63,363 research outputs found
Dynamic Adaptive Point Cloud Streaming
High-quality point clouds have recently gained interest as an emerging form
of representing immersive 3D graphics. Unfortunately, these 3D media are bulky
and severely bandwidth intensive, which makes it difficult for streaming to
resource-limited and mobile devices. This has called researchers to propose
efficient and adaptive approaches for streaming of high-quality point clouds.
In this paper, we run a pilot study towards dynamic adaptive point cloud
streaming, and extend the concept of dynamic adaptive streaming over HTTP
(DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware
point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of
dense point cloud streaming while at the same time can semantically link to
human visual acuity to maintain high visual quality when needed. In order to
describe the various quality representations, we propose multiple thinning
approaches to spatially sub-sample point clouds in the 3D space, and design a
DASH Media Presentation Description manifest specific for point cloud
streaming. Our initial evaluations show that we can achieve significant
bandwidth and performance improvement on dense point cloud streaming with minor
negative quality impacts compared to the baseline scenario when no adaptations
is applied.Comment: 6 pages, 23rd ACM Packet Video (PV'18) Workshop, June 12--15, 2018,
Amsterdam, Netherland
Practical service placement approach for microservices architecture
Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft
Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
In this work, we address the problem of cross-modal comparison of aerial data
streams. A variety of simulated automobile trajectories are sensed using two
different modalities: full-motion video, and radio-frequency (RF) signals
received by detectors at various locations. The information represented by the
two modalities is compared using self-similarity matrices (SSMs) corresponding
to time-ordered point clouds in feature spaces of each of these data sources;
we note that these feature spaces can be of entirely different scale and
dimensionality. Several metrics for comparing SSMs are explored, including a
cutting-edge time-warping technique that can simultaneously handle local time
warping and partial matches, while also controlling for the change in geometry
between feature spaces of the two modalities. We note that this technique is
quite general, and does not depend on the choice of modalities. In this
particular setting, we demonstrate that the cross-modal distance between SSMs
corresponding to the same trajectory type is smaller than the cross-modal
distance between SSMs corresponding to distinct trajectory types, and we
formalize this observation via precision-recall metrics in experiments.
Finally, we comment on promising implications of these ideas for future
integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201
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