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

International Journal of Digital Earth 2013年第2期6卷 158-184页

作者： Hamid Mehmood nitin k.tripathi Remote Sensing&Geographic Information Systems Field of Study School of Engineering and TechnologyAsian Institute of TechnologyPathumthaniThailand

The Global Positioning System(GPS)is expected to play an integral role in the development of digital earth;however,the GPS cannot provide positioning information in regions where a majority of the population spends their time,that is,in urban and indoor ***,alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital ***-Fi-based positioning systems(WPS)stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor *** WPS-based fingerprinting is the most widely adopted technique for position determination,but its accuracy is lower than that of techniques such as time of arrival and angle of *** the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi ***,a novel server-based,genetic-algorithm-optimized,cascading artificial neural network-based positioning model is *** model is tested in 2D and 3D indoor environments under varying *** model is thoroughly investigated on a real Wi-Fi network,and its accuracy is found to be better than that of other well-known techniques.A mean accuracy of 1.9 m is achieved with 87%of the distance error within the range of 0-3 m.

关键词： indoor positioning systems GNSS Wi-Fi positioning artificial neural networks genetic algorithms

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An Artificial Neural Network-Based Snow Cover Predictive Modeling in the Higher Himalayas

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Journal of Mountain Science 2014年第4期11卷 825-837页

作者： Bhogendra MISHRA nitin k.tripathi Muk S.BABEL Remote Sensing and Geographical Information Systems Asian Institute of Technology P.O. Box 4 Klong LuangPathumthani 12120 Thailand Water Engineering and Management Asian Institute of Technology P.O. Box 4 Klong Luang Pathumthani 12120 Thailand

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.

关键词： Snow cover kaligandai river Himalayas Artificial neural network Global warming Climatechange

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Near real-time characterisation of urban environments: a holistic approach for monitoring dengue fever risk areas

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International Journal of Digital Earth 2014年第11期7卷 916-934页

作者： Muhammad Shahzad Sarfraz nitin kumar tripathi Asanobu kitamoto Department of Computer Science COMSATS Institute of Information Technology(CIIT)AbbottabadPakistan Remote Sensing and GIS Asian Institute of Technology(AIT)BangkokThailand Digital Content and Media Sciences Research Division National Institute of Informatics(NII)TokyoJapan

Despite frequent use of digital devices in everyday life,cost-effective measurement of public health issues in urban areas is still *** study was,therefore,planned to extract land-use types using object-based and spatial metric approaches to explore the dengue incidence in relation to the surrounding environment in near real-time using Google and Advanced Land Observation Satellite *** characterised image showed useful classification of an urban areawith 77%accuracy and 0.68 *** analysis on public health data indicated that most of the dengue cases were found in densely populated areas surrounded by dense *** living in independent houses having sparsely vegetated surroundings were found to be less *** incidence was more prevalent in people of 5-24 years of age(67%);while in terms of occupation,mostly students,the unemployed,labourers and farmers(88%)were *** general,males were affected slightly more(10%)than *** analyses indicated that most of the dengue cases were around institutions(40%),religious places(18%)and markets(15%).Thus,usage of Digital Earth scalable tools for monitoring health issues would open new ways for maintaining a healthy and sustainable society in the years ahead.

关键词： dengue fever land-use land-cover monitoring dengue risk urban environment object-based classification

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