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Title: Illumination modelling of a mobile device environment for effective use in driving mobile apps
Authors: Marhoubi, Asmaa H.
Saravi, Sara
Edirisinghe, Eran A.
Bez, Helmut E.
Keywords: Ambient light sensor
Mobile handheld devices
Illumination modelling
Regression analysis
Machine learning
Issue Date: 2015
Publisher: © SPIE
Citation: MARHOUBI. A.H. ... et al, 2015. Illumination modelling of a mobile device environment for effective use in driving mobile apps. Proceedings of SPIE 9481, Image Sensing Technologies: Materials, Devices, Systems, and Applications II, 94810R.
Abstract: The present generation of Ambient Light Sensors (ALS) of a mobile handheld device suffer from two practical shortcomings. The ALSs are narrow angle, i.e. they respond effectively only within a narrow angle of operation and there is a latency of operation. As a result mobile applications that operate based on the ALS readings could perform sub-optimally especially when operated in environments with non-uniform illumination. The applications will either adopt with unacceptable levels of latency or/and may demonstrate a discrete nature of operation. In this paper we propose a framework to predict the ambient illumination of an environment in which a mobile device is present. The predictions are based on an illumination model that is developed based on a small number of readings taken during an application calibration stage. We use a machine learning based approach in developing the models. Five different regression models were developed, implemented and compared based on Polynomial, Gaussian, Sum of Sine, Fourier and Smoothing Spline functions. Approaches to remove noisy data, missing values and outliers were used prior to the modelling stage to remove their negative effects on modelling. The prediction accuracy for all models were found to be above 0.99 when measured using R-Squared test with the best performance being from Smoothing Spline. In this paper we will discuss mathematical complexity of each model and investigate how to make compromises in finding the best model
Description: Copyright 2015 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This item is also available here: http://dx.doi.org/10.1117/12.2177087
Version: Accepted for publication
DOI: 10.1117/12.2177087
URI: https://dspace.lboro.ac.uk/2134/21953
Publisher Link: http://dx.doi.org/10.1117/12.2177087
ISSN: 0277-786X
Appears in Collections:Conference Papers and Contributions (Mechanical, Electrical and Manufacturing Engineering)

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