422 research outputs found
Online Incremental Machine Translation
In this thesis we investigate the automatic improvements of statistical machine translation systems at runtime based on user feedback. We also propose a framework to use the proposed algorithms in large scale translation settings
A novel weighted fusion based efficient clustering for improved wi-fi fingerprint indoor positioning
Information reuse in dynamic spectrum access
Dynamic spectrum access (DSA), where the permission to use slices of radio spectrum is dynamically shifted (in time an in different geographical areas) across various communications services and applications, has been an area of interest from technical and public policy perspectives over the last decade. The underlying belief is that this will increase spectrum utilization, especially since many spectrum bands are relatively unused, ultimately leading to the creation of new and innovative services that exploit the increase in spectrum availability. Determining whether a slice of spectrum, allocated or licensed to a primary user, is available for use by a secondary user at a certain time and in a certain geographic area is a challenging task. This requires 'context information' which is critical to the operation of DSA. Such context information can be obtained in several ways, with different costs, and different quality/usefulness of the information. In this paper, we describe the challenges in obtaining this context information, the potential for the integration of various sources of context information, and the potential for reuse of such information for related and unrelated purposes such as localization and enforcement of spectrum sharing. Since some of the infrastructure for obtaining finegrained context information is likely to be expensive, the reuse of this infrastructure/information and integration of information from less expensive sources are likely to be essential for the economical and technological viability of DSA. © 2013 IEEE
Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles
Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly reduces the efforts for collecting WiFi fingerprints. Furthermore, since these NAVs explore a 3D space, the WiFi fingerprints of a 3D space can be obtained increasing the localization accuracy. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi - fingerprint - based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with the manual WiFi fingerprint survey methods
Adaptive indoor positioning system based on locating globally deployed WiFi signal sources
Recent trends in data driven applications have encouraged expanding
location awareness to indoors. Various attributes driven by location data
indoors require large scale deployment that could expand beyond specific
venue to a city, country or even global coverage. Social media, assets or
personnel tracking, marketing or advertising are examples of applications
that heavily utilise location attributes. Various solutions suggest
triangulation between WiFi access points to obtain location attribution
indoors imitating the GPS accurate estimation through satellites
constellations. However, locating signal sources deep indoors introduces
various challenges that cannot be addressed via the traditional war-driving
or war-walking methods.
This research sets out to address the problem of locating WiFi signal
sources deep indoors in unsupervised deployment, without previous
training or calibration. To achieve this, we developed a grid approach to
mitigate for none line of site (NLoS) conditions by clustering signal readings
into multi-hypothesis Gaussians distributions. We have also employed
hypothesis testing classification to estimate signal attenuation through
unknown layouts to remove dependencies on indoor maps availability.
Furthermore, we introduced novel methods for locating signal sources
deep indoors and presented the concept of WiFi access point (WAP)
temporal profiles as an adaptive radio-map with global coverage.
Nevertheless, the primary contribution of this research appears in
utilisation of data streaming, creation and maintenance of self-organising
networks of WAPs through an adaptive deployment of mass-spring
relaxation algorithm. In addition, complementary database utilisation
components such as error estimation, position estimation and expanding to
3D have been discussed. To justify the outcome of this research, we
present results for testing the proposed system on large scale dataset
covering various indoor environments in different parts of the world.
Finally, we propose scalable indoor positioning system based on received
signal strength (RSSI) measurements of WiFi access points to resolve the
indoor positioning challenge. To enable the adoption of the proposed
solution to global scale, we deployed a piece of software on multitude of
smartphone devices to collect data occasionally without the context of
venue, environment or custom hardware. To conclude, this thesis provides
learning for novel adaptive crowd-sourcing system that automatically deals
with tolerance of imprecise data when locating signal sources
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