Yazar "Cakir, Mehmet" seçeneğine göre listele

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    Dual-Band Electromagnetic Shielding of Concrete Block Walls Using Concentric Double Square-Ring Frequency-Selective Surfaces near the n78 and n79 5G Frequency Regions
    (MDPI, 2026) Cakir, Mehmet
    Featured Application This study demonstrates a concrete-integrated dual-band frequency-selective shielding approach for selectively attenuating the main sub-6 GHz 5G n78 and n79 frequency regions in building walls without increasing wall thickness or altering conventional concrete block geometry.Abstract This paper presents a dual-band shielding approach in which two concentric square rings, fabricated on a single 0.2 mm copper sheet, are embedded in the mortar joint of prefabricated concrete blocks. The outer ring targets n78 (design center 3.5 GHz) and the inner ring targets n79 (design center 4.7 GHz). Both rings are dimensioned from the effective wavelength inside the concrete medium (epsilon r = 5.0): the outer ring has an outer side of 9.93 mm and the inner ring an outer side of 7.43 mm, with a 180 degrees gap offset between the two elements to suppress inter-ring coupling. CST Microwave Studio simulations predicted two shielding peaks of 37.2 dB at 3.48 GHz and 35.8 dB at 4.72 GHz, with -10 dB bandwidths of 580 MHz and 490 MHz, respectively. Computer numerical control (CNC)-milled copper panels cast into ordinary Portland cement (OPC) concrete blocks achieved measured peak SE of 34.9 dB (n78) and 33.4 dB (n79), with bandwidths of 510 MHz and 420 MHz. The 2.3-2.4 dB shortfall relative to simulation is attributed to coupling-gap drift during milling and spacer-induced surface voids. A permittivity sweep from epsilon r = 4.5 to 6.0 showed that peak SE remains above 30 dB throughout.
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    Enhanced Absorption-Dominant EMI Shielding Performance of Pyramidal Cementitious Composites Incorporating Recycled Plastics and Magnetite Minerals for 5G Applications
    (MDPI, 2026) Cakir, Mehmet; Engin, Mustafa Alptekin; Camuzcuoglu, Murat
    In this study, waste polypropylene (PP) and magnetite (Fe3O4) mineral-reinforced cement-based pyramidal composite structures were designed, manufactured, and experimentally characterized to reduce electromagnetic interference (EMI) problems in the 3.3-4.9 GHz frequency band for 5G communication systems. Unlike traditional planar concrete surfaces, the aim was to minimize surface reflections and obtain an absorption-dominant shielding mechanism by providing gradient impedance matching through the pyramidal geometry. Although the use of carbon-based nanomaterials is common in the current literature, their high cost and corrosion risks limit their large-scale applications. This study involves the evaluation of waste polypropylene disposal and self-enriching magnetite mineral together. Theoretical analyses were supported by the Lichtenecker Logarithmic Mixing Rule and the Maxwell-Garnett model, and seven different mixing scenarios (S1-S7) were measured using the free-space method with a Libre vector network analyzer. Experimental results showed that the pure concrete sample exhibited predominantly reflective behaviour, with shielding performance improving significantly as the filler ratio increased. The S4 sample, containing 15% PP and 10% magnetite, offered broadband and balanced absorption performance, while the S7 sample, containing 25% PP and 25% magnetite, provided the highest shielding effectiveness with reflection below -10 dB across the entire band and transmission loss reaching -65 dB.
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    Experimental Electromagnetic Shielding Analysis of a Square-Resonator-Integrated Double-Concrete Structure Using Explainable Machine Learning
    (MDPI, 2026) Cakir, Mehmet
    Electromagnetic shielding has become a practical concern in buildings and structures exposed to persistent interference. This paper reports experimental measurements of the frequency-dependent shielding properties of a square-resonator-integrated double-concrete structure, using a free-space S-parameter setup built around WR229 waveguide adaptors and horn antennas. Three variables were tested: concrete thickness D, relative permittivity epsilon r, and relative magnetic permeability mu r. Both epsilon r and mu r were characterized experimentally from carbon-fibre- and copper-slag-modified concrete rather than taken from standard tables. The novelty of the study lies in combining experimentally characterized concrete electromagnetic properties, an embedded square-resonator geometry, and explainability-driven machine learning analysis within a single experimental framework for cement-based EMI shielding design. A total of 96 parameter combinations were evaluated using calibrated S11 and reference-corrected S21 responses across 3.3-4.9 GHz. Thickness and electromagnetic material properties interacted-neither governed shielding performance on its own. The strongest transmission attenuation occurred at D = 5, epsilon r = 7, and mu r = 1.2, where minimum S21 reached approximately -62.98 dB at 3.6392 GHz. S11 varied considerably less than S21 across the tested combinations, suggesting transmission suppression is the dominant mechanism rather than reflection enhancement. A machine learning analysis confirmed that nonlinear ensemble models outperformed the linear baseline and identified thickness as the most influential predictor of minimum S21.
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    Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters
    (Mdpi, 2025) Engin, Mustafa Alptekin; Cakir, Mehmet
    The precise determination of material type and thickness is of major significance in non-destructive testing, quality assurance, and materials science, as it influences the functionality, reliability, and performance of materials in engineering applications. This study proposes a methodology for the classification of material thickness and type through the analysis of scattering parameters within the 8-12 GHz frequency range. A database was created, encompassing real, imaginary, and dB values of reflection and transmission parameters for nine real-world materials with thicknesses ranging from 1 to 10 mm. This study addressed two main classification tasks, namely material thickness and material type. A variety of training-testing splits were employed in conjunction with 10-fold cross-validation to facilitate a comparison of classifier performance. In the material type classification, incorporating multiple thickness levels of each material enabled the model to distinguish materials with similar reflection characteristics more accurately, thereby enhancing discrimination performance. The results showed that Fine KNN consistently achieved 100% accuracy in thickness classification, while Quadratic SVM achieved 100% accuracy in material type classification, even when utilising only three thickness levels. Furthermore, the 90% training-10% testing split yielded the highest performance in thickness classification, whereas the optimal data split for material type classification differed across classifiers. Overall, this study demonstrates that the combination of scattering parameters and machine learning serves as a reliable and efficient approach for non-destructive material characterization.
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    Non-Destructive Intelligent Microwave Sensing of Water Content in Sand-Water Mixtures Using S-Parameter Features and PLS Regression
    (MDPI, 2026) Cakir, Mehmet
    The accurate and rapid estimation of water content in granular materials is important for geotechnical engineering, construction-material evaluation, agricultural monitoring, and non-destructive material assessment. This study presents a controlled laboratory feasibility framework for estimating water content in sand-water mixtures using microwave S-parameter measurements and physically interpretable features. Sixty measurements were acquired from six nominal mixture levels containing 0%, 5%, 10%, 15%, 20%, and 25% water by mass using a WR-229 waveguide-based fixture over 3.30-4.90 GHz. Descriptors were extracted from the raw and empty-reference-normalized S11 and S21 responses, including extrema, slopes, area-based indicators, band-averaged values, selected-frequency responses, and phase statistics. Ridge regression, partial least squares regression, support vector regression, Gaussian process regression, random forest, and gradient boosting regression were evaluated. The selected PLS model achieved RMSE = 3.56%, MAE = 2.92%, and R2 = 0.826 under leave-one-mixture-level-out group-wise cross-validation, which was used as the primary indicator of generalization to an unseen mixture level. The substantially lower error obtained under repeated-measurement leave-one-out cross-validation primarily reflects within-level repeatability under controlled conditions and should not be interpreted as evidence of universal calibration performance. The results demonstrate that magnitude- and phase-derived microwave descriptors, particularly transmission-based features, provide an interpretable and repeatable framework for water-content estimation under the specific sand type, sample geometry, water source, and laboratory conditions investigated. Further validation using independent preparation batches, different granular materials, measured water conductivity, temperature variation, and field-like conditions is required before practical deployment.