A Framework to Resolve Ambiguities in a Multitarget Environment

Ambiguities are an often encountered nuisance in signal processing and are the source of some of the fundamental trade-offs encountered in radar systems. The goal of this thesis is to extract unambiguous information about targets by combining a limited amount of measurements on a video integration level. A novel framework is proposed to reach...

Integrating fairness in opponent modeling

Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to...

Integrating swarm intelligence with bayesian networks for continuous UAV-based surveillance in dynamic environments

The loss of wildlife due to illegal poaching activity poses threats on both the survival of iconic animal species and the livelihood of local communities. This research proposes a distributed surveillance model in which a UAV swarm autonomously coordinates continuous surveillance in a dynamic environment. The adaptive behaviour of poachers has...

Bayesian Deep Learning for Distilling Physical Laws from Videos

An end-to-end framework is developed to discover physical laws directly from videos, which can help facilitate the study on robust prediction, system stability analysis and gain the physical insight of a dynamic process. In this work, a video information extraction module is proposed to detect and collect the pixel position of moving objects,...

Batch Bayesian Learning of Large-Scale LS-SVMs Based on Low-rank Tensor Networks

Least Squares Support Vector Machines (LS-SVMs) are state-of-the-art learning algorithms that have been widely used for pattern recognition. The solution for an LS-SVM is found by solving a system of linear equations, which involves the computational complexity of O(N^3). When datasets get larger, solving LS-SVM problems with standard methods...

Bayesian Deep Learning for Dynamic System Identification

System identification is a mature field in physical sciences and an emerging field in social sciences, with a vast range of applications. Nevertheless, it remains of great focus in academia. The main challenge is the efficient use of data to generate good model fits. System identification involves multi-disciplinary techniques from statistical,...

Towards Real-Time Human Pose Estimation For Mobile Device

Human pose estimation, a challenging computer vision task of estimating various human body joints' locations, has a wide range of applications such as pedestrian tracking for autonomous cars, baby monitoring, video surveillance, human action recognition, virtual reality, gaming, gait analysis, etc. A majority of the research on the development...

Recursive Tensor Network Bayesian Learning of Large-Scale LS-SVMs

Least-squares support-vector-machines are a frequently used supervised learning method for nonlinear regression and classification. The method can be implemented by solving either its primal problem or dual problem. In the dual problem a linear system needs to be solved, yet for large-scale problems this can be impractical as current methods...

Bayesian Learning Applied to Radio Astronomy Image Formation

Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the ill-posedness, side information should be involved. Based on the sparsity assumption of the sky image, we involve l1-regularization. We formulate the image formation problem into a...

Fault Diagnosis of Neural Network Modelled Mechanical Systems using a Sparse Bayesian Learning Framework

The increasing complexity of mechanical systems has resulted in an increased usage and dependence on data driven modelling techniques in order to obtain simple yet accurate models of these systems. Neural networks have emerged as a popular modelling choice due to their proven ability to learn complex nonlinear relationships between inputs and...

Inferring Arithmetic Expressions from Data

We present a framework for learning arithmetic expressions from a set of observations. Our intention is to introduce a Bayesian method for what is known as equation discovery. Our method is based on measuring a degree of belief (posterior probability) for a set of hypothesized expressions to find those which best explain the observed data. This...