As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

As the demand of indoor Location-Based Services (LBSs) increases, there is a growing interest in developing an accurate indoor positioning and tracking system on mobile devices. The core location determination problem can be reformulated as a sparse natured problem and thus can be solved by applying the Compressive Sensing (CS) theory. This thesis proposes a compact received signal strength (RSS) based real-time indoor positioning and tracking systems using CS theory that can be implemented on personal digital assistants (PDAs) and smartphones, which are both limited in processing power and memory compared to laptops. The proposed tracking system, together with a simple navigation module is implemented on Windows Mobile-operated smart devices and their performance in different experimental sites are evaluated. Experimental results show that the proposed system is a lightweight real-time algorithm that performs better than other traditional fingerprinting methods in terms of accuracy under constraints of limited processing and memory resources.

Humans are exposed to persistent organic pollutants (POPs) such as brominated flame retardants (BFRs, specifically polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane (HBCD)) and perfluoroalkyl acids (PFAAs, specifically perfluoroalkane sulfonate (PFOS) and perfluorooctanoic acid (PFOA)). They are used in consumer products found in cars, offices, homes and day care centers. Diet was earlier thought to be a major human exposure route for legacy POPs, but does not account for body burdens found for many new POPs and indoor exposure from air and dust has been hypothesized as also important. In this thesis, BFRs in air and dust, and PFAAs in dust from different indoor microenvironments in Sweden were analysed, and the results used to estimate human exposure. BFRs and PFAAs were detected in dust from all microenvironments and PBDEs in all air samples. BFR and PFAA exposure occurs mostly in peoples’ homes with toddlers having higher intakes from dust ingestion than adults. Inhalation and dust ingestion play minor roles compared to diet for humans with median exposures, but in worst case scenarios, dust ingestion may be significant for a small part of the Swedish population. Sampling using home vacuum cleaner bag dust and researcher-collected above floor dust was compared. Correlations were seen for ∑OctaBDE and ∑DecaBDE but not for ∑PentaBDE and HBCD. Higher PBDE concentrations were found in above floor dust but higher HBCD concentrations were found in vacuum cleaner bag dust. BDE-47 concentrations were correlated between vacuum cleaner bag dust and breast milk, indicating exposure through dust ingestion. Similar concentrations of PBDEs were measured in indoor and outgoing air from day care centers, apartment and office buildings. Indoor air explained 54-92% of ∑PentaBDE and 24-86% of BDE-209 total emissions to outdoor air in Sweden, supporting the hypothesis that the indoor environment is polluting ambient air via ventilation systems. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Accepted. Paper 3: Accepted. Paper 4: Manuscript.</p>

The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability. / Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.

Thesis (D.P.H.)--University of Michigan.

Thesis (D.P.H.)--University of Michigan.

Thesis (M. Sc.)--University of Hong Kong, 1997. / Includes bibliographical references.

Thesis (M. Sc.)--University of Hong Kong, 1997. / Cover title. Includes bibliographical references (leaf 138-139).

Thesis (M. Sc.)--University of Hong Kong, 1993.

Thesis (M. Sc.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf [44-52]).