首页|Data on Machine Learning Reported by Researchers at University of Technology (Machine Learning-optimized Compact Frequency Re- configurable Antenna With Rssi Enhancement for Long-range Ap- plications)
Data on Machine Learning Reported by Researchers at University of Technology (Machine Learning-optimized Compact Frequency Re- configurable Antenna With Rssi Enhancement for Long-range Ap- plications)
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Investigators publish new report on Machine Learning. According to news originating from Perak, Malaysia, by NewsRx correspondents, research stated, "This study presents an innovative and compact monopole antenna with dual-band frequency reconfigurability for LoRa applications. It operates within the 915 MHz and 868 MHz frequencies, aligning with the designated bands for use in America, Asia and Europe." Financial support for this research came from Universiti Teknologi PETRONAS, Malaysia, YUTP-PRG (Yayasan Universiti Teknologi PETRONAS-Prototype Research Grant). Our news journalists obtained a quote from the research from the University of Technology, "No existing compact reconfigurable antenna with these features for LoRa applications within ISM bands below 1 GHz is known. Employing an economical FR-4 substrate in its design, the antenna attains a compact size of 40 x 42 mm(2) (0.12 lambda(0) x 0.12 lambda(0)), where lambda(0) denotes the wavelength in free space corresponding to 868 MHz. A single RF PIN diode enables seamless switching between 868 MHz and 915 MHz bands. simulation, and optimization employed CST MWS ® software. Supervised regression Machine Learning (ML) models predicted resonance frequencies, with Gaussian Process Regression emerging as optimal, achieving R-squared and variance scores of 92.87% and 93.77%, respectively. A maximum gain of 2 dBi at 915 MHz and 70% efficiency, boasting good radiation patterns and matching was demonstrated by the antenna. Experimental validation in a football field at Universiti Teknologi PETRONAS, Malaysia, assessed the proposed antenna's performance on a LoRa transceiver system based on LoRa SX1276. The Received Signal Strength Indicator (RSSI) of the proposed antenna consistently exceeded the conventional commercially available monopole antenna by an average of -12 dBm at every point up to 300 m, showcasing enhanced signal reception."
PerakMalaysiaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of Technology
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