Electronic polarization-division demultiplexing based on artificial neural networks in optical communication systems

2020 ◽  
Author(s):  
Yuichiro Kurokawa ◽  
Takeru Kyono ◽  
Moriya Nakamura
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Communication ◽  
Communication Systems ◽  
Electronic Polarization ◽  
Optical Communication Systems ◽  
Artificial Neural

Exploring Structural and Dynamical Properties Microtubules by Means of Artificial Neural Networks

2014 ◽  
pp. 191-203
Author(s):  
R. Pizzi ◽  
S. Fiorentini ◽  
G. Strini ◽  
M. Pregnolato
Keyword(s):  
Neural Networks ◽  
Electromagnetic Field ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Dynamical Behaviour ◽  
Information Communication ◽  
Cellular Functions ◽  
Dynamical Properties ◽  
Physical Measures ◽  
Artificial Neural

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

Exploring Structural and Dynamical Properties Microtubules by Means of Artificial Neural Networks

2013 ◽  
pp. 78-91
Author(s):  
R. Pizzi ◽  
S. Fiorentini ◽  
G. Strini ◽  
M. Pregnolato
Keyword(s):  
Neural Networks ◽  
Electromagnetic Field ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Dynamical Behaviour ◽  
Information Communication ◽  
Cellular Functions ◽  
Dynamical Properties ◽  
Physical Measures ◽  
Artificial Neural

Microtubules (MTs) are cylindrical polymers of the tubulin dimer, are constituents of all eukaryotic cells cytoskeleton and are involved in key cellular functions and are claimed to be involved as sub-cellular information or quantum information communication systems. The authors evaluated some biophysical properties of MTs by means of specific physical measures of resonance and birefringence in presence of electromagnetic field, on the assumption that when tubulin and MTs show different biophysical behaviours, this should be due to their special structural properties. Actually, MTs are the closest biological equivalent to the well-known carbon nanotubes (CNTs), whose interesting biophysical and quantum properties are due to their peculiar microscopic structure. The experimental results highlighted a physical behaviour of MTs in comparison with tubulin. The dynamic simulation of MT and tubulin subjected to electromagnetic field was performed via MD tools. Their level of self-organization was evaluated using artificial neural networks, which resulted to be an effective method to gather the dynamical behaviour of cellular and non-cellular structures and to compare their physical properties.

scholarly journals Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

2022 ◽  
Author(s):  
Diego Argüello Ron ◽  
Pedro Jorge Freire De Carvalho Sourza ◽  
Jaroslaw E. Prilepsky ◽  
Morteza Kamalian-Kopae ◽  
Antonio Napoli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Communication Systems ◽  
Complexity Reduction ◽  
Edge Computing ◽  
Optical Channel ◽  
Raspberry Pi ◽  
Artificial Neural ◽  
The Impact

Abstract The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while keeping an acceptable performance level. In this work, we address this problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), and address its complexity reduction for the 30 GBd 1000 km transmission over a standard single-mode fiber. We demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 91.5%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense and examine the impact of using different CPU and GPU settings on power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, using two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano.

scholarly journals Artifical neural networks in RF MEMS switch modelling

2016 ◽  
Vol 29 (2) ◽  
pp. 177-191 ◽  
Author(s):  
Zlatica Marinkovic ◽  
Vera Markovic ◽  
Tomislav Ciric ◽  
Larissa Vietzorreck ◽  
Olivera Pronic-Rancic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Communication Systems ◽  
Rf Mems ◽  
Mems Switches ◽  
Mems Switch ◽  
Artifical Neural Networks ◽  
Rf Mems Switches ◽  
Artificial Neural ◽  
Similar Accuracy

The increased growth of the applications of RF MEMS switches in modern communication systems has created an increased need for their accurate and efficient models. Artificial neural networks have appeared as a fast and efficient modelling tool providing similar accuracy as standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modelling of RF MEMS switches, in particular of the capacitive shunt switches, proposed by the authors of the paper. Models for the most important switch characteristics in electrical and mechanical domains are considered, as well as the inverse models aimed to determine the switch bridge dimensions for specified requirements for the switch characteristics.

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