Active Extraordinary Optical Transmission Metasurfaces Using Phase-Change Materials

The key question that this thesis aims to answer is “can a tuneable bandpass optical filter for the mid-infrared regime be made by combining extraordinary optical transmission (EOT) arrays and phase-change materials (PCMs)?”. It is proposed that such devices may be useful for a wide range of applications where the ability to dynamically change the transmissive (or reflective) properties of a filter is required, include multispectral sensing/imaging and signal modulation amongst others. Current multispectral imaging systems are mainly dependant on either multiple sets of lenses and sensors, or multiple mechanically-exchanged filters exposed through in-sequence; the use of a single, dynamically tuneable phase-change EOT-based filter opens avenues to reducing systems’ size, cost and complexity. The EOT effect is observed with arrays of sub-wavelength-sized holes in thin plasmonic metal (e.g. gold) films, with the transmission peak position dependent on the array geometry and surrounding materials’ optical properties: a PCM layer on top of the array allows shifting of the transmission peak position by switching the PCM phase (and its refractive index) via heat pulses. Specific areas studied in this thesis include the use of different fabrication methods to make phase-change EOT transmission filters for the mid-infrared regime, including electron-beam lithography-based techniques and a novel (and much faster) approach of direct patterning via laser ablation. Tuneable filters for use in various parts of the optical spectrum, especially the mid and long-wave infrared range, were designed, simulated, fabricated and characterised. Good performance was obtained for phase-change EOT filters over a wide range of array pitch sizes. EOT arrays designed for the mid-infrared range and fabricated via wet-etching and measured with FTIR spectroscopy produced very similar spectra to those of finite-element simulations with peak transmittance of Q-factors between 5-6 and a peak transmittance of ~0.8. Laser-ablated arrays showed a similar (though not quite so good) performance, due mainly to slight irregularities in the positioning of holes in the array. The addition of a phase-change layer, specifically Ge2Sb2Te5, to the EOT arrays resulted in a shift in the wavelength of the peak transmission, with the amount of shift depending on the phase-state (crystalline, amorphous, or mixed-state) of the phase-change layer, so demonstrating the ability for dynamic tuning of the filter response by switching of the phase-change layer. An important requirement for proper and prolonged operation of the filter devices was found to be the use of a thin dielectric barrier layer (here Si3N4, between the plasmonic film and the phase-change layer, to prevent inter-diffusion between the two: reflection cavities of Ge2Sb2Te5 on unpatterned gold layers were created to investigate this effect, with resonance features of a 20 nm layer being destroyed without a barrier layer present and the complete assimilation of gold and phase-change layers evident with cross-section TEM imaging

Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background

Detection and representation of moving objects for video surveillance

In this dissertation two new approaches have been introduced for the automatic detection of moving objects (such as people and vehicles) in video surveillance sequences. The first technique analyses the original video and exploits spatial and temporal information to find those pixels in the images that correspond to moving objects. The second technique analyses video sequences that have been encoded according to a recent video coding standard (H.264/AVC). As such, only the compressed features are analyzed to find moving objects. The latter technique results in a very fast and accurate detection (up to 20 times faster than the related work). Lastly, we investigated how different XML-based metadata standards can be used to represent information about these moving objects. We proposed the usage of Semantic Web Technologies to combine information described according to different metadata standards

Phase change thermal energy storage for the thermal control of large thermally lightweight indoor spaces

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Energy storage using Phase Change Materials (PCMs) offers the advantage of higher heat capacity at specific temperature ranges, compared to single phase storage. Incorporating PCMs in lightweight buildings can therefore improve the thermal mass, and reduce indoor temperature fluctuations and energy demand. Large atrium buildings, such as Airport terminal spaces, are typically thermally lightweight structures, with large open indoor spaces, large glazed envelopes, high ceilings and non-uniform internal heat gains. The Heating, Ventilation and Air-Conditioning (HVAC) systems constitute a major portion of the overall energy demand of such buildings. This study presented a case study of the energy saving potential of three different PCM systems (PCM floor tiles, PCM glazed envelope and a retrofitted PCM-HX system) in an airport terminal space. A quasi-dynamic coupled TRNSYS®-FLUENT® simulation approach was used to evaluate the energy performance of each PCM system in the space. FLUENT® simulated the indoor air-flow and PCM, whilst TRNSYS® simulated the HVAC system. Two novel PCM models were developed in FLUENT® as part of this study. The first model improved the phase change conduction model by accounting for hysteresis and non-linear enthalpy-temperature relationships, and was developed using data from Differential Scanning Calorimetry tests. This model was validated with data obtained in a custom-built test cell with different ambient and internal conditions. The second model analysed the impact of radiation on the phase change behaviour. It was developed using data from spectrophotometry tests, and was validated with data from a custom-built PCM-glazed unit. These developed phase change models were found to improve the prediction errors with respect to conventional models, and together with the enthalpy-porosity model, they were used to simulate the performance of the PCM systems in the airport terminal for different operating conditions. This study generally portrayed the benefits and flexibility of using the coupled simulation approach in evaluating the building performance with PCMs, and showed that employing PCMs in large, open and thermally lightweight spaces can be beneficial, depending on the configuration and mode of operation of the PCM system. The simulation results showed that the relative energy performance of the PCM systems relies mainly on the type and control of the system, the night recharge strategy, the latent heat capacity of the system, and the internal heat gain schedules. Semi-active systems provide more control flexibility and better energy performance than passive systems, and for the case of the airport terminal, the annual energy demands can be reduced when night ventilation of the PCM systems is not employed. The semi-active PCM-HX-8mm configuration without night ventilation, produced the highest annual energy and CO2 emissions savings of 38% and 23%, respectively, relative to a displacement conditioning (DC) system without PCM systems.UK Engineering and Physical Sciences Research Counci

Issues in NASA Program and Project Management: Focus on Project Planning and Scheduling

Topics addressed include: Planning and scheduling training for working project teams at NASA, overview of project planning and scheduling workshops, project planning at NASA, new approaches to systems engineering, software reliability assessment, and software reuse in wind tunnel control systems

Sustainability-oriented housing innovation: Using the Solar Decathlon as a knowledge source

The Solar Decathlon competition started in 2002. Since then, Solar Decathlon has acted as a showcase and source of innovation in the field of sustainability for housing and for the construction industry at large. This thesis has utilised data from Solar Decathlon competitions to understand the nature of innovations involved with progressively building and refining the technology required for sustainable housing. As such, the focus and drive of this thesis is to present an image of the Solar Decathlon competition as openly creating and synthesising new knowledge about sustainability-oriented innovation. It can be stated that understanding the precise factors that make innovation happen can be convoluted in nature. The Solar Decathlon portrays these characteristics, is internationally recognised as the premier competition for prototyping sustainability-oriented innovation, and demonstrates the human features involved with progress in the field of sustainable housing. The thesis is original in that it is the first that addresses innovation and its management through human-centred design, and describes its processes, that can be henceforth taken up by the building industry. Utilising the experience of the Innovations Coordinator of Team UOW (University of Wollongong), this thesis describes and analyses the nature of innovation involved with the Desert Rose house (UOW Solar Decathlon entry), including knowledge of how innovation happened in real time during its construction. This thesis asks the question: What is the nature of innovation involved with sustainable housing? The answer to this question is not resolved simply through experiencing the construction of the Desert Rose, or through an objective analysis of the available Solar Decathlon data sets. Rather, this thesis proposes that the answer can be obtained through comprehensive multi-disciplinary research, including: (i) analysis of available innovation related Solar Decathlon data sets from leading houses, (ii) the development of an innovations management framework for sustainability-oriented technology, (iii) a case study of the Desert Rose Solar Decathlon entry in 2018 in the broader context of design, construction, innovation and sustainability, and (iv) tracing innovation through development of a specific sustainability-oriented technology from Desert Rose

Study and development of innovative strategies for energy-efficient cross-layer design of digital VLSI systems based on Approximate Computing

The increasing demand on requirements for high performance and energy efficiency in modern digital systems has led to the research of new design approaches that are able to go beyond the established energy-performance tradeoff. Looking at scientific literature, the Approximate Computing paradigm has been particularly prolific. Many applications in the domain of signal processing, multimedia, computer vision, machine learning are known to be particularly resilient to errors occurring on their input data and during computation, producing outputs that, although degraded, are still largely acceptable from the point of view of quality. The Approximate Computing design paradigm leverages the characteristics of this group of applications to develop circuits, architectures, algorithms that, by relaxing design constraints, perform their computations in an approximate or inexact manner reducing energy consumption. This PhD research aims to explore the design of hardware/software architectures based on Approximate Computing techniques, filling the gap in literature regarding effective applicability and deriving a systematic methodology to characterize its benefits and tradeoffs. The main contributions of this work are: -the introduction of approximate memory management inside the Linux OS, allowing dynamic allocation and de-allocation of approximate memory at user level, as for normal exact memory; - the development of an emulation environment for platforms with approximate memory units, where faults are injected during the simulation based on models that reproduce the effects on memory cells of circuital and architectural techniques for approximate memories; -the implementation and analysis of the impact of approximate memory hardware on real applications: the H.264 video encoder, internally modified to allocate selected data buffers in approximate memory, and signal processing applications (digital filter) using approximate memory for input/output buffers and tap registers; -the development of a fully reconfigurable and combinatorial floating point unit, which can work with reduced precision formats

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed