This thesis explores the potential of utilizing metal-based plasma sources as a sustainable solution for high-powered electric propulsion and its implications for future interplanetary travel. Focusing on the Vacuum Arc Thruster and the Variable Specific Impulse Magnetoplasma Rocket engine, the study encompasses numerical simulations, analytical comparisons, and performance analyses to assess the feasibility of metal plasma fuels in space missions.The numerical analysis employs COMSOL Multiphysics to delve into the magnetohydrodynamics behavior within the VAT. Such simulation setup could provide valuable insights. Although the numerical results are disappointing for this paper, there exist possibilities within future work. The main hurdle is the simulation of vacuum. There are workarounds in COMSOL's Vacuum System Modeling tool which was not available for this thesis. Also, the used material properties were not suited for this high temperature plasma environment. The lack of material properties is a consequence of the insufficient research in the metal plasma field.Performance analysis is conducted on both the VAT and VASIMR engine, exploring efficiency, thrust capabilities, and feasibility for interplanetary missions. The results demonstrate the potential of metal-based plasma sources to reduce dependence on Earth for refueling and decrease mission costs. It is found that aluminum and magnesium have similar performance as the argon gas used in the VASIMR.Although challenges exist, such as integration problems and availability of material properties for metals in plasma states, the study underscores the promise of metal plasma fuels for sustainable space exploration. By advancing high-powered electric propulsion technologies, we move closer to realizing humanity's ambitious journey to distant celestial bodies. This research paves the way for future innovations, enabling a more self-sustaining space economy and unlocking new horizons of interplanetary travel.

Vibration converters based on piezoelectric materials are currently becoming increasingly important for powering low-power wireless sensor nodes and wearable electronic devices. Piezoelectric materials generate variable electrical charges under mechanical stress, requiring an energy management interface to meet load requirements. Resonant interfaces like Parallel Synchronized Switch Harvesting on Inductor (P-SSHI) are highly efficient and robust to energy sources and loads variations. Nevertheless, SSHI circuits require synchronous switch control for efficient energy transfer. At irregular excitation, SSHI circuits may not perform optimally because the resonant frequency of the circuit is typically tuned to match the frequency of the energy source, which in the case of footsteps can be irregular and unpredictable. In addition, the circuit may also be susceptible to noise and interference from irregular excitations, which can further affect its performance. The aim is to design a self-powered energy management solution that can operate autonomously even at low frequencies and for irregular chock excitations, while at the same time allowing higher energy flow to the energy storage device and maintaining high levels of energy efficiency. To evaluate the performance of the proposed circuit, a piezoelectric shoe insole is designed and used for testing with different storage capacitance values and loads as a proof of the circuit’s adaptability to various loading conditions.:1 Introduction 2 Theoretical background 3 State of the art of piezoelectric energy harvesting interfaces 4 Novel approach of SP-PSSHI piezoelectric energy harvesting interface 5 Experimental investigations 6 Conclusions and Outlook

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The dissertation aims to expand access through a low-cost technological innovation system S-MLS to learners in underdeveloped areas with difficulties in accessing education. Technology is advancing rapidly. However, many parts of the world need access to educational advances, which are hindered due to war, political situations, and low literacy and income. A qualitative phenomenological approach explores the lived experience using the solar-powered computing and learning management system (LMS) to support the development of educational access in underrepresented societies, developing countries, and rural areas where access to proper classroom education is non-existent. Proof of concept is used with a group of students in a rural area, a developing country, and within an underrepresented population to check the feasibility of using the equipment in a real-world setting. A technology acceptance model would be used to identify the user's perceived interest and user acceptance. The community of inquiry theory would find the first-hand experience and point of view of the learner. The student group interviews would be through semi-structured interviews. Observations, surveys, video/audio recordings, and artifacts would be gathered for further analysis. The data collected would be analyzed using interpretative phenomenology analysis (IPA), close examination, and management of development themes through thoughts, observations, and reflections on the technological experience and future research and implementations provided. The projected finding would be to check that a solar-powered Raspberry Pi system with MoodleBox operating system that runs Moodle (Modular Object-Oriented Dynamic Learning Environment) LMS would be feasible to provide learning underdeveloped areas to enhance education.

<p dir="ltr">Computer vision (CV) using deep learning can equip machines with the ability to understand visual information. CV has seen widespread adoption across numerous industries, from autonomous vehicles to facial recognition on smartphones. However, alongside these advancements, there have been increasing concerns about reproducing the results. The difficulty of reproducibility may arise due to multiple reasons, such as differences in execution environments, missing or incompatible software libraries, proprietary data, and the stochastic nature in some software. A study conducted by the Nature journal reveals that more than 70% of researchers failed to reproduce other researcher's experiments; over 50% failed to reproduce their own experiments. Given the critical role that computer vision plays in many applications, for example in edge devices like mobile phones and drones, irreproducibility poses significant challenges for researchers and practitioners. To address these concerns, this thesis presents a systematic approach at analyzing and improving the reproducibility of computer vision models through case studies. This approach combines rigorous documentation standards, standardized software environment, and a comprehensive guide of best practices. By implementing these strategies, we aim to bridge the gap between research and practice, ensuring that innovations in computer vision can be effectively reproduced and deployed. </p>

In healthcare, the rising demand for medical services, compounded by a shortage of professionals, presents significant challenges. To address these issues, the healthcare industry has turned to artificial intelligence (AI) to enhance various services such as disease diagnosis, medical imaging interpretation, clinical laboratory tasks, screenings, and health communications. By offering real-time, human-like interactions, AI-driven chatbots facilitate access to healthcare information and services, aiding symptom analysis and providing preliminary disease information before professional consultations. This initiative aims not only to reduce healthcare costs but also to enhance patient access to medical data. Despite their growing popularity, AI-enabled chatbots or conversational agents chatbots in the healthcare disease diagnosis domain continue to encounter obstacles such as a limited user adoption and integration into healthcare systems. This study addresses a gap in the existing literature on the adoption of AI enabled healthcare disease diagnosis chatbots by analysing the elements that influence users' behavioural intention to utilize AI-enabled disease diagnosis chatbots. Employing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical framework, this quantitative study began with exploratory research to define its scope and context, followed by a survey of 130 participants. The study utilized multiple linear regression and Pearson correlation analysis to evaluate the data. The outcomes suggest that performance expectancy, habits, social influence, and trust significantly associated with the individuals’ behavioural intentions to use AI-enabled chatbots for disease diagnosis. The results of this study reveal that performance expectancy, habits, social influence, and trust significant association with intention to use AI-enabled chatbots for disease diagnosis. The outcomes of this study contribute to existing knowledge in information systems, particularly identifying key factors that boost user adoption of AI-enabled chatbot applications for disease diagnosis. These insights can guide system designers, developers, marketers, and promotors involved in developing, revamping, and promoting chatbot applications, considering the influential factors discovered in this research, thereby increasing the usage of chatbot apps. Furthermore, the research model developed here could serve as a valuable model for future studies on disease diagnostic chatbot applications.

Smart sensors have become and will continue to constitute an enabling technology to wirelessly connect platforms and systems and enable improved and autonomous performance. Automobiles have about two hundred sensors. Airplanes have about eight thousand sensors. With technology advancements in autonomous vehicles or fly-by-wireless, the numbers of these sensors is expected to increase significantly. The need to conserve water and energy has led to the development of advanced metering infrastructure (AMI) as a concept to support smart energy and water grid systems that would respond to emergency shut-offs or electric blackouts. Through the Internet of things (IoT) smart sensors and other network devices will be connected to enable exchange and control procedure toward reducing the operational cost and improving the efficiency of residential and commercial buildings in terms of their function or energy and water use. Powering these smart sensors with batteries or wires poses great challenges in terms of replacing the batteries and connecting the wires especially in remote and difficult-to-reach locations. Harvesting free ambient energy provides a solution to develop self-powered smart sensors that can support different platforms and systems and integrate their functionality. In this dissertation, we develop and experimentally assess the performance of harvesters that draw their energy from air or water flows. These harvesters include centimeter-scale micro wind turbines, piezo aeroelastic harvesters, and micro hydro generators. The performance of these different harvesters is determined by their capability to support wireless sensing and transmission, the level of generated power, and power density. We also develop and demonstrate the capability of multifunctional systems that can harvest energy to replenish a battery and use the harvested energy to sense speed, flow rate or temperature, and to transmit the data wirelessly to a remote location. / PHD / Smart sensors are an essential part of planned connected communities, smart cities and buildings, structural health and pollution monitoring, and autonomous systems including air and ground vehicles. For example, these sensors can be used to monitor different buildings functions such as water flow rates, pressure and temperature, smoke detectors, HVAC and fire alarms systems. Most of the current smart sensors are powered by batteries or connected to a power source with wires. Batteries will need to be replaced frequently. Wires will add a cost and weight to the system. On the other hand, energy can be harvested locally from different sources to power these sensors. In this dissertation, we develop and experimentally assess the performance of energy harvesters that draw power from air or water flows. These devices include centimeter-scale micro wind turbines, piezo aeroelastic harvesters, and micro hydro generators. The level of generated power, and power density of these devices and their capability to support wireless sensing and transmission are evaluated. We also develop and demonstrate the capability of using one device to harvest energy to replenish a battery over specified time periods and use the harvested energy and the same device to sense speed, flow rate or temperature, and to transmit the data wirelessly to a remote location over other time periods.

Amyotrophic lateral sclerosis (ALS) is a terminal neurodegenerative disease that leads to a lack of independent mobility. One solution uses a unilateral surface EMG (sEMG) interface on the temporalis muscle to provide autonomous control of a powered wheelchair. Limbitless Journey, an EMG-controlled serious game, intends to provide users with a virtual environment to train in before use in a real-world scenario. A recent study analyzed the effect of video game training on the use of sEMG systems on the forearm, showing significant improvement in the usage of the interface but no difference between Free Play and structured play. The study of interest emulates this investigation while extending its generalizability by using the temporalis muscle and incorporating Limbitless Journey as an alternative training method. Participants first played another training game, Limbitless Runner’s Ring Challenge, as a pre-test, requiring participants to flex at different strengths to jump through hoops of various heights. Participants then completed serious game training, consisting of Journey, Runner’s Ring Challenge, or Runner’s Free Play modes. The pre-test was repeated to detect variation in the score. Two post-assessment surveys were utilized to determine perceptions of the video game training and the usability of the sEMG training system for video game control.

Low-frequency vibrations typically occur in many practical structures and systems when in use, for example, in aerospaces and industrial machines. Piezoelectric materials feature compactness, lightweight, high integration potential, and permit to transduce mechanical energy from vibrations into electrical energy. Because of their properties, piezoelectric materials have been receiving growing interest during the last decades as potential vibration- harvested energy generators for the proliferating number of embeddable wireless sensor systems in applications such as structural health monitoring (SHM). The basic idea behind piezoelectric energy harvesting (PEH) powered architectures, or energy harvesting (EH) more in general, is to develop truly “fit and forget” solutions that allow reducing physical installations and burdens to maintenance over battery-powered systems. However, due to the low mechanical energy available under low-frequency conditions and the relatively high power consumption of wireless sensor nodes, PEH from low-frequency vibrations is a challenge that needs to be addressed for the majority of the practical cases. Simply saying, the energy harvested from low-frequency vibrations is not high enough to power wireless sensor nodes or the power consumption of the wireless sensor nodes is higher than the harvested energy. This represents a main barrier to the widespread use of PEH technology at the current state of the development, despite the advantages it may offer. The main contribution of this research work concerns the proposal of a novel EH circuitry, which is based on a whole-system approach, in order to develop enhanced PEH powered wireless sensor nodes, hence to compensate the existing mismatch between harvested and demanded energy. By whole-system approach, it is meant that this work develops an integrated system-of-systems rather than a single EH unit, thus getting closer to the industrial need of a ready- to-use energy-autonomous solution for wireless sensor applications such as SHM. To achieve so, this work introduces: Novel passive interfaces in connection with the piezoelectric harvester that permit to extract more energy from it (i.e., a complex conjugate impedance matching (CCIM) interface, which uses a PC permalloy toroidal coil to achieve a large inductive reactance with a centimetre- scaled size at low frequency; and interfaces for resonant PEH applications, which exploit the harvester‟s displacement to achieve a mechanical amplification of the input force, a magnetic and a mechanical activation of a synchronised switching harvesting on inductor (SSHI) mechanism). A novel power management approach, which permits to minimise the power consumption for conditioning the transduced signal and optimises the flow of the harvested energy towards a custom-developed wireless sensor communication node (WSCN) through a dedicated energy-aware interface (EAI); where the EAI is based on a voltage sensing device across a capacitive energy storage. Theoretical and experimental analyses of the developed systems are carried in connection with resistive loads and the WSCN under excitations of low frequency and strain/acceleration levels typical of two potential energy- autonomous applications, that are: 1) wireless condition monitoring of commercial aircraft wings through non-resonant PEH based on Macro-Fibre Composite (MFC) material bonded to aluminium and composite substrates; and wireless condition monitoring of large industrial machinery through resonant PEH based on a cantilever structure. shown that under similar testing conditions the developed systems feature a performance in comparison with other architectures reported in the literature or currently available on the market. Power levels up to 12.16 mW and 116.6 µW were respectively measured across an optimal resistive load of 66 277 kΩ for an implemented non-resonant MFC energy harvester on aluminium substrate and a resonant cantilever-based structure when no interfaces were added into the circuits. When the WSCN was connected to the harvesters in place of the resistive loads, data transmissions as fast as 0.4 and s were also respectively measured. By use of the implemented passive interfaces, a maximum power enhancement of around 95% and 452% was achieved in the two tested cases and faster data transmissions obtained with a maximum percentage improvement around 36% and 73%, respectively. By the use of the EAI in connection with the WSCN, results have also shown that the overall system‟s power consumption is as low as a few microwatts during non- active modes of operation (i.e., before the WSCN starts data acquisition and transmission to a base station). Through the introduction of the developed interfaces, this research work takes a whole-system approach and brings about the capability to continuously power wireless sensor nodes entirely from vibration-harvested energy in time intervals of a few seconds or fractions of a second once they have been firstly activated. Therefore, such an approach has potential to be used for real-world energy- autonomous applications of SHM.

This dissertation progressively addresses research problems related to the trajectory optimization for fuel cell powered UAVs, from propulsion system model development, to optimal trajectory analyses and optimal trajectory applications. A dynamic model of a fuel cell powered UAV propulsion system is derived by combining a fuel cell system dynamic model, an electric motor dynamic model, and a propeller performance model. The influence of the fuel cell system dynamics on the optimal trajectories of a fuel cell powered UAV is investigated in two phases. In the first phase, the optimal trajectories of a fuel cell powered configuration and that of a conventional gas powered configuration are compared for point-to-point trajectory optimization problems with different performance index functions. In the second phase, the influence of the fuel cell system parameters on the optimal fuel consumption cost of the minimum fuel point-to-point optimal trajectories is investigated. This dissertation also presents two applications for the minimum fuel point-to-point optimal trajectories of a fuel cell powered UAV: three-dimensional minimum fuel route planning and path generation, and fuel cell system size optimization with respect to a UAV mission.