910 research outputs found
Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation
This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Minimal Infrastructure Radio Frequency Home Localisation Systems
The ability to track the location of a subject in their home allows the provision of a
number of location based services, such as remote activity monitoring, context sensitive
prompts and detection of safety critical situations such as falls. Such pervasive monitoring
functionality offers the potential for elders to live at home for longer periods of their lives
with minimal human supervision.
The focus of this thesis is on the investigation and development of a home roomlevel
localisation technique which can be readily deployed in a realistic home environment
with minimal hardware requirements. A conveniently deployed Bluetooth ®
localisation
platform is designed and experimentally validated throughout the thesis. The platform
adopts the convenience of a mobile phone and the processing power of a remote location
calculation computer. The use of Bluetooth ®
also ensures the extensibility of the platform
to other home health supervision scenarios such as wireless body sensor monitoring.
Central contributions of this work include the comparison of probabilistic and nonprobabilistic
classifiers for location prediction accuracy and the extension of probabilistic
classifiers to a Hidden Markov Model Bayesian filtering framework. New location
prediction performance metrics are developed and signicant performance improvements
are demonstrated with the novel extension of Hidden Markov Models to higher-order
Markov movement models. With the simple probabilistic classifiers, location is correctly
predicted 80% of the time. This increases to 86% with the application of the Hidden
Markov Models and 88% when high-order Hidden Markov Models are employed.
Further novelty is exhibited in the derivation of a real-time Hidden Markov Model
Viterbi decoding algorithm which presents all the advantages of the original algorithm,
while producing location estimates in real-time. Significant contributions are also made
to the field of human gait-recognition by applying Bayesian filtering to the task of motion
detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even
enables a
floor recognition rate of 68% using only accelerometers. The unique application
of time-varying Hidden Markov Models demonstrates the effect of integrating these freely
available motion predictions on long-term location predictions
Virtual reality as an educational tool in interior architecture
Ankara : The Department of Interior Architecture and Environmental Design and the Institute of Fine Arts of Bilkent Univ., 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references.This thesis discusses the use of virtual reality technology as an educational tool in interior architectural design. As a result of this discussion, it is proposed that virtual reality can be of use in aiding three-dimensional design and visualization, and may speed up the design process. It may also be of help in getting the designers/students more involved in their design projects. Virtual reality can enhance the capacity of designers to design in three dimensions. The virtual reality environment used in designing should be capable of aiding both the design and the presentation process. The tradeoffs of the technology, newly emerging trends and future directions in virtual reality are discussed.Aktaş, OrkunM.S
Assessment of and improvements to a stereophotogrammetric patient positioning system for proton therapy
Summary in English.Bibliography: pages 125-129.This thesis describes the construction and use of the facemask at the National Accelerator Centre (NAC) as used to both immobilise and position patients for precision proton radiotherapy. The precision achieved using the stereophotogrammetric (SPG) positioning system is measured, and the shortcomings and errors in using the facemask by the SPG system are measured and analysed. The implementation of improvements made to the SPG system is reported upon, and alternative means of both supporting the fiducial markers and immobilising the patient are investigated and evaluated. The accuracy of positioning a facemask using the SPG system is 1.4 mm and of positioning a newly designed frame is 1.6 mm. These measurements were made without using a patient. It is estimated that the total uncertainty of positioning a patient's tumour at the isocentre is 1.6 (1SD) mm using the facemask and it is estimated that the precision using the frame will be less than this value. The largest component of this error (1.39 mm) is due to the error in obtaining the CT scanner co-ordinates. These results are comparable to those obtained by other investigators. The movement of patient bony landmarks within the facemask was measured to be 1.0 ± 0.8 mm. Three main recommendations are that the CT scanner co-ordinating procedure be improved, the SPG computer program be rewritten in parts to achieve greater speed and accuracy, and that the new frame be used. The frame is easier to manufacture than the facemask and allows real time monitoring of the position of the patient's head by the SPG system thus allowing faster throughput of patients and better positioning quality control
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
Object-Aware Tracking and Mapping
Reasoning about geometric properties of digital cameras and optical physics enabled
researchers to build methods that localise cameras in 3D space from a video
stream, while – often simultaneously – constructing a model of the environment.
Related techniques have evolved substantially since the 1980s, leading to increasingly
accurate estimations. Traditionally, however, the quality of results is strongly
affected by the presence of moving objects, incomplete data, or difficult surfaces
– i.e. surfaces that are not Lambertian or lack texture. One insight of this work is
that these problems can be addressed by going beyond geometrical and optical constraints,
in favour of object level and semantic constraints. Incorporating specific
types of prior knowledge in the inference process, such as motion or shape priors,
leads to approaches with distinct advantages and disadvantages.
After introducing relevant concepts in Chapter 1 and Chapter 2, methods for building
object-centric maps in dynamic environments using motion priors are investigated
in Chapter 5. Chapter 6 addresses the same problem as Chapter 5, but presents
an approach which relies on semantic priors rather than motion cues. To fully exploit
semantic information, Chapter 7 discusses the conditioning of shape representations
on prior knowledge and the practical application to monocular, object-aware
reconstruction systems
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