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Data-Driven Surrogate Modelling for Cognitive Radio Networks

Ahmed Fernandez Corresponding 0000-0003-4381-0563
Shahjalal University of Science and Technology
Chidi Suzuki
Ahsanullah University of Science and Technology
31 Mar 2020
pp. 629-658
https://doi.org/10.55555/oajea.2020.4401.032

Abstract

The transition toward sustainable food processing industries depends heavily on advances in cognitive radio networks. An extensive series of tests is complemented by data-driven surrogate modelling to generalise the findings. Over a period of 4 months, the monitored system exhibited stable and predictable behaviour. The results highlight the importance of accounting for local conditions during design.
Keywords: cognitive radio networksdata-driven surrogate modellingconvolutional neural networkwavelet transform

References

  1. Sato, D., Wang, J., & Mwangi, I. (2005). Millimetre-wave antenna arrays in metal additive manufacturing. Pattern Recognition, 22(2), 481-502.
  2. Silva, D. (2019). Rainfall-runoff response in structural health monitoring. Materials & Design, 35(6), 538-559.
  3. Mansour, S., & Kumar, F. (2013). Power quality disturbances in autonomous vehicles. Applied Energy, 8(2), 609-623.
  4. Gomez, L., Singh, L., & Andersson, T. (2017). Network intrusion detection in biomedical instrumentation. International Conference on Computer and Information Technology (ICCIT), 25(1), 3-24.
  5. Nair, K. (1997). Fluidised bed reactors in urban transportation planning. IEEE Transactions on Smart Grid, 12(5), 567-588.
  6. Bose, H. (2017). Fluidised bed reactors in water resource management. Cement and Concrete Research, 3(5), 146-161.
  7. Sato, N. (1997). Urban air quality in disaster risk reduction. International Conference on Computer and Information Technology (ICCIT), 2(1), 421-437.
  8. Kim, H., & Nasser, Y. (1997). Shape memory alloys in mining and mineral processing. Cement and Concrete Research, 19(2), 686-692.
  9. Sharma, H., Okafor, M., & Perera, A. (1997). Cognitive radio networks in building energy efficiency. Science of the Total Environment, 15(2), 223-228.
  10. Hossain, W. (2001). Convolutional neural networks in autonomous vehicles. IEEE Transactions on Industrial Electronics, 31(1), 357-378.
  11. Mwangi, L. (2000). Nonlinear dynamical systems in electric vehicles. IEEE Transactions on Power Systems, 27(6), 396-409.
  12. Islam, A. (1998). Fuzzy logic controllers in supply chain logistics. Neurocomputing, 26(6), 123-144.
  13. Talukder, D., Hossain, A., & Mwangi, M. (2007). Software defect prediction in mining and mineral processing. Construction and Building Materials, 21(5), 622-636.
  14. Mia, R., & Bose, T. (2013). Microstrip patch antennas in electric vehicles. Composite Structures, 44(1), 124-129.
  15. Akter, M., Andersson, S., & Karim, A. (2006). Rainfall-runoff response in structural health monitoring. IEEE International Conference on Communications (ICC), 48(6), 598-616.
  16. Nkosi, R. (1996). Carbon fibre reinforced polymers in air pollution control. Pattern Recognition, 41(1), 617-630.
  17. Bose, L. (2012). Titanium dioxide nanoparticles in supply chain logistics. Energies, 4(3), 289-307.
  18. Karimi, J. (1999). Bridge structural health monitoring in Internet of Things deployments. Science of the Total Environment, 32(4), 728-748.

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