Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). "January 2007." Includes bibliographical references (p. [209]-223). Also available via the World Wide Web.

Automated driving can help solve the current and future problems of individualtransportation. Automated valet parking is a possible approach to help with overcrowded parking areas in cities and make electric vehicles more appealing. In an automated valet system, drivers are able to drop off their vehicle close to a parking area. The vehicle drives to a free parking spot on its own, while the driver is free to perform other tasks — such as switching the mode of transportation. Such a system requires the automated car to navigate unstructured, possibly three dimensional areas. This goes beyond the scope ofthe tasks performed in the state of the art for automated driving. This thesis describes a visual localization system that provides accuratemetric pose estimates. As sensors, the described system uses multiple monocular cameras and wheel-tick odometry. This is a sensor set-up that is close to what can be found in current production cars. Metric pose estimates with errors in the order of tens of centimeters enable maneuvers such as parking into tight parking spots. This system forms the basis for automated navigationin the EU-funded V-Charge project. Furthermore, we present an approach to the challenging problem of life-long mapping and localization. Over long time spans, the visual appearance ofthe world is subject to change due to natural and man-made phenomena. The effective long-term usage of visual maps requires the ability to adapt to these changes. We describe a multi-session mapping system, that fuses datasets intoiiia single, unambiguous, metric representation. This enables automated navigation in the presence of environmental change. To handle the growing complexityof such a system we propose the concept of Summary Maps, which contain a reduced set of landmarks that has been selected through a combination of scoring and sampling criteria. We show that a Summary Map with bounded complexity can achieve accurate localization under a wide variety of conditions. Finally, as a foundation for lifelong mapping, we propose a relational database system. This system is based on use-cases that are not only concerned with solving the basic mapping problem, but also with providing users with a better understanding of the long-term processes that comprise a map. We demonstrate that we can pose interesting queries to the database, that help us gain a better intuition about the correctness and robustness of the created maps. This is accomplished by answering questions about the appearance and distribution of visual landmarks that were used during mapping. This thesis takes on one of the major unsolved challenges in vision-based localization and mapping: long-term operation in a changing environment. We approach this problem through extensive real world experimentation, as well as in-depth evaluation and analysis of recorded data. We demonstrate that accurate metric localization is feasible both during short term changes, as exemplified by the transition between day and night, as well as longer term changes, such as due to seasonal variation.

Evaluating the efficacy of neuroprotective drugs in rat models of focal cerebral ischemia has involved histological and behavioural batteries to examine pathology and sensorimotor function. However, the behavioural tests used provide little insight into the nature of the neurological impairments. In an effort to gain further insight into the behavioural impairment following ischemic lesions, a battery of tasks were used. The tasks included tests of sensorimotor, motor (paw use), motivation, sensory and attentional function. The use of the potent vasoconstrictor endothelin-1 has allowed cerebral arteries to be occluded. This can be used to occlude the MCA (which is a common target of ischemia research), as well as other arteries, such as the ACA. Typically quantitative volumetric analysis has used nissl stains to assess lesion extent. However, alternative markers of tissue dysfunction are available including GFAP to assess the astroglial response to ischemia. Consequently cresyl violet and GFAP were compared along with different methods for calculating lesion volume. The boundaries of the lesion identified using the two stains corresponded closely providing care was taken when calculating lesion volume to avoid distortion from histological procedures and edema. Following MCA occlusion the rats displayed unilateral somatosensory and motor deficits, however there was no evidence of attentional dysfunction. Performance in the covert orienting task was compared with striatal dopamine depletion and with a posterior parietal cortical lesion. Neither of these manipulations resulted in deficits of covert orienting. Furthermore, the behavioural consequences of ACA occlusion were studied in two experiments using reaction time tasks designed to dissociate response impairments from dysfunction of motivation and attention. The ACA ischemic damage did not disrupt motivation or attention, however, the results were consistent with an impairment in selecting and initiating responses.

M/EEG mechanisms allow determining changes in the brain activity, which is useful in diagnosing brain disorders such as epilepsy. They consist of measuring the electric potential at the scalp and the magnetic field around the head. The measurements are related to the underlying brain activity by a linear model that depends on the lead-field matrix. Localizing the sources, or dipoles, of M/EEG measurements consists of inverting this linear model. However, the non-uniqueness of the solution (due to the fundamental law of physics) and the low number of dipoles make the inverse problem ill-posed. Solving such problem requires some sort of regularization to reduce the search space. The literature abounds of methods and techniques to solve this problem, especially with variational approaches. This thesis develops Bayesian methods to solve ill-posed inverse problems, with application to M/EEG. The main idea underlying this work is to constrain sources to be sparse. This hypothesis is valid in many applications such as certain types of epilepsy. We develop different hierarchical models to account for the sparsity of the sources. Theoretically, enforcing sparsity is equivalent to minimizing a cost function penalized by an l0 pseudo norm of the solution. However, since the l0 regularization leads to NP-hard problems, the l1 approximation is usually preferred. Our first contribution consists of combining the two norms in a Bayesian framework, using a Bernoulli-Laplace prior. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of the model jointly with the source location and intensity. Comparing the results, in several scenarios, with those obtained with sLoreta and the weighted l1 norm regularization shows interesting performance, at the price of a higher computational complexity. Our Bernoulli-Laplace model solves the source localization problem at one instant of time. However, it is biophysically well-known that the brain activity follows spatiotemporal patterns. Exploiting the temporal dimension is therefore interesting to further constrain the problem. Our second contribution consists of formulating a structured sparsity model to exploit this biophysical phenomenon. Precisely, a multivariate Bernoulli-Laplacian distribution is proposed as an a priori distribution for the dipole locations. A latent variable is introduced to handle the resulting complex posterior and an original Metropolis-Hastings sampling algorithm is developed. The results show that the proposed sampling technique improves significantly the convergence. A comparative analysis of the results is performed between the proposed model, an l21 mixed norm regularization and the Multiple Sparse Priors (MSP) algorithm. Various experiments are conducted with synthetic and real data. Results show that our model has several advantages including a better recovery of the dipole locations. The previous two algorithms consider a fully known leadfield matrix. However, this is seldom the case in practical applications. Instead, this matrix is the result of approximation methods that lead to significant uncertainties. Our third contribution consists of handling the uncertainty of the lead-field matrix. The proposed method consists in expressing this matrix as a function of the skull conductivity using a polynomial matrix interpolation technique. The conductivity is considered as the main source of uncertainty of the lead-field matrix. Our multivariate Bernoulli-Laplacian model is then extended to estimate the skull conductivity jointly with the brain activity. The resulting model is compared to other methods including the techniques of Vallaghé et al and Guttierez et al. Our method provides results of better quality without requiring knowledge of the active dipole positions and is not limited to a single dipole activation.

Wireless devices have invaded the medical industry with a wide range of capability as components of a wireless personal area network (WPAN) and Wireless Body Area Network (WBAN). The recent advances in Internet of Things (IoT) promises even larger contributions to the future of medical applications. This paper investigates the Bluetooth Low Energy (Bluetooth Smart- BTLE) for indoor localization of HealthCare devices used in medical telemetry applications and demonstrates the key role that localization plays in tracking of Bluetooth Low Energy enabled medical devices. Proper tracking of these devices provides better management which would directly reduce the transmission of infectious diseases which can result from improper sharing of these devices. This work will investigate the novel indoor localization technologies of BTLE devices with creative research strategies, and their applications as a basis for ultimately improving health standard using BTLE localization.

Location tracking on mobile devices like smartphones has already begun to revolutionize personal navigation. Unfortunately, these services perform poorly indoors when GPS signals are no longer available. Highly accurate indoor location tracking would enhance a wide variety of applications including: building navigation (malls, factories, airports), augmented reality, location-aware pervasive computing, targeted advertising, social networking, participatory sensing and could even support next generation beam forming MIMO wireless networks. Current indoor localization systems for smartphones often use RF signal strength from WiFi access points or Bluetooth Low Energy (BLE) beacons to fingerprint indoor locations. Such systems are sensitive to environmental changes and obstructions, require extensive training procedures and are limited in both absolute as well as semantic localization accuracy. We propose using audio signals in the ultrasound spectrum, just above the human hearing range, to provide ranging and localization for many off-the-shelf mobile devices that are equipped with microphones. Ultrasonic ranging provides several advantages over RF-based ranging and fingerprinting approaches, which make it attractive for indoor localization. A relatively low propagation speed and carrier frequency allow for precise propagation time measurements in software using commodity hardware. Acoustic signals also have a low penetration depth, which confines them to target areas for accurate semantic localization. In this dissertation we address several challenges related to acoustic localization, including system scalability, ranging and localization accuracy, energy efficiency, robustness to noise, elimination of human perceivable audio artifacts, efficient use of limited acoustic bandwidth and rapid deployment strategies.

Vehicular ad hoc networks (VANETs) have attracted much attention over the last few years. VANETs own several significant characteristics, such as the high-rate changing topology led by velocity of vehicles, time-and-location critical safety applications, and Global Positioning System (GPS) devices. In VANETs, as vehicle movement is usually restricted in just bidirectional movements along the roads and streets, geographical location information becomes very useful. In addition, many studies show that position-based routing protocol is a more promising routing strategy for VANETs; therefore security and verification of location information are necessary to be researched. In this thesis, a location verification approach, namely the Cooperative Location Verification (CLV) protocol, is proposed, aiming to prevent position-spoofing attacks on VANETs. The CLV basically uses two vehicles, a Verifier and a Cooperator, to verify the claimed position of a vehicle (Prover), according to two challenge-response procedures. Additionally, the security analysis of the CLV is presented. In order to enhance the CLV by reduce the network overhead, a reputation management system is designed. It utilizes the verification results of the CLV application and maintain every vehicle's reliability in the network. In addition, the solution to sparse networks is briefly discussed. In the simulation, the results show that the proposed CLV performs better than another location verification algorithm, namely the Secure Location Verification (SLV). And the effectiveness of the reputation management system is also demonstrated.

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