Applications of eye-tracking devices aim to understand human activities and behaviors, improve human interactions with robots, and develop assistive technology in helping people with some communication disabilities. This paper proposes an algorithm to detect the pupil center and user’s gaze direction in real-time, using a low-resolution webcam and a conventional computer with no need for calibration. Given the constraints, the gaze space was reduced to five states: left, right, center, up, and eyes closed. A pre-existing landmarks detector was used to identify the user’s eyes. We employ image processing techniques to find the center of the pupil and we use the coordinates of the points found associated with mathematical calculations to classify the gaze direction. By using this method, the algorithm achieved 81.9% overall accuracy results even under variable and non-uniform environmental conditions. We also performed quantitative experiments with noise, blur, illumination, and rotation variation. Smart Eye Communicator, the proposed algorithm, can be used as eye-tracking mechanism to help people with communication difficulties to express their desires.
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC’s weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB’s ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs’ ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.
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