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JACIII Vol.27 No.1 pp. 19-26
doi: 10.20965/jaciii.2023.p0019
(2023)

Research Paper:

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Adrian Genevie G. Janairo*,†, Jonah Jahara G. Baun*, Johndel Garrison Chan*, Joseph Aristotle R. De Leon**, Ronnie S. Concepcion II**, Ryan Rhay P. Vicerra**, Argel A. Bandala*, and Elmer P. Dadios**

*Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

Received:
April 6, 2022
Accepted:
June 4, 2022
Published:
January 20, 2023
Keywords:
computational intelligence, digital multimeter, multi-array antenna, multigene genetic programming, subsurface imaging
Abstract

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices.

Cite this article as:
A. Janairo, J. Baun, J. Chan, J. Leon, R. Concepcion II, R. Vicerra, A. Bandala, and E. Dadios, “MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.1, pp. 19-26, 2023.
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References
  1. [1] A. Srivastav et al., “A Highly Digital Multiantenna Ground-Penetrating Radar (GPR) System,” IEEE Trans. on Instrumentation and Measurement, Vol.69, No.10, pp. 7422-7436, 2020.
  2. [2] H. Ali et al., “Classification of different materials for underground object using artificial neural network,” IOP Conf. Series: Materials Science and Engineering, Vol.705, Article No.012013, 2013.
  3. [3] K. Oliver, “The Capacitive Resistivity Technique for Electrical Imaging of the Shallow Subsurface,” Ph.D. Thesis, University of Nottingham, 2002.
  4. [4] W. Sun et al., “Research on Detection and Visualization of Underground Pipelines,” 2nd Int. Conf. on Remote Sensing, Environment and Transportation Engineering, 2012. https://doi.org/10.1109/RSETE.2012.6260692
  5. [5] Ç. Balkaya et al., “Ground-Penetrating Radar and Electrical Resistivity Tomography Studies in the Biblical Pisidian Antioch City, Southwest Anatolia,” 20th Int. Geophysical Congress and Exhibitions of Turkey, Antalya, 2013.
  6. [6] J. v. d. Kruk, “Tools and Techniques: Ground-Penetrating Radar,” Treatise on Geophysics, Vol.11, pp. 209-232, 2015.
  7. [7] A. V. Kochetov, G. V. Komarov, and D. Y. Kulikova, “Ultra-wideband Multidisc Antenna with Reconfigurable Polarization for Ground-Penetrating Imaging Radar,” 2019 IEEE Conf. of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 870-872, 2019.
  8. [8] E. Eide, P. A. Våland, and J. Sala, “Ground-coupled antenna array for step-frequency GPR,” Proc. of the 15th Int. Conf. on Ground Penetrating Radar, pp. 756-761, 2014.
  9. [9] M. N. A. Karim et al., “Design of Ground Penetrating Radar Antenna for Buried Object Detection,” 2013 IEEE Int. RF and Microwave Conf. (RFM), pp. 253-257, 2013.
  10. [10] M. N. A. Karim et al., “Wideband slotted antenna for microwave imaging system in ground penetrating radar applications,” 2016 IEEE Int. Symp. on Systems Engineering (ISSE), 2016. https://doi.org/10.1109/SysEng.2016.7753172
  11. [11] M. Lu et al., “A kind of MIMO ground penetrating radar plane antenna array and corresponding imaging method,” Proc. of the XIII Int. Conf. on Ground Penetrating Radar, 2010. https://doi.org/10.1109/ICGPR.2010.5550222
  12. [12] Y. Nakano and A. Hirose, “Taper-walled linearly tapered slot antenna: A low direct-coupling antenna for subsurface imaging,” Proc. of the XIII Int. Conf. on Ground Penetrating Radar, 2010. https://doi.org/10.1109/ICGPR.2010.5550152
  13. [13] P. Meincke and O. Kim, “Accurate antenna models in ground penetrating radar diffraction tomography,” IEEE Antennas and Propagation Society Int. Symp., Vol.4, pp. 306-309, 2002.
  14. [14] J. Rayno, N. Celik, and M. F. Iskander, “Synthesis of broadband 3D AMC ground planes using genetic programming,” 2014 IEEE Antennas and Propagation Society Int. Symp. (APSURSI), pp. 35-36, 2014.
  15. [15] G. A. Casula et al., “Genetic Programming Design of Wire Antennas,” IEEE Antennas and Propagation Society Int. Symp., 2009. https://doi.org/10.1109/APS.2009.5171505
  16. [16] J. T. Rayno et al., “3D metamaterial broadband ground plane designed using genetic programming for the long slot array antenna,” 2013 IEEE APSURSI, pp. 400-401, 2013.
  17. [17] T. B. Bach et al., “Evolved Design of Microstrip Patch Antenna by Genetic Programming,” 2019 Int. Conf. on Electromagnetics in Advanced Applications (ICEAA), pp. 1393-1397, 2019.
  18. [18] D. K. Naik et al., “Knowledge-Embedded MGGP Model for Resonant Frequency of Microstrip Antenna,” 2021 IEEE 2nd Int. Conf. on Applied Electromagnetics, Signal Processing, & Communication (AESPC), 2021. https://doi.org/10.1109/AESPC52704.2021.9708521
  19. [19] R. S. Concepcion II, E. P. Dadios, and J. Cuello, “Non-destructive in situ measurement of aquaponic lettuce leaf photosynthetic pigments and nutrient concentration using hybrid genetic programming,” Agrivita: J. of Agricultural Science, Vol.43, No.3, 2021. http://doi.org/10.17503/agrivita.v43i3.2961
  20. [20] R. S. Concepcion II et al., “Aquaphotomics determination of total organic carbon and hydrogen biomarkers on aquaponic pond water and concentration prediction using genetic programming,” 2020 IEEE 8th R10 Humanitarian Technology Conf. (R10-HTC), 2020. https://doi.org/10.1109/R10-HTC49770.2020.9357030
  21. [21] M. G. Palconit et al., “FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming,” 2021 IEEE 9th R10-HTC, 2021. https://doi.org/10.1109/R10-HTC53172.2021.9641654
  22. [22] R. S. Concepcion II et al., “Hybrid Genetic Programming and Multiverse-Based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration,” Int. J. on Advanced Science, Engineering and Information Technology, Vol.11, No.6, pp. 2128-2138, 2021.
  23. [23] R. S. Concepcion II et al., “Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming,” Information Processing in Agriculture, Vol.9, No.4, pp. 497-513, 2022.
  24. [24] O. J. Alajas et al., “Indirect Prediction of Aquaponic Water Nitrate Concentration Using Hybrid Genetic Algorithm and Recurrent Neural Network,” 2021 IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021. https://doi.org/10.1109/HNICEM54116.2021.9731946
  25. [25] C. H. Mendigoria et al., “Optimization of Vacuum Drying Properties for Chlorococcum infusionum Microalgae Moisture Content Using Hybrid Genetic Programming and Genetic Algorithm,” 2021 IEEE 13th Int. Conf. on HNICEM, 2021. https://doi.org/10.1109/HNICEM54116.2021.9732016

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