Gedeane Gomes da Silva Kenshima

The THz range has been under-explored for solar astronomy, mainly due to technological limitations. Only recently, a few new telescopes, such as the High Altitude Terahertz Solar (HATS) photometer, have been monitoring the solar activity in this range of the spectrum. This paper presents the experimental characterization and voltage-to-temperature calibration of the HATS acquisition system. HATS uses a Golay cell for capturing the incoming radiation, modulated by a 20 Hz fork chopper. The amplitude of the signal is then obtained at predetermined time intervals by applying a windowing function and an FFT to the signal. To characterize this acquisition system, a blackbody calibrator, with temperatures varying from 100°C to 500°C (373.15 to 648.15 K), was used as a THz source, and two signal recovery methodologies were compared: sinusoidal curve fitting and FFT combined with six windowing functions (rectangular, Hamming, Hann, Barlett, Blackman, and Flat Top). Our quantitative results demonstrate high linearity in the system’s response over the input source’s temperature range, with the Hamming window achieving the highest precision, yielding a root mean square error (RMSE) of 15.48 and a calibration temperature-to-voltage factor of 10.32±0.13 mV/K. By contrast, the Bartlett window presented the highest error (RMSE 16.02). The choice of windowing function had only a minor effect on the calibration factors, which ranged from 10.32 to 10.43 mV/K, all within the experimental uncertainties. Beyond solar photometry, the signal modulation and windowing concepts established here are highly applicable to other fields requiring high-sensitivity thermal detection, such as industrial pyrometry for high-temperature manufacturing, environmental monitoring of atmospheric water vapor, and the development of medical imaging systems based on Terahertz radiation for noninvasive tissue analysis.

The manufacture of radial tires is complex, and for this reason some defects inevitably end up appearing in the production process. Currently the commonly used defect detection is manual detection, among these techniques are ultrasound testing, thermal detection, X-ray detection, digital holography, among others. The X-ray or technical radiography allows the analysis of all the internal components of the tire, such as the bead structure and the different layers of rubber and cords, whether metallic or textile. The task of detecting defects using X-ray images is done manually, which causes loss of time and costs for the company. Furthermore, it is a subjective, inefficient, time-consuming and even biased process as it requires a high level of concentration and focus. The objective of this work is the digitization of the conventional process of analysis of quality failures in metallic radial tires using algorithms developed with Artificial Intelligence resources, capable of identifying and classifying defects present in X-Ray images. This work presents a brief introduction about what the tire is, as well as the development of the proposed algorithm, using classic image segmentation techniques with a Computer Vision library and two different convolutional neural network structures. Through the comparison, it was verified that the algorithm based on U-Net obtained more relevant results, however, the algorithm based on Mask R-CNN was also promising and could be useful in new works, with a larger database and different settings parameters. The manufacture of radial tires is complex, and for this reason some defects inevitably end up appearing in the production process. Currently the commonly used defect detection is manual detection, among these techniques are ultrasound testing, thermal detection, X-ray detection, digital holography, among others. The X-ray or technical radiography allows the analysis of all the internal components of the tire, such as the bead structure and the different layers of rubber and cords, whether metallic or textile. The task of detecting defects using X-ray images is done manually, which causes loss of time and costs for the company. Furthermore, it is a subjective, inefficient, time-consuming and even biased process as it requires a high level of concentration and focus. The objective of this work is the digitization of the conventional process of analysis of quality failures in metallic radial tires using algorithms developed with Artificial Intelligence resources, capable of identifying and classifying defects present in X-Ray images. This work presents a brief introduction about what the tire is, as well as the development of the proposed algorithm, using classic image segmentation techniques with a Computer Vision library and two different convolutional neural network structures. Through the comparison, it was verified that the algorithm based on U-Net obtained more relevant results, however, the algorithm based on Mask R-CNN was also promising and could be useful in new works, with a larger database and different settings parameters.