امكان سنجي اندازهگيري دماي يك جسم با استفاده از طرح پيسهاي ليزر

Precise temperature measurement is critically important across many scientific an‎d industrial fields because temperature is a fundamental parameter for process control an‎d system behavior analysis. Various methods have been developed for monitoring an‎d determining temperature, each with its own advantages an‎d limitations. Among these methods, the use of laser speckle patterns has attracted attention due to its non-contact, non-destructive nature an‎d high sensitivity.In this study, we designed an experimental setup an‎d recorded images of laser speckle patterns at different temperatures. The images were processed using statistical measures derived from the gray-level co-occurrence matrix (GLCM), an‎d the resulting metrics were analyzed. Two deep neural network models—a classifier an‎d a regression model—were also developed an‎d trained on the experimental data. The classification model achieved 100% accuracy, while the regression model produced a well-fitting curve for temperature prediction. Employing neural networks improved both accuracy an‎d processing speed, demonstrating that this approach can yield reliable an‎d rapid temperature measurements

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