Mobile geographic information systems

Model
Digital Document
Publisher
Florida Atlantic University
Description
High data rate acoustic communications become feasible with the use of communication systems that operate at high frequency. The high frequency acoustic transmission in shallow water endures severe distortion as a result of the extensive intersymbol interference and Doppler shift, caused by the time variable multipath nature of the channel. In this research a Single Input Multiple Output (SIMO) acoustic communication system is developed to improve the reliability of the high data rate communications at short range in the shallow water acoustic channel. The proposed SIMO communication system operates at very high frequency and combines spatial diversity and decision feedback equalizer in a multilevel adaptive configuration. The first configuration performs selective combining on the equalized signals from multiple receivers and generates quality feedback parameter for the next level of combining.
Model
Digital Document
Publisher
Florida Atlantic University
Description
Nowadays the widespread availability of wireless networks has created an interest
in using them for other purposes, such as localization of mobile devices in indoor
environments because of the lack of GPS signal reception indoors. Indoor localization
has received great interest recently for the many context-aware applications it could make possible. We designed and implemented an indoor localization platform for Wi-Fi nodes (such as smartphones and laptops) that identifies the building name, floor number, and room number where the user is located based on a Wi-Fi access point signal fingerprint pattern matching. We designed and evaluated a new machine learning algorithm, KRedpin, and developed a new web-services architecture for indoor localization based on J2EE technology with the Apache Tomcat web server for managing Wi-Fi signal data from the FAU WLAN. The prototype localization client application runs on Android cellphones and operates in the East Engineering building at FAU. More sophisticated classifiers have also been used to improve the localization accuracy using the Weka data mining tool.