Abstract
The popularity of deep learning methods has increased significantly, in no small part due to their impressive performance in several application scenarios. This paper focuses on recognising activities in an in-vehicle environment and measuring the impact that factors such as resolution, aspect ratio, field of view and framerate have on the performance of the model. The use of deep learning methodologies in recent years has increased the amount of data required to train and test the models. However, such data is often insufficient, unavailable, or lacks suitable properties. Publicly available action recognition datasets have been analysed, collected, and prepared to assess the classification results in such scenarios, which provides important guidance for use in a real-world setting. © 2021 IEEE.

Abstract

Abstract

Abstract
We propose supporting hybrid conference experiences using the Augmented Reality Edge Network Architecture (ARENA). ARENA is a platform based on web technologies that simplifies the creation of collaborative mixed reality for standard Web Browsers (Chrome, Firefox) in VR, Headset AR/VR Browsers (Magic Leap, Hololens, Oculus Quest 2), and mobile AR (WebXR Viewer for iOS, Chrome with experimental flags for Android, and our own custom WebXR fork for iOS). We use a 3D scan of the conference venue as the backdrop environment for remote users and a model to stage various AR interactions for in-person users. Remote participants can use VR in a browser or a VR headset to navigate the scene. In-person participants can use AR headsets or mobile AR through WebXR browsers to see and hear remote users. ARENA can scale up to hundreds of users in the same scene and provides audio and video with spatial sound that can more closely capture real-world interactions.

Abstract
Practice is of the essence in Engineering courses. A relevant question in control engineering education is: How to close the gap between theory and practice? Once subjects are introduced in theoretical classes, students want to know about its practical use. Thus, it is important to introduce theoretical control concepts with practical experiments, enabling students to easily test and validate the theory. An Arduino based temperature control laboratory (TCLab) is deployed in this study as a portable kit providing students with a simple and effective means to test some feedback control techniques. Teaching/learning experiments are proposed involving proportional, integral and derivative controllers with Feedforward and Cascade control structures. Preliminary results achieved in a Portuguese university are presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
It is fundamental to diagnose fetal acidemia as early as possible, allowing adequate obstetrical interventions to prevent brain damage or perinatal death. The visual analysis of cardiotocography traces has been complemented by computerized methods in order to overcome some of its limitations in the screening of fetal hypoxia/acidemia. Spectral analysis has been proposed by several studies exploring fetal heart rate recordings while referring to a great variety of frequency bands for integrating the power spectrum. In this paper, the main goal was to systematically review the spectral bands reported in intrapartum fetal heart rate studies and to evaluate their performance in detecting fetal acidemia/hypoxia. A total of 176 articles were reviewed, from MEDLINE, and 26 were included for the extraction of frequency bands and other relevant methodological information. An open-access fetal heart rate database was used, with recordings of the last half an hour of labor of 246 fetuses. Four different umbilical artery pH cutoffs were considered for fetuses' classification into acidemic or non-acidemic: 7.05, 7.10, 7.15, and 7.20. The area under the receiver operating characteristic curve (AUROC) was used to quantify the frequency bands' ability to distinguish acidemic fetuses. Bands referring to low frequencies, mainly associated with neural sympathetic activity, were the best at detecting acidemic fetuses, with the more severe definition (pH <= 7.05) attaining the highest values for the AUROC. This study shows that the power spectrum analysis of the fetal heart rate is a simple and powerful tool that may become an adjunctive method to CTG, helping healthcare professionals to accurately identify fetuses at risk of intrapartum hypoxia and to implement timely obstetrical interventions to reduce the incidence of related adverse perinatal outcomes.