The accurate assessment of hemodynamics and its parameters play an important role when diagnosing cardiovascular diseases. In this context, 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique that facilitates hemodynamic parameter assessment as well as quantitative and qualitative analysis of three-directional flow over time. However, the assessment is limited by noise, low spatio-temporal resolution and long acquisition times. Consequently, in regions characterized by transient, rapid flow dynamics, such as the aorta and heart, capturing these rapid transient flows remains particularly challenging. Recent research has shown the feasibility of machine learning models to effectively denoise and increase the spatio-temporal resolution of 4D Flow MRI. However, temporal super-resolution networks, which can generalize on unseen domains and are independent on boundary segmentations, remain unexplored.  This study aims to investigate the feasibility of a neural network for temporal super-resolution and denoising of 4D Flow MRI data. To achieve this, we propose a residual convolutional neural network (based on the 4DFlowNet from Ferdian et al.) providing an end-to-end mapping from temporal low resolution space to high resolution space. The network is trained on patient-specific cardiac models created with computational-fluid dynamic (CFD) simulations covering a full cardiac cycle. For clinical contextualization, performance is assessed on clinical patient data. The study shows the potential of the 4DFlowNet for temporal-super resolution with an average relative error of 16.6 % on an unseen cardiac domain, outperforming deterministic methods such as linear and cubic interpolation. We find that the network effectively reduces noise and recovers high-transient flow by a factor of 2 on both in-silico and in-vivo cardiac datasets. The prediction results in a temporal resolution of 20 ms, going beyond the general clinical routine of 30-40 ms. This study exemplifies the performance of a residual CNN for temporal super-resolution of 4D flow MRI data, providing an option to extend evaluations to aortic geometries and to further develop different upsampling factors and temporal resolutions. / En noggrann bedömning av hemodynamiken och dess parametrar spelar en viktig roll vid diagnos av kardiovaskulära sjukdomar. I detta sammanhang är 4D Flow Magnetic Resonance Imaging (4D Flow MRI) en icke-invasiv mätteknik som underlättar bedömning av hemodynamiska parametrar samt kvantitativ och kvalitativ analys av flöde. Bedömningen begränsas av brus, låg spatio-temporal upplösning och långa insamlingstider. I områden som karakteriseras av snabb flödesdynamik, såsom aorta och hjärta, är det därför fortfarande särskilt svårt att fånga dessa snabba transienta flöden. Ny forskning har visat att det är möjligt att använda maskininlärningsmodeller för att effektivt reducera brus och öka den spatio-temporala upplösningen i 4D Flow MRI. Nätverk för temporal superupplösning, som kan generaliseras till osedda domäner och är oberoende av segmentering, är fortfarande outforskade.  Denna studie syftar till att undersöka genomförbarheten av ett neuralt nätverk för temporal superupplösning och brusreducering av 4D Flow MRI-data. För att uppnå detta föreslår vi ett residual faltningsneuralt nätverk (baserat på 4DFlowNet från Ferdian et al.) som tillhandahåller en end-to-end-mappning från temporalt lågupplöst utrymme till högupplöst utrymme. Nätverket tränas på patientspecifika hjärtmodeller som skapats med CFD-simuleringar som spänner över en hel hjärtcykel. För klinisk kontextualisering utvärderas nätverkets prestanda på kliniska patientdata. Studien visar potentialen av 4DFlowNet för temporal superupplösning med ett genomsnittligt relativt fel på 16,6 % på en osedd hjärtdomän, vilket överträffar deterministiska metoder som linjär och kubisk interpolation. Vi konstaterar att nätverket effektivt minskar brus och återställer högtransient flöde med en faktor på 2 på både in-silico ochin-vivo hjärtdataset. Förutsägelsen resulterar i en temporal upplösning på 20 ms, vilket är mer än den allmänna kliniska rutinen på 30-40 ms. Denna studie exemplifierar prestandan hos en residual CNN för temporal superupplösning av 4D-flödes-MRI-data, vilket ger möjlighet att utvidga utvärderingarna till aortageometrier och att vidareutveckla olika uppsamplingsfaktorer och temporala upplösningar.

In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. These radar measurements can be used to determine the radar cross-section (RCS) of the target and to reconstruct an estimate of the target. However, the reconstructed images will suffer from spectral leakage effects and are limited in resolution. Many methods of enhancing the images exist and some are based on deep learning. Most commonly the deep learning methods rely on high-resolution ground truth data of the scene to train a neural network to enhance the radar images. In this thesis, a method that does not rely on any high-resolution ground truth data is applied to train a convolutional neural network to enhance radar images. The network takes a conventional ISAR image subject to spectral leakage effects as input and outputs an enhanced ISAR image which contains much more defined features. New RCS measurements are created from the enhanced ISAR image and the network is trained to minimise the difference between the original RCS measurements and the new RCS measurements. A sparsity constraint is added to ensure that the proposed enhanced ISAR image is sparse. The synthetic training data consists of scenes containing point scatterers that are either individual or grouped together to form shapes. The scenes are used to create synthetic radar measurements which are then used to reconstruct ISAR images of the scenes. The network is tested using both synthetic data and measurement data from a cylinder and two aeroplane models. The network manages to minimise spectral leakage and increase the resolution of the ISAR images created from both synthetic and measured RCSs, especially on measured data from target models which have similar features to the synthetic training data.  The contributions of this thesis work are firstly a convolutional neural network that enhances ISAR images affected by spectral leakage. The neural network handles complex-valued signals as a single channel and does not perform any rescaling of the input. Secondly, it is shown that it is sufficient to calculate the new RCS for much fewer frequency samples and angular positions and compare those measurements to the corresponding frequency samples and angular positions in the original RCS to train the neural network.

The global need to eliminate CO2 emissions and its consequent reduction in the use of fossil fuels drives the ongoing energy transition that highly involves the research achievements of the scientific community to reach the goals of this purpose. Renewable sources like photovoltaic and wind energy, are central to this endeavor, however, the intermittency of natural resources makes it non-dispatchable and energy storage is fundamental. According to the European Roadmap [1] just a 60% of the CO2 emissions reduction goal can be achieved with available technologies and existing energy. However, the production, use and specially storage opportunities that hydrogen offers can drive non-dispatchable renewable sources to achieve its full potential by clearing up the intermittency problem as well as covering the remained 40% gap. This master's thesis aims to investigate the techno-economic feasibility of integrating a Solid Oxide Electrolyzer Cell (SOEC) into a hybrid PV-CSP(sCO2) plant. The study focuses on assessing various indicators related to electricity, energy, and hydrogen production prices. To achieve this, three different integration strategies within the hybrid PV-CSP(sCO2) plant were selected for analysis: Soec using heat from the particles coming from the receiver, soec using heat coming from the particles available in the thermal energy storage (TES) and soec recovering heat from the sCO2 power block. A sensitivity analysis was conducted on different PV sizes (MWp), battery capacities (MWh), and SOEC installed capacities (MWh) to investigate the technology's potential in the plant and determine optimal sizing of subsystems. However, the individual optimization of economic indicators presented technical and economic challenges. Scenarios allowing individual optimization of hydrogen production prices (€/kg H2) resulted in 10.9, 11.7, and 14.6 €/kg h2 for receiver, TES, and sCO2 integration strategy, respectively. These scenarios, however, require high SOEC installed capacities, leading to elevated electricity and energy production prices. On the other hand, the individual optimization of electricity and energy production prices led to better and lower results when no hydrogen production presence within the plant. However, this analysis also showed that soec capacities below 5MWh together with no installation of batteries and a new definition for calculating hydrogen production prices (LCOH) allows feasible integration of hydrogen production within the plant. LCOH(€/kg h2) results were 10.2€/kg h2, 7.6€/kg h2, and 9.4€/kg h2 for receiver, TES, and sCO2, respectively, for a soec installed capacity of 0.5MWh (119m2 size) along with energy production values not exceeding 101€/MWh. While the results present a favorable outlook for SOEC installations based on literature review data [2] [3] [4] they still face challenges when competing with the cost-efficient PEM technology, which offers 4.5-5.5€/kg H2 [5] without storage. Nonetheless, this research contributes valuable insights into the integration of SOEC technology within hybrid renewable energy systems and provides a comprehensive analysis of the techno-economic aspects related to hydrogen production following different integration strategies. The findings may inform decision-making processes and promote further advancements in sustainable energy solutions. / Det globala behovet av att eliminera CO2utsläpp och därmed minska användningen av fossila bränslen driver pågående energiomställning, som starkt involverar forskningsresultaten från vetenskapssamhället för att nå syftet med detta mål. Förnybara källor som solceller och vindkraft är centrala i detta arbete, men intermittensen hos naturliga resurser gör dem icke disponibla och energilagring är grundläggande. Enligt den europeiska vägkartan [1] kan endast 60% av målet att minska CO2-utsläppen uppnås med tillgängliga teknologier och befintlig energi. Produktionen, användningen och särskilt lagringsmöjligheterna som väte erbjuder kan emellertid driva icke-disponibla förnybara källor att nå sin fulla potential genom att lösa intermitt ensproblemet och täcka den återstående 40% klyftan. Detta examensarbete syftar till att undersöka den tekniskekonomiska genomförbarheten av att integrera en fastoxid elektrolysör (SOEC) i en hybrid PV CSP(sCO2)-anläggning. Studien fokuserar på att utvärde ra olika indikatorer relaterade till el-, energi- och vätgasproduktionspriser. För att uppnå detta har tre olika integrationsstrategier inom hybrid PV CSP(sCO2) anläggningen valts för analys: SOEC med hjälp av värme från partiklar som kommer från mottagaren, SOEC med hjälp av värme från partiklar som finns i termisk energilagring (TES) och SOEC som återvinner värme från sCO2-kraftblocket. En känslighetsanalys har genomförts för olika PVstorlekar (MWp), batterikapaciteter (MWh) och SOEC installerade kapacit eter (MWh) för att undersöka teknologins potential i anläggningen och bestämma optimal dimensionering av delsystem. Resultaten från individuell optimering av ekonomiska indikatorer ledde dock till flera tekniska och ekonomiska utmaningar. Scenarier som tillåter individuell optimering av vätgasproduktionspriser (€/kg H2) resulterade i 10, 9, 11, 7 respektive 14,6 €/kg H2 för mottagare, TES och sCO2 integrationsstrategi. Dessa scenarier kräver dock höga SOEC installerade kapaciteter, vilket leder till höga el och energipriser. Å andra sidan ledde individuell optimering av el och energiproduktionspriser till bättre och lägre resultat när ingen vätgasproduktion fanns i anläggningen. Denna analys visade också att SOEC kapaciteter under 5MWh tillsammans med ingen installation av batterier och en ny definition för beräkning av vätgasproduktionspriser (LCOH) möjliggör genomförbar integration av vätgasproduktion i anläggningen. LCOH (€/kg H2) resultaten var 10,2 €/kg h2 , 7 ,6 €/kg h2 respektive 9,4 €/kg h2 för mottagare, TES och sCO2, för en SOEC installerad kapacitet på 0,5 MWh (storlek 119m2) tillsammans med energiproduktionsvärden som inte överstiger 101 €/MWh. Medan resultaten visar en gynnsam utsikt för SOECinstallationer baserat på data från litteraturöversikter [2] [3] [4], står de ändå inför utmaningar när de konkurrerar med den kostnadseffektiva PEM teknologin, som erbjuder 4,5-5,5 €/kg H2 [5] utan lagring. Trots detta bidrar forskningen med värdefulla insikter i integrationen av SOEC teknologi i hybrid förnybara energisystem och ger en omfattande an alys av de teknisk-ekonomiska aspekterna relaterade till vätgasproduktion enligt olika integrationsstrategier. Resultaten kan informera beslutsprocesser och främja ytterligare framsteg inom hållbara energilösningar.

Prevailing propagation phenomena in certain areas play a vital role in deciding terrestrial wireless systems performance. Vertical refractivity profile below 1 km is a critical parameter for designing reliable systems; noting that there is a shortage of upper-air data worldwide. Anomalous phenomena may cause severe signal fading and interference beyond the horizon. The objectives of this thesis are to investigate dominant refractive conditions in the subtropical Arabian Gulf region, develop new approaches and empirical models for evaluating vertical refractivity profiles and relevant propagation parameters in the low troposphere, and to examine the impact of frequently experienced anomalous phenomena on terrestrial microwave links. Twenty-three years of meteorological measurements, from 1990 to 2013, are utilized using spatially separated surface stations and a single radiosonde in the United Arab Emirates (UAE). Profiles of sea level, surface and upper refractivity components are statistically analysed. Three major atmospheric layers; namely 65 m, 100 m and 1 km above the ground are studied to analyse relevant propagation parameters such as sub-refraction, super-refraction, anomalous propagation probability parameter β0 and point refractivity gradient not exceeded for 1% of time. The effective earth radius factor k is investigated using a new weighted averaged approach. In addition, the seasonal structure of atmospheric ducting is dimensioned within 350 m layer above ground. Finally, microwave measurement campaign is conducted using multiple radio links operating in UAE using various frequency bands. The link budget simulations are compared with the signal strength measurements. Fading scenarios are studied against the observed anomalous conditions and several recommendations are concluded.

Research suggests that society is facing continued increasing economic inequalities where structural forces such as neoliberalism and globalisation are behind the emergence of a new group of globally super-rich. Since media is a place where social class is expressed, depicted and represented, it can have a particularly important role regarding what discourses about the global upper class are generated. The purpose of this study is to critically examine how the portrayal and re-contextualisation of the global upper class can produce discourses, by specifically analysing semiotic elements in the film Triangle of Sadness by Ruben Östlund. The following research question was formed: What discourses are produced about the globally super-rich in the film Triangle of Sadness?  The theoretical framework of this study has a critical point of departure, according to which media is not a neutral means of communication, and through various expressions media can produce discourses that serve certain groups and interests. This framework also includes contrasting the concept of culture industry to other perspectives such as Bourdieu’s field theory, which serves the study as reflective perspectives that can provide generalising arguments about the role that the film industry plays in a society. The method used in this study is a multimodal critical discourse analysis, which permits a rather detailed analysis of a choice of semiotic resources in the chosen film. The selection of empirical material was made by first watching the film as a whole, then identifying relevant parts for analysis. The sequences were chosen based on where the super-rich are in focus and portrayed in the film. The analysis is structured by the identified discourses in regard to the representation of the super-rich. The result of the study is that Triangle of Sadness produces five discourses about the globally super-rich: The extreme wealth of the global super-rich, the global super-rich as wicked, the fall of the global super-rich, the super-rich as vulnerable and the super-rich as powerless. Overall, the film is a mockery of the super-rich. An explanation for why Östlund produces this discourse, according to Bourdieu, could have to do with his position as a consecrated avant-garde in the production field.

Super-resolution is a subfield of computer vision centered around upsampling low-resolution images to a corresponding high-resolution counterpart. This degree project investigates the suitability of a deep learning method for real-time video super-resolution. Following earlier work in the field, we use bicubic interpolation as a baseline for comparison. The deep learning method selected is specifically suited towards real-time super-resolution and consists of a motion compensation network and an upsampling network. The deep learning method and bicubic interpolation are compared by quantitatively evaluating the methods against each other in quality metrics and performance metrics. Suitable quality metrics are selected from earlier works to provide increased comparability of results, namely peak signal-to-noise ratio and structure similarity index. The performance metrics are: number of operations for a single upsampled frame, latency, throughput, and memory requirements. We apply the methods to a highly challenging publicly available dataset specifically engineered towards video super-resolution research. To further investigate the deep learning method, we propose a few modifications and study the effect on the metrics. Our findings show that the deep learning models outperform bicubic interpolation in the quality metrics, while bicubic interpolation outperformed the deep learning models in the performance metrics. We also find no significant quality metric improvement associated with having a motion compensation network for this dataset, suggesting that the dataset might be too complex for the motion compensation network. We conclude that the deep learning method exhibits real-time capabilities as the method has a throughput of around 500 frames per second for full HD super-resolution. Additionally, we show that by modifying the deep learning method, we achieve similar latency as bicubic interpolation without sacrificing throughput or quality. / Superupplösning är ett område inom datorseende centrerat kring att uppsampla lågupplösta bilder till högupplösta motsvarigheter. Detta examensarbete undersöker hur lämplig en specifik djupinlärningsmetod är för superupplösning i realtid. Enligt tidigare forskning använder vi oss av bikubisk interpolering som grund för jämförelse. Den valda djupinlärningsmetoden är speciellt anpassad till superupplösning i realtid och består av ett rörelsekompensationsnätverk och ett uppsamplingsnätverk. Djupainlärningsmetoden och interpoleringsmetoden jämförs genom att kvantitativt utvärdera metoderna mot varandra i kvalitetsmått och prestandamått. Lämpliga kvalitetsmått väljs från tidigare forskning för att ge ökad jämförbarhet mellan resultaten, nämligen maximalt signaltill- brusförhållande och strukturlikhetsindex. Prestandamätvärdena är: antal operationer för en uppsamplad bild, latens, genomströmning och minnesbehov. Vi utvärderar metoderna på ett utmanande allmänt tillgängligt dataset speciellt konstruerat för superupplösningsforskning inom video. För att ytterligare undersöka den djupa inlärningsmetoden föreslår vi några modifieringar och studerar effekten på mätvärdena. Våra resultat visar att djupinlärningsmodellerna överträffar bikubisk interpolering i kvalitetsmåtten, medan bikubisk interpolering överträffar djupinlärningsmodellerna i prestandamåtten. Vi finner inte heller någon signifikant kvalitetsmässig förbättring förknippad med att ha ett rörelsekompensationsnätverk för detta dataset, vilket kan betyda att datasetet är för komplext för rörelsekompensationnätverket. Vi drar slutsatsen att djupainlärningsmetoden uppvisar realtidsfunktioner eftersom metoden har en genomströmning på cirka 500 bilder per sekund för full HD superupplösning. Dessutom visar vi att genom att modifiera djupainlärningsmetoden uppnår vi liknande latens som bikubisk interpolering utan att offra genomströmning eller kvalitet.

Recent years have witnessed tremendous success in using deep learning approaches to handle low-level vision problems. Most of the deep learning based methods address the low-level vision problem by training a neural network to approximate the mapping from the inputs to the desired ground truths. However, directly learning this mapping is usually difficult and cannot achieve ideal performance. Besides, under the setting of unsupervised learning, the general deep learning approach cannot be used. In this thesis, we investigate and address several problems in low-level vision using the proposed approaches. To learn a better mapping using the existing data, an indirect domain shift mechanism is proposed to add explicit constraints inside the neural network for single image dehazing. This allows the neural network to be optimized across several identified neighbours, resulting in a better performance. Despite the success of the proposed approaches in learning an improved mapping from the inputs to the targets, three problems of unsupervised learning is also investigated. For unsupervised monocular depth estimation, a teacher-student network is introduced to strategically integrate both supervised and unsupervised learning benefits. The teacher network is formed by learning under the binocular depth estimation setting, and the student network is constructed as the primary network for monocular depth estimation. In observing that the performance of the teacher network is far better than that of the student network, a knowledge distillation approach is proposed to help improve the mapping learned by the student. For single image dehazing, the current network cannot handle different types of haze patterns as it is trained on a particular dataset. The problem is formulated as a multi-domain dehazing problem. To address this issue, a test-time training approach is proposed to leverage a helper network in assisting the dehazing network adapting to a particular domain using self-supervision. In lossy compression system, the target distribution can be different from that of the source and ground truths are not available for reference. Thus, the objective is to transform the source to target under a rate constraint, which generalizes the optimal transport. To address this problem, theoretical analyses on the trade-off between compression rate and minimal achievable distortion are studied under the cases with and without common randomness. A deep learning approach is also developed using our theoretical results for addressing super-resolution and denoising tasks. Extensive experiments and analyses have been conducted to prove the effectiveness of the proposed deep learning based methods in handling the problems in low-level vision. / Thesis / Doctor of Philosophy (PhD)

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