Augmented Reality, or simply AR, is the incorporation of information in digital format that includes live footage of a certain user's real-time environment. Also now, various universities are using Augmented Reality. Applying the technology in the education sector can result in having a smart campus. In line with that, this paper will discuss how Augmented Reality is being used now in different learning areas.
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Technology is progressively reshaping the domestic environment as we know it, enhancing home security and the overall ambient quality through smart connected devices. However, demographic shift and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare. Elderly monitoring is only one of the possible service applications an intelligent robotic platform can handle for collective wellbeing. In this paper, we present Marvin, a novel assistive robot we developed with a modular layer-based architecture, merging a flexible mechanical design with state-of-the-art Artificial Intelligence for perception and vocal control. With respect to previous works on robotic assistants, we propose an omnidirectional platform provided with four mecanum wheels, which enable autonomous navigation in conjunction with efficient obstacle avoidance in cluttered environments. Moreover, we design a controllable positioning device to extend the visual range of sensors and to improve the access to the user interface for telepresence and connectivity. Lightweight deep learning solutions for visual perception, person pose classification and vocal command completely run on the embedded hardware of the robot, avoiding privacy issues arising from private data collection on cloud services.
This paper presents and analyses existing taxonomies of virtual and augmented reality and demonstrates knowledge gaps and mixed terminology which may cause confusion among educators, researchers, and developers. Several such occasions of confusion are presented. A methodology is then presented to construct a taxonomy of virtual reality and augmented reality applications based on a combination of: a faceted analysis approach for the overall design of the taxonomy; an existing taxonomy of educational objectives to derive the educational purpose; an information systems analysis to establish important facets of the taxonomy; and two systematic mapping studies to identify categories within each facet. Based onUsing thisthe methodology a new taxonomy is proposed and the implications of its facets (and their combinations of facets)are demonstrated. The taxonomy focuses on technology used to provide the virtual or augmented reality as well as the content presented to the user, including the type of gamification and how it is operated. It also takes into accountaccommodates a large number of devices and approaches developed throughout the years and for multiple industries, and proposes and developsprovides a way to categorize them in order to clarify communication between researchers, developers and as well as educators. Use of the taxonomy and implications of choices made during their development is then demonstrated ion two case studies:, a virtual reality chemical plant for use in chemical engineering education and an augmented reality dog for veterinary education.
The research on differently abled persons, and their use of library is getting global attention in recent years. The field has shown a modest, continuous but wide-scale growth. This research paper aimed at capturing the dynamics of the field using various bibliometrics and text mining tools. The bibliographic data of journal articles published in the field were collected from the Web of Science (WoS) database. The records were collected form the year 1991 to 2021 and analysed to observed the trends of literature growth, core journals, institutes from where most of the literature is being published, prominent keywords and so on. The results show that there is a significant growth of publications since the year 2000. The trends shows that the research in these areas is mostly emerging from developed countries. The developing countries should also pay more attention to do research in this area because differently abled peoples need in developed countries may vary with respect to developed countries.
Ageing civil infrastructure systems require imminent attention before any failure mechanism becomes critical. Structural Health Monitoring (SHM) is employed to track inputs and/or responses of structural systems for decision support. Inspections and structural health monitoring require field visits, and subsequently expert assessment of critical elements at site, which may be both time-consuming and costly. Also, fieldwork including visits and inspections may pose danger, require personal protective equipment and structure closures during the fieldwork. To address some of these issues, a Virtual Reality (VR) collaborative application is developed to bring the structure and SHM data from the field to the office such that many experts from different places can simultaneously virtually visit the bridge structure for final assessment. In this work, we present an SHM system in a VR environment that includes the technical and visual information necessary for the engineers to make decisions for a footbridge on the campus of the University of Central Florida. In this VR application, for the visualization stage, UAV (Unmanned Air Vehicle) photogrammetry and LiDAR (Light Detection and Ranging) methods are used to capture the bridge. For the technical assessment stage, Finite Element Analysis (FEA) and Operational Modal Analysis (OMA) from vibration data as part of SHM are analyzed. To better visualize the dynamic response of the structure, the operational behaviour from the FEA is reflected on the LiDAR point cloud model for immersive. The multi-user feature allowing teams to collaborate simultaneously is essential for decision-making activities. In conclusion, the proposed VR environment offers the potential to provide beneficial features with further automated and real-time improvements along with the SHM and FEA models.
Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this paper, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects. Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.
Level of emotional arousal of one's body changes in response to external stimuli in an environment. Given the risks involved while crossing streets, particularly at unsignalized mid-block crosswalks, one can expect a change in the stress level of pedestrians. In this study, we investigate the levels and changes in pedestrian stress, under different road crossing scenarios in immersive virtual reality. To measure the stress level of pedestrians, we used Galvanic Skin Response (GSR) sensors. To collect the required data for the model, Virtual Immersive Reality Environment (VIRE) tool is used, which enables us to measure participants' stress levels in a controlled environment. The results suggested that the density of vehicles has a positive effect, meaning as the density of vehicles increases, so does the stress level for pedestrians. It was noted that younger pedestrians have a lower amount of stress when crossing as compared to older pedestrians which have higher amounts of stress. Geometric variables have an impact on the stress level of pedestrians. The greater the number of lanes the greater the observed stress, which is due to the crossing distance increasing, while the walking speed remains the same.
The rapid advancements in machine learning, graphics processing technologies and availability of medical imaging data has led to a rapid increase in use of machine learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection, and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models, in general) and possible future trends in the field.
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require that a model learns from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures, like the dependency tree of sentences and the scene graph of images, is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can represent information from its neighborhood with arbitrary depth. Although the primitive GNNs have been found difficult to train for a fixed point, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful learning with them. In recent years, systems based on graph convolutional network (GCN) and gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a detailed review over existing graph neural network models, systematically categorize the applications, and propose four open problems for future research.
A vexing problem in artificial intelligence is reasoning about events that occur in complex, changing visual stimuli such as in video analysis or game play. Inspired by a rich tradition of visual reasoning and memory in cognitive psychology and neuroscience, we developed an artificial, configurable visual question and answer dataset (COG) to parallel experiments in humans and animals. COG is much simpler than the general problem of video analysis, yet it addresses many of the problems relating to visual and logical reasoning and memory -- problems that remain challenging for modern deep learning architectures. We additionally propose a deep learning architecture that performs competitively on other diagnostic VQA datasets (i.e. CLEVR) as well as easy settings of the COG dataset. However, several settings of COG result in datasets that are progressively more challenging to learn. After training, the network can zero-shot generalize to many new tasks. Preliminary analyses of the network architectures trained on COG demonstrate that the network accomplishes the task in a manner interpretable to humans.
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