133 research outputs found
The effect of tele-assistance on task performance of people undertaking a collaborative task
No full textBibliography: p. 155-161Some pages are in colour.For a collaborative task to be effective, it must allow a wide range of people to interact in a seamless manner. Collaborative virtual environments (CVEs) present ways for people to work collaboratively on a task. Research in CVEs has indicated that communication, awareness, and presence are factors that affect the task performance of people involved in a collaborative task. Although researchers have studied the effect of these three factors on performance, they have never identified the interaction strategies used by people involved in a collaborative task. Thus, the research in this thesis has taken an interest in investigating how different interaction strategies affect the task performance of people involved in a collaborative task. To undertake this investigation, the research in this thesis created a CVE that imitates the functionality of the ventral and dorsal streams of the human visual system found in the brain; as neuroscientists discovered that these two streams perform complementary functions, and yet able to interact efficiently. In particular, neuroscientists have proposed that these streams use an interaction strategy called tele-assistance. As a result, this investigation, comprised of three experiments, observed different levels of tele-assistance and tele-operation; and their effect on the task performance of people undertaking a collaborative task. Specifically, tele-assistance allowed people to perform the task using fewer steps and lesser workload than teleoperation. This suggests that tele-assistance can be used as an interaction strategy to allow people to complete a collaborative task efficiently; similar to the ventral and dorsal streams working together in a seamless manner. This finding implies that CVE designers could incorporate tele-assistance as an interaction strategy in facilitating collaboration between people
Multi-sensory integration for intuitive interaction within virtual environments
No full textBibliography: p. 78-9
Feasibility of Mapping Brain Activity to the Levels of Task Complexity within Environments of Virtual Reality
No full textMapping brain activity to certain levels of task complexity is essential for creating environments of Virtual Reality (VR), which could adapt to the mental states of human users. To investigate the feasibility of such mapping, the research work of this thesis took an approach of two steps. At first, the levels of task complexity were defined according to the geometric and appearance parameters of objects that the users interacted with for executing a task. By associating the parameters to the execution of the task, this step remedied qualitative descriptions of the levels in current state-of-the-art. Secondly, an empirical study of two experiments was conducted within a VR to collect brain activities (as brainwaves) of human participants (i.e., users) during the execution involving various task complexity. Using a device of encephalography (EEG) to collect the brainwaves, this step assessed several existing features derived from the brainwaves as potential indicators of feasibility. This thesis produced two significant findings: (1) the definition of task complexity is quantitative and could be suitable for describing object-oriented tasks, and (2) specific EEG features – such as engagement ratio – could indicate increased or decreased levels of task complexity. Hence, the work indicates the feasibility of mapping brain activity to the levels of task complexity. Future investigations are needed to refine the definition, and EEG features for optimizing cognitive engagement and performance by modulating the levels of task complexity. The outcomes of the investigations could have implications for training, simulation, and user experience in various VR-based applications
Supplemental Material, Table_S2_PH-assumption_tests_of_different_variables - High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC
No full textSupplemental Material, Table_S2_PH-assumption_tests_of_different_variables for High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC by Jie Yang, Pengzhou Kong, Jian Yang, Zhiwu Jia, Xiaoling Hu, Zianyi Wang, Heyang Cui, Yanghui Bi, Yu Qian, Hongyi Li, Fang Wang, Bin Yang, Ting Yan, Yanchun Ma, Ling Zhang, Caixia Cheng, Bin Song, Yaoping Li, Enwei Xu, Haiyan Liu, Wei Gao, Juan Wang, Yiqian Liu, Yuanfang Zhai, Lu Chang, Yi Wang, Yingchun Zhang, Ruyi Shi, Jing Liu, Qi Wang, Xiaolong Cheng, and Yongping Cui in Technology in Cancer Research & Treatment</p
Supplemental Material, Table_S1_Information_of_104_ESCC_patients - High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC
No full textSupplemental Material, Table_S1_Information_of_104_ESCC_patients for High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC by Jie Yang, Pengzhou Kong, Jian Yang, Zhiwu Jia, Xiaoling Hu, Zianyi Wang, Heyang Cui, Yanghui Bi, Yu Qian, Hongyi Li, Fang Wang, Bin Yang, Ting Yan, Yanchun Ma, Ling Zhang, Caixia Cheng, Bin Song, Yaoping Li, Enwei Xu, Haiyan Liu, Wei Gao, Juan Wang, Yiqian Liu, Yuanfang Zhai, Lu Chang, Yi Wang, Yingchun Zhang, Ruyi Shi, Jing Liu, Qi Wang, Xiaolong Cheng, and Yongping Cui in Technology in Cancer Research & Treatment</p
Approaches to Reduce Clutter and Enhance Robustness of Vortex Extraction in Flow Visualization
No full textOver the past few decades, extraction and visualization of flow features like vortices has gained tremendous importance and is employed in numerous applications. Several vortex detectors are available in literature that can identify vortices in most empirical and computational datasets. However, despite these efforts, uncertainties in empirical measurements often results in undesired vectors that cause clutter in visualization. Clutter would obscure vortex features and make it hard to understand complex flow behavior. Additionally, floating-point errors in vortex detector computations lead to false positives in vortex extraction. This thesis aims to solve aforementioned problems by implementing - a pre-processing technique to filter undesired vectors from empirical data and a threshold estimation technique to reduce the effect of floating-point errors in vortex extraction. Proposed methodologies are tested on several flow datasets of various sizes and turbulence intensities. Results indicate enhanced visualization by reducing clutter; also, they confirm improved robustness in vortex extraction
RGB Predicted Depth Simultaneous Localization and Mapping (SLAM) for Outdoor Environment
No full textThis thesis focuses on visual simultaneous localization and mapping (V-SLAM) for outdoor applications such as autonomous driving. While most V-SLAM methods have been tested on small-scale settings such as mobile robots, applying them in expansive outdoor spaces introduces additional complexities. The larger scale of the environment, dynamic obstacles, and depth-perception limitations of visual sensors pose challenges for V-SLAM methods. The first contribution introduces a dynamic V-SLAM approach. A novel front-end motion tracking approach is developed to recover multiple motions from image frames, considering key-points observed after map initialization as dynamic with time-varying locations. The proposed approach searches for key-point clusters based on their motion and classifies associated motions probabilistically. A bundle adjustment (BA) optimizes the local map, camera trajectory, and key-points motion within a unified V-SLAM system. BA maintains the geometric relationships between dynamic key-points and camera poses in the co-visibility graph, enhancing the overall robustness and accuracy of V-SLAM in populated environments. The second contribution of this thesis centers around a deep-learning-based depth prediction approach, which proves effective for estimating metric scale maps using a monocular camera. An unsupervised depth prediction approach is proposed using a novel convolution vision transformer (CViT) model architecture to infer depth from monocular images. The proposed encoder features a dual CViT block (DCViT); one block generates self-attention solely based on the spatial context of input feature vectors, and the other learns to generate attention based on the scene’s geometry. Contrastive learning of visual representations is applied to DCViT, where the model takes depth predictions from the same model through a feedback path as a supervisory signal to train the DCViT. Integration with residual blocks enables the learning of local and global receptive fields that produce predicted disparity maps at a higher level of detail and accuracy. Experimental results demonstrate significant improvements over state-of-the-art methods across multiple depth datasets. The third contribution of this thesis involves a comprehensive investigation into the utilization of predicted depth within monocular SLAM. This exploration aims to enhance the accuracy of map estimation in metric scale. Most existing approaches struggle with the non-Gaussian distribution inherent in heavy-tail noise produced by depth prediction models. The proposed monocular SLAM approach utilizes t-distribution for ego-motion, with parameter estimation achieved through maximum likelihood (ML) estimation using the expectation maximization (EM) algorithm. Experiments on real data show that the proposed t-distribution renders the monocular SLAM algorithm inherently robust to outliers and heavy-tail noise produced by depth prediction models
Towards a User-Centered Visual Analytics Platform for Collaborative Flow Pattern Analysis
No full textThe analysis of complex spatiotemporal data such as fluid flows is a non-trivial task making knowledge discovery difficult. Conventional flow analysis methods suffer from several shortcomings: (1) a lack of interpretation in terms of physical parameters (e.g. momentum); (2) restrictions on flow conditions; (3) inconsideration of interactive controls for users; (4) disregard for users’ analysis requirements while processing data; (5) a necessity of domain expertise. The objective of this thesis is a feasibility study of a Visual Analytics (VA) platform to overcome these shortcomings. The thesis has thus two Foci: Focus 1 develops novel flow analysis techniques to address the first 3 shortcomings; and Focus 2 introduces an end-to-end automated, user-centered adaptation of data processing workflows to mitigate the remaining 2 shortcomings. Preliminary evaluation and simulation outcomes indicate that both foci together set the foundation for a VA platform where multiple users of varying experience levels can collaboratively analyze flows
A Trust Model for Human-Machine Systems under Mutual Influence of Human and Machine Reliability: Virtual Reality Use-cases
No full textHuman trust in machines plays a pivotal role in performing a task effectively within human–machine systems (HMS). Prior work defined a three-layered framework for such trust, encasing all three elements of HMS – human, machine, and environment, but also indicated two deficiencies. Firstly, there is an absence of a model with metrics spanning all layers to objectively measure fluctuations of the trust (trust dynamics) in real time. Secondly, there is an inadequate consideration of human and machine reliability for evaluating task performance and the trust. Herein, the aim of this thesis is to propose and evaluate a trust model with metrics spanning all three layers to objectively measure trust dynamics. To achieve this aim, two challenges must be addressed: (1) assessing the mutual influence of human and machine reliability on task performance and (2) formulating the metrics based on objective measurement of behavioral data. By addressing these challenges, this thesis makes three contributions: (a) developing a trust model with metrics aligned with the three-layered framework and formulated to be objectively measurable; (b) enabling concurrent consideration of human and machine reliability within HMS; and (c) investigating the mutual influence of human and machine reliability on task performance and applying these findings in the model’s empirical evaluation across two virtual reality (VR) use-cases. The outcomes of the evaluation demonstrate the pertinence of the model in measuring trust dynamics while accounting for both human and machine reliability. And the model’s objective measurement was notably sensitive to trust dynamics than its subjective counterpart. The proposed model could enable designing trustworthy and adaptive HMS for applications across various domains such as autonomous vehicles, telesurgery, aviation, and immersive VR training
Wake Response to Constant Frequency Actuation of Rectangular Prisms using Synthetic Jet Actuators
No full textThis thesis investigates the wake response of rectangular prisms to synthetic jets driven at a constant frequency. The purpose of this investigation is two-fold. First, to determine the difference in the wake response of rectangular prisms with thickness-to-chord ratios in different dynamic regions. Second, to determine how perturbations of the shear layer affect the vortex shedding response. To this end, the actuator is placed at the leading edge of the obstacle. Experiments were conducted on two geometries (thin-plate: B/D = 0.3, square: B/D = 1.0) in a small-scale wind tunnel. The actuation frequency was varied across a wide range of frequencies and only the pressure response was recorded. This was done for three separate Reynolds numbers, 8000, 10000, and 12000. Cases at select actuation frequencies were chosen for particle image velocimetry experiments. These were conducted at Re = 8000 and 10000 for the thin-plate and square prism, respectively. The thin-plate showed a relatively insensitive response to actuation. The mean coefficient drag was increased with increasing actuation frequency, and there was no effect on the Strouhal number. In contrast, the square prism showed a varied response. The coefficient of drag generally decreased and varied rapidly from a local maximum to a local minimum in synchronization bands. The Strouhal number showed a rapid variation between a local minimum and a local maximum in the synchronization bands. Between these synchronization bands, the St had a step-like increase relative to the baseflow. This difference in the response was attributed to the presence of the afterbody. The characteristics of the wake response to shear layer perturbation were investigated for the square prism. The different observed flow con figurations were due to when the actuation pulse interacted with the shedding cycle. For the synchronized flow cases, the difference across the synchronization band was due to a phase difference in the actuation pulse relative to the shedding cycle. The mechanism in which the actuator influenced the shear layer was predominately via the decay of vorticity in the shear layer via cross-diffusive annihilation. Changes in the St were shown to be from a modification of the circulation density
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