scholarly journals Visible light communications: 375 Mbits/s data rate with a 160 kHz bandwidth organic photodetector and artificial neural network equalization [Invited]

2013 ◽  
Vol 1 (2) ◽  
pp. 65 ◽  
Author(s):  
Zabih Ghassemlooy ◽  
Paul Anthony Haigh ◽  
Francesco Arca ◽  
Sandro Francesco Tedde ◽  
Oliver Hayden ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
Data Rate ◽  
Visible Light Communications ◽  
Organic Photodetector ◽  
Artificial Neural

scholarly journals Visible Light Communications: 170 Mb/s Using an Artificial Neural Network Equalizer in a Low Bandwidth White Light Configuration

2014 ◽  
Vol 32 (9) ◽  
pp. 1807-1813 ◽  
Author(s):  
Paul Anthony Haigh ◽  
Zabih Ghassemlooy ◽  
Sujan Rajbhandari ◽  
Ioannis Papakonstantinou ◽  
Wasiu Popoola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
White Light ◽  
Visible Light Communications ◽  
Artificial Neural

scholarly journals Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yadollah Abdollahi ◽  
Azmi Zakaria ◽  
Nor Asrina Sairi ◽  
Khamirul Amin Matori ◽  
Hamid Reza Fard Masoumi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
Visible Light Irradiation ◽  
Back Propagation ◽  
Light Irradiation ◽  
Maximum Efficiency ◽  
Data Set ◽  
Artificial Neural

The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

scholarly journals Data Rate Enhancement in Optical Camera Communications Using an Artificial Neural Network Equaliser

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42656-42665 ◽  
Author(s):  
Othman Isam Younus ◽  
Navid Bani Hassan ◽  
Zabih Ghassemlooy ◽  
Paul Anthony Haigh ◽  
Stanislav Zvanovec ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Rate ◽  
Rate Enhancement ◽  
Artificial Neural
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