Vision-based navigation for autonomous underwater vehicles

This thesis investigates the use of vision sensors in Autonomous Underwater Vehicle (AUV) navigation, which is typically performed using a combination of dead-reckoning and external acoustic positioning systems. Traditional dead-reckoning sensors such els Doppler Velocity Logs (DVLs) or inertial systems are expensive and result in drifting trajectory estimates. Acoustic positioning systems can be used to correct dead-reckoning drift, however they are time consuming to deploy and have a limited range of operation. Occlusion and multipath problems may also occur when a vehicle operates near the seafloor, particularly in environments such as reefs, ridges and canyons, which are the focus of many AUV applications. Vision-based navigation approaches have the potential to improve the availability and performance of AUVs in a wide range of applications. Visual odometry may replace expensive dead-reckoning sensors in small and low-cost vehicles. Using onboard cameras to correct dead-reckoning drift will allow AUVs to navigate accurately over long distances, without the limitations of acoustic positioning systems. This thesis contains three principal contributions. The first is an algorithm to estimate the trajectory of a vehicle by fusing observations from sonar and monocular vision sensors. The second is a stereo-vision motion estimation approach that can be used on its own to provide odometry estimation, or fused with additional sensors in a Simultaneous Localisation And Mapping (SLAM) framework. The third is an efficient SLAM algorithm that uses visual observations to correct drifting trajectory estimates. Results of this work are presented in simulation and using data collected during several deployments of underwater vehicles in coral reef environments. Trajectory estimation is demonstrated for short transects using the sonar and vision fusion and stereo-vision approaches. Navigation over several kilometres is demonstrated using the SLAM algorithm, where stereo-vision is shown to improve the estimated trajectory produced by a DVL

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Ecological conditions leading to the seep of antibiotic resistance genes in the model-type bacterium Escherichia coli

In antibiotic therapy design, conventional wisdom holds that higher antibiotic dosages always leads to the observation of fewer bacterial cells, resulting in a monotonic decay in cell number as a function of increasing antibiotic dose; accordingly, throughout this thesis, we will call this phenomenon a monotone dose-response profile. When we analysed the evolution of antibiotic resistance mediated by the multi-drug efflux pump AcrAB-TolC in Escherichia coli to study if such a monotone dose-response is maintained at all times, our analysis showed that higher dosages can, in fact, lead to higher bacterial loads. This is because selection for drug resistance is mediated by the duplication of the genes, AcrAB-TolC, that encode the aforementioned efflux pump. As explained in detail below, our work highlights the idea that Darwinian selection on additional copies of AcrAB-TolC is a non-linear function of antibiotic dose and that the observed transition from monotone to non-monotone dose-response is a consequence of AcrAB-TolC being strongly selected at very specific dosages. We term this phenomenon an ‘evolutionary hotspot’. Next, we extended the above experimental system to solid media to study how selection on resistance mediated by AcrAB-TolC leads to a ‘spatio-genomic patterning’ effect that we call a ‘bullseye’. Using a bespoke culture device developed as part of this PhD, we show that spatial selection on resistance also depends non-linearly on the distance of the cell from an antibiotic source, and that the non-linearity can be multi-modal as a function of distance, and therefore also of antibiotic dose. This result also contradicts the aforementioned principle that higher antibiotic dosages necessarily lead to fewer bacterial cells. Following on from this, we then studied the ability of microbial competitors for resources to modulate the antibiotic sensitivity of a particular strain of E. coli, namely Tets , using a range of multi-species experiments. We measured the sensitivity to antibiotics of Tets both with, and without, one bacterial or fungal competitor. When that competitor was equally sensitive to the antibiotic, we observed that Tets was less sensitive to it, in part due to an ‘antibiotic sinking’ effect carried out by the competitor strain. However, when the competitor was not sensitive to the antibiotic, Tets was, accordingly, more sensitive than in the absence of competition. In this latter case, the competitor seemed to reduce the growth of Tets by carbon theft as part of a phenomenon known as ‘competitive suppression’. Moreover, this ecological effect is one that synergises with the action of the antibiotic. Finally, we turned to a study of an ecological trade-off motivated by ribosome-binding antibiotics. So, by manipulating the content of ribosomal RNA in the E. coli cell, a large and essential molecule that is bound by antibiotics such as tetracycline or erythromycin, we could subsequently manipulate what is known as a metabolic trade-off between growth rate and growth yield. The latter is the number of cells produced per molecule of carbon found in the extracellular environment of the bacterial population. Using glucose as carbon source we therefore constructed an empirical fitness landscape that shows how the optimum number of ribosomal rRNA operons depends on extracellular glucose concentration. Whilst this study does not relate directly to the presence of an antibiotic, it does show that by altering the number of operons in a manner that is known to affect antibiotic susceptibility, we can also mediate important growth parameters like cell yield, aka efficiency, and growth rate.Engineering and Physical Sciences Research Council (EPSRC

Deep transformation models for functional outcome prediction after acute ischemic stroke

In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and image data. We show how to apply deep transformation models (DTMs) for distributional regression which fulfill these requirements. DTMs allow the data analyst to specify (deep) neural networks for different input modalities making them applicable to various research questions. Like statistical models, DTMs can provide interpretable effect estimates while achieving the state-of-the-art prediction performance of deep neural networks. In addition, the construction of ensembles of DTMs that retain model structure and interpretability allows quantifying epistemic and aleatoric uncertainty. In this study, we compare several DTMs, including baseline-adjusted models, trained on a semi-structured data set of 407 stroke patients with the aim to predict ordinal functional outcome three months after stroke. We follow statistical principles of model-building to achieve an adequate trade-off between interpretability and flexibility while assessing the relative importance of the involved data modalities. We evaluate the models for an ordinal and dichotomized version of the outcome as used in clinical practice. We show that both, tabular clinical and brain imaging data, are useful for functional outcome prediction, while models based on tabular data only outperform those based on imaging data only. There is no substantial evidence for improved prediction when combining both data modalities. Overall, we highlight that DTMs provide a powerful, interpretable approach to analyzing semi-structured data and that they have the potential to support clinical decision making.Comment: Preprint under revie