Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the abil-ity of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost vol-ume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction net -work to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quanti-tative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.(c) 2021 Published by Elsevier B.V.

Abstract

The role of the digital economy is undoubtful these days, with new digital services and technologies appearing regularly, creating new products, and digitalizing others. With the technology exploring new limits, the growth tendency of this economy is clear, and more incumbents will face serious competition, which can’t be ignored. Several of these startups have untapped markets that are not being addressed, exploring that disruption leads to more innovative solutions that will likely lead to better services for people and organizations. Those startups might, though, move up the ladder and take away the market from those incumbents; with this work, we aim to analyze the role of digital economies in market disruption through the lenses of a case study and understand to what extent that case is looking at disrupt an existing market

Abstract
A linear electromagnetic energy harvesting device for underwater applications, fabricated with a simple manufacturing process, was developed to operate with movement frequencies from 0.1 to 0.4 Hz. The generator has two coils, and the effect of the combination of the two coils was investigated. The experimental study has shown that the energy capture system was able to supply energy to several ocean sensors, producing 7.77 mJ per second with wave movements at 0.4 Hz. This study shows that this energy is enough to restore the energy used by the battery or the capacitor and continue supplying energy to the sensors used in the experimental work. For an ocean wave frequency of 0.4 Hz, the generator can supply power to 8 sensors or 48 sensors, depending on the energy consumed and its optimization.

Abstract
Research into teaching and learning methodologies is intense and demonstrates the academic community's unrelenting need to understand how people learn in a continuous effort to improve efficiency in the transmission of knowledge. Teachers are dealing with a growing disengagement of students in recent decades. In part, this is due to the increase in the spread of ICT technologies outside the classroom, particularly those supporting social networking and video games. New trends, such as gamification and flipped classroom, are emerging to try to find ways to stimulate increased student engagement and motivation. In this chapter, the authors present a critical reflection and field experiences, around the potential of joint implementation of gamification with flipped classroom, demonstrating possibilities of positive increment of efficiency and effectiveness of teaching and learning processes.

Abstract
We present the status and plans for the Keck All sky Precision Adaptive optics (KAPA) program. KAPA includes (1) an upgrade to the Keck I laser guide star adaptive optics (AO) facility to improve image quality and sky coverage, (2) the inclusion of AO telemetry-based point spread function estimates with all science exposures, (3) four key science programs, and (4) an educational component focused on broadening the participation of women and underrepresented groups in instrumentation. For this conference we focus on the KAPA upgrades since the 2020 SPIE proceedings1 including implementation of a laser asterism generator, wavefront sensor, real-time controller, asterism and turbulence simulators, the laser tomography system itself along with new operations software and science tools, and modifications to an existing near-infrared tip-tilt sensor to support multiple natural guide star and focus measurements. We will also report on the results of daytime and on-sky calibrations and testing.

Abstract