Archive

Abstract:

This paper presents a Monte Carlo simulation for analyzing the random vibration of continuous beams with random spring stiffness and 2D random material properties. The spectral modeling approach is utilized to model the 2D stochastic field of material properties and generate realizations. The stiffness of elastic springs is assumed to follow a normal distribution. By applying the standard finite element method to beam structures with random input parameters, including realizations of a 2D random field and joint stiffness, the statistical characteristics of the natural frequencies can be quantified through analysis of the resulting frequency data. Results demonstrate the influence of 2D random material properties and the variability of spring stiffness on the statistical distribution of natural frequencies. The coefficient of variation (COV) of natural frequencies exhibits an upward trend as the standard deviation of either the 2D stochastic field representing material properties or the joint stiffness increases. Notably, the stochastic variations in the 2D random field exert a significantly greater influence than those in spring stiffness. Additionally, at small correlation lengths, the COV increases significantly with increasing correlation length. Conversely, at large correlation lengths, the COV remains on an increasing trend, yet at a much more gradual rate.

Keywords:

Monte Carlo simulation,2D Random field,Beam,Natural frequencies,

Refference:

I. Arregui-Mena, José David, Lee Margetts and Paul M. Mummery. (2016), Practical Application of the Stochastic Finite Element Method. Archives of Computational Methods in Engineering, vol. 23, pp. 171-190. 10.1007/s11831-014-9139-3.
II. Chang, T. P. and H. C. Chang. (1994), Stochastic Dynamic Finite Element Analysis of a Nonuniform Beam. International Journal of Solids and Structures, vol. 31, pp. 587-597. 10.1016/0020-7683(94)90139-2.
III. Das, Sourav and Solomon Tesfamariam. (2024), Reliability Assessment of Stochastic Dynamical Systems Using Physics Informed Neural Network Based Pdem. Reliability Engineering & System Safety, vol. 243, pp. 109849. 10.1016/j.ress.2023.109849.
IV. Ganesan, Rajamohan and Vijay Kumar Kowda. (2005), Free-Vibration of Composite Beam-Columns with Stochastic Material and Geometric Properties Subjected to Random Axial Loads. Journal of Reinforced Plastics and Composites, vol. 24, pp. 69-91. doi: 10.1177/0731684405042951.
V. Ghanem, Roger G and Pol D Spanos (2003). Stochastic Finite Elements: A Spectral Approach. Courier Corporation.
VI. Gladwin, K. T. J. and K. J. Vinoy. (2023), An Efficient Ssfem-Pod Scheme for Wideband Stochastic Analysis of Permittivity Variations. IEEE Transactions on Antennas and Propagation, vol. 71, pp. 1654-1661. 10.1109/TAP.2022.3221061.
VII. Gupta, S. and C. S. Manohar. (2002), Dynamic Stiffness Method for Circular Stochastic Timoshenko Beams: Response Variability and Reliability Analyses. Journal of Sound and Vibration, vol. 253, pp. 1051-1085. 10.1006/jsvi.2001.4082.
VIII. Hien, Ta Duy. (2020), A Static Analysis of Nonuniform Column by Stochastic Finite Element Method Using Weighted Integration Approach Transport and Communications Science Journal, vol. 70, pp. 359-367. 10.25073/tcsj.71.4.5.
IX. Hurtado, J. E. and A. H. Barbat. (1998), Monte Carlo Techniques in Computational Stochastic Mechanics. Archives of Computational Methods in Engineering, vol. 5, pp. 3-29. 10.1007/BF02736747.
X. Kleijnen, Jack PC, Ad Ridder and Reuven Rubinstein (2010). Variance Reduction Techniques in Monte Carlo Methods. Springer.
XI. Le, Toan Minh, Duy Vo, Tinh Quoc Bui, Thu Van Huynh, Suchart Limkatanyu and Jaroon Rungamornrat. “Spectral Stochastic Isogeometric Analysis of Microbeams with Material Uncertainty.” CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure, edited by Cuong Ha-Minh et al., Springer Nature Singapore, 2022, pp. 491-498. 10.1007/978-981-16-7160-9_49.
XII. Li, Xin, Shaopeng Li, Yan Jiang, Qingshan Yang, Yunfeng Zou, Yi Su and Yi Hui. (2024), Higher-Order Spectral Representation Method: New Algorithmic Framework for Simulating Multi-Dimensional Non-Gaussian Random Physical Fields. Probabilistic Engineering Mechanics, vol. 76, pp. 103596. 10.1016/j.probengmech.2024.103596.
XIII. Lien, Pham Thi Ba. (2023), Free Vibration of Porous Functionally Graded Sandwich Beams on Elastic Foundation Based Ontrigonometric Shear Deformation Theory. Transport and Communications Science Journal, vol. 74, pp. 946-961. 10.47869/tcsj.74.8.8.
XIV. Liu, Gui-Rong and Siu Sin Quek (2013). The Finite Element Method: A Practical Course. Butterworth-Heinemann.
XV. Lofrano, Egidio, Achille Paolone and Marcello Vasta. (2016), A Perturbation Approach for the Identification of Uncertain Structures. International Journal of Dynamics and Control, vol. 4, pp. 204-212. doi: 10.1007/s40435-015-0171-4.
XVI. Maksymiuk, Yurii, Viktor Andriievskyi, Ivan Martyniuk and Oleksandr Maksymiuk. (2023), Analysis of Structures with Arbitrary Kinematic Boundary Conditions by the Semi-Analytical Finite Element Method. Strength of Materials Theory of Structures, pp. 140-146. doi: 10.32347/2410-2547.2023.111.140-146.
XVII. Musa, Sarhan M, VK Havanur and AV Kulkarni (2013). Finite Element Analysis. Mercury Learning and Information.
XVIII. Nguyen, Phong T. T., Luan C. Trinh and Kien-Trung Nguyen. “Probabilistic Free Vibration Analysis of Functionally Graded Beams Using Stochastic Finite Element Methods.” Computational Intelligence Methods for Green Technology and Sustainable Development, edited by Yo-Ping Huang et al., Springer International Publishing, 2021, pp. 517-531. 10.1007/978-3-030-62324-1_44.
XIX. Ninh, Vu Thi An. (2021), Fundamental Frequencies of Bidirectional Functionally Graded Sandwich Beams Partially Supported by Foundation Using Different Beam Theories. Transport and Communications Science Journal, vol. 72, pp. 452-467. 10.47869/tcsj.72.4.5.
XX. Schuëller, G. I. and H. J. Pradlwarter. (2009), Uncertain Linear Systems in Dynamics: Retrospective and Recent Developments by Stochastic Approaches. Engineering Structures, vol. 31, pp. 2507-2517. doi: 10.1016/j.engstruct.2009.07.005.
XXI. Shinozuka, Masanobu and George Deodatis. (1991), Simulation of Stochastic Processes by Spectral Representation. Applied Mechanics Reviews, vol. 44, pp. 191-204. 10.1115/1.3119501.
XXII. Shinozuka, Masanobu and George Deodatis. (1996), Simulation of Multi-Dimensional Gaussian Stochastic Fields by Spectral Representation. Applied Mechanics Reviews, vol. 49, pp. 29-53. 10.1115/1.3101883.
XXIII. Thuan, N. V. and T. D. Hien. (2020), Stochastic Perturbation-Based Finite Element for Free Vibration of Functionally Graded Beams with an Uncertain Elastic Modulus. Mechanics of Composite Materials, vol. 56, pp. 485-496. 10.1007/s11029-020-09897-z.
XXIV. Zheng, Zhibao and Udo Nackenhorst. (2024), Stochastic Virtual Element Methods for Uncertainty Propagation of Stochastic Linear Elasticity. Computational Mechanics, vol. 73, pp. 667-684. 10.1007/s00466-023-02384-x.

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Abstract:

This paper is devoted to the study of bipolar vague topological spaces. In this paper, bipolar vague strongly  generalized closed sets are introduced. Also, some of their properties are studied and analyzed.

Keywords:

Bipolar vague sets,Bipolar vague topology,Bipolar vague α generalized closed sets,Bipolar vague strongly α generalized closed sets,

Refference:

I. Arockiarani. I and Cicily Flora. S.: ‘Positive Implicative Bipolar Vague Ideals in BCK-algebras’, International Research Journal of Pure Algebra, Vol. 8, pp. 1–7, 2016.
II. Atanassov. K.: ‘Intuitionistic Fuzzy Sets’, Fuzzy Sets and Systems, Vol. 20, pp. 87–96, 1986.
III. Chang. C. L.: ‘Fuzzy Topological Spaces’, J. Math. Anal. Appl., Vol. 24, pp. 182–190, 1968.
IV. Cicily Flora. S and Arockiarani. I.: ‘A New Class of Generalized Bipolar Vague Sets’, International Journal of Information Research and Review, pp. 3058–3065, 2016.
V. Cicily Flora. S and Arockiarani. I.: ‘On Bipolar Vague Ring in Baire Spaces’, Bulletin of Mathematics and Statistics Research, Vol. 5, pp. 1–9, 2017.
VI. Coker. D.: ‘An Introduction to Intuitionistic Fuzzy Topological Spaces’, Fuzzy Sets and Systems, Vol. 88, pp. 81–89, 1997.
VII. Gau. W. L and Buehrer. D. J.: ‘Vague Sets’, IEEE Trans. Systems, Man and Cybernetics, Vol. 23, pp. 610–614, 1993.
VIII. Lee. K. M.: ‘Bipolar-valued Fuzzy Sets and Their Operations’, Proc. Int. Conf. on Intelligent Technologies, Vol. 7, pp. 307–312, 2000.
IX. Levine. N.: ‘Generalized Closed Sets in Topological Spaces’, Rend. Circ. Mat. Palermo, Vol. 19, pp. 89–96, 1970.
X. Prishka. F and Mariapresenti. L.: ‘Bipolar Vague α Generalized Closed Sets in Topological Spaces’, Journal of Basic Science and Engineering, Vol. 21, pp. 1390–1398, 2024.
XI. Zadeh. L. A.: ‘Fuzzy Sets’, Information and Control, Vol. 8, pp. 338–335, 1965.

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Abstract:

Massive MIMO (multiple-input multiple-output) systems are important for millimeter-wave (mm-Wave) communication. These systems connect base stations (BS) with user equipment (UE) for faster and more efficient data transfer. However, there are challenges in achieving this performance. Beam selection and power control between the BS and UE must be done accurately. This is difficult because the channel state information (CSI) is not always available. Without proper information, selecting the best beam or power level results in inefficiency. To solve this, we propose a deep learning-based framework. This system uses a beam-steering technique to estimate signal strength. The goal is to choose the best beam for the user and the right power level for data transmission. The framework minimizes power usage when the user gets the required data rate. It works even when CSI is unknown. Missing data is another common problem in these systems. Some beams or signal information not be available. To handle this, we use a machine learning model called LSTM (Long Short-Term Memory). LSTM processes time-based data to predict the missing values. Using this, the system still selects the best beam. To verify this approach, we used a dataset called DeepMIMO. This dataset is based on realistic simulations of wireless channels. Tests showed that the proposed framework works better than existing methods. The system performed well even without full CSI and handled missing data effectively. The paper offers a solution to improve communication in mm-Wave systems. It uses advanced deep-learning models to address challenges like beam selection, power control, and missing data. The proposed method is efficient and accurate, outperforming other strategies.

Keywords:

Multiple-input multiple-output,Channel state information,Long short-term memory,Energy consumption,Resource allocation,Signal to noise ratio,Energy efficiency,Spectral efficiency,

Refference:

I. A. A. Ager, C. R. Evers, M. A. Day, H. K. Preisler, A. M. Barros, and M. Nielsen-Pincus, “Network analysis of wildfire transmission and implications for risk governance,” PLoS One, vol. 12, no. 3, p. e0172867, 2017.
II. A. F. Molisch, Wireless communications, vol. 34. John Wiley & Sons, 2012.
III. A. Garcia-Rodriguez, C. Masouros, and T. Ratnarajah,” Hybrid analog digital precoding revisited under realistic RF modeling,” IEEE Wireless Communications Letters, vol. 5, no. 5, pp. 528-531, Oct. 2016.
IV. A. K. Gupta, A. Khreishah, and J. Zhang,” Joint optimization of beamforming and power allocation for multi-user MIMO systems,” IEEE Communications Letters, vol. 21, no. 7, pp. 1577-1580, Jul. 2017.
V. Bharti, Rajendra Kumar, D. Suganthi, S. K. Abirami, Relangi Anil Kumar, B. Gayathri S. Kayathri. “Optimal Extreme Learning Machine based Traffic Congestion Control System in Vehicular Network.” In 2022 6th International Conference on Electronics, Communication and Aerospace Technology, pp. 597-603. IEEE, 2022.
VI. C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo,” Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98-105, Apr. 2017.
VII. E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta,” Massive MIMO for next generation wireless systems,” IEEE Communications Magazine, vol. 52, no. 2, pp. 186-195, Feb. 2014.
VIII. H. Al-Hraishawi and G. Fettweis,” A survey on 5G millimeter wave technologies,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 1616-1653, Third Quarter 2018.
IX. I. Aykin, B. Akgun, and M. Krunz, “Multi-beam transmissions for blockage resilience and reliability in millimeter wave systems,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 12, pp. 2772–2785, 2019.
X. J. Brady, N. Behdad, and A. M. Sayeed, “Beamspace mimo for millimeter wave communications: System architecture, modeling, analysis, and measurements,” IEEE Transactions on Antennas and Propagation, vol. 61, no. 7, pp. 3814–3827, 2013.
XI. J. Choi, V. Va, N. Gonzalez-Prelcic, R. W. Heath, and S. Sangodoyin,” Millimeter-wave vehicular communication to support massive automotive sensing,” IEEE Communications Magazine, vol. 54, no. 12, pp. 160-167, Dec. 2016.
XII. J. Hoydis, S. Ten Brink, and M. Debbah,” Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 160-171, Feb. 2013.
XIII. J. Kim, H. Lee, and D. Hong,” A novel beam selection method for 5G networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 7857-7869, Aug. 2018.
XIV. K. Abratkiewicz, A. Ksie˙zyk, M. Plotka, P. Samczy´nski, J. Wszolek, and T. P. Zieli´nski, “SSB-based signal processing for passive radar using a 5G network,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3469–3484, 2023.
XV. K. Venugopal, M. C. Valenti, and R. W. Heath,” Device-to-device millimeter wave communications: Interference, coverage, and rate,” IEEE Transactions on Wireless Communications, vol. 15, no. 9, pp. 6175-6188, Sept. 2016.
XVI. Kumar, R. Anil, Adireddy Ramesh, Sarala Patchala, U. Sreenivasulu R. Prakash Kumar. “The Performance Analysis of Precoded Space-Time Frequency MIMO-GFDM Over Rayleigh Fading Channels.” Journal of Mechanics of Continua And Mathematical Sciences, pp.35-47, Vol – 19 No – 9, September 2024.
XVII. M. Giordani, M. Mezzavilla, and M. Zorzi,” Initial access in 5G mmWave cellular networks,” IEEE Communications Magazine, vol. 54, no. 11, pp. 40-47, Nov. 2016.
XVIII. M. K. Samimi and T. S. Rappaport, ”3D statistical channel model for millimeter wave urban cellular communication,” IEEE International Conference on Communications (ICC), pp. 1-7, 2016.
XIX. Q. Wu, R. Zhang, and Y. Zeng,” Intelligent reflecting surface-aided wireless energy and information transfer: An overview,” Proceedings of the IEEE, vol. 109, no. 6, pp. 1234-1260, Jun. 2021.
XX. S. Bouktif, A. Fiaz, A. Ouni, and M. A. Serhani, “Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches,” Energies, vol. 11, no. 7, p. 1636, 2018.
XXI. S. M. A. Zaidi, M. Manalastas, H. Farooq, and A. Imran, “Mobility management in emerging ultra-dense cellular networks: A survey, outlook, and future research directions,” IEEE Access, vol. 8, pp. 183505–183533, 2020.
XXII. S. Rangan, T. S. Rappaport, and E. Erkip,” Millimeter-wave cellular wireless networks: Potentials and challenges,” Proceedings of the IEEE, vol. 102, no. 3, pp. 366-385, Mar. 2014.
XXIII. S. Sun, T. S. Rappaport, R. W. Heath, A. Nix, and S. Rangan,” MIMO for millimeter-wave wireless communications: Beamforming, spatial multiplexing, or both?” IEEE Communications Magazine, vol. 52, no. 12, pp. 110-121, Dec. 2014.
XXIV. Singh, Shashank, V. Subburaj, K. Sivakumar, R. Anil Kumar, M. S. Muthuramam, Ravi Rastogi, Vishal Ratansing Patil A. Rajaram. “Optimum power forecasting technique for hybrid renewable energy systems using deep learning.” Electric Power Components and Systems (2024): 1-18.
XXV. T. Bai, A. Alkhateeb, and R. W. Heath,” Coverage and capacity of millimeter wave cellular networks,” IEEE Communications Magazine, vol. 52, no. 9, pp. 70-77, Sept. 2014.
XXVI. X. Liu, Z. Zhang, X. Wu, C. Feng, X. Wang, L. Lei, and W. Zuo, “Learning real world image de-weathering with imperfect supervision,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 3738–3746, 2024.
XXVII. Y. Alsaba, S. K. A. Rahim, and C. Y. Leow, “Beamforming in wireless energy harvesting communications systems: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1329–1360, 2018.
XXVIII. Y. Niu, Y. Li, D. Jin, L. Su, and A. V. Vasilakos, “A survey of millimeter wave communications (mmwave) for 5G: opportunities and challenges,” Wireless networks, vol. 21, pp. 2657–2676, 2015.
XXIX. Z. Pi and F. Khan,” An introduction to millimeter-wave mobile broadband systems,” IEEE Communications Magazine, vol. 49, no. 6, pp. 101-107, Jun. 2011.
XXX. Z. Xiao, P. Xia, and X.-G. Xia,” Enabling UAV cellular with millimeter wave communication: Potentials and approaches,” IEEE Communications Magazine, vol. 54, no. 5, pp. 66-73, May 2016.

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Abstract:

In 2019, the development of 5G organizations started, and it is, by and large, guessed that these organizations would achieve propels that are not confined to the everyday existence of individuals. The hubs of the 5G organization are associated with each other using the utilization of optical handset modules and optical filaments. The most entrancing piece of the 5G correspondence network is the contact that exists between the Focal Office (CO) and the Base Station (BS). This contact has been the subject of a significant review performed by an enormous number of scientists with the end goal of upgrading and improving the viability of the organization. The result of this is that in this article, we illustrate, plan, and do a) by utilizing the Optisystem 17.1 programming and a Thick Frequency Division Multiplexing (DWDM) Radio over Fiber (RoF) procedure. The expression "40x40 Gbps information transmission framework" is utilized to depict this sort of framework, which is intended for higher-speed transmission frameworks that are engaged toward association at the Terabit each second (Tbps) level. A determination of channels, including 1, 4, 8, 12, 16, 20, 24, 28, 32, 36, and 40, were chosen as tests for the examination. The eye graph boundaries, the Quality Element (Q-factor), and the Base Piece Blunder Rate (BER) will all assume a part in the result examination for distances of sixty, hundred and eighty kilometers. Because of the consequences of the examination, it has been resolved that the device can move information at a pace of 1.60 Tbps.

Keywords:

5G,BER,QF,RoF,WDM,

Refference:

I. Hussein Abdulaal, “Deep Learning-based Signal Identification in Wireless Communication Systems: A Comparative Analysis on 3G, LTE, and 5G Standards”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 3, pp. 60–70, Sep. 2024.
II. A. Hameed Rasheed and M. Al-Nouman, “RFID based Infrastructure to Vehicle Communication System for Road Sign Identification”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 2, no. 1, pp. 24–36, Mar. 2023.
III. Abdulwahid, M. M., & Wasel, N. B. M. (2020). Optimum AP Estimation Location for the communication of different mmWave bands. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 1(1), 44-53.
IV. A. Jasim Mohammed, “Impact of Rain Weather Conditions over Hybrid FSO/58GHz Communication Link in Tropical Region ”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 3, pp. 117–134, Sep. 2024.
V. Abdulwahid M. M. and N. B. Mohamad wasel, “Design and implementation of motor speed control model by using a PLC,” iamecs2, vol. 1, no. 1, pp. 54–62, 2020.
VI. Abdulwahid, M. M., Abdullah, H. K., Ateah, W. M., & Ahmed, S. (2023). Implementation of Automated Water based Level Management Model by using SCADA system and PLC.
VII. Abdulwahid, M. M., & Kurnaz, S. (2023, July). Implementation of two polarization DQPSK WDM Is-OWC system with different precoding schemes for long-reach GEO Inter Satellite Link. In International Conference on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023) (Vol. 2023, pp. 134-141). IET.
VIII. Abdulwahid, M. M., Kurnaz, S., Türkben, A. K., Hayal, M. R., Elsayed, E. E., & Juraev, D. A. (2024). Inter-satellite optical wireless communication (Is-OWC) trends: a review, challenges and opportunities. Engineering Applications, 3(1), 1-15.
IX. Abdulwahid, M. M., Kurnaz, S., Hayal, M. R., Elsayed, E. E., & Juraev, D. A. (2025). Performance analysis of input power variations in high data rate DWDM-FSO systems under various rain conditions. Journal of Optics, 1-16.
X. Abdulwahid, M. M., & Kurnaz, S. (2023). The channel WDM system incorporates of Optical Wireless Communication (OWC) hybrid MDM-PDM for higher capacity (LEO-GEO) inter satellite link. Optik, 273, 170449.
XI. Abdulwahid, M. M., & Kurnaz, S. (2024, February). The utilization of different AI methods-based satellite communications: A survey. In AIP Conference Proceedings (Vol. 3051, No. 1). AIP Publishing.
XII. Abd-Alhameed, R. A., Abdulwahid, M. M., & Mosleh, M. F. (2021). Effects of Antenna Directivity and Polarization on Indoor Multipath Propagation Characteristics for different mmWave frequencies. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 2(1), 20-28.
XIII. Abdulwahid, M. M., Kurnaz, S., Türkben, A. K., Hayal, M. R., Elsayed, E. E., & Juraev, D. A. (2024). Inter-satellite optical wireless communication (Is-OWC) trends: a review, challenges and opportunities. Engineering Applications, 3(1), 1-15.
XIV. Agrawal, Govind P. Fiber-optic communication systems. Vol. 222. John Wiley & Sons, 2012.‏
XV. Abdulwahid, M. M., & Wasel, N. B. M. (2020). Optimum AP Estimation Location for the communication of different mmWave bands. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 1(1), 44-53.
XVI. Almetwali, A. S., Bayat, O., Abdulwahid, M. M., & Mohamadwasel, N. B. (2022, November). Design and analysis of 50 channel by 40 Gbps DWDM-RoF system for 5G communication based on fronthaul scenario. In Proceedings of Third Doctoral Symposium on Computational Intelligence: DoSCI 2022 (pp. 109-122). Singapore: Springer Nature Singapore.
XVII. Alhamadani, N. B., & Abdelwahid, M. M. (2021). Implementation of microstrip patch antenna using MATLAB. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 2(1), 29-34.
XVIII. A. M. Mm and A. N. Mohammed, “Design and implementation of 200 G passive optical network,” iamecs2, vol. 2, no. 1, pp. 8–13, 2021.
XIX. AL-Hakeem, M. S., Burhan, I., & Abdulwahid, M. M. (2020). Hybrid Localization Algorithm for Accurate Indoor Estimation Based IoT Services.
XX. Al-Raweshidy, Hamed, and Shozo Komaki. Radio over fiber technologies for mobile communications networks. Artech House, 2002.
XXI. Alan Cartlton, “Front and Backhaul: look out, a fusion is coming” Network Word. http://www.networkworld.com/article/3-096788/virtualization/fronthaul-and-backhaul-look-out-a-fusion-is-coming.html, Jul 18, 2016
XXII. Basil, N., & Moutaz, M. (2021). Design and implementation of chirp fiber bragg grating for long haul transmission system using opti-system. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 2(1), 1-7.
XXIII. Burhan, I. M., Al-Hakeem, M. S., Abdulwahid, M. M., & Mosleh, M. F. (2020, July). Investigating the Access Point height for an indoor IOT services. In IOP Conference Series: Materials Science and Engineering (Vol. 881, No. 1, p. 012116). IOP Publishing.
XXIV. Cheng, Yitai. “Research on 5G optical transport schemes.” IOP Conference Series: Earth and Environmental Science. Vol. 332. No. 4. IOP Publishing, 2019.‏
XXV. D. E. Mohsen, E. M. Abbas, and M. M. Abdulwahid, “Performance analysis of OWC system based (S-2-S) connection with different modulation encoding,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 4s, pp. 400–408, 2023.
XXVI. Fan Wei, Hanyi Zhang, and Bingkun Zhou Tsinghua, “Carrier Phase and Amplitude Manipulation for Linearization and Dispersion compensation in Radio-over-Fiber Systems Using Mach-Zehnder Modulator – Progress in Electromagnetic Research Symposium” Electromagnetics Research Symposium Proceedings, Stockholm, Sweden, Aug. 12-15, 2013 1311.
XXVII. Faris, R. A., Ibrahim, A. A., Abdulwahid, M. M., & Mosleh, M. F. (2021, June). Optimization and Enhancement of Charging Control System of Electric Vehicle Using MATLAB SIMULINK. In IOP Conference Series: Materials Science and Engineering (Vol. 1105, No. 1, p. 012004). IOP Publishing.
XXVIII. M. M. Abdulwahid, S. Kurnaz, M. R. Hayal, E. E. Elsayed, and D. A. Juraev, “Performance analysis of input power variations in high data rate DWDM-FSO systems under various rain conditions,” J. Opt., pp. 1–16, 2025.
XXIX. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. (2022, June). Design and Implementation of DWDM-FSO system for Tbps data rates with different atmospheric Attenuation. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE.
XXX. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. (2024, September). Performance Evaluation of 32 WDM-FSO Systems with Different Weather Turbulence Under Variance Launch Power Values. In 2024 IEEE 1st International Conference on Communication Engineering and Emerging Technologies (ICoCET) (pp. 1-4). IEEE.
XXXI. M.M, A. M., & Mohammed, A. N. (2021). Enhancement of similarity for image segmentation. Informatica: Journal of Applied Machines Electrical Electronics Computer Science and Communication Systems, 2(1), 56-60.
XXXII. Pradeep R and Vijayakumar N, “Performance Analysis of RoF link using Mach-Zehnder Modulator in radio over fiber systems,” International Journal of Advanced Research in Engineering and Technology (IJARET). ISSN Print: 0976-6480 and ISSN Online: 0976-6499 Volume 7, Issue 5, September-October 2016, pp. 45–52, 2016.
XXXIII. Shareef, O. A., Abdulwahid, M. M., Mosleh, M. F., & Abd-Alhameed, R. (2019). The optimum location for access point deployment based on RSS for indoor communication.
XXXIV. Y. Yuan and L. Zhu, “Application scenarios and enabling technologies of 5G,” China Commun., vol. 11, no. 11, pp. 69–79, 2014.
XXXV. Y. S. Mezaal, H. T. Eyyuboğlu, and J. K. Ali, “New microstrip bandpass filter designs based on stepped impedance Hilbert fractal resonators,” IETE J. Res., vol. 60, no. 3, pp. 257–264, 2014.
XXXVI. Y. S. Mezaal, H. T. Eyyuboglu, and J. K. Ali, “A new design of dual band microstrip bandpass filter based on Peano fractal geometry: Design and simulation results,” in 2013 13th Mediterranean Microwave Symposium (MMS), 2013.
XXXVII. Y. S. Mezaal and H. T. Eyyuboglu, “A new narrow band dual-mode microstrip slotted patch bandpass filter design based on fractal geometry,” in 2012 7th International Conference on Computing and Convergence Technology (ICCCT), 2012, pp. 1180–1184.
XXXVIII. Y. S. Mezaal et al., “Cyber Security Readiness in Iraq: Role of the Human Rights Activists,” International Journal of Cyber Criminology, vol. 16, no. 2, pp. 1–14-1–14, 2022.
XXXIX. Y. W. Abduljaleel, B. Al-Obaidi, M. M. Khattab, F. Usman, A. Syamsir, and B. M. Albaker, “Compressive Strength Prediction of Recycled Aggregate Concrete Based on Different Machine Learning Algorithms”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 3, pp. 25–36, Sep. 2024.
XL. Y. S. Mezaal et al., “Cloud of Things and fog computing in Iraq: Potential applications and sustainability,” Heritage and Sustainable Development, vol. 5, no. 2, pp. 339–350, 2023.
XLI. Y. S. Mezaal et al., “New miniature narrow band microstrip diplexer for recent wireless communications,” Electronics (Basel), vol. 12, no. 3, p. 716, 2023.
XLII. Z. Fawzi Hassan, M. N. Al-Turfi, and F. Al Alawy, “Facial Expression Recognition Enhancement Using Convolutional Neural Network”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 2, pp. 83–91, Jun. 2024.
XLIII. Z. Atheer Ali and H. Falah Hasan, “A Review of Artificial Intelligence (AI) Applications in Key Generation for Encryption Algorithms ”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 4, no. 1, pp. 1–8, Mar. 2025.
XLIV. Zaal, R. M., Mustafa, F. M., Abbas, E. I., Mosleh, M. F., & Abdulwahid, M. M. (2020, July). Real measurement of optimal access point localizations. In IOP Conference Series: Materials Science and Engineering (Vol. 881, No. 1, p. 012119). IOP Publishing.
XLV. Zaal, R. M., Mosleh, M. F., Abbas, E. I., & Abdulwahid, M. M. (2020, September). Optimal Coverage Area with Lower Number of Access Point. In IMDC-SDSP 2020: Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP (p. 230).

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Abstract:

This study is based on the analysis of network intrusion detection and the improvement of various machine learning methods that produce high accuracy and guarantee secure network traffic from malicious activities. The work employs Gradient Boosting, Random Forest, and Neural Network classifiers alongside a meta-model that improves the performance of learning models among them. Data enhancements that were used in the models include data normalization and feature selection in a bid to enhance the accuracy of the model’s predictions. Common parameters such as accuracy, precision, recall, and F1-score were computed on each model to allow for a comparative evaluation. Therefore, the meta-model reveals better results than individual base models, meaning the meta-model can be efficient for real-time intrusion detection. This research aids in enhancing the accuracy and reliability of the IDS model for subsequent improvements in cybersecurity applications.

Keywords:

Cybersecurity,Data Normalization,Ensemble Learning,Feature Selection,Intrusion Detection,Machine Learning,Meta-Model,Network Traffic Analysis,Performance Metrics,

Refference:

I. Abdulganiyu, Oluwadamilare Harazeem, Taha Ait Tchakoucht, and Yakub Kayode Saheed. “Towards an efficient model for network intrusion detection system (IDS): systematic literature review.” Wireless Networks 30.1 (2024): 453-482. 10.1007/s11276-023-03495-2
II. Al-Haija, Qasem Abu. “Cost-effective detection system of cross-site scripting attacks using hybrid learning approach.” Results in Engineering 19 (2023): 101266. 10.1016/j.rineng.2023.101266
III. Abu Al-Haija, Qasem, and Mustafa Al-Fayoumi. “An intelligent identification and classification system for malicious uniform resource locators (URLs).” Neural Computing and Applications 35.23 (2023): 16995-17011. 10.1007/s00521-023-08592-z
IV. Azizan, Adnan Helmi, et al. “A machine learning approach for improving the performance of network intrusion detection systems.” Annals of Emerging Technologies in Computing (AETiC) 5.5 (2021): 201-208. 10.33166/AETiC.2021.05.025
V. Al-Haija, Qasem Abu, Charles D. McCurry, and Saleh Zein-Sabatto. “Intelligent self-reliant cyber-attacks detection and classification system for IoT communication using deep convolutional neural network.” Selected Papers from the 12th International Networking Conference: INC 2020 12. Springer International Publishing, 2021. 10.1007/978-3-030-64758-2_8
VI. Alsulami, Abdulaziz A., et al. “An intrusion detection and classification system for IoT traffic with improved data engineering.” Applied Sciences 12.23 (2022): 12336. 10.3390/app122312336
VII. Ashiku, Lirim, and Cihan Dagli. “Network intrusion detection system using deep learning.” Procedia Computer Science 185 (2021): 239-247.https://doi.org/10.1016/j.procs.2021.05.025
VIII. Azzaoui, Hanane, et al. “Developing new deep-learning model to enhance network intrusion classification.” Evolving Systems 13.1 (2022): 17-25. 10.1007/s12530-020-09364-z
IX. Düzgün, Berkant, et al. “Network intrusion detection system by learning jointly from tabular and text‐based features.” Expert Systems 41.4 (2024): e13518. 10.1111/exsy.13518
X. Hnamte, Vanlalruata, and Jamal Hussain. “DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system.” Telematics and InformaticsReports10(2023):100053. 10.1016/j.teler.2023.100053
XI. Kizza, Joseph Migga. “System intrusion detection and prevention.” Guide to computer network security. Cham: Springer international publishing, 2024. 295-323. 10.1007/978-3-031-47549-8_13
XII. Kizza, Joseph Migga. “System intrusion detection and prevention.” Guide to computer network security. Cham: Springer international publishing, 2024. 295-323. 10.1007/978-3-031-47549-8_13
XIII. Madhusudhan, R., Shubham Kumar Thakur, and P. Pravisha. “Enhancing Intrusion Detection System Using Machine Learning and Deep Learning.” International Conference on Advanced Information Networking and Applications. Cham: Springer Nature Switzerland, 2024. 10.1007/978-3-031-57870-0_29
XIV. Maseer, Ziadoon K., et al. “Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges.” IET Networks 13.5-6 (2024): 339-376. 10.1049/ntw2.12128
XV. Medina-Arco, Joaquín Gaspar, et al. “Methodology for the detection of contaminated training datasets for machine learning-based network intrusion-detection systems.” Sensors 24.2 (2024): 479. 10.3390/s24020479
XVI. Paya, Antonio, et al. “Apollon: a robust defense system against adversarial machine learning attacks in intrusion detection systems.” Computers & Security 136 (2024): 103546. 10.1016/j.cose.2023.103546
XVII. Bhandari, Rahul, et al. “AINIS: An Intelligent Network Intrusion System.” International Journal of Performability Engineering 20.1 (2024). 10.23940/ijpe.24.01.p4.2431
XVIII. Saleh, Hadeel M., Hend Marouane, and Ahmed Fakhfakh. “Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning.” IEEE Access 12 (2024): 3825-3836. 10.1109/ACCESS.2023.3349248
XIX. Sayem, Ibrahim Mohammed, et al. “ENIDS: a deep learning-based ensemble framework for network intrusion detection systems.” IEEE transactions on network and service management (2024). 10.1109/TNSM.2024.3414305.
XX. Saheed, Yakub Kayode, et al. “Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System.” Journal of Information and Telecommunication 8.2 (2024): 189-207. 10.1080/24751839.2023.2272484
XXI. Younisse, Remah, Ashraf Ahmad, and Qasem Abu Al-Haija. “Explaining intrusion detection-based convolutional neural networks using shapley additive explanations (shap).” Big Data and Cognitive Computing 6.4 (2022): 126. 10.3390/bdcc6040126
XXII. Yuan, Xinwei, et al. “A simple framework to enhance the adversarial robustness of deep learning-based intrusion detection system.”Computers& Security 137 (2024): 103644. 10.1016/j.cose.2023.103644

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Abstract:

Tipped FBGs are designed to have specific geometrical modifications at their ends, which can enhance their interaction with external stimuli such as magnetic fields. This design can lead to improved sensitivity and specificity in sensing applications.  An amplitude splitting interferometer (ASI)  with a very sensitive stepping stage allows for precise control over measurements and adjustments during fabrication. This technique is used for achieving high-quality FBGs with minimal defects.  The   405 nm laser will be chosen to work in an optimal wavelength range for writing FBGs into photosensitive materials and olive oil, as this wavelength is effective in inducing refractive index changes. The manufactured Tipped FBGs chose the reflected wavelength at 532.1 nm, ‎0.2 pm/Gauss sensitivity of the sensor.

Keywords:

Amplitude Splitting Interferometer,Organic oil magnetic sensor Photosensitive Materials,Tipped Fiber Bragg Grating, ,

Refference:

I. Al-Thahaby, Farah S., and Anwaar A. Al-Dergazly. “Tuneable Fiber Bragg Grating for Magnetic Field Sensor.” Al-Nahrain Journal for Engineering Sciences (NJES), vol. 20, no. 5, 2017, pp. 1112-1123. 10.29194/NJES.2005.1112
II. He, Panting, Shulian Yang, and Qinqin Wei. “Intensity-Modulated Magnetic Field Sensor Based on Fiber Bragg Grating.” AIP Advances, vol. 9, 2019, 105303. 10.1063/1.5122678
III. Jixiang, D., Minghong, Y., Xiaobing, L., et al. “Magnetic Field Sensor Based on Magnetic Fluid Clad Etched Fiber Bragg Grating.” Optical Fiber Technology, vol. 17, no. 3, 2011, pp. 210–213. 10.1016/j.yofte.2011.01.002
IV. Meltz, G., W.W. Morey, and W.H. Glenn. “Formation of Bragg Gratings in Optical Fibers by a Transverse Holographic Method.” Optics Letters, vol. 14, no. 15, 1989, pp. 823-825. 10.1364/OL.14.000823
V. Musa, Ruaa K., Salah Al Deen A. Taha, and Ali Hammadi. “Tipped Fiber Bragg Grating Sensor for Concentration Measurements.” International Journal of Computation and Applied Sciences, vol. 2, no. 3, June 2017, pp. 123-127. URL
VI. Shakir, A., R.D. Al-Mudhafa, and A. Al-Dergazly. “Verdet Constant Measurement of Olive Oil for Magnetic Field Sensor.” International Journal of Advances in Electrical & Electronics Engineering, vol. 2, no. 2, 2013, pp. 362-368. 10.13140/RG.2.1.1805.4646
VII. Zhao, Xiaojun, Liang Zhuohang, Li Yansong, and Liu Jun. “Clustered Magneto-Optical Current Sensor to Eliminate the Interference of a Phase-to-Phase Magnetic Field.” Optics Continuum, vol. 1, no. 2, 2022, pp. 197-214. 10.1364/OPTCON.450701
VIII. Zhu, Hong, Penghong Guo, Tao Wen, Jiaxun Li, Zhiyuan Sha, and Qiaogen Zhang. “Magnetic-Collecting Optical Current Sensor Based on Magneto-Optic Crystal.” 6th China International Electrical and Energy Conference (CIEEC), IEEE, 2023. 10.1109/CIEEC56163.2023.1000034

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Abstract:

This article addresses the problem of visual detection of moving objects in bad weather conditions. A multi-frame semantic information-based background subtraction scheme is proposed here. The camera is static, hence, the viewpoint is assumed to be fixed. It exploits the spatial as well as temporal neighbourhood at the pixel level by using motion parameters to detect the position of the objects in the field of view. A local attribute map is generated by analyzing the Discrete Cosine Transform coefficients. Further, a spatio-contextual framework is used to obtain the global attributes. Then, the local and global attributes are combined using an entropy-based fusion strategy to get the moving objects in bad weather sequences. The efficacy of the scheme is evaluated using the benchmark bad-weather dataset of CDNet. To check the stand of the proposal among seven recent state-of-the-art schemes, qualitative as well as quantitative analyses are carried out. The results are found to be encouraging.

Keywords:

Cosine Transform,DCT,Bad weather video,Background subtraction,Five-frame difference,Multi-frame feature space,Object detection,Visual surveillance,

Refference:

I. Ahmad, Naeem, Mattias O’Nils, and Najeem Lawal. A taxonomy of visual surveillance systems. 2013.
II. Blin, Rachel, et al. “Road scenes analysis in adverse weather conditions by polarization-encoded images and adapted deep learning.” 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019. 10.1109/ITSC.2019.8916853
III. Boussakta, Said, and Hamoud O. Alshibami. “Fast algorithm for the 3-D DCT-II.” IEEE Transactions on Signal Processing 52.4 (2004): 992-1001. 10.1109/TSP.2004.823472
IV. Chaturvedi, Saket S., Lan Zhang, and Xiaoyong Yuan. “Pay” Attention” to Adverse Weather: Weather-aware Attention-based Object Detection.” 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. 10.1109/ICPR56361.2022.9956149
V. Chen, Y., Zhou, R., Guo, B., Shen, Y., Wang, W., Wen, X., Suo, X.: Discrete cosine transform for filter pruning. Applied Intelligence,1–17(2022). 10.1007/s10489-022-03604-
VI. Cioppa, Anthony, Marc Van Droogenbroeck, and Marc Braham. “Real-time semantic background subtraction.” 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. 10.48550/arXiv.2002.04993
VII. El-Khoreby, Mohamed A., and Syed Abd Rahman Abu-Bakar. “Vehicle detection and counting for complex weather conditions.” 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. 10.1109/ICSIPA.2017.8120648
VIII. Gao, Fei, Yunyang Li, and Shufang Lu. “Extracting moving objects more accurately: a CDA contour optimizer.” IEEE Transactions on Circuits and Systems for Video Technology 31.12 (2021): 4840-4849. 10.1109/TCSVT.2021.3055539
IX. Garcia-Garcia, Belmar, Thierry Bouwmans, and Alberto Jorge Rosales Silva. “Background subtraction in real applications: Challenges, current models and future directions.” Computer Science Review 35 (2020): 100204. 10.1016/j.cosrev.2019.100204.
X. Jegham, Imen, and Anouar Ben Khalifa. “Pedestrian detection in poor weather conditions using moving camera.” 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2017. 10.1109/AICCSA.2017.35.
XI. Jia, Zhen, et al. “A two-step approach to see-through bad weather for surveillance video quality enhancement.” Machine Vision and Applications 23 (2012): 1059-1082. 10.26906/SUNZ.2023.1.040
XII. Kalsotra, Rudrika, and Sakshi Arora. “Background subtraction for moving object detection: explorations of recent developments and challenges.” The Visual Computer 38.12 (2022):4151-4178. 10.1007/s00371-021-02286-0
XIII. Krišto, Mate, Marina Ivasic-Kos, and Miran Pobar. “Thermal object detection in difficult weather conditions using YOLO.” IEEE access 8 (2020): 125459-125476. 10.1109/ACCESS.2020.3007481
XIV. Leroux, Sam, Bo Li, and Pieter Simoens. “Multi-branch neural networks for video anomaly detection in adverse lighting and weather conditions.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022. 10.1109/WACV51458.2022.00308
XV. Levine, Martin D., Xiangjing An, and Hangen He. “Saliency detection based on frequency and spatial domain analysis.” Neuroscience 8.8 (2005): 975-977.
XVI. Lim, Long Ang, and Hacer Yalim Keles. “Foreground segmentation using convolutional neural networks for multiscale feature encoding.” Pattern Recognition Letters 112 (2018): 256-262. 10.1016/j.patrec.2018.08.002
XVII. Liu, Wenyu, et al. “Image-adaptive YOLO for object detection in adverse weather conditions.” Proceedings of the AAAI conference on artificial intelligence. Vol. 36. No. 2. 2022. 10.1609/aaai.v36i2.20072
XVIII. Otsu, Nobuyuki. “A threshold selection method from gray-level histograms.” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979. 10.1109/TSMC.1979.4310076
XIX. Rahmon, Gani, et al. “Motion U-Net: Multi-cue encoder-decoder network for motion segmentation.” 25th IEEE International conference on pattern recognition, 2021. 10.1109/ICPR48806.2021.9413211
XX. Rout, Deepak Kumar, et al. “A novel five-frame difference scheme for local change detection in underwater video.” Fourth IEEE International Conference on Image Information Processing, pp. 1-6, 2017. 10.1109/ICIIP.2017.8313727
XXI. Rout, Deepak Kumar, and Sharmistha Puhan. “Video object detection using inter-frame correlation based background subtraction.” IEEE Recent Advances in Intelligent Computational Systems, pp. 1-6, 2013. 10.1109/RAICS.2013.6745467
XXII. Rout, Deepak Kumar, et al. “Walsh–Hadamard-kernel-based features in particle filter framework for underwater object tracking.” IEEE Transactions on Industrial Informatics 16.9 (2019): 5712-5722. 10.1109/TII.2019.2937902
XXIII. Sajid, Hasan, and Sen-Ching Samson Cheung. “Universal multimode background subtraction.” IEEE Transactions on Image Processing 26.7 (2017): 3249-3260. 10.1109/TIP.2017.2695882
XXIV. Singha, Anu, and Mrinal Kanti Bhowmik. “Salient features for moving object detection in adverse weather conditions during night time.” IEEE Transactions on circuits and systems for video technology 30.10 (2019): 3317-3331. DOI: 10.1109/TCSVT.2019.2926164
XXV. St-Charles, Pierre-Luc, Guillaume-Alexandre Bilodeau, and Robert Bergevin. “SuBSENSE: A universal change detection method with local adaptive sensitivity.” IEEE Transactions on Image Processing 24.1 (2014): 359-373. 10.1109/TIP.2014.2378053
XXVI. Strang, Gilbert. “The discrete cosine transform.” SIAM review 41.1 (1999): 135-147.
XXVII. Sun, Dayang, Binbin Li, and Zhihong Qian. “Research of vehicle counting based on DBSCAN in video analysis.” IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp.1523-1527,2013. 10.1109/GreenCom-iThings CPSCom.2013.270
XXVIII. Tezcan, M. Ozan, Prakash Ishwar, and Janusz Konrad. “BSUV-Net 2.0: Spatio-temporal data augmentations for video-agnostic supervised background subtraction.” IEEE Access 9 (2021): 53849-53860. 10.1109/ACCESS.2021.3071163
XXIX. Walambe, Rahee, et al. “Lightweight object detection ensemble framework for autonomous vehicles in challenging weather conditions.” Computational Intelligence and Neuroscience 2021.1 (2021): 5278820. 10.1155/2021/5278820
XXX. Wang, Kunfeng, Chao Gou, and Fei-Yue Wang. “$ M^{4} CD $: A robust change detection method for intelligent visual surveillance.” IEEE Access 6 (2018): 15505-15520. 10.1109/ACCESS.2018.2812880
XXXI. Yaghoobi Ershadi, Nastaran, José Manuel Menéndez, and David Jiménez. “Robust vehicle detection in different weather conditions: UsingMIPM.” PloSone 13.3(2018):e0191355. 10.1371/journal.pone.0191355
XXXII. Zhong, Yijie, et al. “Detecting camouflaged object in frequency domain.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Pp. 1-6, 2022. 10.1109/WACV57701.2024.00146

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Abstract:

In 5G communication networks, the use of Dense Wavelength Division Multiplexing Radio over Fiber (DWDM-RoF) technology has produced several benefits, including cost savings, increased capacity, and faster data rates. This thesis uses OptiSystem software version 17.1 to illustrate the design performance of three (DWDM-RoF) systems. Systems with eight, sixteen, and thirty-two channels with a 40 Gbps data bit rate per channel have been suggested, using the Mack Zander Modulator (MZM) as an external modulator. The system's chosen wavelengths fell between 1527.99 and 1565.496 nm in wavelength range, with 1.6 nm separating each pair of channels. Thus, this choice was made in light of the fiber optic's reduced optical signal losses as well as the greater gain spectra of the above-mentioned Erbium Doped Fiber Amplifier (EDFA). The systems' quality factor and minimal bit error rate (BER) were chosen as assessment metrics based on the variations in distances (60, 120, and 180 kilometers). While the BER findings ranged from 3.39e-21 to 2e-100, the Quality Factor results fell between 9.3 (channel 4 at 180 km from the 8-channel system) to 25 (channel at 60 km from the 32-channel system). Analyzing data points to a system with improved performance approaching 320 Gbps. The three suggested technologies can transmit data at speeds of 640 Gbps and 1.28 Tbps, respectively. The non-uniformity of the EDFA gain spectra and the non-uniformity of the fiber optic losses at various wavelengths were the causes of the difference in Quality Factor values and BER for different channels. Additionally, there is a noticeable improvement in the system's performance when the laser's output power is increased from 0 dBm to 5 dBm. Consequently, the system capacity that was attained with a high-quality signal was deemed to be very supportive of the 5 G communication network's needs.

Keywords:

BER,DWDM,QF,RoF,WDM,

Refference:

I. A. Hussein Abdulaal, “Deep Learning-based Signal Identification in Wireless Communication Systems: A Comparative Analysis on 3G, LTE, and 5G Standards”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 3, pp. 60–70, Sep. 2024.
II. A. S. Almetwali, O. Bayat, M. M. Abdulwahid, and N. B. Mohamadwasel, “Design and analysis of 50 channel by 40 Gbps DWDM-RoF system for 5G communication based on fronthaul scenario,” Proc. Third Doctoral Symposium on Computational Intelligence: DoSCI 2022, pp. 109–122, Singapore: Springer Nature Singapore, Nov. 2022.
III. D. E. Mohsen, E. M. Abbas, and M. M. Abdulwahid, “Design and Implementation of DWDM-FSO system for Tbps data rates with different atmospheric Attenuation,” 2022 Int. Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–7, Jun. 2022.
IV. R. A. Faris, A. A. Ibrahim, M. M. Abdulwahid, and M. F. Mosleh, “Optimization and Enhancement of Charging Control System of Electric Vehicle Using MATLAB SIMULINK,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1105, no. 1, p. 012004, Jun. 2021.
V. N. Basil and M. Moutaz, “Design and implementation of chirp fiber bragg grating for long haul transmission system using opti-system,” Informatica: J. Appl. Mach. Electr. Electron. Comput. Sci. Commun. Syst., vol. 2, no. 1, pp. 1–7, 2021.
VI. M. M. Abdulwahid and S. Kurnaz, “The utilization of different AI methods-based satellite communications: A survey,” AIP Conf. Proc., vol. 3051, no. 1, Feb. 2024.
VII. M. M. Abdulwahid, S. Kurnaz, A. K. Türkben, M. R. Hayal, E. E. Elsayed, and D. A. Juraev, “Inter-satellite optical wireless communication (Is-OWC) trends: a review, challenges and opportunities,” Eng. Appl., vol. 3, no. 1, pp. 1–15, 2024.
VIII. M. M. Abdulwahid and S. Kurnaz, “Implementation of two polarization DQPSK WDM Is-OWC system with different precoding schemes for long-reach GEO Inter Satellite Link,” Int. Conf. on Green Energy, Computing and Intelligent Technology (GEn-CITy 2023), vol. 2023, pp. 134–141, Jul. 2023.
IX. D. E. Mohsen, E. M. Abbas, and M. M. Abdulwahid, “Performance analysis of OWC system based (S-2-S) connection with different modulation encoding,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 4s, pp. 400–408, 2023.
X. M. M. Abdulwahid and S. Kurnaz, “The channel WDM system incorporates of Optical Wireless Communication (OWC) hybrid MDM-PDM for higher capacity (LEO-GEO) inter satellite link,” Optik, vol. 273, p. 170449, 2023.
XI. R. A. Abd-Alhameed, M. M. Abdulwahid, and M. F. Mosleh, “Effects of Antenna Directivity and Polarization on Indoor Multipath Propagation Characteristics for different mmWave frequencies,” Informatica: J. Appl. Mach. Electr. Electron. Comput. Sci. Commun. Syst., vol. 2, no. 1, pp. 20–28, 2021.
XII. N. B. Alhamadani and M. M. Abdelwahid, “Implementation of microstrip patch antenna using MATLAB,” Informatica: J. Appl. Mach. Electr. Electron. Comput. Sci. Commun. Syst., vol. 2, no. 1, pp. 29–34, 2021.
XIII. A. M. MM and A. N. Mohammed, “Enhancement of similarity for image segmentation,” Informatica: J. Appl. Mach. Electr. Electron. Comput. Sci. Commun. Syst., vol. 2, no. 1, pp. 56–60, 2021.
XIV. Fan Wei, Hanyi Zhang, and Bingkun Zhou Tsinghua, “Carrier Phase and Amplitude Manipulation for Linearization and Dispersion compensation in Radio-over-Fiber Systems Using Mach-Zehnder Modulator – Progress in Electromagnetic Research Symposium.” Electromagnetics Research Symposium Proceedings, Stockholm, Sweden, p.1311, 2013.
XV. Htet, Sai Naing Min. “Generation of Optical Carrier Suppressed Signal for Radio-over-Fiber (RoF) System Using Dual-Drive Mach-Zehnder Modulator.” International Journal of Scientific and Research Publications, Vol.4, No.9, pp. 2250-3153, 2014.‏
XVI. Husein, Abdel Hakeim M., and Fady I. El Nahal. “Optimal design of 32 channels spectrum slicing WDM for optical fiber access network system.” Optik Vol. 125, No.18, pp, 5141-5143, 2014.‏
XVII. Johny, Jincy, and Sreenesh Shashidharan. “Design and simulation of a Radio over Fiber system and its performance analysis.” 2012 IV International Congress on Ultra-Modern Telecommunications and Control Systems. IEEE, 2012.‏
XVIII. Kabonzo, Fabrice Mfuamba, and Muhammad Umar Dilshad. “Impact of radio over fiber technology for integrated 5G front and back-haul applications.” 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017.‏
XIX. Karthikeyan, R., and S. Prakasam. “A survey on Radio over Fiber (RoF) for wireless broadband access technologies.” International Journal of Computer Applications Vol.64, No.12, 2013.‏
XX. Khan, Muhammad Faisal Nadeem, et al. “FSO Communication: Benefits, Challenges and Its Analysis in DWDM Communication System.” Sir Syed University Research Journal of Engineering & Technology (2020).‏ 10.33317/ssurj.181
XXI. Kumar, Suresh, Deepak Sharma, and Rajbir Singh. “Performance Analysis of Radio over Fiber Link Using MZM External Modulator.” First International Conference on Sustainable Technologies for Computational Intelligence. Springer, Singapore, pp. 229-240, 2020.‏
XXII. Kumar, Ghanendra, and Sandeep Kumar. “Effect of different channel spacings for DWDM system using optical amplifiers.” National Academy Science Letters pp. 1-4, 2020.‏ 10.1007/s40009-020-01014-x
XXIII. M. M. Abdulwahid, S. Kurnaz, M. R. Hayal, E. E. Elsayed, and D. A. Juraev, “Performance analysis of input power variations in high data rate DWDM-FSO systems under various rain conditions,” J. Opt., pp. 1–16, 2025.
XXIV. Marwanto, Arief, Sevia M. Idrus, and A. Sahmah Mohd. “Performance analysis of EDFA for SCM/WDM radio over fiber communication link.” Supa’at Photonic Technology Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia Vol.209, No.12, 2017. ‏
XXV. Mousa, Mokhalad Isam, Galip Cansevera, and Thanaa H. Abd. “Design and Implementation DWDM toward Terabit for Long-Haul Transmission System.” 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2020.‏
XXVI. Mohsen, D. E., Abbas, E. M., & Abdulwahid, M. M. (2023). Performance Analysis of OWC System based (S-2-S) Connection with Different Modulation Encoding. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 400-408.
XXVII. Mohan, Arya, and A. P. Anisha. “Full Duplex Transmission in RoF System using WDM and OADM Technology.” International Journal of Engineering Research & Technology, Vol. 4, pp. 513-516, 2015.
XXVIII. Qureshi, Khurram Karim, et al. “Radio-over-fiber front-haul link design using optisystem.” Journal of Optical Communications 1.ahead-of-print (2020).‏
XXIX. Rani, Nisha. “Design and Implementation of Various Advanced Modulation format over 8-WDM Bidirectional PON.” International Journal of Advance Research, Ideas and Innovations in Technology, Vol.3, No.6, pp.300-305, 2017.‏
XXX. Sharma, Savita Rana Abhishek, and Savita Rana. “Comprehensive study of radio over fiber with different modulation techniques–a review.” International Journal of Computer Applications, Vol.170, No.4 pp. 22-25, 2017.‏
XXXI. Velasco, Luis, et al. “Monitoring and data analytics for optical networking: benefits, architectures, and use cases.” IEEE Network, Vol.33, No.6, pp.100-108, 2019‏.
XXXII. Y. Yuan and L. Zhu, “Application scenarios and enabling technologies of 5G,” China Commun., Vol. 11, No. 11, pp. 69–79, 2014.
XXXIII. Y. S. Mezaal, H. T. Eyyuboğlu, and J. K. Ali, “New microstrip bandpass filter designs based on stepped impedance Hilbert fractal resonators,” IETE J. Res., vol. 60, no. 3, pp. 257–264, 2014.
XXXIV. Y. S. Mezaal, H. T. Eyyuboglu, and J. K. Ali, “A new design of dual band microstrip bandpass filter based on Peano fractal geometry: Design and simulation results,” in 2013 13th Mediterranean Microwave Symposium (MMS), 2013.
XXXV. Y. S. Mezaal and H. T. Eyyuboglu, “A new narrow band dual-mode microstrip slotted patch bandpass filter design based on fractal geometry,” in 2012 7th International Conference on Computing and Convergence Technology (ICCCT), 2012, pp. 1180–1184.
XXXVI. Y. S. Mezaal et al., “Cyber Security Readiness in Iraq: Role of the Human Rights Activists,” International Journal of Cyber Criminology, vol. 16, no. 2, pp. 1–14-1–14, 2022.
XXXVII. Y. W. Abduljaleel, B. Al-Obaidi, M. M. Khattab, F. Usman, A. Syamsir, and B. M. Albaker, “Compressive Strength Prediction of Recycled Aggregate Concrete Based on Different Machine Learning Algorithms”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 3, pp. 25–36, Sep. 2024.
XXXVIII. Y. S. Mezaal et al., “Cloud of Things and fog computing in Iraq: Potential applications and sustainability,” Heritage and Sustainable Development, vol. 5, no. 2, pp. 339–350, 2023.
XXXIX. Y. S. Mezaal et al., “New miniature narrow band microstrip diplexer for recent wireless communications,” Electronics (Basel), vol. 12, no. 3, p. 716, 2023.
XL. Z. Fawzi Hassan, M. N. Al-Turfi, and F. Al Alawy, “Facial Expression Recognition Enhancement Using Convolutional Neural Network”, AL-IRAQIA JOURNAL FOR SCIENTIFIC ENGINEERING RESEARCH, vol. 3, no. 2, pp. 83–91, Jun. 2024.
XLI. M. M. Abdulwahid, S. Kurnaz, M. R. Hayal, E. E. Elsayed, and D. A. Juraev, “Performance analysis of input power variations in high data rate DWDM-FSO systems under various rain conditions,” J. Opt., pp. 1–16, 2025.
XLII. Zhu, Ming, et al. “Radio-over-fiber access architecture for integrated broadband wireless services.” Journal of Lightwave Technology Vol.31, No.23 pp. 3614-3620, 2013.‏

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Abstract:

Motor symptoms, such as tremors, bradykinesia, stiffness, and posture issues, are produced by the loss of dopamine-producing neurons in the spinal column portion of the brain, which is characteristic of Parkinson's disease (PD). To properly control and treat PD, the condition must be identified as soon as possible. Machine learning techniques, which use data-driven methodologies, provide intriguing possibilities for reaching this aim. These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. These models support early diagnosis and enable personalized treatment strategies tailored to the specific needs of patients. Additionally, integrating wearable sensors and mobile health technologies further enhances the feasibility of continuous monitoring and early detection, providing patients and healthcare practitioners with the tools they need to manage PD proactively. To identify diseases, one can access vast databases of medical information. To diagnose PD, the proposed method uses two different data sets. Algorithms for machine learning are also capable of helping in producing specific details from such data. The proposed research applies a few Machine Learning ways to anticipate Parkinson's disease by human guidance, with the dataset acting as the source of the process understanding. By applying the hyperparameter optimization process, the accuracy is estimated. When used to diagnose Parkinson's disease (PD), the proposed methods produce accuracy rates of 98.9% for Naive Bayes and 97.3% for Logistic Regression.

Keywords:

KNN,logistic regression,machine learning,Naive Bayes,Parkinson’s Disease,Speech Disorder,

Refference:

I. Anastasia M Bougea, Nikolas Papagiannakis, Athina-Maria Simitsi, & Leonidas Stefanis. (2023). Ambiental Factors in Parkinson’s disease Progression: A Systematic Review. Medicina (Kaunas, Lithuania), 59(2). 10.3390/medicina59020294.
II. Arora, S., & Paliwal, K. K. (2020). Early diagnosis of Parkinson’s disease using deep learning and machine learning techniques: A review. Journal of Neural Engineering, 17(3), 031001. 10.1109/ACCESS.2020.3016062
III. Arora, S., & Paliwal, K. K. (2021). An ensemble classifier approach for early detection of Parkinson’s disease using handwriting dynamics. Biocybernetics and Biomedical Engineering, 41(3), 1175-1187. 10.1016/j.compbiomed.2023.107031
IV. Delrobaei, M., Baktash, N., & Gilmore, G. (2018). Wearable sensor-based classification of Parkinson’s disease tremor and essential tremor using hybrid dual tree complex wavelet transform-based features. Journal of Neuro Engineering and Rehabilitation, 15(1), 95. 10.1109/TNSRE.2017.2690578
V. Gao, F., Zhang, J., & Duan, H. (2020). Diagnosis of Parkinson’s Disease Using a Stacked Deep Polynomial Network Based on Functional Magnetic Resonance Imaging Data. Frontiers in Neuroscience, 14, 586. 10.1186/s40035-015-0039-8
VI. Giuffrida, J. P., Riley, D. E., & Maddux, B. N. (2020). Wearable sensors for advanced therapy referral in Parkinson’s disease. Journal of Parkinson’s Disease, 10(1), 373-379. doi: 10.1002/mds.22445
VII. Guo, Y., Huang, D., Zhang, W., Wang, L., Li, Y., Olmo, G., … & Chan, P. (2022). High-accuracy wearable detection of freezing of gait in Parkinson’s disease based on pseudo-multimodal features. Computers in Biology and Medicine, 146, 105629. 10.1016/j.compbiomed.2022.105629
VIII. Jankovic, J. (2020). Parkinson’s disease: clinical features and diagnosis. Journal of Neurology. Neurosurgery & Psychiatry, 91(8), 795-808. 10.1136/jnnp.2007.131045
IX. Liu, J., Du, H., Bi, Q., Liao, H., & Pan, Y. (2022, December). MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1072-1077. 10.1109/BIBM55620.2022.9995498.
X. Moshkova, A., Samorodov, A., Voinova, N., Volkov, A., Ivanova, E., & Fedotova, E. (2020). Parkinson’s disease detection by using machine learning algorithms and hand movement signal from Leap Motion sensor, In 2020 26th Conference of Open Innovations Association, 321-327. 10.23919/FRUCT48808.2020.9087433.
XI. Pahuja, G., & Nagabhushan, T. N. (2021). A comparative study of existing machine learning approaches for Parkinson’s disease detection. IETE Journal of Research, 67(1), 4-14. 10.1080/03772063.2018.1531730
XII. Ponsen, M.M., Stoffers, D., Booij, J., van Eck‐Smit, B.L., Wolters, E.C., & Berendse, H.W. (2004). Idiopathic hyposmia as a preclinical sign of Parkinson’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 56(2),173-181. 10.1:002/ana.20160
XIII. Prashanth, R., Roy, S.D., Mandal, P.K., & Ghosh, S. (2016). High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. International Journal of Medical Informatics, 90, 13-21. 10.1016/j.ijmedinf.2016.03.001
XIV. Salari, N., Kazeminia, M., Sagha, H., Daneshkhah, A., Ahmadi, A., & Mohammadi, M. (2023). The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. Current Psychology, 42(20), 16637-16660. 10.1007/s12144-022-02949-8
XV. Sharma, S., Jain, A., & Nowacki, A. S. (2021). Deep learning-based diagnosis of Parkinson’s disease using statistical features and convolutional neural networks. Computers in Biology and Medicine, 130, 104187. doi:10.48550/arXiv.2101.05631
XVI. Silveira‐Moriyama, L., Carvalho, M.D.J., Katzenschlager, R., Petrie, A., Ranvaud, R., Barbosa, E.R., & Lees, A.J. (2008). The use of smell identification tests in the diagnosis of Parkinson’s disease in Brazil, Movement disorders: official journal of the Movement Disorder Society, 23(16), 2328-2334. 10.1002/mds.22241
XVII. Skibinska, J., & Burget, R. (2020). Parkinson’s disease detection based on changes of emotions during speech. In 2020 12th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops, 124-130. 10.1109/ICUMT51630.2020.9222446.
XVIII. Vikas Ukani (2020) Parkinson’s Disease Data Set, https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set
XIX. Mhetre, H., Kaduskar, V., Chougule, P., Chendake, Y., Naik, N., Hiremath, P., & Bhat, R. (2024). Binder Molecular Weight, Concentration, and Flow Rate Optimization for ZnO Nanofiber Synthesis for Electronic Device Applications. Engineering Proceedings, 59(1), 177.
XX. Jomy, J., Sharma, S., Prabhu, P. R., Hiremath, P., & Prabhu, D. (2023). Microstructural changes and their influence on corrosion post-annealing treatment of copper and AISI 5140 steel in 3.5 wt% NaCl medium. Cogent Engineering, 10(1), 2244770.
XXI. Shivaprakash, Y. M., Prabhu, S., Anne, G., Gurumurthy, B. M., Hiremath, P., Sharma, S., & Sowrabh, B. S. (2023). High-temperature dry sliding wear behaviour of pre-aged 3-step T6-treated Al7075 hybrid matrix composite. Cogent Engineering, 10(1), 2235820.
XXII. Sharma Uppangala, R., Pai, S., Patil, V., Smriti, K., Naik, N., Shetty, R., … & Rathnakar, R. (2022). Influence of thermal and thermomechanical stimuli on dental restoration geometry and material properties of cervical restoration: a 3D finite element analysis. Journal of Composites Science, 7(1), 6.
XXIII. Prabhu, D., Hiremath, P., Prabhu, P. R., & Gowrishankar, M. C. (2022). Optimization of the parameters influencing the control of dual-phase AISI1040 steel corrosion in sulphuric acid solution with pectin as inhibitor using response surface methodology. Protection of Metals and Physical Chemistry of Surfaces, 58(2), 394-413.
XXIV. Vu, T.C., Nutt, J.G., & Holford, N.H. (2012). Progression of motor and nonmotor features of Parkinson’s disease and their response to treatment. British journal of clinical pharmacology, 74(2), 267-283. 10.1111/j.1365-2125.2012.04192.x
XXV. Wroge, T.J., Ozkanca, Y., Demiroglu, C., Si, D., Atkins, D.C., & Ghomi, R.H. (2018). Parkinson’s disease diagnosis using machine learning and voice. In 2018 IEEE signal processing in medicine and biology symposium, 1-7. 10.1109/SPMB.2018.8615607

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Abstract:

This manuscript aims to formulate an innovative method called Covariance Matrix Adaptation Evolution Strategy based Cat Mouse Based Optimization (CMAES-CMBO) integrated with a Hybrid Fast Fuzzy-2-Degree-of-Freedom Fractional-Order Tilt Integral Derivative Controller (CMAES-CMBO-HFF-2DoF-FOTIDC) aimed at enhancing the performance of Grid-Interfaced Solar Inverter Systems (GISIS) while reducing total harmonic distortion. The proposed solar inverter system comprises several elements, including a photovoltaic array, a Relift Luo Converter (RLC), and a 15-Level Switch-Minimized Multilevel Inverter (15L-SMMI), alongside the CMAES-CMBO-HFF-2DoF-FOTIDC controller. The choice of the RLC over the others from the category stems from its capability to mitigate parasitic capacitance effects, achieve high efficiency, increase power density, reduce ripple voltage magnitude, and lower duty cycle requirements. This control strategy employs a fuzzy-logic-based, optimized 2DoF fractional-order tilt integral derivative controller (2DoF-FOTIDC). The CMAES-CMBO algorithm optimizes the controller's parameters. Comparative analysis of the CMAES-CMBO-HFF-2DoF-FOTIDC controller with other state-of-the-art controllers demonstrates its superior performance and effectiveness. Additionally, the manuscript explores the implementation of the Random Pulse Position Pulse Width Modulation (RPPPWM) method alongside the proposed approach. The proposed GISIS aims to address harmonic distortion reduction, alongside improvements in the performance of solar inverters, robustness, stability, and enhanced capabilities to deal with system uncertainties.

Keywords:

CMAES-CMBO-HFF-2DoF-FOTIDC,CMAES-CMBO,RLC,robustness,15L-SMMI,RPPPWM,

Refference:

I. Andela, M., & Salkuti, S. R. “Solar GISIS-based reduced switch multilevel inverter for improved power quality.” Clean Technologies, vol. 4, no. 1, 2022, pp. 1-13.
II. Dehghani, M., Hubálovský, Š., & Trojovský, P. “Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm.” Sensors, vol. 21, no. 15, 2021, pp. 5214.
III. Dheeban, S. S., and Krishnaveni, L. “Performance Improvement of Photo-Voltaic Panels by Super-Lift Luo Converter in Standalone Application.” Materials Today: Proceedings, vol. 37, no. 1, 2021, pp. 1163-1171.
IV. Kumar, P., Arya, S. R., Mistry, K. D., & Yadav, S. “A self-tuning ANFIS DC link and ANN-LM controller based DVR for power quality enhancement.” CPSS Transactions on Power Electronics and Applications, no. 99, 2023, pp. 1-11.
V. Li, Guohua and Li, Feng. “Single-Phase Voltage Source Multi-Level Inverter Hysteresis RPPPWM Reconfigurable Fault-Tolerant Control Method.” Energies, vol. 15, no. 7, 2022, p. 2557.
VI. Lin, H., & He, X., et al. “A Simplified 3-D NLM-Based RPPPWM Technique With Voltage-Balancing Capability for 3LNPC Cascaded Multilevel Converter.” IEEE Transactions on Power Electronics, vol. 35, no. 4, 2020, pp. 3506-3518.
VII. Logasri, R. “Study on Performance Analysis of Luo Converter with Fuzzy Controller.” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 9, 2021, pp. 2793-2798.
VIII. Logeswaran, T., Senthilkumar, A., & Karuppusamy, P. “Adaptive Neuro-Fuzzy Model for Grid-Interfaced GISIS.” International Journal of Fuzzy Systems, vol. 17, no. 4, pp. 585-594, 2015.
IX. Marrero, L., & González, V. J. “Harmonic distortion characterization in groups of distribution networks applying the IEEE Standard 519-2014.” IEEE Latin America Transactions, vol. 19, no. 4, 2022, pp. 526-533.
X. Nguyen, P. C., and Nguyen, D. T. “A New Decentralized Space Vector PWM Method for Multilevel Single-Phase Full Bridge Converters.” Energies, vol. 15, no. 3, 2022, p. 1010.
XI. Palanisamy, R., and Vijayakumar, V. “Artificial Neural Network Based RPPPWM for Five Level Cascaded H-Bridge Inverter Fed Grid Connected PV System.” Journal of Intelligent and Fuzzy Systems, vol. 39, no. 6, 2020, pp. 8453-8462.
XII. Rai, A., & Das, D. K. “The development of a fuzzy tilt integral derivative controller based on the sailfish optimizer to solve load frequency control in a microgrid incorporating energy storage systems.” Journal of Energy Storage, vol. 48, no. 1, pp. 1-30, 2022.
XIII. Ranjan, A., & Mehta, U. “Improved control of integrating cascade processes with time delays using fractional-order internal model controller with the Smith predictor.” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 237, no. 9, 2023, pp. 1523-1541.
XIV. Rath, D., Kar, S., & Patra, A. K. “Harmonic Distortion Assessment in the Single-Phase Photovoltaic (PV) System Based on SPWM Technique.” Arab Journal of Science and Engineering, vol. 46, no. 4, 2021, pp. 9601–9615.
XV. Rezaei, M. A., & Khooban, M. H. “A New Hybrid Cascaded Switched-Capacitor Reduced Switch Multilevel Inverter for Renewable Sources and Domestic Loads.” IEEE Access, vol. 10, no. 1, pp. 14157-14183, 2022.
XVI. Singh, A., & Udhayakumar, R. “Asymptotic Stability of Fractional Order (1, 2] Stochastic Delay Differential Equations in Banach Spaces.” Chaos, Solitons and Fractals, vol. 150, no. 1, 2021, p. 111095.
XVII. Sulttan, M. Q., Jaber, M. H., & Shneen, S. W. “Proportional-Integral Genetic Algorithm Controller for Stability of TCP Network.” International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 6, 2020, pp. 6225-6232.
XVIII. Wang, P., and Montanari, G. C. “Considering the Parameters of Pulse Width Modulation Voltage to Improve the Signal-to-Noise Ratio of Partial Discharge Tests for Inverter-Fed Motors.” IEEE Transactions on Industrial Electronics, vol. 69, no. 5, 2021, pp. 4545-4554.

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Abstract:

Conventionally the solution of any quadratic equation in one unknown quantity (say x) is represented in real or complex numbers. Instead of finding the solution of any quadratic equation in one unknown in complex numbers, the author introduced Bhattacharyya's Theorem – 1 & 2 to find its solution in real numbers only. Bhattacharyya's Theorem – 1 states that the square root of any negatively directed number is a negatively directed number and Bhattacharyya's Theorem -2 states that the square of any negatively directed number is a negatively directed number. Both theorems are based on the newly invented concept of the Theory of Dynamics of Numbers by the author. To find the root of any quadratic equation it must satisfy the two criteria: 1) The value of x must satisfy the equation (as conventional method). (2) The inherent nature of x must satisfy the quadratic equation (new concept). The inherent nature of x may be a positively directed number, a negatively directed number, or a neutral number which can be determined depending on the constant term, c<0, c>0, or c= 0 respectively of the quadratic equation. The author states that the quadratic expression which is factorizable into two linear functions may be defined as a pseudo-quadratic equation but all factorizable quadratic equations are not pseudo-quadratic equations. Using the unique concept of the Theory of Dynamics of Numbers the solution of the quadratic equation, ax²+bx+c=0, in one unknown quantity (say x) can be determined in real numbers only even if the discriminant, b² – 4ac < 0, without using the concept of the complex numbers.

Keywords:

Bhattacharyya's Theorem – 1 & 2,Number theory,Pseudo - quadratic equation,Quadratic equation,Theory of Dynamics of numbers,

Refference:

I. Alper Yorulmaz & M. Cihangir Doğan. : ‘An Action Research to Eliminate Mistakes in Multiplication and Division Operations through Realistic Mathematics Education’. Eric. Vol17(3) 2022. 10.29329/epasr.2022.461.12.
II. Brahma-Sphuta Siddhanta Vol I – edited by Acharyavara Ramswarup Sharma pg 158.
III. Brahma-Sphuta Siddhanta Vol I – edited by Acharyavara Ramswarup Sharma pg 160.
IV. Dutta, B.B. ( 1929). The Bhakshali Mathematics, Calcutta, West Bengal: Bulletin of the Calcutta Mathematical Society.
V. Datta, B. B., & Singh, A. N. (1938). History of Hindu Mathematics, A source book. Mumbai, Maharashtra: Asia Publishing House.
VI. Gandz, S. (1937). The origin and development of the quadratic equations in Babylonian, Greek, and Early Arabic algebra. History of Science Society, 3, 405-557.
VII. Ganita-manjari by Ganesa.
VIII. G. H. Hardy and E. M. Wright. : ‘An Introduction to the Theory of Numbers’. Sixth Edition. Page 245 – 247.
IX. Ganita-Sara-Sangraha – ed. L. C. Jain (1963).
X. Hansa, S. (2017). Exploring multiplicative reasoning with grade four learners through structured problem solving. (Unpublished MA thesis), University of the Witwatersrand, Johannesburg.
XI. History of Hindu Mathematics by Bibhutibhusan Datta and Avadhesh Narayan Singh, 1935, pg 138.
XII. Katz, V. J. (1997), Algebra and its teaching: An historical survey. Journal of Mathematical Behavior, 16(l), 25-36.
XIII. Katz, V., J. (1998). A history of mathematics (2nd edition). Harlow, England: Addison Wesley Longman Inc.
XIV. Katz Victor, (2007). The Mathematics of Egypt, Mesopotamia, China, India and Islam: A source book 1st ed., New Jersey, USA: Princeton University Press.
XV. Kennedy, P. A., Warshauer, M. L. & Curtin, E. (1991). Factoring by grouping: Making the connection. Mathematics and Computer Education, 25(2), 118-123.
XVI. Ling, W. & Needham, J., (1955). Horner’s method in Chinese Mathematics: Its root in the root extraction procedures of the Han Dynasty, England: T’oung Pao.
XVII. Nataraj, M. S., & Thomas, M. O. J. (2006). Expansion of binomials and factorisation of quadratic expressions: Exploring a vedic method. Australian Senior Mathematics Journal, 20(2), 8-17.
XVIII. Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2017). Improving mathematics teaching in kindergarten with realistic mathematical education. Early Childhood Education Journal, 45(3), 369-378. 10.1007/s10643-015-0768-4
XIX. Prabha S. Rastogi, Sandhya Nitin. : ‘HISTORY OF MULTIPLICATION CLASSICAL PERIOD IN INDIAN MATHEMATICS’.
https://instavm.org/wp-content/uploads/2021/05/H16.pdf
XX. Prabir Chandra Bhattacharyya. : ‘AN INTRODUCTION TO THEORY OF DYNAMICS OF NUMBERS: A NEW CONCET’. J. Mech. Cont. & Math. Sci., Vol.-17, No.-1, pp 37-53, January (2022). 10.26782/jmcms.2022.01.00003
XXI. Prabir Chandra Bhattacharyya. : A NOVEL CONCEPT IN THEORY OF QUADRATIC EQUATION. J. Mech. Cont. & Math. Sci., Vol.-17, No.-3, March (2022) pp 41-63. l : 10.26782/jmcms.2022.03.00006
XXII. Prabir Chandra Bhattacharyya. : ‘A NOVEL CONCEPT FOR FINDING THE FUNDAMENTAL RELATIONS BETWEEN STREAM FUNCTION AND VELOCITY POTENTIAL IN REAL NUMBERS IN TWO-DIMENSIONAL FLUID MOTIONS’. J. Mech. Cont. & Math. Sci., Vol.-18, No.-02, February (2023) pp 1-19
XXIII. Prabir Chandra Bhattacharyya, : ‘A NEW CONCEPT TO PROVE, √(−1) = −1 IN BOTH GEOMETRIC AND ALGEBRAIC METHODS WITHOUT USING THE CONCEPT OF IMAGINARY NUMBERS’. J. Mech. Cont. & Math. Sci., Vol.-18, No.-9, pp 20-43. 10.26782/jmcms.2023.09.00003
XXIV. Prabir Chandra Bhattacharyya. : ‘A NOVEL CONCEPT: THE PRODUCT OF TWO NEGATIVELY DIRECTED NUMBERS IS A NEGATIVELY DIRECTED NUMBER THOUGH THE NEGATIVE OF A NEGATIVE NUMBER IS A POSITIVE NUMBER’. J. Mech. Cont.& Math. Sci., Vol.-19, No.-10, October (2024) pp 1-18. 10.26782/jmcms.2024.10.00001
XXV. Prabir Chandra Bhattacharyya. : AN INTRODUCTION TO RECTANGULAR BHATTACHARYYA’S CO-ORDINATES: A NEW CONCEPT. J. Mech. Cont. & Math. Sci., Vol.-16, No.-11, November (2021) pp 76. 10.26782/jmcms.2021.11.00008
XXVI. Smith, D. (1953). History of mathematics, Vol. 2. New York: Dover.
XXVII. Swapan Banerjee : ‘Ramanujan & Ancient Indian Mathematicians. Milton Keynes, UK, (2019). ISBN – 9781981156283, P. 27.
XXVIII. Thapar, R., (2000). Cultural pasts: Essays in early Indian History, New Delhi: Oxford University Press.
XXIX. Trishatika (Pati-Ganit-Sara) by V. Kutumbshastri, 2004, pg 13.
XXX. Yorulmaz, A., Uysal, H. & Sidekli, S. (2021). The use of mind maps related to the four operations in primary school fourth-grade students as an evaluation tool. Journal of Education and Learning, 15(2), 257-266. 10.11591/edulearn.v15i2.19894
XXXI. Yong, L. L. (1970). The geometrical basis of the ancient Chinese square-root method. The History of Science Society, 61(1), 92-102.
XXXII. http://en. wikipedia.org/wiki/Shridhara

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