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1. Abstract
1. Abstract
This experiment investigates Mie scattering phenomena using two detection methods, Photo-Multiplier Tube (PMT) mode, and Charge-Coupled Device (CCD) mode, within an AIoTs (Artificial Intelligence of Things) framework. Mie scattering theory, which describes the interaction of light with spherical particles whose sizes are comparable to the wavelength, is used to study light intensity distribution as a function of scattering angle.
This experiment investigates Mie scattering phenomena using two detection methods, Photo-Multiplier Tube (PMT) mode, and Charge-Coupled Device (CCD) mode, within an AIoTs (Artificial Intelligence of Things) framework. Mie scattering theory, which describes the interaction of light with spherical particles whose sizes are comparable to the wavelength, is used to study light intensity distribution as a function of scattering angle.
The system measures scattered light in the PMT mode by rotating the diffraction angle from -90° to +90°, capturing the light intensity at each step using a high-sensitivity PMT detector. This data is then analyzed in real-time using Python, enabling precise particle size estimation. In CCD mode, scattered light is captured as an image, and the intensity distribution is analyzed using ImageJ image processing techniques to extract fringe positions and calculate diffraction angles. Integrating IoT-enabled sensors allows for continuous data collection and real-time analysis, enhancing the precision and efficiency of the experiment. By comparing the results from both detection modes, students gain a deeper understanding of Mie scattering and its applications while leveraging AI techniques to optimize data analysis and interpretation.
The system measures scattered light in the PMT mode by rotating the diffraction angle from -90° to +90°, capturing the light intensity at each step using a high-sensitivity PMT detector. This data is then analyzed in real-time using Python, enabling precise particle size estimation. In CCD mode, scattered light is captured as an image, and the intensity distribution is analyzed using ImageJ image processing techniques to extract fringe positions and calculate diffraction angles. Integrating IoT-enabled sensors allows for continuous data collection and real-time analysis, enhancing the precision and efficiency of the experiment. By comparing the results from both detection modes, students gain a deeper understanding of Mie scattering and its applications while leveraging AI techniques to optimize data analysis and interpretation.
References:
References:
2.https://www.taiwaniot.com.tw/product/ambient-light-sensor-breakout-temt6000
2.https://www.taiwaniot.com.tw/product/ambient-light-sensor-breakout-temt6000
4.Inexpensive Mie scattering experiment forthe classroom manufactured by 3D printing (Christian Scholz, Achim Sack, Michael Heckel and Thorsten Pöschel, Eur. J. Phys. 37 (2016) 055305
4.Inexpensive Mie scattering experiment forthe classroom manufactured by 3D printing (Christian Scholz, Achim Sack, Michael Heckel and Thorsten Pöschel, Eur. J. Phys. 37 (2016) 055305
5.Sub-Micro Particle Matter Detection for Metal 3-D Printing Workshop (Yingying Qiao , Jifang Tao, Yao Zhang, Jifang Qiu , Xiaobin Hong , Jian Wu, and Chia-Hung Chen, IEEE SENSORS JOURNAL, VOL. 19, NO. 13, JULY 1, 2019)
5.Sub-Micro Particle Matter Detection for Metal 3-D Printing Workshop (Yingying Qiao , Jifang Tao, Yao Zhang, Jifang Qiu , Xiaobin Hong , Jian Wu, and Chia-Hung Chen, IEEE SENSORS JOURNAL, VOL. 19, NO. 13, JULY 1, 2019)
6.Tsang L, Kong J A and Ding K H 2000 Scattering of Electromagnetic Waves: Theories and Applications(NewYork:Wiley) http://inis.jinr.ru/sl/vol2/Physics/Kolebania%20i%20volni/Tsang,Kong,Ding,_Scattering_of_Electromagnetic_Waves,Numerical_Simulations,2001.pdf
6.Tsang L, Kong J A and Ding K H 2000 Scattering of Electromagnetic Waves: Theories and Applications(NewYork:Wiley) http://inis.jinr.ru/sl/vol2/Physics/Kolebania%20i%20volni/Tsang,Kong,Ding,_Scattering_of_Electromagnetic_Waves,Numerical_Simulations,2001.pdf
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