2,269 research outputs found
Enabling Self-aware Smart Buildings by Augmented Reality
Conventional HVAC control systems are usually incognizant of the physical
structures and materials of buildings. These systems merely follow pre-set HVAC
control logic based on abstract building thermal response models, which are
rough approximations to true physical models, ignoring dynamic spatial
variations in built environments. To enable more accurate and responsive HVAC
control, this paper introduces the notion of "self-aware" smart buildings, such
that buildings are able to explicitly construct physical models of themselves
(e.g., incorporating building structures and materials, and thermal flow
dynamics). The question is how to enable self-aware buildings that
automatically acquire dynamic knowledge of themselves. This paper presents a
novel approach using "augmented reality". The extensive user-environment
interactions in augmented reality not only can provide intuitive user
interfaces for building systems, but also can capture the physical structures
and possibly materials of buildings accurately to enable real-time building
simulation and control. This paper presents a building system prototype
incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy
Systems (e-Energy), 201
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Inferring Room Semantics Using Acoustic Monitoring
Having knowledge of the environmental context of the user i.e. the knowledge
of the users' indoor location and the semantics of their environment, can
facilitate the development of many of location-aware applications. In this
paper, we propose an acoustic monitoring technique that infers semantic
knowledge about an indoor space \emph{over time,} using audio recordings from
it. Our technique uses the impulse response of these spaces as well as the
ambient sounds produced in them in order to determine a semantic label for
them. As we process more recordings, we update our \emph{confidence} in the
assigned label. We evaluate our technique on a dataset of single-speaker human
speech recordings obtained in different types of rooms at three university
buildings. In our evaluation, the confidence\emph{ }for the true label
generally outstripped the confidence for all other labels and in some cases
converged to 100\% with less than 30 samples.Comment: 2017 IEEE International Workshop on Machine Learning for Signal
Processing, Sept.\ 25--28, 2017, Tokyo, Japa
Mobile AR Depth Estimation: Challenges & Prospects -- Extended Version
Metric depth estimation plays an important role in mobile augmented reality
(AR). With accurate metric depth, we can achieve more realistic user
interactions such as object placement and occlusion detection. While
specialized hardware like LiDAR demonstrates its promise, its restricted
availability, i.e., only on selected high-end mobile devices, and performance
limitations such as range and sensitivity to the environment, make it less
ideal. Monocular depth estimation, on the other hand, relies solely on mobile
cameras, which are ubiquitous, making it a promising alternative for mobile AR.
In this paper, we investigate the challenges and opportunities of achieving
accurate metric depth estimation in mobile AR. We tested four different
state-of-the-art monocular depth estimation models on a newly introduced
dataset (ARKitScenes) and identified three types of challenges: hard-ware,
data, and model related challenges. Furthermore, our research provides
promising future directions to explore and solve those challenges. These
directions include (i) using more hardware-related information from the mobile
device's camera and other available sensors, (ii) capturing high-quality data
to reflect real-world AR scenarios, and (iii) designing a model architecture to
utilize the new information
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