WirelessDT: A Digital Twin Platform for AI-Enabled Wireless Systems

Wireless technologies play a critical role in various domains, including communication, pedestrian localization, autonomous vehicles, and healthcare. Their dependence on radio signals for transmitting information exposes them to vulnerabilities from signal fluctuations, influenced by both wireless sensing systems and environmental dynamics (like crowd densities and stochastic events). This aspect complicates the analysis of wireless signals and related applications. Moreover, integrating AI into these systems requires substantial, high-quality data for training deep learning models, but acquiring such data is often laborious and time-consuming. To overcome these challenges, WirelessDT, a groundbreaking digital twin platform, is proposed for simulating and generating wireless signals in dynamic 3D environments in real-time. It combines a high-performance 3D game engine, a GPU-based real-time ray-tracing (RTRT) rendering pipeline, and adaptive learning algorithms, enhancing data fidelity, application efficiency, and scalability. WirelessDT revolutionizes wireless signal propagation simulation using the 3D engine. Its novel application of GPU RTRT for wireless analysis integrates seamlessly with digital twin technology for a cohesive real-virtual environment blend. Furthermore, an innovative learning-based calibration technique viewing the digital twin system as a generative model is presented to fine-tune parameters using realworld data, significantly improving simulation realism and accuracy. These advancements make WirelessDT a vital tool for applications needing interactive simulation and immediate feedback. Comprehensive evaluations confirm WirelessDT's exceptional data accuracy, application efficiency, and scalability performance

High Accuracy WiFi Sensing for Vital Sign Detection with Multi-Task Contrastive Learning

WiFi sensing has emerged as a promising technique in the healthcare industry, enabling contact-free monitoring of vital signs by detecting changes in WiFi signals resulting from physiological activities. State-of-the-art WiFi sensing uses channel state information (CSI) to analyze signal characteristics, capturing subtle changes due to heartbeats and breathing. However, existing methods face challenges in concurrently measuring respiration and heart rates, and they exhibit high sensitivity to environmental factors and individual differences, limiting the detection accuracy of a trained model in real-world environments. In this paper, we propose a novel multi-task contrastive learning framework for concurrent detection of respiration and heart rates. We introduce multi-task learning with hard-shared layers to exploit the physiological link between breathing and heartbeat. Additionally, we leverage contrastive learning to improve our model's ability to differentiate and prioritize CSI changes related to respiratory and cardiac activities. The experimental results demonstrate the proposed model's ability to accurately measure respiratory and heart rates in challenging scenarios, including long-distance and non-line-of-sight conditions, even when utilizing omnidirectional antennas

The Politics and Governance of the Global Plastics Crisis

Plastics are everywhere and everlasting. They permeate every human and natural system such that they have come to structure political, economic, and social life. Despite increasing evidence of plastic pollution’s adverse effects on human health and the environment, few studies to date have systematically tried to understand who is governing the global plastics crisis (GPC) and how. Individual actors and institutions with idiosyncratic attributes, attitudes, and interests generate collective behaviours informed by their norms, values, and shared ideas. Examining these aspects of the institutional landscape is essential for understanding how patterned interactions constitute governance. Based on a constructivist perspective, this thesis employs network science to operationalise a systems approach to international regime theory. Interviews with 84 professionals in multi-level and multi-sectoral institutions are analysed using visualisations, a social network analysis, and a qualitative analysis of the interview content to reveal the composition of the governance landscape, patterns of institutional linkage, and intersubjective understandings. In doing so, this PhD thesis identifies the elemental institutional building blocks; international regimes at the micro-level; regime complexes at the meso-level; and governance architecture at the macro-level of GPC governance. Based on this original empirical research, this thesis argues that the GPC is governed by two partially overlapping regime complexes for the emerging global plastics treaty and evolving hazardous waste trade within a governance system that perpetuates the unmitigated and accelerating proliferation of plastic pollution. This lays the foundation for ongoing analysis of the evolution of the politics and governance of the GPC

Projection-free methods for solving smooth convex bilevel optimisation problems

When faced with multiple minima of an "inner-level" convex optimisation problem, the convex bilevel optimisation problem selects an optimal solution which also minimises an auxiliary "outer-level" convex objective of interest. Bilevel optimisation requires a different approach compared to single-level optimisation problems since the set of minimisers for the inner-level objective is not given explicitly. In this thesis, we propose new projection-free methods for convex bilevel optimisation which require only a linear optimisation oracle over the base domain. We provide convergence guarantees for both inner- and outer-level objectives that hold under our proposed projection-free methods. In particular, we highlight how our guarantees are affected by the presence or absence of an optimal dual solution. Lastly, we conduct numerical experiments that demonstrate the performance of the proposed methods

Development of a novel ECG-based metric and device for monitoring changes in cerebral blood flow in stroke and other neurological disorders

Stroke is the second leading cause of death and the third leading cause of disability globally. The most common type of stroke, ischaemic stroke, is caused by a sudden restriction in cerebral blood flow (CBF). Computed tomography (CT) perfusion imaging is the most common method to diagnose stroke. However, to limit radiation exposure, typically only a single CT scan is performed at presentation, with the possibility of one or two follow-up scans over the next 72 hours. Therefore, although useful for the initial assessment and treatment decision making, these scans provide only sparse snapshots of CBF in the brain. Following diagnosis and treatment, patients are at risk of early neurological deterioration (END). Notably, there is currently no continuous monitoring of stroke in-between CT scans, and no technologies have been clinically validated to provide such monitoring. This thesis introduces a novel device and electrocardiography (ECG)-based metric, termed the Electrocardiography Brain Perfusion index (EBPi), which may serve as a proxy for changes in CBF. Apparent changes in CBF were monitored in healthy volunteers using a custom-built head-worn device to wirelessly capture the signals required for EBPi computation. A next-generation device was shown to be safe and well tolerated by ischaemic stroke patients in an acute care environment providing a preliminary assessment of the clinical utility of EBPi. Finally, the feasibility of repurposing existing multi-electrode electroencephalography (EEG)/ECG protocols used in seizure monitoring of epilepsy patients to compute EBPi retrospectively (or simultaneously) was demonstrated and suggests that EBPi could augment current monitoring techniques in that context. Future work should aim to explore the clinical utility of EBPi (in isolation and in combination with quantitative EEG measures) for the continuous monitoring of stroke patients between CT scans to monitor disease progression, treatment outcome and detect END

Uncertain Waters: A Game of Collective Cooperation and Climate Change

This cooperative tabletop game immerses players in the complexities of managing flood risk under climate change. Players collaborate to implement adaptation strategies to protect community assets from flood impacts. Through dynamic gameplay and real-world decision-making scenarios, players must plan for evolving uncertainty and carefully allocate their resources to build a flood-resilient community. The game is suitable for 4-7 players and for ages 10+. The game is suitable for a range of applications and audiences, from use as a teaching tool for students to facilitating conversations between governments and communities

Anticipating Hazards in Machine Translations of Public Health Resources via Advanced Text Classification Pipelines

Public health educational resources developed by health institutions aim for high accessibility of information. The translation of these resources, which provide the public with a basic understanding of health risks and diseases, is commonly conducted by professional translators to cater to diverse linguistic and cultural backgrounds. In recent years, the global advancement of information technology has broadened the use of Machine Translation (MT) in online health education and promotion. MT tools such as Google Translate, DeepL, and ChatGPT have significantly improved performance, yet they face challenges posed by the language complexity, content complexity, and formality of professional medical resources. In this study, we leverage Natural Language Processing (NLP) and Machine Learning (ML) tools to harness the power of text classification, a vital task that assigns text to one or more predefined categories. We aim to develop machine learning classifiers within our newly proposed Multi-Dimensional Text Classification Pipeline (MD-TCP) framework. As a risk-prevention mechanism, MD-TCP assists medical professionals with limited knowledge of the patient's language and helps patients who wish to self-navigate. Our model predicts the likelihood of clinical mistakes or incomprehensible machine translation outputs based on the features of English source input to the machine translation systems. MD-TCP is a new, comprehensive pipeline for data mining and feature extraction that we developed to achieve this goal. The pipeline has demonstrated significant improvements in both of our datasets. Regarding Accuracy, AUC, Sensitivity, Precision, and Specificity, our method improved by 24% - 33% compared to baseline methods. This underscores the potential of machine learning, mainly when implemented through MD-TCP, in predicting translation errors, thereby ensuring more accurate and understandable translations for health resources across diverse populations

Applying principles of motor learning to speech intervention for children with cleft palate: is it effective?

Cleft palate with/without cleft lip (CP+/-L) is a common congenital defect and it is known to impact speech development. Minimising speech and resonance difficulties is a primary aim of CP+/-L repair. However, it is estimated that between 50-70 percent of children with CP+/-L will require speech pathology intervention, even after surgical repair. There is currently a paucity of evidence to support any speech intervention approach with children with CP+/-L, making it difficult for clinicians to choose an evidence-based intervention approach. Principles of motor learning (PML) provide clinicians with advice on the structure and frequency of practice and how to provide feedback that best facilitates learning, retention, and transfer of skill. However, despite the growing body of literature to support the principles of motor learning in the treatment of motor speech disorders in children, little is known about the effectiveness of using PML with children with CP+/-L. This research aimed to determine the effectiveness of speech interventions that apply PML, both clinician-led and parent-led app-based, in improving speech outcomes at word level for children with CP+/-L. Both clinician-led and parent-led app-based speech interventions were effective for children with cleft related speech errors. Nine of the eleven children experienced improvement for all phases of treatment completed, and the remaining two children made improvements in one of the two phases completed

Explore the anticancer and antimetastatic effect of soluble epoxide hydrolase inhibitors (sEHI) in the treatment of human uveal melanoma (UM) in 2d and 3d cell culture models

Uveal melanoma is the primary form of eye cancer– a rare but deadly disease with up to 50% of the patients’ died from metastasis. Patients with metastatic UM have a lifespan of up to 18 months. There are few proven pharmacological treatments for UM, which are associated with suboptimal clinical outcomes. The pathogenesis of UM is largely unknown, which greatly limited the progress of UM drug discovery. This project is to screen novel candidate drugs from a group of kinase inhibitors for the treatment of UM, which will address the unmet need to treat this rare but deadly cancer. Soluble epoxide hydrolase (sEH) is a key enzyme involved in fatty acid metabolism, which is involved in cell mitochondrial function. Its inhibitors have been shown to exhibit anti-cancer effect; however, little is known regarding their applications in the treatment of UM. This study screened the anti-cancer and anti-metastatic effect of a group of in-house synthesised epoxide hydrolase inhibitors (sEHIs) in UM cell lines. UC2288 is the lead molecule selected from this compound library due to its potent effect in reducing cell viability in UM cell lines. Further molecular assays have been performed to evaluate the anti-UM effect of this lead molecule in both 2D and 3D UM cell culture models. The findings in this study indicated that 2288 is a novel drug candidate for UM, which shall be further investigated in in vivo settings

Smart Technologies, Loneliness and Young Adults: Towards an Updated Understanding of the People-Place Relationship in Urban Open Space

With the rise of smart cities, increasing technologies have been introduced in the cities’ public realm and are intertwined with our social life. Although technology enables our cities to be smarter places, it is nevertheless a double-edged sword that may reinforce urban dwellers’ loneliness. Loneliness is a personal feeling, but it also has become a widespread social phenomenon that might have been affected by the built environment. This research asks how smart technologies in public open spaces impact people-place relationships and perceptions of loneliness. This study contributes to the theoretical discussion on public space planning and public social life in the smart city era, with special attention given to young city dwellers. A case study framework and qualitative methods are used in this study. It investigates three smart technology case studies in public open spaces: smart furniture, a smart device and a smart festival as case studies. The three case studies represent three modalities of smartness mediated in the public realm and are likely to lead to various scales of social interactions. Through unpacking 41 in-depth interviews of young adult users, technology designers, and academic experts in urban and loneliness fields, the study sheds light on how public spaces’ form and social function and people-place relationships are altered by smart technologies. The study also offers nuanced insight into contemporary public spaces as a social resource, young adults’ public social life in real and virtual spaces, and links between smart technology use and young adults’ loneliness