Medical image transmission in multi-state synchronization of chaotic systems using polynomial fuzzy modeling

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Zabol Branch, Islamic Azad University, Zabol, Iran.

2 Department of Mathematics and Statistics, Velayat University, Iranshahr, Iran.

Abstract
Purpose: The most important effects of the Internet of Things in healthcare include the ability to exchange information, reduce hospitalization costs, and improve healthcare costs. The primary challenges of the Internet of Things in healthcare are security and privacy, with image transmission particularly crucial for communication and security. The primary objective of this paper is to design a suitable channel for transmitting medical data via chaotic synchronization that employs fuzzy modeling.
Methodology: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order convolutional neural networks based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this scheme, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.
Findings: Simulations have been performed on color and black-and-white medical images. Encrypted and recovered images have been obtained using this scheme. The simulation and accuracy of the proposed method's results have been investigated using MATLAB software. To evaluate the performance of the proposed method, various criteria, including image histogram, signal-to-noise ratio, correlation, and information entropy, were assessed. The results demonstrate the effectiveness of the proposed method in image encryption.
Originality/Value: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order multi-convolutional systems based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this project, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the multi-state synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.

Keywords


[1]   Moafimadani, S. S., Chen, Y., & Tang, C. (2019). A new algorithm for medical color images encryption using chaotic systems. Entropy, 21(6), 577. https://doi.org/10.3390/e21060577
[2]   Lima, V. S., Madeiro, F., & Lima, J. B. (2020). Encryption of 3D medical images based on a novel multiparameter cosine number transform. Computers in biology and medicine, 121, 103772. https://doi.org/10.1016/j.compbiomed.2020.103772
[3]   Benssalah, M., Rhaskali, Y., & Azzaz, M. S. (2018). Medical images encryption based on elliptic curve cryptography and chaos theory. 2018 international conference on smart communications in network technologies (saconet) (pp. 222–226). IEEE. https://doi.org/10.1109/SaCoNeT.2018.8585512
[4]   Pareek, N. K., & Patidar, V. (2016). Medical image protection using genetic algorithm operations. Soft computing, 20(2), 763–772. https://doi.org/10.1007/s00500-014-1539-7
[5]   Pecora, L. M., & Carroll, T. L. (1990). Synchronization in chaotic systems. Physical review letters, 64(8), 821–824. https://doi.org/10.1103/PhysRevLett.64.821
[6]   Çiçek, S., Ferikoğlu, A., & Pehlivan, İ. (2016). A new 3D chaotic system: Dynamical analysis, electronic circuit design, active control synchronization and chaotic masking communication application. Optik, 127(8), 4024–4030. https://doi.org/10.1016/j.ijleo.2016.01.069
[7]   Tirandaz, H., & Hajipour, A. (2017). Adaptive synchronization and anti-synchronization of TSUCS and Lü unified chaotic systems with unknown parameters. Optik, 130, 543–549. https://doi.org/10.1016/j.ijleo.2016.10.093
[8]   Chen, X., Park, J. H., Cao, J., & Qiu, J. (2017). Sliding mode synchronization of multiple chaotic systems with uncertainties and disturbances. Applied mathematics and computation, 308, 161–173. https://doi.org/10.1016/j.amc.2017.03.032
[9]   yaghoubi,  hassan., & Zare, A. (2022). stability analysis of fuzzy polynomial fractional differential systems using sum-of-squares. Journal of modeling in engineering, 20(71), 61-76. (In Persian). https://doi.org/10.22075/jme.2022.24735.2181
[10] Dong, H., Huang, C., Cao, J., & Liu, H. (2024). Adaptive fuzzy quantized prescribed performance synchronization of uncertain non-strict feedback chaotic systems with time-varying actuator failure. Information sciences, 681, 121241. https://doi.org/10.1016/j.ins.2024.121241
[11] Chen, Y. J., Chou, H. G., Wang, W. J., Tsai, S. H., Tanaka, K., Wang, H. O., & Wang, K. C. (2020). A polynomial-fuzzy-model-based synchronization methodology for the multi-scroll Chen chaotic secure communication system. Engineering applications of artificial intelligence, 87, 103251. https://doi.org/10.1016/j.engappai.2019.103251
[12] Senouci, A., & Boukabou, A. (2016). Fuzzy modeling, stabilization and synchronization of multi-scroll chaotic systems. Optik, 127(13), 5351–5358. https://doi.org/10.1016/j.ijleo.2016.03.019
[13] Vaidyanathan, S., & Boulkroune, A. (2016). A novel 4-D hyperchaotic chemical reactor system and its adaptive control. In advances and applications in chaotic systems (pp. 447–469). Springer, Cham. https://doi.org/10.1007/978-3-319-30279-9_19
[14] Feki, M. (2017). Sliding mode based control and synchronization of chaotic systems in presence of parametric uncertainties. In applications of sliding mode control in science and engineering. studies in computational intelligence (pp. 35–59). Springer, Cham. https://doi.org/10.1007/978-3-319-55598-0_2
[15] Tabasi, M., & Balochian, S. (2018). Synchronization of the chaotic fractional-order genesio–tesi systems using the adaptive sliding mode fractional-order controller. Journal of control, automation and electrical systems, 29(1), 15–21. https://doi.org/10.1007/s40313-017-0350-y
[16] Hamri, N., & Ouahabi, R. (2017). Modified projective synchronization of different chaotic systems using adaptive control. Computational and applied mathematics, 36(3), 1315–1332. https://doi.org/10.1007/s40314-015-0294-4
[17] Onma, O. S., Olusola, O. I., & Njah, A. N. (2014). Control and Synchronization of chaotic and hyperchaotic lorenz systems via extended backstepping techniques. Journal of nonlinear dynamics, 2014, 1–15. https://doi.org/10.1155/2014/861727
[18] Chen, Y. J., Chou, H. G., & Wang, W. J. (2017). Polynomial fuzzy control design for synchronizing multi-scroll chen chaotic systems. 2017 international conference on applied system innovation (ICASI) (pp. 785–788). IEEE. https://doi.org/10.1109/ICASI.2017.7988548
[19] Zhou, S. S., Jahanshahi, H., Din, Q., Bekiros, S., Alcaraz, R., Alassafi, M. O., …, & Chu, Y. M. (2021). Discrete-time macroeconomic system: Bifurcation analysis and synchronization using fuzzy-based activation feedback control. Chaos, solitons & fractals, 142, 110378. https://doi.org/10.1016/j.chaos.2020.110378
[20] Yu, G. R., Chang, Y. D., & Chang, C. H. (2021). Synthesis of polynomial fuzzy model-based designs with synchronization and secure communications for chaos systems with h∞ performance. Processes, 9(11), 2088. https://doi.org/10.3390/pr9112088
[21] Huang, C., & Cao, J. (2017). Active control strategy for synchronization and anti-synchronization of a fractional chaotic financial system. Physica a: Statistical mechanics and its applications, 473, 262–275. https://doi.org/10.1016/j.physa.2017.01.009
[22] Prajna, S., Papachristodoulou, A., & Parrilo, P. A. (2002). Introducing sostools: A general purpose sum of squares programming solver. Proceedings of the 41st IEEE conference on decision and control, 2002. (pp. 741–746). IEEE. https://doi.org/10.1109/CDC.2002.1184594
[23] Ahmad, I., Saaban, A. Bin, Ibrahim, A. B., Shahzad, M., & Mossa Al-sawalha, M. (2016). Reduced-order synchronization of time-delay chaotic systems with known and unknown parameters. Optik, 127(13), 5506–5514. https://doi.org/10.1016/j.ijleo.2016.02.078
[24] Ren, H. P., Bai, C., Huang, Z. Z., & Grebogi, C. (2017). Secure communication based on hyperchaotic chen system with time-delay. International journal of bifurcation and chaos, 27(05), 1750076. https://doi.org/10.1142/S0218127417500766
[25] Wang, W., Jia, X., Luo, X., Kurths, J., & Yuan, M. (2019). Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication. Chaos, solitons & fractals, 126, 85–96. https://doi.org/10.1016/j.chaos.2019.05.041
[26] Kekha Javan, A. A., & Keikha, A. (2024). Fuzzy polynomial application in the synchronization of fractional order chaotic systems based on sum of squares method. Fuzzy systems and its applications, 7(1), 163-187. (In Persian). https://jfsa.fuzzy.ir/article_205960.html?lang=en
[27] Kekha Javan, A. A., Zare, A., & Alizadehsani, R. (2022). Multi-state synchronization of chaotic systems with distributed fractional order derivatives and its application in secure communications. Big data and cognitive computing, 6(3), 82. https://doi.org/10.3390/bdcc6030082
[28] Hosny, K. M., Kamal, S. T., Darwish, M. M., & Papakostas, G. A. (2021). New image encryption algorithm using hyperchaotic system and fibonacci Q-matrix. Electronics, 10(9), 1066. https://doi.org/10.3390/electronics10091066