Masonry infill walls under blast loading using confined underwater blast wave generators (WBWG)

The vulnerability of the masonry envelop under blast loading is considered critical due to the risk of loss of lives. The behaviour of masonry infill walls subjected to dynamic out-of-plane loading was experimentally investigated in this work. Using confined underwater blast wave generators (WBWG), applying the extremely high rate conversion of the explosive detonation energy into the kinetic energy of a thick water confinement, allowed a surface area distribution avoiding also the generation of high velocity fragments and reducing atmospheric sound wave. In the present study, water plastic containers, having in its centre a detonator inside a cylindrical explosive charge, were used in unreinforced masonry infills panels with 1.7m by 3.5m. Besides the usage of pressure and displacement transducers, pictures with high-speed video cameras were recorded to enable processing of the deflections and identification of failure modes. Additional numerical studies were performed in both unreinforced and reinforced walls. Bed joint reinforcement and grid reinforcement were used to strengthen the infill walls, and the results are presented and compared, allowing to obtain pressure-impulse diagrams for design of masonry infill walls.

Automatic eye blink detection using consumer web cameras

This research aims to advance blinking detection in the context of work activity. Rather than patients having to attend a clinic, blinking videos can be acquired in a work environment, and further automatically analyzed. Therefore, this paper presents a methodology to perform the automatic detection of eye blink using consumer videos acquired with low-cost web cameras. This methodology includes the detection of the face and eyes of the recorded person, and then it analyzes the low-level features of the eye region to create a quantitative vector. Finally, this vector is classified into one of the two categories considered —open and closed eyes— by using machine learning algorithms. The effectiveness of the proposed methodology was demonstrated since it provides unbiased results with classification errors under 5%