<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>

<p>The "Maker Movement" is a cultural phenomena rooted in DIY culture, which stresses making devices and creations on your own rather than purchasing it ready-made. At the core of the Maker Movement, is the "Maker Mindset"; a collection of attitudes, beliefs and behaviors that emphasize the importance of creativity, experimentation and innovation in the learning process. Since the Maker Mindset embodies constructionist principles at its core that push makers to experiment and problem-solve by collaborating with fellow makers through hands-on activities, it can be said that these activities comprise of Creative, Learning and Collaborative experiences. While Internet-of-Things devices have long been used to enhance these activities, research pertaining to using Augmented Reality in tandem with IoT for the purpose of enhancing experiences core to the Maker Mindset is relatively unexplored. Three different systems were developed with the goal of addressing this -- MicrokARts, ShARed IoT and MechARspace. Each system focuses on enhancing one of the three core experiences through AR-compatible IoT devices, whilst ensuring that they do not require prerequisite knowledge in order to author AR experiences. These systems were evaluated through user studies and testing over a variety of age-groups, with each system successfully enhancing one core experience each through the use of AR-IoT interactions.</p>