| 1. |
- Shahroozi, Zahra, 1992-
(författare)
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Survivability control using data-driven approaches and reliability analysis for wave energy converters
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Wave energy, with five times the energy density of wind and ten times the power density of solar, offers a compelling carbon-free electricity solution. Despite its advantages, ongoing debates surround the reliability and economic feasibility of wave energy converters (WECs). To address these challenges, this doctoral thesis is divided into four integral parts, focusing on optimizing the prediction horizon for power maximization, analyzing extreme waves' impact on system dynamics, ensuring reliability, and enhancing survivability in WECs.Part I emphasizes the critical importance of the prediction horizon for maximal power absorption in wave energy conversion. Using generic body shapes and modes, it explores the effect of dissipative losses, noise, filtering, amplitude constraints, and real-world wave parameters on the prediction horizon. Findings suggest achieving optimal power output may be possible with a relatively short prediction horizon, challenging traditional assumptions.Part II shifts focus to WEC system dynamics, analyzing extreme load scenarios. Based on a 1:30 scaled wave tank experiment, it establishes a robust experimental foundation, extending into numerical assessment of the WEC. Results underscore the importance of damping to alleviate peak forces. Investigating various wave representations highlights conservative characteristics of irregular waves, crucial for WEC design in extreme sea conditions.Part III explores the computational intricacies of environmental design load cases and fatigue analyses for critical mechanical components of the WEC. The analysis is conducted for hourly sea state damage and equivalent two-million-cycle loads. Finally, a comparison of safety factors between the ultimate limit state and fatigue limit state unfolds, illustrating the predominant influence of the ultimate limit state on point-absorber WEC design.Part IV, centers on elevating survivability strategies for WECs in extreme wave conditions. Three distinct controller system approaches leverage neural networks to predict and minimize the line force. Distinct variations emerge in each approach, spanning from rapid detection of optimal damping to integrating advanced neural network architectures into the control system with feedback. The incorporation of a controller system, refined through experimental data, showcases decreases in the line force, providing a practical mechanism for real-time force alleviation.This thesis aims to contribute uniquely to the goal of advancing wave energy conversion technology through extensive exploration.
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| 2. |
- Chang, Ribooga
(författare)
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Design and Optimization of CO2 sorbents for Point Source Emissions and Direct Air Capture
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents a comprehensive study on the design and optimization of CO2 sorbents, targeting two distinct applications: CO2 capture from point source emissions and Direct Air Capture (DAC). The research first introduces Na2HfO3 as a potential CO2 sorbent for point source emissions, using the molten salt effects of Na2CO3 and the thermal stability of HfO2. This combination results in a CO2 sorbent with impressive thermal and cyclic stability, through various optimization steps to enhance CO2 capture performance and efficiency. The study then shows into the structural disorder in Na2ZrO3, a chemically similar CO2 sorbent. This investigation fills a knowledge gap, offering new insights into the sorbent's behavior in CO2 capture. For DAC applications, the thesis explores the design of inorganic anion pillared metal-organic frameworks, focusing on the adjustment of M5+−F− bond lengths in inorganic anion pillars within M5+OFFIVE-1-Ni samples. These structural modifications impact the CO2 capture properties, particularly in terms of capacity and kinetics, demonstrating the potential of structural tuning in enhancing sorbent effectiveness. The synthesized samples exhibit good cyclic and water stability, suggesting their potential for practical DAC applications.
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| 3. |
- Ghandour, Salim
(författare)
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Novel Materials and Implant Designs for the Treatment of Degenerative Disc Disease
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The overarching objective of this thesis was to develop intervertebral implants for degenerated discs through design, fabrication, and mechanical validation. The research had four primary aims, each addressing different facets of implant development.The first aim was to design a structurally optimized fusion intervertebral cage capable of accommodating weak bioactive materials. Topology optimization was employed to design cages using titanium and calcium phosphate. The cage’s integrity was verified using finite element simulations, fabricated using additive manufacturing, and validated using ASTM F2077. Imaging techniques were utilized to assess the quality of the produced cages. These evaluations confirmed the mechanical reliability of the produced cage, able to incorporate around 75% in volume of a bioactive calcium phosphate material, protected by the titanium.The second aim was to develop a clinically relevant degeneration model for the biomechanical evaluation of percutaneous cement discoplasty (PCD). To this end, voids were generated in ovine functional spinal units (FSUs) using papain. The results were compared with clinical data where parameters such as void volume percentage, disc height, and morphology of the induced injury were assessed. FSUs were treated with PCD using bone cement, and mechanically evaluated under healthy, injured, and treated conditions to determine if PCD could stabilize the spinal segment. The void induced showed similar parameters compared to the clinical data. Further, the stability of the spine was significantly reduced after degeneration and restored after treatment, highlighting the effectiveness of the degeneration method and PCD treatment.The third aim was to evaluate the suitability of novel bone cements for their use in PCD. This study examined the tensile and fatigue properties of a low-modulus cement (VSLA) primarily intended for vertebroplasty. The formulation was tested in tensile and fatigue. VSLA showed a significant decrease in tensile and fatigue properties when compared to commercial cements. This study set a baseline for future low-modulus cements that may be tested for use in PCD.The fourth aim was to evaluate an alternative cement due to the low viscosity of VSLA, which may not be suitable for discoplasty. This study assessed the fatigue and long-term properties of a high-viscosity low-modulus cement (hv-LA-PMMA). The hv-LA-PMMA showed a significant reduction of mechanical and fatigue properties when compared to its commercial base. Notably, the fatigue properties were similar to those of the annulus fibrosus in the disc. Additionally, its high viscosity renders it a promising alternative to the bone cements currently utilized in PCD.In conclusion, this thesis successfully addressed the design, fabrication, and mechanical validation of two types of intervertebral implants for degenerated discs. The research outcomes contribute with valuable insights into the design of fusion cages, a degeneration model to evaluate PCD, and the assessment of low-modulus cements for use in PCD.
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| 4. |
- Langhammer, David Michael, 1991-
(författare)
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Capturing Air Pollutants : Photochemical Adsorption and Degradation of SO2, NO2 and CO2 on Titanium Dioxide
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Titanium dioxide (TiO2) is a material with many useful properties. It is used most widely as a pigment in white paint, although in technological research it is better known for its ability to catalyze chemical reactions during light absorption. This process is referred to as photocatalysis, where the energy of the light is used to power the chemical reactions. This has enabled several interesting applications of TiO2, where it can for instance be applied to windows or façade walls to make their surfaces self-cleaning. Another implementation that has received much attention lately is artificial photosynthesis, where the light energy is used to transform CO2 and H2O into synthetic fuels. This thesis work contributes to the development of both these applications, although the main ambition is to show how three of the most common ambient air pollutant molecules, SO2, NO2 and CO2, can be captured at the surface of TiO2 by means of photocatalysis. Specifically, infrared (IR) spectroscopy and density functional theory (DFT) has been used as complementary tools of analysis to study the photocatalytic reactions that enable transformation of SO2, NO2 and CO2 into strongly bound sulfates, nitrates and carbonates, respectively. This combined experimental and theoretical approach has enabled a detailed description of how these reactions proceed and has further shown how the fundamental reactivity of the TiO2 surface changes upon light absorption.The work presented herein contributes to an increased understanding of photocatalysis and shows, quite generally, how molecules can be effectively captured at the surface of metal oxides by forming surface-integrated ionic adsorbates.
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| 5. |
- Aziz, Imran
(författare)
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Ultra-wideband Millimeter-wave Antenna Arrays and Front-end Systems : For high data rate 5G and high energy physics applications
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The demand for wireless data communications is rapidly increasing due to several factors including increased internet access, increasingly growing number of mobile users and services, implementation of the Internet of Things (IoT), high-definition (HD) video streaming and video calling. To meet the bandwidth requirement of new and emerging applications, it is necessary to move from the existing microwave bands towards millimeter-wave bands. This thesis presents different antenna arrays at 60 GHz and 28 GHz that are integrated with the front-end RFIC to steer the beam in ≈ ±50° in the azimuth plane. The 5G antenna arrays at 28 GHz are designed to provide broadband high data rate services to the end users. In order to transport this high-volume data to the core network, a fixed wireless access (FWA) link demands the implementation of a broadband, high gain and steerable narrow-beam array. The 60 GHz antenna arrays, presented in this thesis, are good candidates for both FWA as well as backhaul communications. The two proposed arrays at 60 GHz (57-66 GHz) are i) a stacked patches array and ii) a connected slots array feeding a high gain lens antenna. The 2×16 stacked patches antenna array shows more than 20 dBi realized gain. The array is integrated with the front-end RFIC and the resulting module shows > 40 dBm measured effective isotropic radiated power (EIRP). The other 60 GHz antenna array is designed as linear connected slots with sixteen equidistant feeding points. The latest is then used as a feeder of a high gain dielectric lens. Peak measured gain of 25.4 dBi is achieved with this antenna. Moreover, instead of experiencing scan loss, the lens is designed to get higher gain when the beam is steered away from the broadside direction.Furthermore, two compact antenna arrays are designed at 28 GHz (24.25 - 29.50 GHz). A linear polarized (LP) and a circular polarized (CP) array are realized in the fan-out embedded wafer level ball-grid-array (eWLB) package. In comparison with the PCB arrays, this antenna in package (AiP) solution is not only cost-effective but it also reduces the integration losses because of shorter feed lines and no geometrical discontinuity. The LP array is realized as a dipole antenna array feeding a novel horn-shaped heatsink. The RF module gives 34 dBm peak EIRP with beam-steering in ±35°. Besides, the CP antenna array is realized with the help of crossed dipoles and the RF module provides 31 dBm peak EIRP with beam-steering in ±50°.The data demands are not limited to the telecom industry as the upgradation of accelerators and experiments at the large hadron collider (LHC) at CERN will result in increased event rate thus demanding higher data rate front-end readout systems. This work thus investigates the feasibility of 60 GHz wireless links for the data readout at CERN. For this purpose, the 60 GHz wireless chips are irradiated with 17 MeV protons [dose 7.4 Mrad (RX) & 4.2 Mrad (TX)] and 200 MeV electrons [dose 270 Mrad (RX) & 314 Mrad (TX)] in different episodes. The chips have been found operational in the post-irradiation investigations with some performance degradation. The encouraging results motivate to move forward and investigate the realization of wireless links in such a complex environment.
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| 6. |
- He, Zhitao
(författare)
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Enabling Scalable Security in Internet of Things
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The popular notion of Internet of Things (IoT) implies two salient features: 1. a diversity of small things, i.e., constrained devices; 2. their seamless integration with the Internet. Pioneering work in Wireless Sensor Networks (WSNs) have laid a solid technological foundation for autonomous, low power wireless communication among battery-powered, microcontroller-based devices. On the other hand, as devices are being connected to the Internet in large numbers, industry experts and regulators have associated IoT with enormous security risk. Sensitive personal information, highly complex business workflows, and critical infrastructure for public safety are at stake. In this dissertation, we first explore the scalability of IoT. Approaching from the particular angle of radio interference, we study unstable and faulty network behavior when links between low power radios are disrupted. Our low cost and practical interference generation tools fill a gap between protocol design and test. We then underline the threat of novel attacks at the physical layer, which lead to denial of service and battery draining of low power radios. Launched from low cost hardware, the attacks we devise are power-efficient and hard to detect; and they reach longer ranges than jamming. Finally, we take a step closer to realization of secure and large-scale IoT deployment by enabling certificate enrollment, a key component in a public key infrastructure, for small devices. We show that automated enrollment of device certificates becomes feasible when a memory and power efficient IoT protocol stack is leveraged. Spanning between the physical layer and the application layer, our work has enriched the knowledge domain of IoT and advanced the technological frontier of scalable and secure IoT deployment.
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| 7. |
- Pérez-Penichet, Carlos
(författare)
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Seamless Integration of Battery-Free Communications in Commodity Wireless Networks
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ubiquitous sensing applications have countless potential benefits to society. However, batteries have long been an obstacle to their full development. Harvesting energy from the environment is a promising alternative to battery power, but traditional radio transceivers consume too much for most harvesters. This work is motivated by backscatter communications, a technique that reduces the energy that devices spend exchanging data by up to three orders of magnitude relative to regular radios. This reduction enables sensing devices that operate indefinitely without having to replace batteries; instead they leverage energy harvesting. My goal is to enable the seamless integration of battery-free devices with widespread low-power commodity networks such as Bluetooth or ZigBee/IEEE 802.15.4. Making this integration seamless is critical for the broad adoption of the new class of devices.At a high level, my dissertation outlines a series of challenges to the seamless integration of the new devices with regular low-power networks. We then propose ways to address these challenges, and demonstrate how we could integrate ultra-low-power battery-free devices with regular networks, while avoiding hardware modifications and minimizing any disruption that the addition may cause to existing and co-located communication devices.This work advances the state of the art by: First, demonstrating how to augment an existing sensor network with new sensors without any hardware modification to the pre-existing hardware. The existing network provides the unmodulated carrier that the battery-free nodes need to communicate. Second, we demonstrate a radio receiver that, if implemented in silicon, can directly receive low-power commodity wireless signals when assisted by an unmodulated carrier, and with a power consumption of a few hundred microwatts. The receiver makes battery-free devices directly compatible with regular networks. We introduce simulation models and a first-of-its-kind tool to simulate battery-free communications that integrate with regular networks. Finally, we demonstrate how to efficiently provide unmodulated carrier support for battery-free devices in the previous scenarios without unnecessarily spending energy and spectrum and without undue disturbance to co-located devices.
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| 8. |
- Yan, Wenqing, Ph.D. Student, 1994-
(författare)
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Design and Identification of Wireless Transmitters for a Low-power and Secure Internet of Things
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Wireless communication is a key enabler for connecting billions of Internet of Things devices. For networked embedded devices operating on limited energy resources, wireless communication dominates the power consumption. Moreover, as networked devices increasingly handle sensitive data, security concerns in wireless communication are continuously expanding. This dissertation develops novel solutions for low-power and secure wireless communication. Wireless transmitters consist of a series of steps, involving both analog and digital components, each playing a distinct role in the transmit chain. Conventional transmitters employ power-hungry analog components, leading to power consumption on the order of milliwatt. Backscatter transmitters significantly reduce communication power consumption to levels well below one milliwatt. This remarkable power efficiency is achieved by offloading power-hungry components to an external carrier emitter. However, backscatter transmitters encounter challenges in applications that demand medium to long communication range, because they rely heavily on powerful emitters in their proximity for an effective communication range. Instead of removing power-hungry components, our solution integrates the functions of these components into a low-power design. While still requiring an emitter, our transmitter does not reflect the carrier signal. Instead, we utilize a weak carrier signal to stabilize the transmitter, allowing a communication range of over one hundred meters even when the emitter is far away. This contribution takes a step forward in moving low-power communication beyond backscatter.Passive radiometric fingerprinting leverages imperfections of hardware components to identify and authenticate transmitters. Its passive nature fits well to secure low-power transmitters operating within constrained resources. To enhance the viability of radiometric fingerprinting, we make three contributions in this dissertation to facilitate its widespread deployment. First, compared to conventional radios, low-power backscatter communication has a fundamentally different composition of hardware components in its transmit chain. In our work, we decompose fingerprints in a backscatter system for dual identification of tags and emitters. Beyond security purposes, recognizing the emitter embeds a notion of locality, enabling fingerprinting usage in backscatter network management tasks such as emitter coordination. Second, the dynamic nature of real-world wireless channels significantly impacts the robustness of fingerprinting. We decompose channel impacts and develop a hybrid system. This system employs pertinent strategies for different channel factors, ensuring reliable performance across complex wireless conditions. Lastly, based on the understanding of components' contributions to the transmit chain, we design a lightweight fingerprinting system. We demonstrate a complete implementation seamlessly integrated within the constraints of a single low-cost off-the-shelf chip. This contribution simplifies the conventionally bulky setup using sophisticated signal acquisition equipment and dedicated computer processing resources, which facilitates the practical deployment of fingerprinting on low-cost embedded devices.
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| 9. |
- Zhou, Yijun, 1993-
(författare)
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Mechanical optimization of orthopaedic bone screw constructs
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Orthopaedic screw implants are crucial in surgical procedures for bone structures, with a longstanding issue being screw loosening. Improving screw stability requires an enhanced understanding of the bone-implant interactions and associated failure mechanisms. This knowledge can be used to optimize screw designs.In this thesis, a first step towards this end was taken through the development of enhanced trabecular bone models. Due to the trabecular bone's complex structure and the difficulties in obtaining micro-structural information, we proposed a numerical model based on Voronoi tessellation to mimic the trabecular bone morphology across varying porosities. This model's mechanical properties aligned well with analytical formulas and finite element modelling of real bone specimens, showing strong agreement with experimental results.Further investigation into screw-trabecular bone interaction was carried out using two numerical models. The explicit finite element models were able to replicate experimental screw push-in results. While only one of the models accounted for the screw insertion step, both models showed strong congruence with key experimental results. The two-step simulation however led to a more physically plausible Young’s modulus for trabecular bone material.Utilizing a validated numerical model, a numerical optimization process was initiated, where novel screw designs were proposed. Several neural network surrogate models were introduced, reducing evaluation costs while maintaining prediction accuracy. We found that screw insertion position, trabecular bone porosity, and orientation were significant factors, explaining about 96% of the variance in predicted response. Furthermore, a neural network workflow was developed to generate super-resolution trabecular bone models from clinical CT data, improving accuracy by up to 700% both morphologically and mechanically, using micro-CT models as a benchmark.Lastly, the potential enhancement of screw's primary stability was explored, by injection of hyaluronic acid mixed with hydroxyapatite particles. The augmentation effect was influenced by the hydroxyapatite particles' size and shape. Considering that these particles can also promote bone growth, a particle-laden hydrogel injection could potentially enhance screw stability throughout its lifespan.In conclusion, this study proposed methods to elucidate bone-implant interaction and enhance screw stability in trabecular bone, encompassing trabecular bone modelling, bone-screw interaction investigations, screw geometric optimization, super-resolution trabecular bone model generation from clinical CT data, and hydrogel optimization for stability. The results provide an enhanced understanding as well as optimization of complex geometric interactions, and could lead to future enhancements of clinical practice in terms of screw stability.
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| 10. |
- Osama, Muhammad
(författare)
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Robust machine learning methods
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We are surrounded by data in our daily lives. The rent of our houses, the amount of electricity units consumed, the prices of different products at a supermarket, the daily temperature, our medicine prescriptions, our internet search history are all different forms of data. Data can be used in a wide range of applications. For example, one can use data to predict product prices in the future; to predict tomorrow's temperature; to recommend videos; or suggest better prescriptions. However in order to do the above, one is required to learn a model from data. A model is a mathematical description of how the phenomena we are interested in behaves e.g. how does the temperature vary? Is it periodic? What kinds of patterns does it have? Machine learning is about this process of learning models from data by building on disciplines such as statistics and optimization. Learning models comes with many different challenges. Some challenges are related to how flexible the model is, some are related to the size of data, some are related to computational efficiency etc. One of the challenges is that of data outliers. For instance, due to war in a country exports could stop and there could be a sudden spike in prices of different products. This sudden jump in prices is an outlier or corruption to the normal situation and must be accounted for when learning the model. Another challenge could be that data is collected in one situation but the model is to be used in another situation. For example, one might have data on vaccine trials where the participants were mostly old people. But one might want to make a decision on whether to use the vaccine or not for the whole population that contains people of all age groups. So one must also account for this difference when learning models because the conclusion drawn may not be valid for the young people in the population. Yet another challenge could arise when data is collected from different sources or contexts. For example, a shopkeeper might have data on sales of paracetamol when there was flu and when there was no flu and she might want to decide how much paracetamol to stock for the next month. In this situation, it is difficult to know whether there will be a flu next month or not and so deciding on how much to stock is a challenge. This thesis tries to address these and other similar challenges.In paper I, we address the challenge of data corruption i.e., learning models in a robust way when some fraction of the data is corrupted. In paper II, we apply the methodology of paper I to the problem of localization in wireless networks. Paper III addresses the challenge of estimating causal effect between an exposure and an outcome variable from spatially collected data (e.g. whether increasing number of police personnel in an area reduces number of crimes there). Paper IV addresses the challenge of learning improved decision policies e.g. which treatment to assign to which patient given past data on treatment assignments. In paper V, we look at the challenge of learning models when data is acquired from different contexts and the future context is unknown. In paper VI, we address the challenge of predicting count data across space e.g. number of crimes in an area and quantify its uncertainty. In paper VII, we address the challenge of learning models when data points arrive in a streaming fashion i.e., point by point. The proposed method enables online training and also yields some robustness properties.
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