An artificial neural network-based non-destructive microwave technique for monitoring fluoride contamination in water

References

• Sankhla MS, Kumar R. Fluoride contamination of water in India and its impact on public health. ARC J Forensic Sci. 2018;3(2):10–15.
   Google Scholar
• Roy S, Dass G. Fluoride contamination in drinking water – a review. Resour Environ. 2013;3(3):53–58.
   Google Scholar
• Das B, Talukdar J, Sarma S, et al. Fluoride and other inorganic constituents in groundwater of Guwahati, Assam, India. Curr Sci. 2003;85(5):657–660.
   Web of Science ®Google Scholar
• Dhillona A, Nairb M, Kumara D. Analytical methods for determination and sensing of fluoride in biotic and abiotic sources: a review. Anal Methods. 2016;8:5338–5352. doi: 10.1039/C6AY01534D
   Web of Science ®Google Scholar
• Hussain I, Ahamad KU, Nath P. Low-cost, robust, and field portable smartphone platform photometric sensor for fluoride level detection in drinking water. Anal Chem. 2016;89(1):767–775. doi: 10.1021/acs.analchem.6b03424
   PubMed Web of Science ®Google Scholar
• Levin S, Krishnan S, Rajkumar S, et al. Monitoring of fluoride in water samples using a smartphone. Sci Total Environ. 2016;551–552:101–107. doi: 10.1016/j.scitotenv.2016.01.156
   PubMed Web of Science ®Google Scholar
• Korostynska O, Mason A, Al-Shamma'a AI. Flexible microwave sensors for real-time analysis of water contaminants. J Electromagn Waves Appl. 2013;27(16):2075–2089. doi: 10.1080/09205071.2013.832393
   Web of Science ®Google Scholar
• Dasoqi NAl, Mason A, Alkhaddar R, et al. Real-time nondestructive microwave sensor for nutrient monitoring in waste water treatment. J Phys. 2011;307(1):012043–012047.
   Google Scholar
• Gorji A, Kaleita A, Bowler N. Towards low-cost sensors for real-time monitoring of contaminant ions in water sources. 2017 IEEE MTT-S International Microwave Symposium (IMS); Honololu, HI; 2017. p. 529–532.
   Google Scholar
• Frau I, Wylie S, Byrne P, et al. Continuous Detection of Copper and Bromide in Polluted Water using f-EM Sensors. 2018 12th International Conference on Sensing Technology (ICST); Limerick; 2018. p. 35–38.
   Google Scholar
• Turgul V, Kale I. Permittivity extraction of glucose solutions through artificial neural networks and non-invasive microwave glucose sensing. Sens Actuators. 2018;277:65–72. doi: 10.1016/j.sna.2018.03.041
   Web of Science ®Google Scholar
• Belrhiti MD, Bri S, Nakheli A, et al. Dielectric constant determination of liquid using rectangular waveguide structure combined with EM simulation. J Mater Environ Sci. 2012;3(3):575–584.
   Google Scholar
• Gadani DH, Rana VA, Bhatnagar SP, et al. Effect of salinity on the dielectric properties of water. Indian J Pure Appl Phys. 2012;50:405–410.
   Web of Science ®Google Scholar
• Kaatze U. Reference liquids for the calibration of dielectric sensors and measurement instruments. Meas Sci Technol. 2007;18:967–976. doi: 10.1088/0957-0233/18/4/002
   Web of Science ®Google Scholar
• Roy S, Neihart NM, Bowler N. Coaxial microwave resonant sensor design for monitoring ionic concentration in aqueous solutions. 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, 2018. p. 1–6.
   Google Scholar
• Mathur P, Thakur A, Augustine R, et al. A novel non-invasive microwave technique for monitoring salinity in water. TENCON 2019 – 2019 IEEE Region 10 Conference (TENCON); Kochi, India; 2019. p. 226–229.
   Google Scholar
• Belhadj-Tahar N, Fourrier-Lamer A. Broad-band analysis of discontinuity used for dielectric measurement. IEEE Trans Microw Theory Tech. 1986;34(3):346–350. doi: 10.1109/TMTT.1986.1133342
   Web of Science ®Google Scholar
• Díaz-Morcillo A, Monzó Cabrera J, Requena Pérez ME, et al. Application of genetic algorithms in the determination of dielectric properties of materials at microwave frequencies. International Work Conference on the Interplay Between Natural and Artificial Computation; IWINAC 2007. p. 608–616. (Lecture notes in computer science; 4528).
   Google Scholar
• Acikgoz H, Le Bihan Y, Meyer O, et al. Neural networks for broad-band evaluation of complex permittivity using a coaxial discontinuity. Eur Phys J Appl Phys. 2007;39:197–201. doi: 10.1051/epjap:2007073
   Web of Science ®Google Scholar
• Qaddoumi NN, El-Hag AH, Saker Y. Outdoor insulators testing using artificial neural network-based near-field microwave technique. IEEE Trans Instrum Meas. 2014;63(2):260–266. doi: 10.1109/TIM.2013.2280486
   Web of Science ®Google Scholar
• Maazi M, Benzaim O, Glay D, et al. Detection and characterization of buried macroscopic cracks inside dielectric materials by microwave techniques and artificial neural networks. IEEE Trans Instrum Meas. 2008;57(12):2819–2826. doi: 10.1109/TIM.2008.926396
   Web of Science ®Google Scholar
• Hasan A, Peterson AF. Measurement of complex permittivity using artificial neural networks. IEEE Antennas Propag Mag. 2011;53(1):200–203. doi: 10.1109/MAP.2011.5773614
   Web of Science ®Google Scholar
• Panda S, Tiwari NK, Akhtar MJ. Computationally intelligent sensor system for microwave characterization of dielectric sheets. IEEE Sens J. 2016;16(20):7483–7493. doi: 10.1109/JSEN.2016.2599856
   Web of Science ®Google Scholar
• Acikgoz H, Santandrea L, Le Bihan Y, et al. Generation and use of optimised databases in microwave characterisation. IET Sci Meas Tech. 2008;2(6):467–473. doi: 10.1049/iet-smt:20080086
   Web of Science ®Google Scholar
• Ali A, Hu B, Ramahi OM. Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements. Sensor. 2015;15(5):11402–11416. doi: 10.3390/s150511402
   PubMed Web of Science ®Google Scholar
• Mathur P, Perez MD, Augustine R, et al. NDECOAX: a software package for nondestructive evaluation of stratified dielectric media. SoftwareX. 2019;9:187–192. doi: 10.1016/j.softx.2019.02.001
   Web of Science ®Google Scholar
• Jarvis JB, Kim S. The interaction of radio-frequency fields with dielectric materials at macroscopic to mesoscopic scales. J Res Natl Inst Stand Technol. 2012;117(8):1–60. doi: 10.6028/jres.117.001
   PubMedGoogle Scholar
• Bishop CM. Neural networks for pattern recognition. New York: Oxford University Press; 1995.
   Google Scholar
• Nissen S. Implementation of a fast artificial neural network library (Fann). Copenhagen, Denmark: Department of Computer Science, University of Copenhagen (DIKU); 2003. (Report 31).
   Google Scholar
• Igel C, Husken M. Improving the Rprop learning algorithm. Second International Symposium on Neural Computation. ICSC Academic Press, Canada/Switzerland; 2000. p. 115–121.
   Google Scholar
• Buchner R, Hefter GT, Barthel J. Dielectric relaxation of aqueous NaF and KF solutions. J Chem Soc Faraday Trans. 1994;90(17):2475–2479. doi: 10.1039/ft9949002475
   Google Scholar
• Fawcett WR, Tikanen AC. Role of solvent permittivity in estimation of electrolyte activity coefficients on the basis of the mean spherical approximation. J Phys Chem. 1996;100(10):4251–4255. doi: 10.1021/jp952379v
   Web of Science ®Google Scholar
• Misra D, Chabbra M, Epstein BR, et al. Noninvasive electrical characterization of materials at microwave frequencies using an open-ended coaxial line: test of an improved calibration technique. IEEE Trans Microw Theory Tech. 1990;38(1):8–14. doi: 10.1109/22.44150
   Web of Science ®Google Scholar
• Buckley F, Maryott AA. Tables of dielectric dispersion data for pure liquids and dilute solutions. Vol. 589. U.S. Department of Commerce, National Bureau of Standards; 1958.
   Google Scholar
• Li S, Li S, Shahzad A, et al. Determination of microwave conductivity of electrolyte solutions from debye Drude model. PIERS Proceedings; Guangzhou, China; 2014. p. 670–673.
   Google Scholar