Exploring space situational awareness using neuromorphic event-based cameras

The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

Movement recognition from wearable sensors data: power-aware evolutionary training for template matching and data annotation recovery methods

Human activities recognition finds numerous applications for example in sport training, patient rehabilitation, gait analysis and surgical skills evaluation. Wearable sensing and Template Matching Methods (TMMs) offer significant advantages compared to manual assessment methods as well as to more cumbersome camera-based setups and other machine learning (ML) algorithms. TMMs require less data for training than other ML methods, they are low-power and therefore suitable for integration on wearable sensor. They compute a sample-by-sample distance between two time series. When applied to gestures sensors data, this even enables a richer and more movement-specific assessment and feedback. However, TMMs lack of a standard training procedure. In this thesis, we introduce an innovative evolutionary training algorithm for TMMthat not only can maximize recognition performance, but it can also prefer power-minimisation by reducing the TMM’s computational cost with a configurable trade-off. We exhibit that a reduction is even possible without sacrificing recognition performance by exploiting the long-established concept of “time warping”. We demonstrate that our method is suitable for a wide variety of raw data as well as processed, fused and encoded sensor data. We present a new original multi-modal, multi-user dataset of beach volleyball movements that allowed to evaluate our training methods on a real-case of sport training actions. Moreover, the collection of this dataset helped to generate a set of guidelines for the collection of movement data in the wild, using wearable sensors. We introduce a 3D human model that can be animated through inertial wearable sensors data for troubleshooting, movement analysis and privacy-safe annotation of human activities. Finally, through a case study on a dataset of drinking actions, we demonstrate how TMM can improve the quality of a badly annotated but highly valuable dataset

Piezoelectric energy harvesting from wind-induced flutter

Piezoelectric energy harvesting from fluid flow, to power Ultra Low Power (ULP) devices, has gained interest among researchers over the last decade. In this research, a "leaf and stalk" construct was investigated to harvest energy from wind-induced flutter. Fundamental Fluid Structure Interaction (FSI) studies were carried out to experimentally determine the dependence of physical properties of highly compliant cantilever beams on their flutter onset and flutter frequency. The results indicated that the theoretical 2D scaling laws could be extended to 3D environment. Also, for the first time, theoretical and experimental analyses were carried out to understand flutter characteristics of slender connected body systems consisting of revolute hinge, when placed at various positions along the beam. The analysis showed that as the hinge position was varied from the leading edge to the trailing edge of the beam, the system transitioned to higher modes of flutter, thereby reducing the amount of harvestable energy. Several leaf-stalk configurations were also experimentally tested to investigate the possibility of energy harvesting from coupled bending-torsional flutter. High-speed videos were used to identify and differentiate the flutter modes of the configurations to validate the power output results. The results indicated that asymmetrical configurations, when subjected to flutter, are more prone to chaotic flapping and fatigue, thereby reducing the overall power output and effective lifespan of these harvesters. Therefore, two symmetrical-type harvesters were placed in stream-wise, cross-stream and vertical directions to identify the proximity effects of these harvesters on their power output. It was found that when two harvesters were placed in stream-wise direction, at a particular separation distance, the downstream harvester provided 20-40% high power compared to the upstream harvester. The overall purpose of this work was to develop a scalable energy harvesting system for urban sustainability, mainly to power ULP devices like sensors and LED lights