1,330 research outputs found
Southern Adventist University Undergraduate Catalog 2023-2024
Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp
Breaking Virtual Barriers : Investigating Virtual Reality for Enhanced Educational Engagement
Virtual reality (VR) is an innovative technology that has regained popularity in recent years. In the field of education, VR has been introduced as a tool to enhance learning experiences. This thesis presents an exploration of how VR is used from the context of educators and learners. The research employed a mixed-methods approach, including surveying and interviewing educators, and conducting empirical studies to examine engagement, usability, and user behaviour within VR. The results revealed educators are interested in using VR for a wide range of scenarios, including thought exercises, virtual field trips, and simulations. However, they face several barriers to incorporating VR into their practice, such as cost, lack of training, and technical challenges. A subsequent study found that virtual reality can no longer be assumed to be more engaging than desktop equivalents. This empirical study showed that engagement levels were similar in both VR and non-VR environments, suggesting that the novelty effect of VR may be less pronounced than previously assumed. A study against a VR mind mapping artifact, VERITAS, demonstrated that complex interactions are possible on low-cost VR devices, making VR accessible to educators and students. The analysis of user behaviour within this VR artifact showed that quantifiable strategies emerge, contributing to the understanding of how to design for collaborative VR experiences. This thesis provides insights into how the end-users in the education space perceive and use VR. The findings suggest that while educators are interested in using VR, they face barriers to adoption. The research highlights the need to design VR experiences, with understanding of existing pedagogy, that are engaging with careful thought applied to complex interactions, particularly for collaborative experiences. This research contributes to the understanding of the potential of VR in education and provides recommendations for educators and designers to enhance learning experiences using VR
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
Human Detection And Tracking For Human-Robot Interaction On The REEM-C Humanoid Robot
The interactions between humanoid robots and humans is a growing area of research, as frameworks and models are being continuously developed to improving the ways in which humanoids may integrate into society. These humanoids often require intelligence beyond what they are originally endowed with in order to handle more complex human-robot interaction scenarios. This intelligence can come from the use of additional sensors, including microphones and cameras, which can allow the robot to better perceive its environment. This thesis explores the scenarios of moving conversational partners, and the ways in which the REEM-C Humanoid Robot may interact with them. The additional developed intelligence focuses on external microphones deployed to the robot, with a consideration for computer vision algorithms built using the camera in the REEM-C's head.
The first topic of this thesis explores how binaural acoustic intelligence can be used to estimate the direction of arrival of human speech on the REEM-C Humanoid. This includes the development of audio signal processing techniques, their optimization, and their deployment for real-time use on the REEM-C.
The second topic highlights the computer vision approaches that can be used for a robotic system that may allow better human-robot interaction. This section describes the relevant algorithms and their development, in a way that is efficient and accurate for real-time robot usage.
The third topic explores the natural behaviours of humans in conversation with moving interlocutors. This is measured via a motion capture study and modeled with mathematical formulations, which are then used on the REEM-C Humanoid Robot. The REEM-C uses this tracking model to follow detected human speakers using the intelligence outlined in previous sections.
The final topic focuses on how the acoustic intelligence, vision algorithms and tracking model can be used in tandem for human-robot interaction with potentially multiple human subjects. This includes sensor fusion approaches that help correct for limitations in the audio and video algorithms, synchronization and evaluation of behaviour in the form of a short user study. Applications of this framework are discussed, and relevant quantitative and qualitative results are presented.
A chapter to introduce the work done to establish a chatbot conversational system is also included.
The final thesis work is an amalgamation of the above topics, and presents a complete and robust human-robot interaction framework with the REEM-C based on tracking moving conversational partners with audio and video intelligence
OASIS: Optimal Arrangements for Sensing in SLAM
The number and arrangement of sensors on an autonomous mobile robot
dramatically influence its perception capabilities. Ensuring that sensors are
mounted in a manner that enables accurate detection, localization, and mapping
is essential for the success of downstream control tasks. However, when
designing a new robotic platform, researchers and practitioners alike usually
mimic standard configurations or maximize simple heuristics like field-of-view
(FOV) coverage to decide where to place exteroceptive sensors. In this work, we
conduct an information-theoretic investigation of this overlooked element of
mobile robotic perception in the context of simultaneous localization and
mapping (SLAM). We show how to formalize the sensor arrangement problem as a
form of subset selection under the E-optimality performance criterion. While
this formulation is NP-hard in general, we further show that a combination of
greedy sensor selection and fast convex relaxation-based post-hoc verification
enables the efficient recovery of certifiably optimal sensor designs in
practice. Results from synthetic experiments reveal that sensors placed with
OASIS outperform benchmarks in terms of mean squared error of visual SLAM
estimates
Northeastern Illinois University, Academic Catalog 2023-2024
https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp
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