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Image-based models for defect quantification are fast and accurate but they are neither designed for real-time image processing in the field, nor do they incorporate humans in their decision-making process. Recently, researchers have integrated image-based inspection models for real-time defect quantification in Augmented Reality (AR) headsets to include human input in models’ decisions. However, deploying real-time image-based models in immersive devices is limited by their current minimal embedded processing capabilities. As a result, the model faces challenges with processing complexity timely, which limits human immersion during inspection using AR. To address this problem, this study introduces AR-ROI algorithm which integrates an automatic Region of Interest (ROI) selection method into an image-based defect quantification model and investigates the impact on processing time when deployed in an AR headset. This approach divides images into segments and initially processes all segments horizontally using the Canny algorithm until the number of positive pixels in a segment meets a threshold. The algorithm then vertically processes adjacent segments in subsequent row that both meet the threshold and are next to the segment from the previous row with the highest positive pixel count. This process continues iteratively and terminates when reaching a row without segments meeting the threshold or the final segment. Analytically, the algorithm reduces the asymptotic runtime by a factor of m’/m, where m and m’ are the pixel count in each row of an images and a segment, respectively. The results of this study are validated experimentally under various scenarios. The outcome of the experiments quantify the optimized processing time, while confirming the accuracy and analytical complexity assessment. 

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, costs, fabrication and implementation challenges often hinder engineers’ exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.