Deep Learning for Decision Making and Autonomous Complex Systems

Deep learning consists of various machine learning algorithms that aim to learn multiple levels of abstraction from data in a hierarchical manner. It is a tool to construct models using the data that mimics a real world process without an exceedingly tedious modelling of the actual process. We show that deep learning is a viable solution to decision making in mechanical engineering problems and complex physical systems. In this work, we demonstrated the application of this data-driven method in the design of microfluidic devices to serve as a map between the user-defined cross-sectional shape of the flow and the corresponding arrangement of micropillars in the flow channel that contributed to the flow deformation. We also present how deep learning can be used in the early detection of combustion instability for prognostics and health monitoring of a combustion engine, such that appropriate measures can be taken to prevent detrimental effects as a result of unstable combustion. One of the applications in complex systems concerns robotic path planning via the systematic learning of policies and associated rewards. In this context, a deep architecture is implemented to infer the expected value of information gained by performing an action based on the states of the environment. We also applied deep learning-based methods to enhance natural low-light images in the context of a surveillance framework and autonomous robots. Further, we looked at how machine learning methods can be used to perform root-cause analysis in cyber-physical systems subjected to a wide variety of operation anomalies. In all studies, the proposed frameworks have been shown to demonstrate promising feasibility and provided credible results for large-scale implementation in the industry

Effective image enhancement and fast object detection for improved UAV applications

As an emerging field, unmanned aerial vehicles (UAVs) feature from interdisciplinary techniques in science, engineering and industrial sectors. The massive applications span from remote sensing, precision agriculture, marine inspection, coast guarding, environmental monitoring, natural resources monitoring, e.g. forest, land and river, and disaster assessment, to smart city, intelligent transportation and logistics and delivery. With the fast growing demands from a wide range of application sectors, there is always a bottleneck how to improve the efficiency and efficacy of UAV in operation. Often, smart decision making is needed from the captured footages in a real-time manner, yet this is severely affected by the poor image quality, ineffective object detection and recognition models, and lack of robust and light models for supporting the edge computing and real deployment. In this thesis, several innovative works have been focused and developed to tackle some of the above issues. First of all, considering the quality requirements of the UAV images, various approaches and models have been proposed, yet they focus on different aspects and produce inconsistent results. As such, the work in this thesis has been categorised into denoising and dehazing focused, followed by comprehensive evaluation in terms of both qualitative and quantitative assessment. These will provide valuable insights and useful guidance to help the end user and research community. For fast and effective object detection and recognition, deep learning based models, especially the YOLO series, are popularly used. However, taking the YOLOv7 as the baseline, the performance is very much affected by a few factors, such as the low quality of the UAV images and the high-level of demanding of resources, leading to unsatisfactory performance in accuracy and processing speed. As a result, three major improvements, namely transformer, CIoULoss and the GhostBottleneck module, are introduced in this work to improve feature extraction, decision making in detection and recognition, and running efficiency. Comprehensive experiments on both publicly available and self-collected datasets have validated the efficiency and efficacy of the proposed algorithm. In addition, to facilitate the real deployment such as edge computing scenarios, embedded implementation of the key algorithm modules is introduced. These include the creative implementation on the Xavier NX platform, in comparison to the standard workstation settings with the NVIDIA GPUs. As a result, it has demonstrated promising results with improved performance in reduced resources consumption of the CPU/GPU usage and enhanced frame rate of real-time processing to benefit the real-time deployment with the uncompromised edge computing. Through these innovative investigation and development, a better understanding has been established on key challenges associated with UAV and Simultaneous Localisation and Mapping (SLAM) based applications, and possible solutions are presented. Keywords: Unmanned aerial vehicles (UAV); Simultaneous Localisation and Mapping (SLAM); denoising; dehazing; object detection; object recognition; deep learning; YOLOv7; transformer; GhostBottleneck; scene matching; embedded implementation; Xavier NX; edge computing.As an emerging field, unmanned aerial vehicles (UAVs) feature from interdisciplinary techniques in science, engineering and industrial sectors. The massive applications span from remote sensing, precision agriculture, marine inspection, coast guarding, environmental monitoring, natural resources monitoring, e.g. forest, land and river, and disaster assessment, to smart city, intelligent transportation and logistics and delivery. With the fast growing demands from a wide range of application sectors, there is always a bottleneck how to improve the efficiency and efficacy of UAV in operation. Often, smart decision making is needed from the captured footages in a real-time manner, yet this is severely affected by the poor image quality, ineffective object detection and recognition models, and lack of robust and light models for supporting the edge computing and real deployment. In this thesis, several innovative works have been focused and developed to tackle some of the above issues. First of all, considering the quality requirements of the UAV images, various approaches and models have been proposed, yet they focus on different aspects and produce inconsistent results. As such, the work in this thesis has been categorised into denoising and dehazing focused, followed by comprehensive evaluation in terms of both qualitative and quantitative assessment. These will provide valuable insights and useful guidance to help the end user and research community. For fast and effective object detection and recognition, deep learning based models, especially the YOLO series, are popularly used. However, taking the YOLOv7 as the baseline, the performance is very much affected by a few factors, such as the low quality of the UAV images and the high-level of demanding of resources, leading to unsatisfactory performance in accuracy and processing speed. As a result, three major improvements, namely transformer, CIoULoss and the GhostBottleneck module, are introduced in this work to improve feature extraction, decision making in detection and recognition, and running efficiency. Comprehensive experiments on both publicly available and self-collected datasets have validated the efficiency and efficacy of the proposed algorithm. In addition, to facilitate the real deployment such as edge computing scenarios, embedded implementation of the key algorithm modules is introduced. These include the creative implementation on the Xavier NX platform, in comparison to the standard workstation settings with the NVIDIA GPUs. As a result, it has demonstrated promising results with improved performance in reduced resources consumption of the CPU/GPU usage and enhanced frame rate of real-time processing to benefit the real-time deployment with the uncompromised edge computing. Through these innovative investigation and development, a better understanding has been established on key challenges associated with UAV and Simultaneous Localisation and Mapping (SLAM) based applications, and possible solutions are presented. Keywords: Unmanned aerial vehicles (UAV); Simultaneous Localisation and Mapping (SLAM); denoising; dehazing; object detection; object recognition; deep learning; YOLOv7; transformer; GhostBottleneck; scene matching; embedded implementation; Xavier NX; edge computing

SEPARATING LAYERS IN IMAGES AND ITS APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Communicating the Unspeakable: Linguistic Phenomena in the Psychedelic Sphere

Psychedelics can enable a broad and paradoxical spectrum of linguistic phenomena from the unspeakability of mystical experience to the eloquence of the songs of the shaman or curandera. Interior dialogues with the Other, whether framed as the voice of the Logos, an alien download, or communion with ancestors and spirits, are relatively common. Sentient visual languages are encountered, their forms unrelated to the representation of speech in natural language writing systems. This thesis constructs a theoretical model of linguistic phenomena encountered in the psychedelic sphere for the field of altered states of consciousness research (ASCR). The model is developed from a neurophenomenological perspective, especially the work of Francisco Varela, and Michael Winkelman’s work in shamanistic ASC, which in turn builds on the biogenetic structuralism of Charles Laughlin, John McManus, and Eugene d’Aquili. Neurophenomenology relates the physical and functional organization of the brain to the subjective reports of lived experience in altered states as mutually informative, without reducing consciousness to one or the other. Consciousness is seen as a dynamic multistate process of the recursive interaction of biology and culture, thereby navigating the traditional dichotomies of objective/subjective, body/mind, and inner/outer realities that problematically characterize much of the discourse in consciousness studies. The theoretical work of Renaissance scholar Stephen Farmer on the evolution of syncretic and correlative systems and their relation to neurobiological structures provides a further framework for the exegesis of the descriptions of linguistic phenomena in first-person texts of long-term psychedelic selfexploration. Since the classification of most psychedelics as Schedule I drugs, legal research came to a halt; self-experimentation as research did not. Scientists such as Timothy Leary and John Lilly became outlaw scientists, a social aspect of the “unspeakability” of these experiences. Academic ASCR has largely side-stepped examination of the extensive literature of psychedelic selfexploration. This thesis examines aspects of both form and content from these works, focusing on those that treat linguistic phenomena, and asking what these linguistic experiences can tell us about how the psychedelic landscape is constructed, how it can be navigated, interpreted, and communicated within its own experiential field, and communicated about to make the data accessible to inter-subjective comparison and validation. The methodological core of this practice-based research is a technoetic practice as defined by artist and theoretician Roy Ascott: the exploration of consciousness through interactive, artistic, and psychoactive technologies. The iterative process of psychedelic self-exploration and creation of interactive software defines my own technoetic practice and is the means by which I examine my states of consciousness employing the multidimensional visual language Glide