Mr Patrick Enenche, Dr Michael David, Dr Caroline Alenoghena, Mr Supreme Okoh,
Volume 15, Issue 2 (7-2021)
Abstract
The value of ozone absorption cross section (OACS) is a key parameter used in the configuration of gas sensors. Sadly, the variations of certain parameters among others such as temperature, pressure, and optical path-length in a given spectrum can affect the values of OACS. As a result, there have been several discrepancies in the value of OACS. Recently, the simultaneous effects of optical path-length were investigated in the visible spectrum. Hence, there is the need to also carry out the same investigation in the UV spectrum. So, in this paper, we have reported the combined variation effects of temperature (100 K–350 K), and optical path-length (0.75 cm–130 cm) on OACS in the UV spectrum. We used the method of optical absorption spectroscopy as deployed in a model software called Spectralcalc. The software comprising the HITRAN12 latest line list was used to simulate OACS values. Simulated results were obtained using the latest available line list on the HITRAN12 Spectralcalc simulator. Our obtained results were slightly different from those reported for the visible spectrum but followed a similar trend, in that it showed a decrease in the OACS with an increase in the temperature from 100 K to 350 K at 279.95 nm and 257.34 nm by 1.09 % and 1.43 % respectively. While optical path-length had zero effect on it. We, therefore, conclude that at constant pressure, OACS depends on both temperature and absorption wavelength but not on optical path-length. The analysis reported in this work only seeks to address the differences in the OACS relative to temperature in the UV spectrum. So, the results obtained in this paper can be used to optimally configure ozone gas sensors to obtain an accurate measurement.
Jafar Bakhtiar Shohani, Morteza Hajimahmoodzadeh, Hamidreza Fallah,
Volume 16, Issue 2 (7-2022)
Abstract
In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two kinds of intensity are used, one is in-focus and the other is out-of-focus of the telescope. After these simulations, a convolutional neural network (CNN) is configured and designed and its input is simulated intensity patterns. After learning the network, we could recognize double stars at severe turbulence without needing phase correction with a very high accuracy level of more than 98%.
