A field study on the fusion of terrestrial and satellite location methods in urban cellular networks

Barcelo-Arroyo, Francisco; Martín-Escalona, Israel; Manente, Cyrille

doi:10.1002/ett.1433

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Due to the high variability in the composition of the terrain and the voluminous amount of terrain categories, optimal identification with labeling for terrain types in very high resolution (V.H.R.) Data have become challenging to solve using machine learning methods 8‐10 . This challenge has ignited interest in the domain of remote sensing 6,11 .…”

Section: Introductionmentioning

confidence: 99%

Multilabel classification of remote sensed satellite imagery

Kumar

Abhishek

Singh

et al. 2020

Trans Emerging Tel Tech

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Multilabel scene classification has emerged as a critical research area in the domain of remote sensing. Contemporary classification models primarily emphasis on a single object or multiobject scene classification of satellite remote sensed images. These classification models rely on feature engineering from images, deep learning, or transfer learning. Comparatively, multilabel scene classification of very high resolution (V.H.R.) images is a fairly unexplored domain of research. Models trained for single label scene classification are unsuitable for the application of recognizing multiple objects in a single remotely sensed V.H.R. satellite image. To overcome this research gap, the current inquiry proposes to fine‐tune the state of the art convolutional neural network (C.N.N.) architectures for multilabel scene classification. The proposed approach pre trains C.N.N on the ImageNet dataset and further fine‐tunes them to the task of detecting multiple objects in V.H.R. images. To understand the efficacy of this approach, the final models are applied on a V.H.R. dataset: the U.C.M.E.R.C.E.D. image dataset containing 21 different terrestrial land use categories with a submeter resolution. The performance of the final models is compared with graph convolutional network‐based model by Khan et al. From the results on performance metrics, it was observed that proposed models achieve comparable results in significantly fewer epochs.

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Section: Introductionmentioning

confidence: 99%

Multilabel classification of remote sensed satellite imagery

Kumar

Abhishek

Singh

et al. 2020

Trans Emerging Tel Tech

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“…The purpose of all these is to put network resource to its best use and optimise network performance. In recent years, this task has become more challenging because of heterogeneous traffic that often has highly varying or even unknown characteristics [1][2][3][4][5][6][7][8]. As a result, channel availability can change dynamically over time owing to local congestion [9].…”

Section: Introductionmentioning

confidence: 99%

3G network traffic sources measurement and analysis

Goleva

Atamian

Mirtchev

et al. 2013

Trans. Emerging Tel. Tech.

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In this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows.

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“…Moreover, fusion of hybrid LDPs may compensate the imprecision of some measurements by another more precise measurements and thus enhance localisation accuracy offered by the system. Nevertheless, hybrid techniques must be smart enough to fuse different LDPs and enhance localisation accuracy .…”

Section: Introductionmentioning

confidence: 99%