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

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.

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Channel Path-Loss Measurement and Modeling in Wireless Data Network (IEEE 802.11n) Using Artificial Neural Network

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.1.157 ◽

2020 ◽

Vol 4 (1) ◽

Author(s):

Olasoji Y. Olajide ◽

Samson Musa Yerima

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wireless Local Area Network ◽

Local Area Network ◽

Network Planning ◽

Path Loss ◽

Local Area ◽

Line Of Sight ◽

Area Network ◽

Artificial Neural

Careful network planning has become increasingly critical with the rising deployment, coverage, and congestion of wireless local area networks (WLANs).This paper investigates and determine the Path-loss exponent value for the ubiquitous wireless local area network at the Federal University Oye-Ekiti for the line of sight and non-line of sight (N-LOS). Aside this, the paper also models the wireless network using artificial neural network (ANN) technology by training some neurons based on data collected from a drive-test. The proposed ANN model performed with accuracy and is offered as a simple, yet strong predictive model for network planning – having both speed and accuracy. Results show, that for the area under study, Oye Campus has a higher standard deviation of 5.76dBm as against ikole Campus with 1.44dBm, this is because of dense vegetation at Oye Campus. In view of this, the paper provides a predictive site survey for rapid wireless Access point deployment.

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Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

Multidisciplinary Aspects of Production Engineering ◽

10.2478/mape-2018-0026 ◽

2018 ◽

Vol 1 (1) ◽

pp. 197-204

Author(s):

Tomasz Cepowski

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Multiple Regression ◽

Nonlinear Models ◽

Linear Equations ◽

Regression Method ◽

Wave Resistance ◽

Identification Accuracy ◽

Linear Regression Method ◽

Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

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Fault location estimation for VSC-HVDC system using Artificial Neural Network

2016 IEEE Region 10 Conference (TENCON) ◽

10.1109/tencon.2016.7848050 ◽

2016 ◽

Cited By ~ 3

Author(s):

Somasundaram Vasanth ◽

Yew Ming Yeap ◽

Abhisek Ukil

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Location ◽

Location Estimation ◽

Hvdc System ◽

Artificial Neural

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Optimizing artificial neural network-based indoor positioning system using genetic algorithm

International Journal of Digital Earth ◽

10.1080/17538947.2011.606337 ◽

2013 ◽

Vol 6 (2) ◽

pp. 158-184 ◽

Cited By ~ 8

Author(s):

Hamid Mehmood ◽

Nitin K. Tripathi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Indoor Positioning ◽

Positioning System ◽

Artificial Neural ◽

Indoor Positioning System

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SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

CommIT (Communication and Information Technology) Journal ◽

10.21512/commit.v2i1.482 ◽

2008 ◽

Vol 2 (1) ◽

pp. 1

Author(s):

Anny Tandyo ◽

Martono Martono ◽

Adi Widyatmoko

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Speaker Recognition ◽

Speaker Identification ◽

Back Propagation ◽

Identification Accuracy ◽

Identification System ◽

Accuracy Rate ◽

Discrete Transformation ◽

Artificial Neural

Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.

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