35,770 research outputs found
Feasibility and performances of compressed-sensing and sparse map-making with Herschel/PACS data
The Herschel Space Observatory of ESA was launched in May 2009 and is in
operation since. From its distant orbit around L2 it needs to transmit a huge
quantity of information through a very limited bandwidth. This is especially
true for the PACS imaging camera which needs to compress its data far more than
what can be achieved with lossless compression. This is currently solved by
including lossy averaging and rounding steps on board. Recently, a new theory
called compressed-sensing emerged from the statistics community. This theory
makes use of the sparsity of natural (or astrophysical) images to optimize the
acquisition scheme of the data needed to estimate those images. Thus, it can
lead to high compression factors.
A previous article by Bobin et al. (2008) showed how the new theory could be
applied to simulated Herschel/PACS data to solve the compression requirement of
the instrument. In this article, we show that compressed-sensing theory can
indeed be successfully applied to actual Herschel/PACS data and give
significant improvements over the standard pipeline. In order to fully use the
redundancy present in the data, we perform full sky map estimation and
decompression at the same time, which cannot be done in most other compression
methods. We also demonstrate that the various artifacts affecting the data
(pink noise, glitches, whose behavior is a priori not well compatible with
compressed-sensing) can be handled as well in this new framework. Finally, we
make a comparison between the methods from the compressed-sensing scheme and
data acquired with the standard compression scheme. We discuss improvements
that can be made on ground for the creation of sky maps from the data.Comment: 11 pages, 6 figures, 5 tables, peer-reviewed articl
Robust transmission techniques for block compressed sensing
[[abstract]]Compressed sensing is famous for its compression performances
over existing schemes in this field. Conventional researches aim at reaching the
larger compression ratio at the encoder, with acceptable quality of reconstructed
images at the decoder. This implies the error-free transmission between the
encoder and the decoder. Unlike existing researches which look for
compression performances, we apply compressed sensing to digital images for
robust transmission in this paper. For transmitting compressed sensing signals
over lossy channels, error propagation would be expected, and the ways to
apply some means of protection for compressed sensing signals would be much
required for guaranteed quality of reconstructed images. We propose to transmit
compressed sensing signals over multiple independent channels for robust
transmission. By introducing the correlations between the compressed sensing
signals from different channels, induced errors from the lossy channels can be
effectively alleviated. Simulation results have presented the reconstructed
image qualities, which depict the effectiveness for the protection of compressed
sensing signals.[[notice]]補æ£å®Œ
Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks
Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs
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