43,392 research outputs found
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
A Multiscale Pyramid Transform for Graph Signals
Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.Comment: 16 pages, 13 figure
Airborne LiDAR for DEM generation: some critical issues
Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution.Comment: Final publication available at ACS Nano Letter
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
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