Hybrid genetic algorithm and particle filter optimization model for simultaneous localization and mapping problems

Determining position of a robot and knowing position of the required objects on the map in unknown environments such as underwater, other planets and the remaining areas of natural disasters has led to the development of efficient algorithms for Simultaneous Localization and Mapping (SLAM). The current solutions for solving the SLAM have some drawbacks. For example, the solutions based on Extended Kalman Filter (EKF) are faced with limitation in non-linear models and non-Gaussian errors which are causes for decrease of accuracy. The solutions based on particle filter are also suffering from high memory complexity and time complexity. One of the major approaches to solve the SLAM problem is the approach based on Evolutionary Algorithm (EA). The main advantage of the EA is that it can be used in search space which is too large to be used with high convergence while its disadvantage is high time and computational complexity. This thesis proposes two optimization models in solving SLAM problem namely Hybrid Optimization Model (HOM) and Lined-Based Genetic Algorithm Optimization Model (LBGAOM). These models do not have the limitations of EKF, memory complexity of particle filter, and disadvantages of EA in search space. When the results of HOM compared with original EA, it showed an increase of accuracy based on presented fitness function. The best fitness in original EA was 16.36 but in HOM has reached to 16.68. Both models applied a proposed new representation model. The representation model is designed and used to represent the robot and its environment and is based on occupancy grid and genetic algorithm. There are two types of representation models proposed in this thesis namely Layer 1 and Layer 2. For each layer, related fitness function is created to evaluate the accuracy of map in the model that was tested with some different parameters. The proposed HOM is designed based on genetic algorithm and particle filter by creating a new mutation model inspired by particle filter. The search space is reduced and only suitable space will be explored based on proposed functions. The proposed LBGAOM is a new optimization model based on extraction line from laser sensor data to increase the speed. In this model, search space in the map is a set of lines instead of pixel by pixel and it makes searching time faster. The evaluation of the proposed representation model shows that Layer 2 has better fitness value than Layer 1. The HOM has better performance compared to original GA Layer 1. The LBGAOM has decreased the search space compared to pixel based model. In conclusion, the proposed optimization models have good performance in solving the SLAM problem in terms of speed and accuracy

Hidden states, hidden structures: Bayesian learning in time series models

This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7)This work was supported by the Engineering and Physical Sciences Research Council (EPSRC

Visual motion estimation and tracking of rigid bodies by physical simulation

This thesis applies knowledge of the physical dynamics of objects to estimating object motion from vision when estimation from vision alone fails. It differentiates itself from existing physics-based vision by building in robustness to situations where existing visual estimation tends to fail: fast motion, blur, glare, distractors, and partial or full occlusion. A real-time physics simulator is incorporated into a stochastic framework by adding several different models of how noise is injected into the dynamics. Several different algorithms are proposed and experimentally validated on two problems: motion estimation and object tracking. The performance of visual motion estimation from colour histograms of a ball moving in two dimensions is improved considerably when a physics simulator is integrated into a MAP procedure involving non-linear optimisation and RANSAC-like methods. Process noise or initial condition noise in conjunction with a physics-based dynamics results in improved robustness on hard visual problems. A particle filter applied to the task of full 6D visual tracking of the pose an object being pushed by a robot in a table-top environment is improved on difficult visual problems by incorporating a simulator as a dynamics model and injecting noise as forces into the simulator.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modelling and Identification of Immune Cell Migration during the Inflammatory Response

Neutrophils are the white blood cells that play a crucial role in the response of the innate immune system to tissue injuries or infectious threats. Their rapid arrival to the damaged area and timely removal from it define the success of the inflammatory process. Therefore, understanding neutrophil migratory behaviour is essential for the therapeutic regulation of multiple inflammation-mediated diseases. Recent years saw rapid development of various in vivo models of inflammation that provide a remarkable insight into the neutrophil function. The main drawback of the \textit{in vivo} microscopy is that it usually focuses on the moving cells and obscures the external environment that drives their migration. To evaluate the effect of a particular treatment strategy on neutrophil behaviour, it is necessary to recover the information about the cell responsiveness and the complex extracellular environment from the limited experimental data. This thesis addresses the presented inference problem by developing a dynamical modelling and estimation framework that quantifies the relationship between an individual migrating cell and the global environment. \par The first part of the thesis is concerned with the estimation of the hidden chemical environment that modulates the observed cell migration during the inflammatory response in the injured tail fin of zebrafish larvae. First, a dynamical model of the neutrophil responding to the chemoattractant concentration is developed based on the potential field paradigm of object-environment interaction. This representation serves as a foundation for a hybrid model that is proposed to account for heterogeneous behaviour of an individual cell throughout the migration process. An approximate maximum likelihood estimation framework is derived to estimate the hidden environment and the states of multiple hybrid systems simultaneously. The developed framework is then used to analyse the neutrophil tracking data observed in vivo under the assumption that each neutrophil at each time can be in one of three migratory modes: responding to the environment, randomly moving, and stationary. The second part of the thesis examines the process of neutrophil migration at the subcellular scale, focusing on the subcellular mechanism that translates the local environment sensing into the cell shape change. A state space model is formulated based on the hypothesis that links the local protrusions of the cell membrane and the concentration of the intracellular pro-inflammatory signalling protein. The developed model is tested against the local concentration data extracted from the in vivo time-lapse images via the classical expectation-maximisation algorithm

Bayesian inference for stochastic processes

This thesis builds upon two strands of recent research related to conducting Bayesian inference for stochastic processes. Firstly, this thesis will introduce a new residual-bridge proposal for approximately simulating conditioned diffusions formed by applying the modified diffusion bridge approximation of Durham and Gallant, 2002 to the difference between the true diffusion and a second, approximate, diffusion driven by the same Brownian motion. This new proposal attempts to account for volatilities which are not constant and can, therefore, lead to gains in efficiency over recently proposed residual-bridge constructs (Whitaker et al., 2017) in situations where the volatility varies considerably, as is often the case for larger interobservation times and for time-inhomogeneous volatilities. These gains in efficiency are illustrated via a simulation study for three diffusions; the Birth-Death (BD) diffusion, the Lotka-Volterra (LV) diffusion, and a diffusion corresponding to a simple model of gene expression (GE). Secondly, this thesis will introduce two new classes of Markov Chain Monte Carlo samplers, named the Exchangeable Sampler and the Exchangeable Particle Gibbs Sampler, which, at each iteration, use exchangeablility to simulate multiple, weighted proposals whose weights indicate how likely the chain is to move to such a proposal. By generalising the Independence Sampler and the Particle Gibbs Sampler respectively, these new samplers allow for the locality of moves to be controlled by a scaling parameter which can be tuned to optimise the mixing of the resulting MCMC procedure, while still benefiting from the increase in acceptance probability that typically comes with using multiple proposals. These samplers can lead to chains with better mixing properties, and, therefore, to MCMC estimators with smaller variances than their corresponding algorithms based on independent proposals. This improvement in mixing is illustrated, numerically, for both samplers through simulation studies, and, theoretically, for the Exchangeable Sampler through a result which states that, under certain conditions, the Exchangeable Sampler is geometrically ergodic even when the importance weights are unbounded and, hence, in scenarios where the Independence Sampler cannot be geometrically ergodic. To provide guidance in the practical implementation of such samplers, this thesis derives asymptotic expected squared-jump distance results for the Exchangeable Sampler and the Exchangeable Particle Gibbs Sampler. Moreover, simulation studies demonstrate, numerically, how the theory plays out in practice when d is finite

Deep Gaussian Processes and Variational Propagation of Uncertainty

Uncertainty propagation across components of complex probabilistic models is vital for improving regularisation. Unfortunately, for many interesting models based on non-linear Gaussian processes (GPs), straightforward propagation of uncertainty is computationally and mathematically intractable. This thesis is concerned with solving this problem through developing novel variational inference approaches. From a modelling perspective, a key contribution of the thesis is the development of deep Gaussian processes (deep GPs). Deep GPs generalise several interesting GP-based models and, hence, motivate the development of uncertainty propagation techniques. In a deep GP, each layer is modelled as the output of a multivariate GP, whose inputs are governed by another GP. The resulting model is no longer a GP but, instead, can learn much more complex interactions between data. In contrast to other deep models, all the uncertainty in parameters and latent variables is marginalised out and both supervised and unsupervised learning is handled. Two important special cases of a deep GP can equivalently be seen as its building components and, historically, were developed as such. Firstly, the variational GP-LVM is concerned with propagating uncertainty in Gaussian process latent variable models. Any observed inputs (e.g. temporal) can also be used to correlate the latent space posteriors. Secondly, this thesis develops manifold relevance determination (MRD) which considers a common latent space for multiple views. An adapted variational framework allows for strong model regularisation, resulting in rich latent space representations to be learned. The developed models are also equipped with algorithms that maximise the information communicated between their different stages using uncertainty propagation, to achieve improved learning when partially observed values are present. The developed methods are demonstrated in experiments with simulated and real data. The results show that the developed variational methodologies improve practical applicability by enabling automatic capacity control in the models, even when data are scarce

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality