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Mukhamejanova, Almira
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Electrical & Electronics Engineering

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Analytical broadband impedance matching using modified approximating functions with embedded transmission zeros Yerzhan, Assel; Manbetova, Zhanat; Mussapirova, Gulzada; Karnakova, Gayni; Mukhamejanova, Almira; Imankul, Manat; Kaliyev, Zhanybek; Bakirova, Nagima
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11220

Abstract

This paper proposes a modified approximating function (MAF)-based analytical method for broadband impedance matching in radio-electronic systems. Unlike traditional Chebyshev and Butterworth approaches, which rely on fixed pole distributions and predefined amplitude responses, the proposed method analytically embeds load-specific transmission zeros directly into the approximation function. This modification enables more accurate reconstruction of frequency-dependent impedance behavior without increasing the network order or circuit complexity. The method establishes a unified analytical synthesis framework linking impedance modeling, ladder-network realization, and constrained optimization. Validation was performed over the 1–10 GHz band using numerical simulations, Monte Carlo tolerance analysis, and prototype measurements. Compared with classical Chebyshev and Butterworth designs, the MAF-based approach achieves a 15–25% reduction in maximum reflection coefficient, a 30–40% decrease in optimization iterations, and improved robustness, with reflection variations remaining within 2% under ±10% parameter deviations. The results confirm that the proposed method provides superior analytical flexibility, improved matching accuracy, and reduced computational effort, making it suitable for automated broadband radio frequency (RF) design applications.
Exponential smoothing-based forecasting of self-similar internet of things traffic Mukhamejanova, Almira; Chezhimbayeva, Katipa; Kaliyeva, Samal; Lechshinskaya, Eleonora; Tumanbayeva, Kumyssay; Garmashova, Yuliya; Abisheva, Tolganay; Zhumay, Inkar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11219

Abstract

The rapid growth of internet of things (IoT) devices generate highly variable and self-similar traffic patterns, creating challenges for maintaining quality of service (QoS) in modern telecommunication networks. Accurate short-term forecasting of such traffic is essential for efficient resource allocation, yet its fractal characteristics and long-range dependence complicate prediction. This study investigates the use of simple exponential smoothing for short-term forecasting of self-similar IoT traffic by evaluating three smoothing coefficients (a=0.1, 0.5, and 0.9). The Hurst exponent (H=0.5) confirms the presence of self-similarity in the observed traffic. Experimental results show that a=0.1 provides the highest prediction accuracy, achieving a mean absolute percentage error (MAPE) of 25.82% when forecasting traffic values within a 32-minute horizon. The method effectively captures underlying trends while reducing noise sensitivity. These findings demonstrate that exponential smoothing offers a lightweight, interpretable, and practical solution for real-time IoT traffic forecasting, supporting dynamic network load management under highly variable traffic conditions.
Co-Authors Abisheva, Tolganay Bakirova, Nagima Chezhimbayeva, Katipa Garmashova, Yuliya Imankul, Manat Kaliyev, Zhanybek Kaliyeva, Samal Karnakova, Gayni Lechshinskaya, Eleonora Manbetova, Zhanat Mussapirova, Gulzada Tumanbayeva, Kumyssay Yerzhan, Assel Zhumay, Inkar