) technique and convolutional neural networks (CNNs), allowing simultaneous monitoring of many phenomena occurring in the physical layer of the optical network. The ADTS method makes it possible to visualize the course of the optical signal in the form of characteristics (so-called phase portraits), which change their shape under the influence of phenomena (including chromatic dispersion, amplified spontaneous emission noise and other). Using the VPIphotonics software, a simulation model of the ADTS technique was built. After the simulation tests, 10 000 images were obtained, which after proper preparation were subjected to further analysis using CNN algorithms. The main goal of the study was to train a CNN to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses on the analysis of images containing simultaneously the phenomena of chromatic dispersion and optical signal to noise ratio.

Effect of signal to noise ratio on a convolutional neural network for source ranging and environmental classification

2019 ◽  
Vol 146 (4) ◽  
pp. 2961-2962
Author(s):  
Kira Howarth ◽  
David F. Van Komen ◽  
Tracianne B. Neilsen ◽  
David P. Knobles ◽  
Peter H. Dahl ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Environmental Classification ◽  
Noise Ratio

scholarly journals Active Sonar Target Classification with Power-Normalized Cepstral Coefficients and Convolutional Neural Network

2020 ◽  
Vol 10 (23) ◽  
pp. 8450
Author(s):  
Seungwoo Lee ◽  
Iksu Seo ◽  
Jongwon Seok ◽  
Yunsu Kim ◽  
Dong Seog Han
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Target Classification ◽  
Signal To Noise ◽  
Mel Frequency Cepstral Coefficients ◽  
Active Sonar ◽  
Cepstral Coefficients ◽  
Noise Ratio

Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation.

Optical signal-to-noise ratio restoration algorithm applied to optical network resilience to node failures

2011 ◽  
Author(s):  
Rodrigo C. de Freitas ◽  
Joaquim F. Martins-Filho ◽  
Daniel A. R. Chaves ◽  
Rodrigo C. L. Silva ◽  
Carmelo J. A. Bastos-Filho ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Optical Network ◽  
Optical Signal ◽  
Network Resilience ◽  
Signal To Noise ◽  
Restoration Algorithm ◽  
Noise Ratio ◽  
Node Failures
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