Artificial Neural Network Optimal Modelling of Received Signal Strength in Mobile Communications Using UAV Measurements

2018 ◽  
Author(s):  
S.K. Goudos ◽  
G. Tsoulos ◽  
G. Athanasiadou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Communications ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Artificial Neural

Artificial Neural Network Optimal Modeling and Optimization of UAV Measurements for Mobile Communications Using the L-SHADE Algorithm

2019 ◽  
Vol 67 (6) ◽  
pp. 4022-4031 ◽  
Author(s):  
Sotirios K. Goudos ◽  
George V. Tsoulos ◽  
Georgia Athanasiadou ◽  
Michael C. Batistatos ◽  
Dimitra Zarbouti ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Communications ◽  
Modeling And Optimization ◽  
Artificial Neural ◽  
Optimal Modeling

scholarly journals An improved hybrid indoor positioning system based on surface tessellation artificial neural network

2020 ◽  
pp. 002029402096424
Author(s):  
Imran Ullah Khan ◽  
Tariq Ali ◽  
Zahid Farid ◽  
Edgar Scavino ◽  
Mohd Amiruddin Abd Rahman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Local Area Network ◽  
Average Distance ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Identification Accuracy ◽  
Area Network ◽  
Artificial Neural

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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