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Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

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September 2022, Volume 31, Number 3 [DOI: 10.13164/re.2022-3]

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S. M. Feng, S. Y. Wang, L. F. Wang [references] [full-text] [DOI: 10.13164/re.2022.0255] [Download Citations]
Two-Dimensional Signal Detection Algorithm for Omni-Directional Signal Receiving Using Low-Frequency Orthogonal Magnetic Antenna

This paper proposes a two-dimensional signal detection algorithm for low-frequency signal receiving using orthogonal magnetic antenna. According to the directional properties of a single antenna, the direction coefficient is introduced into the model. The algorithm based on Markov Chain Monte Carlo (MCMC) method can accurately estimate the direction coefficient and parameters of the noise in order to perform signal detection. The results show that the proposed algorithm is less affected by the direction of arrival and performs better, in terms of bit error rate, than that based on one-dimensional model. This study provides a valuable reference to omni-directional receiving of signals in low-frequency communication.

  1. ABRAHAM, D. A. Detection-threshold approximation for non-Gaussian backgrounds. IEEE Journal of Oceanic Engineering, 2010, vol. 35, no. 2, p. 355–365. DOI: 10.1109/JOE.2010.2043752
  2. SCHLEGEL, C., MALLAY, M., TOUESNARD, C. Atmospheric magnetic noise measurements in urban areas. IEEE Magnetics Letters, 2014, vol. 5, p. 1–4. DOI: 10.1109/LMAG.2014.2330337
  3. MCDONALD, K. F., BLUM, R. S. A statistical and physical mechanisms-based interference and noise model for array observations. IEEE Transactions on Signal Processing, 2000, vol. 48, no. 7, p. 2044–2056. DOI: 10.1109/78.847789
  4. GASDIA, F., MARSHALL, R. A. Assimilating VLF transmitter observations with an LETKF for spatial estimates of the D-region ionosphere. IEEE Transactions on Geoscience and Remote Sensing, 2020, vol. 58, no. 5, p. 3526–3543. DOI: 10.1109/TGRS.2019.2957716
  5. CHEN, Q. D., LIU, R., LIU, Y., et al. LF/VLF electromagnetic pulse measurement system. Chinese Journal of Radio Science, 2020, vol. 35, no. 5, p. 791–798. (In Chinese) DOI: 10.13443/j.cjors.2019092001
  6. GU, X., LI, G., PANG, H., et al. Statistical analysis of very low frequency atmospheric noise caused by the global lightning using ground-based observations in China. Journal of Geophysical Research: Space Physics, 2021, vol. 126, no. 6, p. 1–11. DOI: 10.1029/2020JA029101
  7. LOMBARDI, M. J. Bayesian inference for alpha-stable distribution: A random walk MCMC approach. Computation Statistics & Data Analysis, 2007, vol. 51, no. 5, p. 2688–2700. DOI: 10.1016/j.csda.2006.01.009
  8. YING, W. W., JIANG, Y. Z., LIU, Y. L. Parameters estimation for mixture model of atmospheric noise through MCMC method. Systems Engineering Electronics, 2012, vol. 34, no. 6, p. 1241–1245. (In Chinese) DOI: 10.3969/j.issn.1001-506X.2012.06.29
  9. YING, W. W., JIANG, Y. Z., LIU, Y. L. A blind receiver with multiple antennas in impulsive noise modeled as the sub-Gaussian distribution via the MCMC algorithm. IEEE Transactions on Vehicular Technology, 2013, vol. 62, no. 7, p. 3492–3497. DOI: 10.1109/TVT.2013.2250535
  10. WEISS, E., ALIMI, R. Low-Power and High-Sensitivity Magnetic Sensors and Systems. London (UK): Artech House, 2018. ISBN: 9781630812430
  11. RIPKA, P. (Ed.). Magnetic Sensors and Magnetometers. London (UK): Artech House, 2021. ISBN: 9781630817428
  12. FULLEKRUG, M., MEZENTSEV, A., WATSON, R., et al. Array analysis of electromagnetic radiation from radio transmitters for submarine communication. Geophysical Research Letters, 2014, vol. 41, no. 24, p. 9143–9149. DOI: 10.1002/2014GL062126
  13. GHAFFAR, A., AWAN, W. A., ZAIDI, A., et al. Compact ultra wide-band and tri-band antenna for portable device. Radioengineering, 2020, vol. 29, no. 4, p. 601–608. DOI: 10.13164/re.2020.0601
  14. ZAIDI, A., AWAN, W. A., HUSSAIN, N., et al. A wide and triband flexible antennas with independently controllable notch bands for sub-6-GHz communication system. Radioengineering, 2020, vol. 29, no. 1, p. 44–51. DOI: 10.13164/re.2020.0044
  15. AWAN, W. A., ALIBAKHSHIKENARI, M., LIMITI, E. A polydimethyl-siloxane based conformal ultra-wideband antenna with additional GSM band. In 2021 IEEE Asia-Pacific Microwave Conference (APMC). Brisbane (Australia), 2021, p. 67–69. DOI: 10.1109/APMC52720.2021.9661872
  16. KHALID, H., AWAN, W. A., HUSSAIN, M., et al. Design of an integrated sub-6 GHz and mmwave MIMO antenna for 5G handheld devices. Applied Sciences, 2021, vol. 11, no. 18, p. 1–21. DOI: 10.3390/app11188331
  17. ZAHRA, H., AWAN, W. A., ALI, W., et al. A 28 GHz broadband helical inspired end-fire antenna and its MIMO configuration for 5G pattern diversity applications. Electronics, 2021, vol. 10, no. 4, p. 1–15. DOI: 10.3390/electronics10040405
  18. HUSSAIN, N., AWAN, W. A., ALI, W., et al. Compact wideband patch antenna and its MIMO configuration for 28 GHz applications. AEU-International Journal of Electronics and Communications, 2021, vol. 132, p. 1–8. DOI: 10.1016/j.aeue.2021.153612
  19. SHI, W., LI, G. M., QIN, J. P. A method for directional receiving of VLF signals with magnetic antennas. In ICWMMN 2013-5th IET International Conference on Wireless, Mobile & Multimedia Networks. Beijing (China), 2013, p. 51–54. DOI: 10.1049/cp.2013.2376
  20. KHAN, M. A., SUN, J., LI, B. D., et al. Magnetic sensors–A review and recent technologies. Engineering Research Express, 2021, vol. 3, no. 2, p. 1–22. DOI: 10.1088/2631-8695/ac0838
  21. WEI, S., LIAO, X., ZHANG, H., et al. Recent progress of fluxgate magnetic sensors: Basic research and application. Sensors, 2021, vol. 21, no. 4, p. 1–18. DOI: 10.3390/s21041500
  22. GUO, H. B. The research and realization of active omni directional small loops antenna loaded ferrite core. Harbin: Harbin Engineering University, 2007. (In Chinese) DOI: 10.7666/d.y1097838
  23. FU, T. H., SU, M. Research on long wave communication technique for UUV. Journal of Sichuan Ordnance, 2013, vol. 34, no. 3, p. 83–85. (In Chinese) DOI: 10.11809/scbgxb2013.03.024
  24. XIE, C. F., RAO, K. J., ZHAO, J. S. Electromagnetic Fields and Waves. 5nd ed. Beijing (China): Higher Education Press, 2019. (In Chinese) ISBN: 9787040525182
  25. SONG, Z., ZHANG, J. H., HUANG, Z. Antenna and Propagation. 3nd ed. Xi’an (China): Xidian University Publishing House, 2016. (In Chinese) ISBN: 9787560640563
  26. ROBERT, C. P., ELVIRA, V., TAWN, N., et al. Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews: Computational Statistics, 2018, vol. 10, no. 5, p. 1–14. DOI: 10.1002/wics.1435

Keywords: Orthogonal antenna, omni-directional receiving, parameters estimation, non-Gaussian noise

H. S. Zhu, W. H. Hu, B. F. Guo, X. X. Zhu, D. F. Xue, C. A. Zhu [references] [full-text] [DOI: 10.13164/re.2022.0262] [Download Citations]
Bistatic ISAR sparse aperture maneuvering target translational compensation imaging algorithm

For bistatic inverse synthetic aperture radar (Bi-ISAR), the non-uniform motion state of maneuvering target and the time-varying bistatic angle make the traditional imaging method of moving target face the problem of translation compensation, and the traditional translation compensation method is not suitable for the return wave in the case of sparse aperture. In this paper, a compensation imaging method combining two-dimension joint linearized Bregman iteration and image contrast search is proposed. The translation compensation problem can be transformed into two-dimension joint compressed sensing sparse reconstruction and moving target motion parameter estimation. The proposed algorithm makes use of the gain of echo two-dimension compression, greatly improves the accuracy of translation compensation and the quality of target image and has strong robustness to noise. The processing results of simulation data verify the effectiveness and superiority of the algorithm.

  1. ZHU, X. X., SHI, L., HU, W. H., et al. Bi-ISAR sparse imaging algorithm with complex Gaussian scale mixture prior. IET Radar, Sonar & Navigation, 2019, vol. 13, no. 12, p. 2202–2211. DOI: 10.1049/iet-rsn.2019.0296
  2. CABRERA, S. D., PARKS, T. W. Extrapolation and spectral estimation with iterative weighted norm modification. IEEE Transactions on Signal Processing, 1991, vol. 39, no. 4, p. 842–851. DOI: 10.1109/78.80906
  3. KIM, K. T., KIM, S. W., KIM, H. T. Two-dimension ISAR imaging using full polarisation and super-resolution processing techniques. IET Radar, Sonar and Navigation, 1998, vol. 145, no. 4, p. 240 to 246. DOI: 10.1049/ip-rsn:19982033
  4. LARSSON, E. G., LI, J., Spectral analysis of periodically gapped data. IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39, no. 3, p. 1089–1097. DOI: 10.1109/TAES.2003.1238761
  5. WANG, Y., LIU, Q. Super-resolution sparse aperture ISAR imaging of maneuvering target via the RELAX algorithm. IEEE Sensors Journal, 2018, vol. 18, no. 21, p. 8726–8738. DOI: 10.1109/JSEN.2018.2868308
  6. XU, X. J., NARAYANAN, R. M., et al. Enhanced resolution in SAR/ISAR imaging using iterative sidelobe apodization. IEEE Transactions on Image Processing, 2005, vol. 14, no. 4, p. 537 to 547. DOI: 10.1109/tip.2004.841198
  7. LI, S. D., CHEN, Y. B., YANG, J., et al. Imagine method with high resolution for maneuvering target based on compressive sensing. Journal of Air Force Early Warning Academy, 2015, vol. 29, no. 5, p. 313–321. (In Chinese)
  8. ZHANG, S., SUN, S., ZHANG, W., et al. High-resolution bistatic ISAR image formation for high-speed and complex-motion targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, vol. 8, no. 7, p. 3520–3531. DOI: 10.1109/JSTARS.2015.2417192
  9. ZHANG, S., LIU, Y., LI, X., et al. Autofocusing for sparse aperture ISAR imaging based on joint constraint of sparsity and minimum entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, vol. 10, no. 3, p. 998–1011. DOI: 10.1109/JSTARS.2016.2598880
  10. KANG, M., LEE, S., KIM, K., et al. Bistatic ISAR imaging and scaling of highly maneuvering target with complex motion via compressive sensing. IEEE Transactions on Aerospace and Electronic Systems, 2018, vol. 54, no. 6, p. 2809–2826. DOI: 10.1109/TAES.2018.2830598
  11. LI, S. Y., ZHAO, G. Q., ZHANG, W., et al. ISAR imaging by twodimensional convex optimization-based compressive sensing. IEEE Sensors Journal, 2016, vol. 16, no. 19, p. 7088–7093. DOI: 10.1109/JSEN.2016.2599540
  12. WANG, W., HU, Z., HUANG, P. 3-D MIMO radar imaging of ship target with rotational motions. Radioengineering, 2019, vol. 28, no. 4, p. 776–784. DOI: 10.13164/re.2019.0776
  13. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
  14. YU, T., DENG, S. J. Research of remote sensing image compression technology based on compressed sensing. In Tan, T., Ruan, Q., Wang, S., et al. (Eds.) Advances in Image and Graphics Technologies (Proceedings of the 10th Chinese Conference IGTA), 2015, p. 214–223. DOI: 10.1007/978-3-662-47791-5_25
  15. YANG, J. G., JIN, T., XIAO, C., et al. Compressed sensing radar imaging: Fundamentals, challenges, and advances. Sensors, 2019, vol. 19, no. 14, p. 1–19. DOI: 10.3390/s19143100
  16. DUAN, H. P., ZHANG, L. Z., FANG, J., et al. Pattern-coupled sparse Bayesian learning for inverse synthetic aperture radar imaging. IEEE Signal Processing Letters, 2015, vol. 22, no. 11, p. 1995–1999. DOI: 10.1109/LSP.2015.2452412
  17. ZHU, X. X., GUO, B. F., HU, W. H., et al. Scene segmentation of multi-band ISAR fusion imaging based on MB-PCSBL. IEEE Sensors Journal, 2021, vol. 21, no. 3, p. 3520–3532. DOI: 10.1109/JSEN.2020.3026109
  18. ZHANG, D., ZHANG, Y. S., FENG, C. Q., et al. Joint-2D-SL0 algorithm for joint sparse matrix reconstruction. International Journal of Antennas and Propagation, 2017, p. 1–7. DOI: 10.1155/2017/6862852
  19. ZHANG, L., SHENG, J. L., DUAN, J., et al. Translational motion compensation for ISAR imaging under low SNR by minimum entropy. EURASIP Journal on Advances in Signal Processing, 2013, p. 1–19. DOI: 10.1186/1687-6180-2013-33
  20. ZHANG, S. S., ZHANG, W., ZONG, Z. L., et.al. High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 5, p. 2098–2011. DOI: 10.1109/TAP.2015.2408337
  21. ZHU, X. X., LIU, L. M., GUO, B. F., et al. Two-dimensional multiradar ISAR fusion imaging based on fast linearized Bregman iteration algorithm. Journal of Applied Remote Sensing, 2021, vol. 15, no. 2, p. 1–21. DOI: 10.1117/1.JRS.15.026507
  22. FENG, J. J., ZHANG, G. ISAR imaging based on iterative reweighted Lp block sparse reconstruction algorithm. Progress In Electromagnetics Research M, 2016, vol. 48, p. 155–162. DOI: 10.2528/pierm16041501

Keywords: Bistatic-ISAR, maneuvering target, sparse aperture, translational compensation

B. Minnaert [references] [full-text] [DOI: 10.13164/re.2022.0273] [Download Citations]
Unified Expression of the Conjugate Image Impedances for Two-port Representations

Conjugate image impedances are used to minimize power reflections in a variety of domains, including amplifier design, microwave engineering, wireless power transfer, antenna design and millimeter wave applications. For a two-port network, they can be described as function of different parameters including impedance, admittance, hybrid, inverse hybrid, chain, scattering and chain scattering parameters. In this work, a general unified structure for the conjugate image impedances is provided, valid for each of the two-port representations. It highlights its close relationship with the Rollett stability factor and provides insight into the structure of conjugate image impedances.

  1. ROBERTS, S. Conjugate-image impedances. Proceedings of the IRE, 1946, vol. 34, no. 4, p. 198–204. DOI: 10.1109/JRPROC.1946.234242
  2. POZAR, D. M. Microwave Engineering. 4th ed., JohnWiley & Sons, 2011. ISBN: 0470631554
  3. INAGAKI, N. Theory of image impedance matching for inductively coupled power transfer systems. IEEE Transactions on Microwave Theory and Techniques, 2014, vol. 62, no. 4, p. 901–908. DOI: 10.1109/TMTT.2014.2300033
  4. MINNAERT, B., STEVENS, N. Conjugate image theory applied on capacitive wireless power transfer. Energies, 2017, vol. 10, no. 1, p. 1–15. DOI: 10.3390/en10010046
  5. HONG, J.-S., LANCASTER, M. J. Microstrip Filters for RF/Microwave Applications. John Wiley & Sons, 2004. ISBN: 9780471388777
  6. DOBROWOLSKI, J. Microwave Network Design using the Scattering Matrix. Artech House, 2010. ISBN: 9781608071296
  7. YARMAN, B. S. Design of Ultra Wideband Antenna Matching Networks: Via Simplified Real Frequency Technique. Springer Science & Business Media, 2008. ISBN: 9781402084171
  8. MINNAERT, B., STEVENS, N. Single variable expressions for the efficiency of a reciprocal power transfer system. International Journal of Circuit Theory and Applications 2017, vol. 45, no. 10, p. 1418–1430. DOI: 10.1002/cta.2299
  9. HUANG, K. C., WANG, Z. Millimeter Wave Communication Systems. John Wiley & Sons, 2011. ISBN: 9780470404621
  10. FRICKEY, D. A. Conversions between S, Z, Y, H, ABCD, and T parameters which are valid for complex source and load impedances. IEEE Transactions on Microwave Theory and Techniques 1994, vol. 42, no. 2, p. 205–211. DOI: 10.1109/22.275248
  11. COOMAN, A. Conversions between electrical network representations. arXiv:1607.03642, 2016, p. 1–3. DOI: 10.48550/arXiv.1607.03642
  12. DIONIGI, M., KOZIEL, S., MONGIARDO, M., et al. Iterative determination of conjugate image impedances for N-port networks. In IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO). Ottawa, (Canada), 2015, p. 1–4. DOI: 10.1109/NEMO.2015.7415007
  13. OHIRA, T. Extended kQ product formulas for capacitiveand inductive-coupling wireless power transfer schemes. IEICE Electronics Express, 2014, vol. 11, no. 9, p. 1–7. DOI: 10.1587/elex.11.20140147
  14. ROLLETT, J. Stability and power-gain invariants of linear twoports. IRE Transactions on Circuit Theory, 1962, vol. 9, no. 1, p. 29–32. DOI: 10.1109/TCT.1962.1086854
  15. ROLLETT, J. Some Invariant Properties of Linear Electrical Circuits. PhD thesis. University of Surrey, 1971.
  16. CARLI, E., CORZANI, T. General representation for the Rollett stability factor of a two-port network. IEEE Transactions on Circuit Theory, 1969, vol. 16, no. 2, p. 215–217. DOI: 10.1109/TCT.1969.1082928
  17. REVEYRAND, T. Multiport conversions between S, Z, Y, h, ABCD, and T parameters. In IEEE International Workshop on Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMIC). Brive La Gaillarde (France), 2018, p. 1–3. DOI: 10.1109/INMMIC.2018.8430023
  18. WOODS, D. Multiport-network analysis by matrix renormalisation employing voltage-wave S-parameters with complex normalisation. Proceedings of the Institution of Electrical Engineers, 1977, vol. 124, no. 3, p. 198–204. DOI: 10.1049/piee.1977.0037
  19. MASTRI, F, MONGIARDO, M., MONTI, G., et al. Characterization of wireless power transfer links by network invariants. In IEEE International Conference on Electromagnetics in Advanced Applications (ICEAA). Verona (Italy), 2017, p. 590–593. DOI: 10.1109/ICEAA.2017.8065314
  20. MARKS, R. B., WILLIAMS, D. F. A general waveguide circuit theory. Journal of Research of the National Institute of Standards and Technology, 1992, vol. 97, no. 5, p. 533. DOI: 10.6028/jres.097.024
  21. AMAKAWA, S. Scattered reflections on scattering parameters - Demystifying complex-referenced S parameters. IEICE Transactions on Electronics, 2016, vol. 99, no. 10, p. 1100–1112. DOI: 10.1587/transele.E99.C.1100
  22. WILLIAMS, D. Traveling waves and power waves: Building a solid foundation for microwave circuit theory. IEEE Microwave Magazine, 2013, vol. 14, no. 7, p. 38–45. DOI: 10.1109/MMM.2013.2279494
  23. LLORENTE-ROMANO, S., GARCA-LAMPEREZ, A., SARKAR, T. K., et al. An exposition on the choice of the proper S parameters in characterizing devices including transmission lines with complex reference impedances and a general methodology for computing them. IEEE Antennas and Propagation Magazine, 2013, vol. 55, no. 4, p. 94–112. DOI: 10.1109/MAP.2013.6645145

Keywords: Circuit theory, conjugate image, two-port networks

E. M. Cheng, K. Y. Lee, S. F. Khor, N. F. Mohd Nasir, C. W. S. R. Mohamad, N. A. Abdul Aziz, E. Z. Mohd Tarmizi, S. A. Baharuddin [references] [full-text] [DOI: 10.13164/re.2022.0281] [Download Citations]
Microwave Dielectric and Reflection Analysis on Pure and Adulterated Trigona Honey and Honey Gold

Honey adulteration is common in food industry, as it provides cheaper alternative for user to consume honey. However, it has been abused by industry runners with unsavory practices. It leads to business fraudulency. Pure honey is very precious due to its powerful health-giving properties. It rises attention of beekeeper, wholesaler, food manufacturer, retailer and consumer because this issue has been sensationally reported in media mass. Enforcement of law is initiated to mitigate the abuse and fraudulency. Apart from that, it motivates scientists, technologists and engineers to strive for an effective solution. Microwave sensing method is well known in agricultural product and food. Hence, dielectric and reflection response is explored for the potential of development of instrumentation system in gauging edible honeys. In this work, the dielectric and reflection measurement were conducted using Agilent E8362B PNA Network Analyzer in conjunction with Agilent 85070E Performance Probe from 0.5 GHz to 4.5 GHz. Dielectric and reflection measurement were conducted to investigate its dielectric behavior and mismatch impedance due to water and sucrose content in honey. It can be noticed that dielectric constant, ε’ decreases when frequency increases. In the meantime, ε’ decreases with increment of water and sucrose content for Honey Gold and Trigona Honey. Meanwhile, for water adulterated Honey Gold and Trigona Honey, loss factor, ε” decrease when frequencies increases. In addition, ε” decreases when water content < 36% and < 43% for Honey Gold and Trigona Honey, respectively. It can be found that at 1 GHz to 4 GHz, ε” increases when sucrose content increases which applicable for Honey Gold and Trigona Honey. In reflection measurement, magnitude of reflection coefficient, |Γ| decrease when frequency increases for all percentage of water and sucrose content for both honeys. Withal, phase, -φ increases as frequency increases for both water adulterated honeys. -φ varies insignificantly when sucrose content increases for both sucrose adulterated honeys.

  1. ZABRODSKA, B., VORLOVA, L. Honey quality inspection and adulteration identification. Acta Veterinaria Brno, 2015, vol. 83, no. 10, p. S85–S102. DOI: 10.2754/avb201483S10S85
  2. VANDAMME, L., HEYNEMAN, A., HOEKSEMA, H., et al. Honey in modern wound care : A systematic review. Burns, 2013, vol. 39, no.8, p. 1514–1525. DOI: 10.1016/j.burns.2013.06.014
  3. MARGHITAS, L. A., DEZMIREAN, D., MOISE, A., et al. Physicochemical and bioactive properties of different floral origin honeys from Romania. Food Chemistry, 2009, vol. 112, no. 4, p. 863–867. DOI: 10.1016/j.foodchem.2008.06.055
  4. BALL, J. A. R., HORSFIELD, B., HOLDEM, J. R., et al. Cheese curd moisture measurement using a 6-port reflectometer. In Asia Pacific Microwave Conference. New Delhi (India), 1996, vol. 2, p. 479–482.
  5. CHENG, E. M., SHAHRIMAN, A. B., RAHIM, H., et al. Microwave reflection based dielectric spectroscopy for moisture content in Melele mango fruit (Mangifera Indica L.). Journal of Telecommunication, Electronic and Computer Engineering, 2018, vol. 10, no. 1–14, p. 1–6. e-ISSN: 2289-8131
  6. GUO, W., TRABELSI, S., NELSON, S. O., et al. Storage effects on dielectric properties of eggs from 10 to 1800 MHz. Journal of Food Science, 2007, vol. 72, no. 5, p. 335–340. DOI: 10.1111/j.1750-3841.2007.00392.x
  7. AHN, J. Y., KIL, D. Y., KONG, C., et al. Comparison of ovendrying methods for determination of moisture content in feed ingredients. Asian-Australasian Journal of Animal Sciences, 2014, vol. 27, no. 11, p. 1615–1622. DOI: 10.5713/ajas.2014.14305
  8. GUO, W., ZHU, X., LIU, Y., et al. Sugar and water contents of honey with dielectric property sensing. Journal of Food Engineering, 2010, vol. 97, no. 2, p. 275–281. DOI: 10.1016/j.jfoodeng.2009.10.024
  9. GUO, W., LIU, Y., ZHU, X., et al. Dielectric properties of honey adulterated with sucrose syrup. Journal of Food Engineering, 2011, vol. 107, no. 1, p. 1–7. DOI: 10.1016/j.jfoodeng.2011.06.013
  10. CALAY, R. K., NEWBOROUGH, M., PROBERT, D., et al. Predictive equations for the dielectric properties of foods. International Journal of Food Science and Technology, 1995, vol. 29, no. 6, p. 699–713. DOI: 10.1111/j.1365-2621.1994.tb02111.x
  11. KOMAROV, V., WANG, S., TANG, J. Permittivity and measurement. In Encyclopedia of RF and Microwave Engineering. 1st ed. New York (United States): John Wiley and Sons, 2005. Online ISBN: 9780471654506 DOI: 10.1002/0471654507.eme308
  12. PURANIK, S., KUMBHARKHANE, A., MEHROTRA, S. Dielectric properties of honey-water mixtures between 10 MHz and 10 GHz using time domain technique. Journal of Microwave Power and Electromagnetic Energy, 1991, vol. 26, no. 4, p. 196–201. DOI: 10.1080/08327823.1991.11688157
  13. AHMED, J., PRABHU, S. T., RAGHAVAN, G. S. V., et al. Physico-chemical, rheological, calorimetric and dielectric behaviour of selected Indian honey. Journal of Food Engineering, 2007, vol. 79, no. 4, p. 1207–1213. DOI: 10.1016/j.jfoodeng.2006.04.048
  14. PENG, S., LING, N., ANIZA, Y., et al. Total phenolic contents and colour intensity of Malaysian honeys from the Apis spp. and Trigona spp. bees. Agriculture and Agricultural Science Procedia, 2014, vol. 2, p. 150–155. DOI: 10.1016/j.aaspro.2014.11.022
  15. SHUKLA, A., TEWARI, P. R., KHAN, G. Study of phenol and its derivatives based on dipole moment. Journal of Chemical and Pharmaceutical Research, 2011, vol. 3, no. 1, p. 79–83. ISSN: 0975-7384
  16. KEK, S. P., CHIN, N. L., TAN, S. W., et al. Classification of honey from its bee origin via chemical profiles and mineral content. Food Analytical Methods, 2017, vol. 10, no. 1, p. 19–30. DOI: 10.1007/s12161-016-0544-0
  17. UENO, S. (Ed.) Biological Effects of Magnetic and Electromagnetic Fields. 1st ed. New York (United States): Springer, 1996. ISBN: 978-0-585-31661-1
  18. GALGANI, J., AGUIRRE, C., DIAZ, E. Acute effect of meal glycemic index and glycemic load on blood glucose and insulin responses in humans. Nutrition Journal, 2006, vol. 5, no. 22, p. 1–7. DOI: 10.1186/1475-2891-5-22
  19. KUMAR, M., GUPTA, R. Diamagnetic Susceptibility of Organic Compounds, Oils, Paraffins and Polyethylenes. 1st ed. Berlin (Germany): Springer Berlin, Heidelberg, 2008. ISBN: 978-3-540-45860-9

Keywords: Honey, sucrose, water, dielectric, reflection