4,121 research outputs found
Information Centric Networking in the IoT: Experiments with NDN in the Wild
This paper explores the feasibility, advantages, and challenges of an
ICN-based approach in the Internet of Things. We report on the first NDN
experiments in a life-size IoT deployment, spread over tens of rooms on several
floors of a building. Based on the insights gained with these experiments, the
paper analyses the shortcomings of CCN applied to IoT. Several interoperable
CCN enhancements are then proposed and evaluated. We significantly decreased
control traffic (i.e., interest messages) and leverage data path and caching to
match IoT requirements in terms of energy and bandwidth constraints. Our
optimizations increase content availability in case of IoT nodes with
intermittent activity. This paper also provides the first experimental
comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.Comment: 10 pages, 10 figures and tables, ACM ICN-2014 conferenc
A Channel Coding Benchmark for Meta-Learning
Meta-learning provides a popular and effective family of methods for
data-efficient learning of new tasks. However, several important issues in
meta-learning have proven hard to study thus far. For example, performance
degrades in real-world settings where meta-learners must learn from a wide and
potentially multi-modal distribution of training tasks; and when distribution
shift exists between meta-train and meta-test task distributions. These issues
are typically hard to study since the shape of task distributions, and shift
between them are not straightforward to measure or control in standard
benchmarks. We propose the channel coding problem as a benchmark for
meta-learning. Channel coding is an important practical application where task
distributions naturally arise, and fast adaptation to new tasks is practically
valuable. We use our MetaCC benchmark to study several aspects of
meta-learning, including the impact of task distribution breadth and shift,
which can be controlled in the coding problem. Going forward, MetaCC provides a
tool for the community to study the capabilities and limitations of
meta-learning, and to drive research on practically robust and effective
meta-learners
OctNetFusion: Learning Depth Fusion from Data
In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and provides great results, it is not able to reconstruct (partially)
occluded surfaces and requires a large number frames to filter out sensor noise
and outliers. Motivated by the availability of large 3D model repositories and
recent advances in deep learning, we present a novel 3D CNN architecture that
learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric
fusion approach in terms of noise reduction and outlier suppression. By
learning the structure of real world 3D objects and scenes, our approach is
further able to reconstruct occluded regions and to fill in gaps in the
reconstruction. We demonstrate that our learning based approach outperforms
both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric
fusion. Further, we demonstrate state-of-the-art 3D shape completion results.Comment: 3DV 2017, https://github.com/griegler/octnetfusio
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