This paper reports a central spiral split-rectangular-shaped metamaterial absorber surrounded by a polarization-insensitive ring resonator for s-band applications. The rated absorption is 99.9% at 3.1 GHz when using a three-layer structure where the top and ground are made of copper and the center dielectric material is a commonly used FR-4 substrate. The central split gaps have an impact on the unit cell by increasing high absorption, and an adequate electric field is apparent in the outer split ring gap. At 3.1 GHz, the permittivity and permeability are negative and positive, respectively, so the proposed unit cell acts as an epsilon negative (ENG) metamaterial absorber. In a further analysis, Roger4450B was used as a substrate and obtained excellent absorption rates of 99.382%, 99.383%, 99.91%, and 95.17% at 1.44, 3.96, 4.205, and 5.025 GHz, respectively, in the S- and C-band regions. This unit cell acts as a single negative metamaterial (SNG) absorber at all resonance frequencies. The S11 and S21 parameters for FR-4 and Rogers4450B were simulated while keeping the polarization angle (θ and φ) at 15, 30, 45, 60, 75, and 90 degrees to measure, permittivity, permeability, reflective index, absorption, and reflection. The values of the reflective index are near zero. Near-zero reflective indexes (NZRI) are widely used in antenna gain propagation. The unit cell fabricated for the FR-4 substrate attained 99.9% absorption. S-band values in the range of (2–4) GHz can be applied for low-frequency radar detection.
Intelligent transport systems have been in research and development in recent decades. However, not all countries can afford to deploy such systems for the public usage. Conventional public transport systems such as public buses are still the main mode of public transportation system in many developing countries. Due to the issue of public transportation's inaccurate bus arrival timing, the general public still prefers private transportation. The goal of this study is to investigate the use of machine learning to improve the prediction accuracy of bus arrival timing. Two machine learning models, a multi-layer perceptron (MLP) and a MLP regressor, were compared in terms of their performance on small datasets. The experiment data was collected from Kulai-Johor Bahru Sentral bus route in Malaysia and cleaned to negate errors that influenced the accuracy of the models. The performance of the models were analysed and discussed and we observed that the MLP outperforms the MLP regressor. A limitation of this study is the small dataset that only comprises bus location data collected on a single bus route.
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