首页|New Machine Learning Study Findings Recently Were Published by Researchers at Universiti Kebangsaan Malaysia (A unique SWB multi-slotted four-port highly isolated MIMO antenna loaded with metasurface for IOT applications-based machine learning ...)
New Machine Learning Study Findings Recently Were Published by Researchers at Universiti Kebangsaan Malaysia (A unique SWB multi-slotted four-port highly isolated MIMO antenna loaded with metasurface for IOT applications-based machine learning ...)
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NETL
NSTL
Elsevier
Investigators publish new report on artificial intelligence. According to news reporting from Bangi, Malaysia, by NewsRx journalists, research stated, “This study introduces a MIMO antenna system incorporating an epsilon negative Meta Surface (MS). The system’s architects intended for it to have a large usable frequency range, high gain, narrow inter-component spacing, and superior isolation properties with four elements of MIMO antenna that are strategically organized in an orthogonal arrangement and a compact form factor measuring 41 x 41 x 1.6 mm3, utilizing a low-loss Rogers RT5880 substrate.” Funders for this research include King Saud University. The news editors obtained a quote from the research from Universiti Kebangsaan Malaysia: “The architecture of the antenna is characterized by integrating a multi-slotted radiating patch, a partial ground plane, and an epsilon-negative Meta Surface. This integration is done by a 7 x 7 Metamaterial array at the back of the MIMO antenna with a dimension of 41 x 41 x 1.6 mm3, resulting in a collective enhancement of the antenna’s overall performance by affecting the phase, amplitude, electromagnetic field and reducing the backward radiation. The separation between the Meta-surface and the MIMO antenna is established at a distance of 6 mm. The antenna’s exceptional super wideband performance is increased from 2-19 GHz to 1.9-20 GHz after using the MS. Moreover, isolation increases from 20 dB to 25.5 dB, Realized gain from 4.5 dBi to 8 dBi, and radiation efficiency from 77% to 89% across the operational bandwidth. The MIMO antenna exhibits remarkable diversity characteristics, as indicated by an envelope correlation coefficient (ECC) of <0.004, a diversity gain (DG) surpassing 9.98 dB, a channel capacity loss (CCL) below 0.3, and a total active reflection coefficient (TARC) measuring 12 dB. Furthermore, a circuit analogous to a resistor-inductor-capacitor (RLC) system is constructed, and four regression methods from the field of machine learning are employed to validate the gain and efficiency achieved. Notably, the linear regression model exhibits exceptional performance, achieving an accuracy of 99%.”
Universiti Kebangsaan MalaysiaBangiMalaysiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
