8,516 research outputs found
Compressing web Geodata for real-time environmental applications
The advent of connected mobile devices has caused an unprecedented availability of geo-referenced user-generated content, which can be exploited for environment monitoring. In particular, Augmented Reality (AR) mobile applications can be designed to enable citizens collect observations, by overlaying relevant meta-data on their current view. This class of applications rely on multiple meta-data, which must be properly compressed for transmission and real-time usage. This paper presents a two-stage approach for the compression of Digital Elevation Model (DEM) data and geographic entities for a mountain environment monitoring mobile AR application. The proposed method is generic and could be applied to other types of geographical data
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
Observations of an extreme storm in interplanetary space caused by successive coronal mass ejections
Space weather refers to dynamic conditions on the Sun and in the space
environment of the Earth, which are often driven by solar eruptions and their
subsequent interplanetary disturbances. It has been unclear how an extreme
space weather storm forms and how severe it can be. Here we report and
investigate an extreme event with multi-point remote-sensing and in-situ
observations. The formation of the extreme storm showed striking novel
features. We suggest that the in-transit interaction between two closely
launched coronal mass ejections resulted in the extreme enhancement of the
ejecta magnetic field observed near 1 AU at STEREO A. The fast transit to
STEREO A (in only 18.6 hours), or the unusually weak deceleration of the event,
was caused by the preconditioning of the upstream solar wind by an earlier
solar eruption. These results provide a new view crucial to solar physics and
space weather as to how an extreme space weather event can arise from a
combination of solar eruptions.Comment: 23 pages, 7 figure
Efficient Data Compression with Error Bound Guarantee in Wireless Sensor Networks
We present a data compression and dimensionality reduction scheme for data
fusion and aggregation applications to prevent data congestion and reduce
energy consumption at network connecting points such as cluster heads and
gateways. Our in-network approach can be easily tuned to analyze the data
temporal or spatial correlation using an unsupervised neural network scheme,
namely the autoencoders. In particular, our algorithm extracts intrinsic data
features from previously collected historical samples to transform the raw data
into a low dimensional representation. Moreover, the proposed framework
provides an error bound guarantee mechanism. We evaluate the proposed solution
using real-world data sets and compare it with traditional methods for temporal
and spatial data compression. The experimental validation reveals that our
approach outperforms several existing wireless sensor network's data
compression methods in terms of compression efficiency and signal
reconstruction.Comment: ACM MSWiM 201
Polypiectropus species (Trichoptera: Polycentropodidae) from China : with consideration of their phylogeny
Six species of the genus Polyplectropns are recorded from the People's Republic of China. All the species are new to science. A key to the males is given. The larva of Polyplectropns nanjingensis sp. nov. is illustrated. The phylogenetic relationships among these species and with Polyplectropns species of the New World are discussed
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