Rainfall Estimation Based on the Intensity of the Received Signal in a LTE/4G Mobile Terminal by Using a Probabilistic Neural Network

Beritelli, Francesco; Capizzi, Giacomo; Sciuto, Grazia Lo; Napoli, Christian; Scaglione, Francesco

doi:10.1109/access.2018.2839699

Cited by 48 publications

(37 citation statements)

References 21 publications

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“…To estimate the attenuation in a communication system, it is necessary to take into account not only the attenuation due to the propagation medium but also all components of which the system is composed [8,9]. In fact, the two mechanisms that can negatively interfere with the frequency spectrum utilization concern the signal attenuation and cross polarization.…”

Section: Methodsmentioning

confidence: 99%

Air pollution effects on the intensity of received signal in 3G/4G mobile terminal

Sciuto

2019

Int J Energy Environ Eng

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In this paper, the existence of the interaction between the atmospheric particulate and the electromagnetic waves attenuation in the frequency band used by the 3G/4G mobile network was investigated. A few papers present in literature indicate, in presence of air pollution, the possibility of a significant cross polarization of the electromagnetic waves, while none of the papers indicated the possibility of a significant attenuation. This paper has tried to partially fill this gap of knowledge in this field with some new contributions. In all the experiments performed at the department of electrical, electronics and informatics engineering in the University of Catania (87 experiments each one lasting about 36 min), significant variations have not been found in the received signal level (RSL), while a polynomial dependency of the RSL local variance from the particulate concentration has been found. To demonstrate that the results obtained in the laboratory have general validity, a campaign test was conducted in an outdoors environment in the frequency band used by the 3G/4G mobile network. The site of the considered measurement station is selected to represent a typical residential area with high traffic influences in the city of Catania. Also, in this measures campaign conducted in the outdoors environment in the 3G/4G frequency band, significant variations have not been found in the RSL. While a marked dependence of the RSL local variance from the PM 10 concentration has been found. This is a noteworthy result, because certain non-negligible RSL local variance variations indicate the possibility of a pronounced cross polarization. The impact of the cross polarization becomes significant for systems reusing frequency, because the cross polarization counteracts the optimum frequency spectrum utilization.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Methodsmentioning

confidence: 99%

Air pollution effects on the intensity of received signal in 3G/4G mobile terminal

Sciuto

2019

Int J Energy Environ Eng

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“…Francesco Beritelli et al [32] proposed a new classification method applied to classify the precipitation into four rainfall intensities, which is based on a probabilistic neural network with three received signal level local features of the 4G/LTE. The performance of their model obtained an overall correct classification rate of 96.7%.…”

Section: Related Workmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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“…Many studies have been carried out regarding the classification of rainfall levels using alternative methods, parameters, and signals such as video, audio, and radio signals [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

Avanzato

Beritelli

2020

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An accurate estimate of rainfall levels is fundamental in numerous application scenarios: weather forecasting, climate models, design of hydraulic structures, precision agriculture, etc. An accurate estimate becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for estimating rainfall levels present numerous critical issues. The algorithm applies the Convolution Neural Network (CNN) directly to the audio signal, using 3 s sliding windows with an offset of only 100 milliseconds. Therefore, by using low cost and low power hardware, the proposed algorithm allows implementing critical high rainfall event alerting mechanisms with short response times and low estimation errors. More specifically, this paper proposes a new approach to rainfall estimation based on the classification of different acoustic timbres that rain produces at different intensities and on CNN. The results obtained on seven classes ranging from “No rain” to “Cloudburst” indicate an average accuracy of 75%, which rises to 93% if the misclassifications of the adjacent classes are not considered. Some application contexts concern smart cities for which the integration of an audio sensor inside the luminaire of a street lamp is foreseen, precision agriculture, as well as highway safety, by minimizing the risks of aquaplaning.

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Rainfall Estimation Based on the Intensity of the Received Signal in a LTE/4G Mobile Terminal by Using a Probabilistic Neural Network

Cited by 48 publications

References 21 publications

Air pollution effects on the intensity of received signal in 3G/4G mobile terminal

Air pollution effects on the intensity of received signal in 3G/4G mobile terminal

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

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