most citedAmbient Intelligence for Next-Generation AR

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2023

SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Tim Scargill, Ying Chen, Tianyi Hu +1

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, whe…

cs.CR2023

PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information

Lin Duan, Jingwei Sun, Yiran Chen +1

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preservin…

cs.HC20233 cited

Ambient Intelligence for Next-Generation AR

Tim Scargill, Sangjun Eom, Ying Chen +1

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR ex…

eess.SY20231 cited

AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization

Ying Chen, Hazer Inaltekin, Maria Gorlatova

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most…

cs.CY20142 cited

Project-based Learning within a Large-Scale Interdisciplinary Research Effort

Robert Margolies, Maria Gorlatova, John Sarik +3

The modern engineering landscape increasingly requires a range of skills to successfully integrate complex systems. Project-based learning is used to help students build profession…