Object characterisation using wideband sonar pulses

Characterisation of objects in an underwater environment is challenging. Success in the task can be beneficial in a variety of scenarios, which include oil and gas pipe maintenance, archaeology, and assistance to general underwater object identification. This work focuses on object characterisation, providing a solution for material identification. To do this, one must sense the underwater environment for which there are several different ways. Some of the most popular rely on sonar images. These provide limited information about the objects,mostly the shape, size and distance to the object. The study of acoustic wave scattering over a wide frequency range provides more information about the targets characteristics. This work builds on the principles of sound scattering. An acoustic echo reflected from an object has a different pulse shape and frequency composition than its initial pulse. These changes in the pulse are due to the interaction of the sound wave with an object during the reflection process and the pulses interaction with the transmission medium. Study of the reflected pulse can provide information about physical properties such as size, material and shell thickness. The objects used in this work are limited to spherical shells made of a variety of materials, and filled with different liquids or air. The task of material identification is approached in two different ways. The first one is a machine learning based approach. The classification is not based on the object’s shape, but on its physical properties including the composition material. Two approaches will be presented: one, where the spherical shell is described by the echo’s representation in time frequency domain and one, where it is described by the form function. The objects are classified using a number of machine learning techniques including support vector machine, gradient boosting and neural networks. The machine learning approaches give good results for a number of tasks, but are not sufficient to distinguish between materials with similar properties, like water and salt water. An alternative solution is presented in this thesis, which identifies the filler and the shell materials separately. This material identification approach is based on the timing of the sound scattering components. The echo reflected from an object is formed by a number of processes. The information about these processes can be extracted from the echoes and used to identify the material. This approach does not require any training data and shows good results, which are demonstrated on both the simulated and experimental data. This work focuses on object characterisation, providing a solution for material identification using underwater acoustics and signal processing techniques

TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience