8,333 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Reliable indoor optical wireless communication in the presence of fixed and random blockers

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    The advanced innovation of smartphones has led to the exponential growth of internet users which is expected to reach 71% of the global population by the end of 2027. This in turn has given rise to the demand for wireless data and internet devices that is capable of providing energy-efficient, reliable data transmission and high-speed wireless data services. Light-fidelity (LiFi), known as one of the optical wireless communication (OWC) technology is envisioned as a promising solution to accommodate these demands. However, the indoor LiFi channel is highly environment-dependent which can be influenced by several crucial factors (e.g., presence of people, furniture, random users' device orientation and the limited field of view (FOV) of optical receivers) which may contribute to the blockage of the line-of-sight (LOS) link. In this thesis, it is investigated whether deep learning (DL) techniques can effectively learn the distinct features of the indoor LiFi environment in order to provide superior performance compared to the conventional channel estimation techniques (e.g., minimum mean square error (MMSE) and least squares (LS)). This performance can be seen particularly when access to real-time channel state information (CSI) is restricted and is achieved with the cost of collecting large and meaningful data to train the DL neural networks and the training time which was conducted offline. Two DL-based schemes are designed for signal detection and resource allocation where it is shown that the proposed methods were able to offer close performance to the optimal conventional schemes and demonstrate substantial gain in terms of bit-error ratio (BER) and throughput especially in a more realistic or complex indoor environment. Performance analysis of LiFi networks under the influence of fixed and random blockers is essential and efficient solutions capable of diminishing the blockage effect is required. In this thesis, a CSI acquisition technique for a reconfigurable intelligent surface (RIS)-aided LiFi network is proposed to significantly reduce the dimension of the decision variables required for RIS beamforming. Furthermore, it is shown that several RIS attributes such as shape, size, height and distribution play important roles in increasing the network performance. Finally, the performance analysis for an RIS-aided realistic indoor LiFi network are presented. The proposed RIS configuration shows outstanding performances in reducing the network outage probability under the effect of blockages, random device orientation, limited receiver's FOV, furniture and user behavior. Establishing a LOS link that achieves uninterrupted wireless connectivity in a realistic indoor environment can be challenging. In this thesis, an analysis of link blockage is presented for an indoor LiFi system considering fixed and random blockers. In particular, novel analytical framework of the coverage probability for a single source and multi-source are derived. Using the proposed analytical framework, link blockages of the indoor LiFi network are carefully investigated and it is shown that the incorporation of multiple sources and RIS can significantly reduce the LOS coverage blockage probability in indoor LiFi systems

    Undergraduate Catalog of Studies, 2023-2024

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    Broadband Coherent Anti-Stokes Raman Spectroscopy: A Comprehensive Approach to Analyzing Crystalline Materials

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    Broadband Coherent Anti-Stokes Raman scattering (B-CARS) is an advanced Raman spectroscopy technique used to investigate the vibrational properties of materials. B-CARS combines the spectral sensitivity of spontaneous Raman scattering with the enhanced signal intensity of coherent Raman techniques. While B-CARS has been successfully applied in biomedicine for ultra-fast imaging of biological tissue, its potential in solid-state physics remains largely unexplored. This work delves into the challenges and adaptations necessary to apply B-CARS to crystalline materials and shows its potential as a powerful tool for high-speed, hyperspectral investigations. The theoretical part of this work covers inelastic light-matter scattering fundamentals and the signal generation process of B-CARS, with special attention given to the so-called Non-Resonant Background (NRB). This sample-unspecific signal amplifies the B-CARS intensity but also distorts the shape and position of the measured spectral peaks. A reliable NRB correction becomes crucial to retrieve precise spectral parameters containing information on the investigated material's crystallographic structure, defect density, and stress distribution. The first results chapter presents a practical guideline for an optimized workflow of sample preparation, measurement procedure, and data analysis. The influences of sample surfaces, focus positioning, and polarization sensitivity are discussed. The successful NRB removal is achieved by adapting an algorithm initially designed for biomedical purposes. The second chapter involves a transnational Round Robin investigating the same set of materials using different experimental setups. The influences of laser source, detection range, and transmission vs. epi detection are explored to optimize the experimental parameters. This work showcases applications such as high-speed, hyperspectral imaging of ferroelectric domain walls in LiNbO3, demonstrating the potential of B-CARS in the cutting-edge field of domain wall engineering. Additionally, imaging and polarization-sensitive measurements are shown for MoO3 flakes, paving the way for B-CARS investigations of 2D materials. The final chapter presents advanced techniques, such as Three-Color CARS and Time-Delay CARS, applied to crystalline materials. Three-Color CARS is especially promising, as it enhances the signal intensity for low-frequency Raman modes, which are particularly interesting for solid-state physics compared to the usual large-shift modes investigated in biomedical research. Meanwhile, Time-Delay CARS is sensitive to relaxation processes of vibrational and NRB states, enabling experimental NRB removal and lifetime measurements. Additionally, a neural network-based NRB removal method is presented, eliminating the need for a prior NRB spectrum and offering rapid computation. In summary, this work demonstrates the successful implementation of B-CARS for crystalline materials and provides a comprehensive guideline for the optimal experimental setup, workflow, and data processing. The application of B-CARS for imaging bulk crystalline materials, ferroelectric domain walls, and 2D structures shows promising possibilities for future research

    Dataflow Programming and Acceleration of Computationally-Intensive Algorithms

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    The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping

    Graduate Catalog of Studies, 2023-2024

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    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Optical Filter Design for Daylight Outdoor Electroluminescence Imaging of PV Modules

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    This paper presents an advanced outdoor electroluminescence (EL) imaging system for inspecting solar photovoltaic (PV) modules under varying daylight conditions. EL imaging, known for its effectiveness in non-destructively detecting PV module defects, is enhanced through specialized optical filters. These filters, including a bandpass filter targeting EL emissions and a neutral density filter to reduce background light, significantly improve the system’s signal-to-noise ratio (SNR). The experimental results demonstrate the system’s enhanced performance, with superior clarity and detail in EL emissions, enabling precise defect localization and characterization at the cellular level. Notably, the system achieves an SNR improvement, with values consistently above two, outperforming previous systems and confirming its suitability for efficient solar PV maintenance and diagnostics. This research offers a flexible approach to optimizing EL imaging quality across various solar irradiance levels and angles, essential for improved PV module performance and reliability. The system effectively handles different PV module configurations, orientations, and types, including monofacial and bifacial arrays. It showcases robust imaging capabilities under high solar irradiance and different sun illumination levels, maintaining high-quality imaging due to its optimized filter design. Additionally, the system’s adaptability in detecting EL emissions from series-connected PV modules is highlighted, demonstrating its comprehensive evaluation capabilities for PV array performance
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