Estimation of potential field environments from heterogeneous behaviour of sensing agents

This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Markov non-linear system (JMNLS) model of agent dynamics with a basis function decomposition of the environmental field. An approximate expectation-maximisation algorithm is employed for joint estimation of the global field and of the agent behavioural modes from observed agent trajectories. To avoid prohibitive computational costs associated with the state estimation of JMNLS, we utilise two approximation steps. First, an interacting multiple model smoother is used to account for the hybrid structure that emerges in this problem. Second, we propose two approaches to approximating the non-linear sufficient statistics during the expectation step. This results in the maximization step being exact. The performance of the developed framework is tested on simulation examples and demonstrated on an application study in which the observed movement patterns of immune cells are utilised in quantifying the underlying chemical concentration field that governs their migration. The results showcase that the proposed framework can be readily applied to problems where agents assume several behavioural modes

TEchMA2020: 3rd International Conference on Technologies for the Wellbeing and Sustainable Manufacturing Solutions: book of abstracts

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Design, manufacture, and applications of high mass resolution orbital trapping for secondary ion mass spectrometry

Ph. D. ThesisSecondary Ion Mass Spectrometry (SIMS) is a widely used analytical technique for characterizing surface chemistry in numerous technical applications, including medical implant surfaces, fault-finding in semi-conductors, proteomics, pharmaceutical development, and in the field of astrobiology and the search for extra-terrestrial life. The mass resolving power of modern time-of-flight SIMS (ToF-SIMS) instruments does not exceed 12,000. This means that for some mass spectral peaks, a single molecular formula cannot be definitively specified. This is particularly important for high mass molecules, where there can be many possible peak attributions for a given nominal mass. Orbital ion traps have been shown to achieve mass resolution in excess of 100,000, without the need for the large and expensive superconducting magnets required in Fouriertransform ion cyclotron resonance (FT-ICR) instruments. Therefore, an orbital ion trapping mass analyser has been designed, fabricated, and coupled to the Ionoptika J105 SIMS. Computational modelling has been developed to evaluate proposed designs and examine the effects of manufacturing imperfections on the performance of the orbital ion trap. A method of exciting a precise mass range of trapped ions has also been developed, using a Stored Waveform Inverse Fourier Transform (SWIFT) technique. This allows fast, high mass resolution analysis after a low-resolution spectrum has been gathered using the time-of-flight analyser. This thesis will cover the function and capabilities of the Ionoptika J105 SIMS, the need for high mass resolution in SIMS, the mathematical background of electrostatic harmonic ion traps, and the simulation, design, manufacture, and operation of the orbital trapping mass analyser. This research allows mass spectra to be gathered with both high mass and spatial resolution, an advancement with numerous potential applications, including labelfree biological imaging and the unambiguous identification of biosignatures in astrobiology

Bayesian multi-target tracking: application to total internal reflection fluorescence microscopy

This thesis focuses on the problem of automated tracking of tiny cellular and sub-cellular structures, known as particles, in the sequences acquired from total internal reflection fluorescence microscopy (TIRFM) imaging technique. Our primary biological motivation is to develop an automated system for tracking the sub-cellular structures involving exocytosis (an intracellular mechanism) which is helpful for studying the possible causes of the defects in diseases such as diabetes and obesity. However, all methods proposed in this thesis are generalized to be applicable for a wide range of particle tracking applications. A reliable multi-particle tracking method should be capable of tracking numerous similar objects in the presence of high levels of noise, high target density and complex motions and interactions. In this thesis, we choose the Bayesian filtering framework as our main approach to deal with this problem. We focus on the approaches that work based on detections. Therefore, in this thesis, we first propose a method that robustly detects the particles in the noisy TIRFM sequences with inhomogeneous and time-varying background. In order to evaluate our detection and tracking methods on the sequences with known and reliable ground truth, we also present a framework for generating realistic synthetic TIRFM data. To propose a reliable multi-particle tracking method for TIRFM sequences, we suggest a framework by combining two robust Bayesian filters, the interacting multiple model and joint probabilistic data association (IMM-JPDA) filters. The performance of our particle tracking method is compared against those of several popular and state-of-the art particle tracking approaches on both synthetic and real sequences. Although our approach performs well in tracking particles, it can be very computationally demanding for the applications with dense targets with poor detections. To propose a computationally cheap, but reliable, multi-particle tracking method, we investigate the performance of a recent multi-target Bayesian filter based on random finite theory, the probability hypothesis density (PHD) filter, on our application. To this end, we propose a general framework for tracking particles using this filter. Moreover, we assess the performance of our proposed PHD filter on both synthetic and real sequences with high level of noise and particle density. We compare its results from both aspects of accuracy and processing time against our IMM-JPDA filter. Finally, we suggest a framework for tracking particles in a challenging problem where the noise characteristic and the background intensity of sequences change during the acquisition process which make detection profile and clutter rate time-variant. To deal with this, we propose a bootstrap filter using another type of the random finite set based Bayesian filters, the cardinalized PHD (CPHD) filter, composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability while the tracker estimates the state of the targets. We evaluate the performance of our bootstrap on both synthetic and real sequences under these time-varying conditions. Moreover, its performance is compared against those of our other particle trackers as well as the state-of-the art particle tracking approaches

Energy Management of Distributed Generation Systems

The book contains 10 chapters, and it is divided into four sections. The first section includes three chapters, providing an overview of Energy Management of Distributed Systems. It outlines typical concepts, such as Demand-Side Management, Demand Response, Distributed, and Hierarchical Control for Smart Micro-Grids. The second section contains three chapters and presents different control algorithms, software architectures, and simulation tools dedicated to Energy Management Systems. In the third section, the importance and the role of energy storage technology in a Distribution System, describing and comparing different types of energy storage systems, is shown. The fourth section shows how to identify and address potential threats for a Home Energy Management System. Finally, the fifth section discusses about Economical Optimization of Operational Cost for Micro-Grids, pointing out the effect of renewable energy sources, active loads, and energy storage systems on economic operation