首页|Iran University of Science and Technology Researchers Publish New Data on Machine Learning (A General Study for the Complex Re- fractive Index Extraction Including Noise Effect Using a Machine Learning-Aided Method)
Iran University of Science and Technology Researchers Publish New Data on Machine Learning (A General Study for the Complex Re- fractive Index Extraction Including Noise Effect Using a Machine Learning-Aided Method)
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NETL
NSTL
IEEE
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Tehran, Iran, by NewsRx editors, research stated, “This article investigates the extraction of complex refractive indices from the amplitude and phase of the transmitted electric field. In the first step, an incident plane wave has been assumed and the amplitude and phase of the transmitted plane wave is calculated analytically.” The news journalists obtained a quote from the research from Iran University of Science and Technology: “In this calculation, different values of the complex refractive index have been assumed for the non-magnetic material under test. In fact, the real part and imaginary part of the refractive index are assumed in the range of [1-10] and [0-1], respectively. Furthermore, a general study is made by an assumption of the material thickness to simulation wavelength ratio in the range of [0.01-20]. Due to examining the measurement noise, noisy data are produced for different values of signal-to-noise ratio in the range of [25-40] dB. Due to the difficulties of estimating the refractive index confronted in the theoretical or iterative methods, a Long short-term memory (LSTM) network is proposed and used for the estimation of complex refractive index based on the amplitude and phase of the transmitted electric field. It is shown that the estimation accuracy of about 97% can be achieved in the trained network. Furthermore, the estimation accuracy as a function of thickness-to-wavelength ratio, signal-to-noise ratio, and the values of real and imaginary parts of the refractive index are studied in detail and shown that higher estimation accuracy can be achieved.”
Iran University of Science and TechnologyTehranIranAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
