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<p>Our group aims to develop a dedicated PET/MR system for breast cancer imaging. In order to evaluate and optimize the performance of the PET component, Monte Carlo simulation was made to help us choose the configuration parameters for hardware design. A resolution modeling method was also proposed and implemented on the GPU device to not only improve the spatial resolution of the reconstructed images but also accelerate the reconstruction speed. The PET component is configured with a ring shape composed of LYSO+SiPM detectors. Such design is compatible to MRI, and feasible for time of flight PET. Several aspects are included to be investigated in the simulation which are geometry configuration, counting performance and image quality. From the simulation result, the system configured using 2x2x20mm3 LYSO crystal with two DOI layers and 3 detector rings results in 6.2% photon sensitivity. The Noise equivalent count rate is improved with better time resolution, the peak NEC is found to be 7886 cps with 250 ps time resolution. The system is able to achieve 2.0 mm spatial resolution which is found to be more uniform with the addition of DOI layers. With the help of TOF, the lesion is visualizable with shorter scan time than the non-TOF system. The resolution modeling method is based on the coincidence detection response function modeling and multiray projection. It is found to improve the spatial resolution uniformity and contrast recovery. At the same time it reduces the position offset and background noise. The speed and accuracy improvement for this model is also discussed.</p> / Master of Science (MSc)

Next-generation cellular systems will rely on millimeter wave (mmWave) bands to meet the increasing demand for wireless connectivity from end user equipment. Given large available bandwidth and small-sized antenna elements, mmWave frequencies can support high communication rates and facilitate the use of multiple-input-multiple-output (MIMO) techniques to increase the wireless capacity. However, the small wavelength of mmWave yields severe path loss and high channel uncertainty. Meanwhile, using a large number of antenna elements requires a high energy consumption and heavy communication overhead for MIMO transmissions and channel measurement. To facilitate efficient mmWave communications, in this dissertation, the challenges of energy efficiency and communication overhead are addressed. First, the use of unmanned aerial vehicle (UAV), intelligent signal reflector, and device-to-device (D2D) communications are investigated to improve the reliability and energy efficiency of mmWave communications in face of blockage. Next, to reduce the communication overhead, new channel modeling and user localization approaches are developed to facilitate MIMO channel estimation by providing prior knowledge of mmWave links. Using advance mathematical tools from machine learning (ML), game theory, and communication theory, this dissertation develops a suite of novel frameworks using which mmWave communication networks can be reliably deployed and operated in wireless cellular systems, UAV networks, and wearable device networks. For UAV-based wireless communications, a learning framework is developed to predict the cellular data traffic during congestion events, and a new framework for the on-demand deployment of UAVs is proposed to offload the excessive traffic from the ground base stations (BSs) to the UAVs. The results show that the proposed approach enables a dynamical and optimal deployment of UAVs that alleviates the cellular traffic congestion. Subsequently, a novel energy-efficient framework is developed to reflect mmWave signals from a BS towards mobile users using a UAV-carried intelligent reflector (IR). To optimize the location and reflection coefficient of the UAV-carried IR, a deep reinforcement learning (RL) approach is proposed to maximize the downlink transmission capacity. The results show that the RL-based approach significantly improves the downlink line-of-sight probability and increases the achievable data rate. Moreover, the channel estimation challenge for MIMO communications is addressed using a distributional RL approach, while optimizing an IR-aided downlink multi-user communication. The results show that the proposed method captures the statistic feature of MIMO channels, and significantly increases the downlink sum-rate. Moreover, in order to capture the characteristics of air-to-ground channels, a data-driven approach is developed, based on a distributed framework of generative adversarial networks, so that each UAV collects and shares mmWave channel state information (CSI) for cooperative channel modeling. The results show that the proposed algorithm enables an accurate channel modeling for mmWave MIMO communications over a large temporal-spatial domain. Furthermore, the CSI pattern is analyzed via semi-supervised ML tools to localize the wireless devices in the mmWave networks. Finally, to support D2D communications, a novel framework for mmWave multi-hop transmissions is investigated to improve the performance of the high-rate low-latency transmissions between wearable devices. In a nutshell, this dissertation provides analytical foundations on the ML-based performance optimization of mmWave communication systems, and the anticipated results provide rigorous guidelines for effective deployment of mmWave frequency bands into next-generation wireless systems (e.g., 6G). / Doctor of Philosophy / Different kinds of new smart devices are invented and deployed every year. Emerging smart city applications, including autonomous vehicles, virtual reality, drones, and Internet-of-things, will require the wireless communication system to support more data transmissions and connectivity. However, existing wireless network (e.g., 5G and Wi-Fi) operates at congested microwave frequency bands and cannot satisfy needs of these applications due to limited resources. Therefore, a different, very high frequency band at the millimeter wave (mmWave) spectrum becomes an inevitable choice to manage the exponential growth in wireless traffic for next-generation communication systems. With abundant bandwidth resources, mmWave frequencies can provide the high transmission rate and support the wireless connectivity for the massive number of devices in a smart city. Despite the advantages of communications at the mmWave bands, it is necessary to address the challenges related to high-frequency transmissions, such as low energy efficiency and unpredictable link states. To this end, this dissertation develops a set of novel network frameworks to facilitate the service deployment, performance analysis, and network optimization for mmWave communications. In particular, the proposed frameworks and efficient algorithms are tailored to the characteristics of mmWave propagation and satisfy the communication requirements of emerging smart city applications. Using advanced mathematical tools from machine learning, game theory, and wireless communications, this dissertation provides a comprehensive understanding of the communication performance over mmWave frequencies in the cellular systems, wireless local area networks, and drone networks. The anticipated results will promote the deployment of mmWave frequencies in next-generation communication systems.

Emerging integrated satellite-drone-terrestrial communication (ISDTC) technologies are expected to contribute to our life by bringing forth high speed wireless connectivity to every corner of the world. On the one hand, the Internet of Things (IoT) provides connectivity to various physical objects by enabling them to share information and to coordinate decisions. On the other hand, the non-terrestrial components of an ISDTC system, i.e. unmanned aerial vehicles (UAVs), and satellites, can boost the capacity of wireless networks by providing services to hotspots, disaster affected, and rural areas. Despite the several benefits and practical applications of ISDTC technologies, one must address many technical challenges such as, resource management, trajectory design, device cooperation, data routing, and security. The key goal of this dissertation is to develop analytical foundations for the optimization of ISDTC operations, and the deployment of non-terrestrial networks (NTNs). First, the problem of resource management within ISDTC systems is investigated for service-effective cooperation among the terrestrial networks and NTNs. The performance of a multi-layer ISDTC system is analyzed within a competitive market setting.Using a novel decentralized algorithm, spectrum resources are allocated to each one of the communication links, considering the fairness among devices. The proposed algorithm is proved to reach a Walrasian equilibrium, at which the sum-rate of the network is maximized. The results also show that the proposed algorithm can reach the equilibrium with a practical convergence speed. Then, the effective deployment of NTNs under environmental dynamics is investigated using machine learning solutions with meta training capabilities. First, the use of satellites for on-demand coverage to unforeseeable radio access needs is investigated using game theory. The optimal data routing strategies are learned by the satellite system, using a novel reinforcement learning approach with distribution-robust meta training capability. The results show that, the proposed meta training mechanism significantly reduces the learning cost on the satellites, and is guaranteed to reach the maximal service coverage in the system. Next, the problem of control of UAV-carried radio access points under energy constraints is studied. In particular, novel frameworks are proposed to design trajectories for UAVs that seek to deliver data service to distributed, dynamic, and unforeseeable wireless access requests. The results show that the proposed approaches are guaranteed to converge to an optimal trajectory, and can get a faster convergence speed and lower computation cost using decomposition, cross validation and meta learning. Finally, this dissertation looks at the security of an IoT system. In particular, the impact of human intervention on the system security is analyzed under specific resource constraints. Psychological game theory frameworks are proposed to analyze the human psychology and its impact on the security of the system. The results show that the proposed solution can help the defender optimize its connectivity within the IoT system by estimating the attacker's behavior. In summary, the outcomes of this dissertation provide key guidelines for the effective deployment of ISDTC systems. / Doctor of Philosophy / In the past decade, the goal of providing wireless connectivity to all individuals and communities, including the most disadvantaged ones, has become a national priority both in the US and globally. Yet, remarkably, until today, there is still a great portion of the Earth's population who falls out of today's wireless broadband coverage. While people who live in under-developed or rural areas are still in "wireless darkness", communities in megacities often experience below-par wireless service due to their overloaded communication systems. To provide high-speed, reliable wireless connectivity to those on the less-served side of the digital divide, an integrated space-air-ground communication system can be designed. Indeed, airborne and space-based non terrestrial networks (NTNs) can enhance the capacity and coverage of existing wireless cellular networks (e.g., 5G and beyond) by providing supplemental, affordable, flexible, and reliable service to users in rural, disaster affected, and over-crowded areas. In order to fill the coverage holes and bridge the digital divide, seamless integration among NTNs and terrestrial networks is needed. In particular, when deploying an integrated communication system, one must consider the problems of spectrum management, device cooperation, trajectory design, and data routing within the system. Meanwhile, with the increased exposure to malicious attacks on high altitude platforms and vulnerable IoT devices, the security within the integrated system must be analyzed and optimized for reliable data service. To overcome all the technological challenges that hinder the realization of global digital inclusion, this dissertation uses techniques from the fields of game theory, meta learning, and optimization theory to deploy, control, coordinate, and manage terrestrial networks and NTNs. The anticipated results show that a properly integrated satellite-drone-terrestrial communication (ISDTC) system can deliver cost-effective, high speed, seamless wireless service to our world.

Computational Fluid Dynamics (CFD) applications usually involve intensive computations, which can be accelerated through using open accelerators, especially GPUs due to their common use in the scientific computing community. In addition to code acceleration, it is important to ensure that the code and algorithm are implemented numerically correctly, which is called code verification. This dissertation focuses on accelerating research CFD codes on multi-CPUs/GPUs using MPI and OpenACC, as well as the code verification for turbulence model implementation using the method of manufactured solutions and code-to-code comparisons. First, a variety of performance optimizations both agnostic and specific to applications and platforms are developed in order to 1) improve the heterogeneous CPU/GPU compute utilization; 2) improve the memory bandwidth to the main memory; 3) reduce communication overhead between the CPU host and the GPU accelerator; and 4) reduce the tedious manual tuning work for GPU scheduling. Both finite difference and finite volume CFD codes and multiple platforms with different architectures are utilized to evaluate the performance optimizations used. A maximum speedup of over 70 is achieved on 16 V100 GPUs over 16 Xeon E5-2680v4 CPUs for multi-block test cases. In addition, systematic studies of code verification are performed for a second-order accurate finite volume research CFD code. Cross-term sinusoidal manufactured solutions are applied to verify the Spalart-Allmaras and k-omega SST model implementation, both in 2D and 3D. This dissertation shows that the spatial and temporal schemes are implemented numerically correctly. / Doctor of Philosophy / Computational Fluid Dynamics (CFD) is a numerical method to solve fluid problems, which usually requires a large amount of computations. A large CFD problem can be decomposed into smaller sub-problems which are stored in discrete memory locations and accelerated by a large number of compute units. In addition to code acceleration, it is important to ensure that the code and algorithm are implemented correctly, which is called code verification. This dissertation focuses on the CFD code acceleration as well as the code verification for turbulence model implementation. In this dissertation, multiple Graphic Processing Units (GPUs) are utilized to accelerate two CFD codes, considering that the GPU has high computational power and high memory bandwidth. A variety of optimizations are developed and applied to improve the performance of CFD codes on different parallel computing systems. The program execution time can be reduced significantly especially when multiple GPUs are used. In addition, code-to-code comparisons with some NASA CFD codes and the method of manufactured solutions are utilized to verify the correctness of a research CFD code.

This thesis compares three modern JavaScript frameworks—Next.js, SvelteKit, and Astro—for e-commerce web development, focusing on how different rendering strategies (Static Site Generation, Server-Side Rendering, and Client-Side Rendering) affect performance metrics like load times, interactivity, and resource efficiency. Prototype e-commerce applications were developed using each framework, and performance testing was conducted to measure First Contentful Paint (FCP), Largest Contentful Paint (LCP), Interaction to Next Paint (INP), page size, and SEO-friendliness. Results show that Astro and SvelteKit consistently outperforms Next.js in load times and resource efficiency. However, the differences in performance metrics are not drastically large. All three frameworks are capable of delivering high-quality e-commerce applications. Therefore, decision-makers should also consider factors such as developer experience, community support, and specific project requirements when selecting a framework.

The widespread adoption of containerization for application deployment emphasizes the need for improved performance and efficiency, particularly in Java Virtual Machine (JVM)-based applications. Traditional JVMs often struggle with resource efficiency and long startup times, leading to increased operational costs and a larger environmental footprint. This is especially significant in microservices architectures where resource optimization is crucial for sustainability and cost-effectiveness. This thesis aims to evaluate the effectiveness of GraalVM’s Ahead-of-Time (AOT)compilation and native image support in enhancing the performance of JVM-based applications within a microservices architecture. The focus is reducing startup times, improving resource utilization, and overall operational efficiency at Ericsson, contributing to a greener economy. The research involved migrating a JVM-based program to run on GraalVM. Systematic performance tests measured key metrics such as startup time, memory usage, and performance. Semi-structured interviews with industry professionals were also utilized to gather qualitative insights into the migration challenges and benefits. The results indicated that native images significantly reduced startup times and memory usage compared to standard JVMs. Profile Guided Optimization (PGO)massively increased performance but at the cost of compilation time, memory usage, and build size. Interviews revealed that developers are cautiously optimistic about the benefits of AOT compilation with GraalVM but also revealed major concerns about complexity and compatibility. GraalVM’s AOT benefits make it a viable option for both microservice and serverless architectures. However, the decision to adopt GraalVM requires careful consideration of the trade-offs. These include larger build sizes, longer compilation times, and the need for specialized knowledge to maintain stability. Future work should focus on case studies in real-world environments to further validate and explore the benefits and drawbacks of AOT compilation..

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module designs toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

Microbial communities are key drivers of biogeochemical cycles and several important industrial processes rely on complex, undefined microbial ecosystems for production or conversion of substrates for example in wastewater treatment or anaerobic digestion plants. Despite their significance, such communities are often poorly defined, if at all. This project concerned previously undefined secondary microbial communities (SMCs) from photobioreactors culturing cyanobacterium Arthrospira platensis, known for producing high-value protein-pigment complex C-phycocyanin (C-PC). C-PC has a range of applications in the biochemical/pharmaceutical and food industries. Next-generation sequencing methods were applied to characterize the SMCs sampled over the course of various batch runs. The bioreactor exerted a strong selective pressure on the SMC, initially diverse and dynamic, succeeded by a stable and predictable SMC dominated by a few species. SMC stability and diversity correlated with reactor performance, especially proliferation and instability of the rare-abundance sub-population; dominant species ratios were likely less important. The substantially larger (compared to other species present) A. platensis filaments may represent a dynamic microenvironment in itself, and if so, constitutes a significant parameter when optimizing culture conditions. Denser and carefully pre-acclimated inocula reduce the ecological space available to undesirable taxa (e.g. pathogens) otherwise below detectable/significant limits. This has implications for other processes that rely on mixed cultures and may be a control strategy in manufacturing active pharmaceutical ingredients to cGMP standards. Molecular data was used to obtain several pure isolates which were characterized further. Strategies to optimize performance with respect to SMCs were explored and evaluated. A significant aspect of this CASE project was an industrial placement with Scottish Bioenergy. The placement involved set-up of a production facility and incremental scale-up of cultivation from 2 L to 1000 L reactors; development of a downstream processing protocol covering harvesting, pigment extraction and protein purification, and some formulation/stability testing. A very low-cost method is described for obtaining relatively high-purities of C-PC, broadly considered the most costly part of the entire production process.