DCNN oscillator design, implementation, and performance evaluation

• Autorzy: Salih Thair A. , Mohammed Emad A. , Abdal Yasir M.

Identyfikatory

DOI

10.15199/48.2023.04.14

Warianty tytułu

PL

Projekt, implementacja i ocena oscylatora DCNN

• Języki publikacji: EN

Abstrakty

EN

FPAAs technology are the ideal solution for creating an analog system. FPAA describes and implements the architecture of a simple CNN model used to build a delayed cellular neural network (DCNN) oscillator in this letter. Matlab simulation was carried out in order to analyze the proposed system. The attraction spectrum with different initial conditions, as well as Lyapunov's motives, were used to investigate the fundamental characteristics of the proposed system. The effect of noise on the proposed system was investigated. The promising results obtained encourage the application of the proposed model in secure communication systems.

PL

Technologia FPAA jest idealnym rozwiązaniem do stworzenia systemu analogowego. W tym liście FPAA opisuje i implementuje architekturę prostego modelu CNN używanego do budowy oscylatora opóźnionej komórkowej sieci neuronowej (DCNN). W celu analizy proponowanego systemu przeprowadzono symulację w Matlabie. Widmo przyciągania z różnymi warunkami początkowymi, a także motywy Lapunowa zostały wykorzystane do zbadania podstawowych cech proponowanego systemu. Zbadano wpływ hałasu na proponowany system. Uzyskane obiecujące wyniki zachęcają do zastosowania proponowanego modelu w bezpiecznych systemach komunikacyjnych.

Słowa kluczowe

EN

DCNN; FPAA; chaotic signal; Lyapunov exponent

PL

sygnał chaotyczny; generator DCNN

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 4
• Strony: 84--88
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Salih Thair A.

• Northern Technical University, Engineering Technical College, Department of Computer Engineering Technology

autor

Mohammed Emad A.

• Northern Technical University, Engineering Technical College, Department of Computer Engineering Technology

autor

Abdal Yasir M.

• Northern Technical University, Engineering Technical College, Department of Computer Engineering Technology

Bibliografia

• [1] Jia, L., Yuling, L., Junxiu L., Jinjie, B., Senhui, Q., Mingcan, C., & Zhixian, L., An Image Compression-Encryption Algorithm Based on Cellular Neural Network and Compressive Sensing", 3rd IEEE International Conference on Image, Vision and Computing, 2018, pp. 673-677.
• [2] Renxiu, Z., Longfei, Y., Donghua, J., Wei, D., Jian, S., Kuncheng, H, & Qun, D., A Novel Plaintext-Related Color Image Encryption Scheme Based on Cellular Neural Network and Chen’s Chaotic System, Symmetry journal, 13(2021), no.3, pp. 393-411.
• [3] Chunlei, F., Qun, D, Analyzing the dynamics of digital chaotic maps via a new period search algorithm, Nonlinear Dynamics journal, 1997, pp. 831–8.
• [4] Enis, G., & Kenan, A., Lorenz-like System Design Using Cellular Neural Networks, Turk J Elec Eng & Comp Sci, 26(2018), pp. 1812–1819.
• [5] Ammar, D., Dhammika, J., Peter V.H., Bouchra, S. & Jasmine B., A Generalized Multilevel-Hybrid Chaotic Oscillator for Low-Cost and Power-Efficient short-Range Chaotic Communication Systems, EURASIP Journal on Wireless Communications and Networking, 23(2020), pp. 1-14.
• [6] Sundarapandian, V., 3-Cells Cellular Neural Network (CNN) Attractor and its Adaptive Biological Control, International Journal of Pharm Tech Research, 8(2015), no.4, pp. 632-640.
• [7] Larptwee and W. San-Um, Implementation of Rössler Chaotic System through Inherent Exponential Nonlinearity of a Diode with Two-Channel Chaotic Synchronization Applications, IEEE 4th International Conference on Intelligent Control and Information Processing, 2013, pp. 787-791.
• [8] Marcin, D , Noncommutative Sprott Systems, Their Jerk Dynamics, and Their Chaos Synchronization by Active Control, IOP Conf. Series: Journal of Physics: Conf. Series. 1194 012024, 2019, pp.1-9.
• [9] Farzaneh, M.,Sara, D.,Foroogh, M., & Sadjaad O. Controlling chaos in Arneodo system,17th Mediterranean Conference on Control and Automation, , 2009, pp. 314-319.
• [10] İsmail, K., Murat, T., Can, B. F., & İhsan, P, FPGA-based Real time Implementation of Lü-Chen Chaotic Generator, International Advanced Researches & Engineering Congress,2017, pp. 1-6.
• [11] V. Sundarapandian, Anti-Synchronization of Li and Cai Chaotic Systems by Active Nonlinear Control, Int. J. of Mathematical Sciences and Applications, 1(2011), 1, no.3, pp. 1129-1137.
• [12] Sundarapandian, V. , & R.Karthikeyan, Adaptive Anti Synchronization of Uncertain Tigan and Li Systems, Journal of Engineering and Applied Sciences, 7(2012), no.1, pp. 45-52.
• [13] Sundarapandian V., Oumate Alhadji Abba and Gambo Betchewe,and Mohamadou Alidou, A new three- dimensional chaotic system: its adaptive control and circuit design, Int. J. Automation and Control, 13 (2019), no. 1, pp. 101-121.
• [14] S. Vaidyanathan, Hybrid Synchronization of Lorenz and Pehlivan Chaotic Systems by Active Nonlinear Control, International Journal of Advances in Science and Technology, 2(2011), no. 6, pp. 10-19-102.
• [15] Wei-Feng, S., Shi-Long, X., Novel Chaotic Neural Networks And Application", IEEE proceedings of the 4th International Conference on Machine Learning and Cybernetics, 2005, pp. 4651-4656.
• [16] P. Arena, S. Baglio, L. Fortuna, and G. Manganaro, Cellular Neural Networks: A Survey, 7th IFAC Symposium on Large Scale Systems: Theory and Applications, 28(1995), no. 10, pp. 43-48.
• [17] Birong X., Hairong L., and Guangyi W., Hidden Multistability in a Memristor-Based Cellular Neural Network, Advances in Mathematical Physics, 2020, pp. 1-10.
• [18] L.,O. Chue, and L.Yang., Cellular neural networks: theory, IEEE Transactions on circuits and systems, 35(1988), no.10, pp. 1257-1272.
• [19] Pavel, S., "Cellular Neural Networks and Their Applications, Sixth International Conference on New Trends in the Applications of Differential Equations in Sciences, 2159 (2019). No. 1, pp.030034-1-5.
• [20] Zainab, A., Sajad, J., Jun, M., Julien, C. S., and Sareh, Z, Using chaotic artificial neural networks to model memory in the brain, Communications in Nonlinear Science and Numerical Simulation, 44(2017), pp. 449-459.
• [21] Samuel X.-de-S., Optimization and Robustness of Cellular Neural Networks, Thesis Submitted to the Faculty of Elektrotechniek, Katholieke, University Leuven, 2007.
• [22] F. Corinto, M. Gilli,"Comparison between the dynamic behaviour of Chua–Yang and full-range cellular neural networks", International Journal of Circuit Theory and Applications, v 31(2003), no.5, pp. 423–441.
• [23] João, L.F.F.,"Cellular Neural Networks design for sensor networks. Thesis Submitted to the School of Engineering , University of Porto. 2021., v31(2003), no.5, pp. 423–441.
• [24] Ülkü. A. Ş., Cuma . B., Osman. N. U., Application of cellular neural network (CNN) to the prediction of missing air pollutant data, Atmospheric Research, 101(2011), pp. 314–326.
• [25] Baris. K., Vedat. Ç. and Arif A., Chaotic cellular neural network-based true random number generator, International Journal of Circuit Theory and Applications,45(2017), no.11.
• [26] Giuseppe G. and Saverio M., Synchronizing High Dimensional Chaotic Systems Via Eigenvalue Placement with Application to Cellular Neural Networks, International Journal of Bifurcation and Chaos, 9(1999), no. 4, pp. 705–711.
• [27] B. Karakaya, V. Celik & A. Gulten , Realization of Delayed Cellular Neural Network Model on FPGA, Electric Electronics, Computer Science, Biomedical Engineerings' Meeting conference, 2018, pp.1-4.
• [28] Jia L.,Yuling L.,Junxiu L., Jinjie B., Senhui O., Mingcan C., Zhixian L, An Image Compression-Encryption Algorithm Based on Cellular Neural Network and Compressive Sensing, 3rd IEEE International Conference on Image, Vision and Computing, 2018, pp. 673-677.
• [29] Kalamullah R.,Yohan S., Magfirawaty, Nur H., Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set, IEEE Access journal, 8(2020), pp. 110351–110361.
• [30] Fatih Ö.,Brief review on application of nonlinear dynamics in image encryption, An International Journal of Nonlinear Dynamics and Chaos in Engineering Systems, 92(2020), no.5, pp. 305-313.
• [31] Xingyuan W., Le F., Hongyu Z., Fast image encryption algorithm based on parallel computing system, Journal of Information Sciences, 486(2020), pp. 340-358.
• [32] Chanil P., Lilian H., A New Color Image Encryption Using Combination of The 1D Chaotic Map, Journal of Signal Processing, 138(2017), pp. 129-137.
• [33] Baran T., Atilla. Ö., and Yasin Ö., Design and Implementation of a Cellular Neural Network Based Oscillator Circuit", Recent advances in circuits, systems, electronics, control and signal processing, 2009, pp. 34-39.
• [34] Xuemei L., Lihong H. Jianhong W.,"Further Results on the Stability of Delayed Cellular Neural Networks, IEEE Transactions on Circuits and Systems—I: Fundamental Theory and Applications, 50(2003),no.9, pp.1239-1242.
• [35] Marek B., Danylo P., The Fastest, Simplified Method of Estimation of the Largest Lyapunov Exponent for Continuous Dynamical Systems with Time Delay, Journal of Mechanics and Mechanical Engineering. 21(2018), no.4, pp. 985–994.
• [36] Shengyao C., Feng X., and Zhong L., Supreme Local Lyapunov Exponents and Chaotic Impulsive Synchronization, International Journal of Bifurcation and Chaos, 23(2013), no.10, pp. 1-20.
• [37] Mazhar B. T., Eslam I. A., Robust and Sensitive Method of Lyapunov Exponent for Heart Rate Variability, International Journal of Biomedical Engineering and Science, 2(2015), no.3, pp. 31-48.
• [38] Silvio L.T.de S., and Lbere. L. C., Calculation of Lyapunov exponents in systems with impacts", Chaos, Solitons and Fractals, 19(2004), pp. 569–579.
• [39] Tomáš. G., Advanced Algorithms for the Analysis of Data Sequences in Matlab. Thesis Submitted to the Faculty of Electrical Engineering and Communication Department of Radio Electronics, Brno University of Technology. 2010.
• [40] Yifu S., Fault Detection in Dynamic Systems Using the Largest Lyapunov Exponent. Thesis Submitted to the Office of Graduate Studies of Texas, A & M University. 2011.
• [41] Maha. S. D., and Soliman A.M., Survey on Field Programmable Analog Array Architectures Eliminating Routing Network, IEEE ACCESS. 8(2020), pp. 220779-220794.
• [42] Alejandro M., Pablo B., Manuel B. Félix G.-L., Elio S., Work in progress: Proof of concept: Remote Laboratory Raspberry Pi + FPAA. IEEE World Engineering Education Conference, 2019, pp. 1-4.
• [43] AN231E04 Datasheet Rev 1.0, Dynamically Reconfigurable dpASP, 3rdGeneration, www.anadigm.com.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).