Development of a Solution for OLED Display Smartphones for Pilot Training in Low-Visibility Flight Scenarios

Visual illusions and spatial disorientation are common causes of air accidents and incidents, especially during low-visibility flight conditions in small aircraft. It is therefore essential that pilots receive training regarding adaptation of the visual and vestibular systems to the aerospace environment. This project aimed to develop a device capable of simulating different visual illusions and aspects related to central and peripheral vision (colour and visual acuity), through the use of smartphones with OLED display technology (model: Galaxy S5 SM-G900M, screen: 5.1 inches, 1080 x 1920 pixels resolution, 432 pixels per inch). The phone was coupled with augmented reality glasses (model: ColorCross 3D Virtual Reality) with a 70 mm focal length lens, supporting devices of 4 to 6 inches. The smartphone is attached to the front of the glasses, giving an impression of three-dimensionality, and the visual tests are either transmitted from a computer or saved on the device itself. The images and videos selected, such as the Farnsworth-Munsell 100 Hue Test and Cambridge Colour Test, are commonly used in pilot training and are validated for use in clinical ophthalmology. Technical adaptations were necessary so these tests functioned adequately on the smartphone. Both tests are designed exclusively for use on a computing platform and, therefore, the Trinus VR application was first used to convert the computer image to 3D, before making it available on the smartphone screen. This solution for training pilots in visual illusions during low-visibility flight scenarios using smartphones with OLED displays is easy to implement, user-friendly and low-cost. Mobile technology adaptation for use in aviation training is of great value, as it can have a positive influence on the reduction of human errors that can result from alterations in human physiology secondary to exposure to the aerospace environment, thereby reducing the occurrence of air accidents and incidents

ATLANTIC-PRIMATES: a dataset of communities and occurrences of primates in the Atlantic Forests of South America

Primates play an important role in ecosystem functioning and offer critical insights into human evolution, biology, behavior, and emerging infectious diseases. There are 26 primate species in the Atlantic Forests of South America, 19 of them endemic. We compiled a dataset of 5,472 georeferenced locations of 26 native and 1 introduced primate species, as hybrids in the genera Callithrix and Alouatta. The dataset includes 700 primate communities, 8,121 single species occurrences and 714 estimates of primate population sizes, covering most natural forest types of the tropical and subtropical Atlantic Forest of Brazil, Paraguay and Argentina and some other biomes. On average, primate communities of the Atlantic Forest harbor 2 ± 1 species (range = 1–6). However, about 40% of primate communities contain only one species. Alouatta guariba (N = 2,188 records) and Sapajus nigritus (N = 1,127) were the species with the most records. Callicebus barbarabrownae (N = 35), Leontopithecus caissara (N = 38), and Sapajus libidinosus (N = 41) were the species with the least records. Recorded primate densities varied from 0.004 individuals/km 2 (Alouatta guariba at Fragmento do Bugre, Paraná, Brazil) to 400 individuals/km 2 (Alouatta caraya in Santiago, Rio Grande do Sul, Brazil). Our dataset reflects disparity between the numerous primate census conducted in the Atlantic Forest, in contrast to the scarcity of estimates of population sizes and densities. With these data, researchers can develop different macroecological and regional level studies, focusing on communities, populations, species co-occurrence and distribution patterns. Moreover, the data can also be used to assess the consequences of fragmentation, defaunation, and disease outbreaks on different ecological processes, such as trophic cascades, species invasion or extinction, and community dynamics. There are no copyright restrictions. Please cite this Data Paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data. © 2018 by the The Authors. Ecology © 2018 The Ecological Society of Americ

Development of a skateboarding trick classifier using accelerometry and machine learning

<div><p>Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU) use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN). Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE). The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation). Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.</p></div