A mobile robot system provides a grounded platform for a wide variety of interactive systems to be developed and deployed. The mobility provided by the robot presents unique challenges as it must observe the state of the surroundings while observing the state of itself with respect to the environment. The scope of the discipline includes the mechanical and hardware issues, which limit and direct the capabilities of the software considerations. The systems that are integrated into the mobile robot platform include both specific task oriented and fundamental modules that define the core behaviour of the robot. While the earlier can sometimes be developed separately and integrated at a later stage, the core modules are often custom designed early on to suit the individual robot system depending on the configuration of the mechanical components. This thesis covers the issues encountered and the resolutions that were implemented during the development of a low cost mobile robot platform using off the shelf sensors, with a particular focus on the algorithmic side of the system. The incrementally developed modules target the localisation and mapping aspects by incorporating a number of different sensors to gather the information of the surroundings from different perspectives by simultaneously or sequentially combining the measurements to disambiguate and support each other. Although there is a heavy focus on the image processing techniques, the integration with the other sensors and the characteristics of the platform itself are included in the designs and analyses of the core and interactive modules. A visual odometry technique is implemented for the localisation module, which includes calibration processes, feature tracking, synchronisation between multiple sensors, as well as short and long term landmark identification to calculate the relative pose of the robot in real time. The mapping module considers the interpretation and the representation of sensor readings to simplify and hasten the interactions between multiple sensors, while selecting the appropriate attributes and characteristics to construct a multi-attributed model of the environment. The modules that are developed are applied to realistic indoor scenarios, which are taken into consideration in some of the algorithms to enhance the performance through known constraints. As the performance of algorithms depends significantly on the hardware, the environment, and the number of concurrently running sensors and modules, comparisons are made against various implementations that have been developed throughout the project.

This thesis proposes two acoustic localisation techniques that are accurate in Non-Line-of-Sight (NLoS) conditions and system implementation of the proposed techniques. Such conditions can cause positive bias errors, namely NLoS errors, in the measured Time-of-Arrivals (ToAs) of first-arrival signals received by microphones and thus reduce the positioning accuracy. The primary issue of the thesis is to precisely estimate and correct the NLoS errors by modelling the received first-arrival signals. The first proposed technique uses multiple on-ground microphones to locate a sound source. The proposed technique approximately estimates and corrects the NLoS errors based on an initial guess of the sound source position and a map. The localisation is then achieved by iteratively correcting the ToAs and updating the sound source location. The strength of the proposed technique is that its accuracy is not notably affected by small or known obstacles. The proposed technique is implemented into two localisation systems of controlled and uncontrolled sound sources. The performance of the proposed technique is investigated by its comparison with three other time-based localisation techniques in series of experiments and simulations, showing at least 10% improvement by the proposed technique under various background noise levels. The second proposed technique localises a sound source using a single on-ground microphone subject to an assumption of a single diffraction in the first-arrival signal. To predict the angular and radial coordinates of the sound source relative to the diffraction point, a new magnitude delay frequency profile is proposed. The profile can be estimated by applying the uniform geometrical theory of diffraction and be extracted from measured signals using a derived formulation. Similar to the first technique, the second proposed technique estimates the measured delay of the first-arrival signal for computing the radial coordinate. The angular coordinate is then obtained by matching the estimated and measured profiles at the measured delay. A key achievement of the second proposed technique is enabling NLoS localisation using only one microphone without any time-consuming pre-measurement. This technique is implemented into a localisation system of a controlled sound source and validated experimentally with three different sound sources and under two background noise levels.

Doctor of Philosophy (PhD) / This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology.

This thesis explores approaches to two problems in the frame-rate computation of a priori unknown 3D scene structure and camera pose using a single camera, or monocular simultaneous localisation and mapping. The thesis reflects two trends in vision in general and structure from motion in particular: (i) the move from directly recovered and towards learnt geometry; and (ii) the sparsification of otherwise dense direct methods. The first contributions mitigate scale drift. Beyond the inevitable accumulation of random error, monocular SLAM accumulates error via the depth/speed scaling ambiguity. Three solutions are investigated. The first detects objects of known class and size using fixed descriptors, and incorporates their measurements in the 3D map. Experiments using databases with ground truth show that metric accuracy can be restored over kilometre distances; and similar gains are made using a hand-held camera. Our second method avoids explicit feature choice, instead employing a deep convolutional neural network to yield depth priors. Relative depths are learnt well, but absolute depths less so, and recourse to database-wide scaling is investigated. The third approach uses a novel trained network to infer speed from imagery. The second part of the thesis develops sparsified direct methods for monocular SLAM. The first contribution is a novel camera tracker operating directly using affine image warping, but on patches around sparse corners. Camera pose is recovered with an accuracy at least equal to the state of the art, while requiring only half the computational time. The second introduces a least squares adjustment to sparsified direct map refinement, again using patches from sparse corners. The accuracy of its 3D structure estimation is compared with that from the widely used method of depth filtering. It is found empirically that the new method's accuracy is often higher than that of its filtering counterpart, but that the method is more troubled by occlusion.

The subcellular localisation of mRNA is a widespread mechanism to determine the fate of mRNAs in eukaryotes. Translationally repressed mRNAs localise to P-bodies and stress granules where their decay and storage, respectively, are directed. In a study from the Ashe lab, specific mRNAs were identified to localise, in actively growing S. cerevisiae, to cytoplasmic granules that do not seem to be related to P-bodies or stress granules but appear to be associated with active translation (Lui et al., 2014).It is possible that this might represent a strategy to co-regulate the expression of proteins from the same pathway. In the work of this thesis, microscopy techniques to visualise RNAs in live cells were used to extend the localisation analysis to several mRNAs encoding translation factors. The investigated transcripts were all found to localise to mostly one or two cytoplasmic granules per cell and would sometimes overlap with other transcripts, suggesting that each granule contains a mixture of mRNAs. Granules tend to migrate to the bud tip and may provide the daughter cell with a "start-up kit" of transcripts essential for rapid growth. A similar pattern can be observed in yeast cells growing undergoing filamentous growth, with granules harbouring translation factor transcripts often found in the apical quarter of the elongated cell. Although the mechanism by which the granules form and their protein composition are not yet known, high-throughput genetic screens performed as part of this work offer some insight into factors that might be involved in granule assembly and proteins that partially overlap with the granules. We propose that granules containing translation factor mRNAs might be functioning as a specialised factory for the translation machinery and are possibly being directed to the point in the cell where the rhythm of protein production is highest.

No description available.

This thesis is an attempt to make visible how Christians within a minority people in the southern Philippines view the Bible conceptually as a source of spiritual authority and also how they read and interpret the Bible, both privately and within the context of community worship. Reading and studying the Bible is now universally practised by people from multitudes of cultures, a reality that has naturally engendered a great deal of interest on the part of scholars. The resultant scholarship however, has been preoccupied with the findings of the professional researcher, and little has been published which reveals how “ordinary indigenous readers” view the Bible and/or how they interpret it. Using qualitative data gathered by this author among Manobo Christians living in the hills of central Mindanao, this thesis will endeavour to redress this imbalance and provide access to the voices of ordinary Manobo readers. The thesis also makes an important contribution to the Bible’s place within Philippine Christianity. Despite the expanding readership of the Bible within the Philippines almost no research has focused on how the Bible is actually interpreted by ordinary readers. The thesis will major on the appropriation of the Bible by Christians from within the Manobo Bible Church Association of Mindanao, an association of churches born out of the church planting efforts of missionaries belonging to the Overseas Missionary Fellowship. At the centre of the thesis is an encounter between conservative evangelical missionaries and the unique culture and cosmology of the Manobo. The central argument is that the missionaries’ prototypically, evangelical doctrine of Scripture was appropriated and reconfigured by Manobo Christians in ways that reveal the persistent ability of elements of their own cosmology, and customary law, to exert influence upon their localisation of Christianity. In particular, the thesis focuses on how the localisation process has led to innovations by the Manobo on what is meant by the Bible as “spiritual authority” and to reinterpretations of significant theological themes within the evangelical gospel message. At the same time the thesis also outlines how adoption of the Christian Scriptures has redefined the position that indigenous sources of authority, such as spirit priest and village chief, now occupy within Manobo Christian communities.

Retinoblastoma Binding Protein 6 (RBBP6) is a 200 KDa protein shown to play a role in 3'- polyadenylation of mRNA transcripts, as well as to function as an E3 ligase catalysing ubiquitination of cancer-associated proteins. RBBP6 has been previously reported to localise to nuclear speckles, which are thought to play a role in mRNA splicing, presumably as a result of its RS domain, which is known to target mRNA splicing factors to nuclear speckles. However recent studies in our laboratory have shown that isoform 3 of RBBP6, consisting mainly of the DWNN domain, also localises to speckles in resting cells, but more strongly in cells subjected to various stresses, suggesting that the DWNN domain may be the speckle-targeting domain. / Magister Scientiae - MSc (Biotechnology)

Les techniques M/EEG permettent de déterminer les changements de l'activité du cerveau, utiles au diagnostic de pathologies cérébrales, telle que l'épilepsie. Ces techniques consistent à mesurer les potentiels électriques sur le scalp et le champ magnétique autour de la tête. Ces mesures sont reliées à l'activité électrique du cerveau par un modèle linéaire dépendant d'une matrice de mélange liée à un modèle physique. La localisation des sources, ou dipôles, des mesures M/EEG consiste à inverser le modèle physique. Cependant, la non-unicité de la solution (due à la loi fondamentale de physique) et le faible nombre de dipôles rendent le problème inverse mal-posé. Sa résolution requiert une forme de régularisation pour restreindre l'espace de recherche. La littérature compte un nombre important de travaux traitant de ce problème, notamment avec des approches variationnelles. Cette thèse développe des méthodes Bayésiennes pour résoudre des problèmes inverses, avec application au traitement des signaux M/EEG. L'idée principale sous-jacente à ce travail est de contraindre les sources à être parcimonieuses. Cette hypothèse est valide dans plusieurs applications, en particulier pour certaines formes d'épilepsie. Nous développons différents modèles Bayésiens hiérarchiques pour considérer la parcimonie des sources. En théorie, contraindre la parcimonie des sources équivaut à minimiser une fonction de coût pénalisée par la norme l0 de leurs positions. Cependant, la régularisation l0 générant des problèmes NP-complets, l'approximation de cette pseudo-norme par la norme l1 est souvent adoptée. Notre première contribution consiste à combiner les deux normes dans un cadre Bayésien, à l'aide d'une loi a priori Bernoulli-Laplace. Un algorithme Monte Carlo par chaîne de Markov est utilisé pour estimer conjointement les paramètres du modèle et les positions et intensités des sources. La comparaison des résultats, selon plusieurs scenarii, avec ceux obtenus par sLoreta et la régularisation par la norme l1 montre des performances intéressantes, mais au détriment d'un coût de calcul relativement élevé. Notre modèle Bernoulli Laplace résout le problème de localisation des sources pour un instant donné. Cependant, il est admis que l'activité cérébrale a une certaine structure spatio-temporelle. L'exploitation de la dimension temporelle est par conséquent intéressante pour contraindre d'avantage le problème. Notre seconde contribution consiste à formuler un modèle de parcimonie structurée pour exploiter ce phénomène biophysique. Précisément, une distribution Bernoulli-Laplacienne multivariée est proposée comme loi a priori pour les dipôles. Une variable latente est introduite pour traiter la loi a posteriori complexe résultante et un algorithme d'échantillonnage original de type Metropolis Hastings est développé. Les résultats montrent que la technique d'échantillonnage proposée améliore significativement la convergence de la méthode MCMC. Une analyse comparative des résultats a été réalisée entre la méthode proposée, une régularisation par la norme mixte l21, et l'algorithme MSP (Multiple Sparse Priors). De nombreuses expérimentations ont été faites avec des données synthétiques et des données réelles. Les résultats montrent que notre méthode a plusieurs avantages, notamment une meilleure localisation des dipôles. Nos deux précédents algorithmes considèrent que le modèle physique est entièrement connu. Cependant, cela est rarement le cas dans les applications pratiques. Au contraire, la matrice du modèle physique est le résultat de méthodes d'approximation qui conduisent à des incertitudes significatives. / 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.

Teachers, locals, a designer, communities, students, materials and co-design are the resources of this written report. These elements have tried to find a balance between each other during the process to stimulate the core of the project: a sustainable future in the local community of Älmhult. Starting of through of changing the global perspective of education to a more local sharing-learning experiences, the project made a turn to focus more on a problematic community namely that of Älmhult, Sweden. This local community has in the recent years changed dramatically because of growth in population and cultures. The community in which international and local do not mingle enough, has split. Instead of using these possibilities of learning from each other’s cultures and languages they have decided to live in mostly separate communities. Through the course of this project a network was built of people from both these communities to start building a stronger community in Älmhult. Education has been used as tool to stimulate this change, bringing the local community to the students. Teaching the students about strong communities they are the tool to develop this into the future. Thereby the student is the seed of change. The project has worked with co-design, using the tools to connect teachers and local initiators to create these meetings between student and local community. The research can be a learning tool to build communities, by using the knowledge of its inhabitants.