Ferroelectric crystals are commonly used in nonlinear optics for frequency conversion of laser radiation. The quasi-phase matching (QPM) approach uses a periodically modulated nonlinearity that can be achieved by periodically inverting domains in ferroelectric crystals and allows versatile and efficient frequency conversion in the whole transparency region of the material. KTiOPO4 (KTP) is one of the most attractive ferroelectric non-linear optical material for periodic domain-inversion engineering due to its excellent non-linearity, high resistance for photorefractive damage, and its relatively low coercive field. A periodic structure of reversed domains can be created in the crystal by lithographic patterning with subsequent electric field poling. The performance of the periodically poled KTP crystals (PPKTP) as frequency converters rely directly upon the poling quality. Therefore, characterization methods that lead to a deeper understanding of the polarization switching process are of utmost importance. In this work, several techniques have been used and developed to study domain structure in KTP, both in-situ and ex-situ. The results obtained have been utilized to characterize different aspects of the polarization switching processes in KTP, both for patterned and unpatterned samples. It has also been demonstrated that it is possible to fabricate sub-micrometer (sub-μm) PPKTP for novel optical devices. Lithographic processes based on e-beam lithography and deep UV-laser lithography have been developed and proven useful to pattern sub- μm pitches, where the later has been the most convenient method. A poling method based on a periodical modulation of the K-stoichiometry has been developed, and it has resulted in a sub-μm domain grating with a period of 720 nm for a 1 mm thick KTP crystal. To the best of our knowledge, this is the largest domain aspect-ratio achieved for a bulk ferroelectric crystal. The sub-micrometer PPKTP samples have been used for demonstration of 6:th and 7:th QPM order backward second-harmonic generation with continuous wave laser excitation, as well as a demonstration of narrow wavelength electrically-adjustable Bragg reflectivity. / QC 20100930

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Lithium niobate (LiNbO3) and KTiOPO4 (KTP) are ferroelectric crystals of considerable interest in different fields of optics and optoelectronics. Due to its large values of the nonlinear optical, electro-optic (EO), piezoelectric and acousto-optical coefficients, LiNbO3 is widely used for laser frequency conversion using the quasiphase matching (QPM) approach where the sign of nonlinearity has been periodically modulated by electric field poling (EFP). In the microwave and telecommunication field LiNbO3 is used for surface acoustic devices and integrated optical modulators. KTP and its isomorphs, on the other hand, exhibit slightly lower nonlinear coefficients but have much higher photorefractive damage thresholds, so that it is mainly used in the fabrication of QPM devices for both UV, IR and visible light generation and in high power applications. This thesis focus on different key issues: (1) accurate characterization of specific optical properties of LiNbO3, which are of interest in nonlinear and EO applications; (2) in-situ visualization and characterization of domain reversal by EFP in LiNbO3 and KTP crystals for a through understanding of the ferroelectric domain switching; (3) fabrication of periodic surface structures at sub-micron scale in LiNbO for photonic applications. An interferometric method is used for accurate measurement of ordinary and extraordinary refractive indices in uniaxial crystals, which is of great interest in the proper design of QPM crystals. A digital holography (DH) based method is presented here for 2D characterization of the EO properties of LiNbO , which is considerably interesting in the applications where the proper design of the EO device requires a spatially resolved information about the EO behaviour and the existing pointwise techniques are not sufficient. A DH method for novel in-situ monitoring of domain reversal by EFP in both LiNbO3 and KTP, is also presented here. The technqiue could be used as a tool for high fidelity periodic domain engineering but also provides information about domain kinetics, internal field and crystals defects. 3 3 3 Finally this thesis presents novel results concerning nanoscale periodic surface structuring of congruent LiNbO3. Holographic lithography (HL) is used for sub-micron period resist patterning and electric overpoling for surface domain reversal. Surface structures are obtained by selective etching. Moiré effect is also used in the HL to fabricate complicated structures with multiple periods. The depth compatibility with waveguide implementation allows foreseeing possible applications of these structures for Bragg gratings or innovative photonic crystal devices, exploiting the additional nonlinear and EO properties typical of LiNbO3. / QC 20100824

Nonlinear optical interaction in quasi-phase-matched structures opens up unique possibilities to build compact and efficient parametric devices such as optical parametric oscillators, generators, and amplifiers with tailored spectral properties. The focus of this thesis is on novel parametric interactions with periodically-poled KTiOPO4 (PPKTP) as the parametric gain medium. Optical parametric oscillators (OPOs) are attractive light sources for many applications, particularly in spectroscopy, and plays a central role in this thesis. Special attention is put on simple, yet powerful, spectral-manipulation and bandwidth-narrowing techniques for OPOs. The overall knowledge gained from these studies has been used for device construction of several tunable ultraviolet sources for biological sensing. In the case of bandwidth narrowing, the observation of decreasing spectral bandwidth in a noncollinear, idler-resonant OPO, as compared with a signal-resonant one, has been found to be due to the interplay between the material properties and the angular dispersion of PPKTP. To further reduce the bandwidth, we have shown that it is very beneficial to replace the output mirror in an OPO with a bulk Bragg grating. In fact, even close to degeneracy, where the bandwidth is typically wide, this approach is able to decrease the bandwidth drastically. Moreover, different OPO cavity designs have been examined in order to spectrally manipulate the resonant waves. By deploying a grating in a ring OPO cavity, it becomes possible to access the resonant wave and spectrally manipulated it in a zero-dispersion arrangement; the filtered wave is subsequently sent back into its own cavity as a seed signal, in a self-seeding arrangement. This particular cavity design decreases the bandwidth close to ~ 1000 times as compare to the free-running mode. An interesting phenomenon arises when two mutually coherent laser beams are used to pump a linear OPO cavity. When the pump beams intersect within the PPKTP crystal, an interference grating is formed and acts as a catalyst for the generation of new spectral sidebands through multiple cascaded four-wave mixing, in the pump, the idler and the signal directions. The spacing of these sidebands is determined geometrically by the incident pump angle, while the signals are continuously tunable over the c-band telecom window (λ ~ 1.5 μm) by rotating the cavity. Ultrabroad bandwidths have been generated in an optical parametric generator (OPG) pumped by an amplified picosecond Ti:sapphire laser. In the collinear direction the output spectrum extends over three octaves in the mid-infrared region. This enormously broad spectrum is also Fourier-filtered and subsequently used for narrowband seeding of an optical parametric amplifier (OPA). Finally, the spectral range between 285 nm and 340 nm is of importance for detection of biological substances through fluorescence spectroscopy. With this spectral region in mind a practical way to generate a tunable parametric device in the ultraviolet region is presented in the thesis. The developed ultraviolet laser is used for studies of the characteristics of biological particles. The ultraviolet source and the results from these studies, will be utilized in an integrated detection system, a so called early-warning system. / QC 20100923

Nonlinear optical frequency conversion is one of the most important key techniques in order to obtain lasers with wavelengths targeted for specific applications. In order to realize efficient and tailored lasers, the quasi-phase-matching (QPM) approach using periodically-poled ferroelectric crystals is getting increasingly important. Also understanding of damage mechanisms in nonlinear materials is necessary to be able to design reliable and well working lasers. This is especially true for high power application lasers, which is a rapidly growing field, where the damage problem normally is the ultimate limiting factor. In this thesis work, several promising novel ferroelectric materials have been investigated for nonlinear optical applications and the emphasis has been put on QPM devices consisting of periodically-poled structures. The materials were selected from three different types of ferroelectric materials: 1) MgO-doped stoichiometric LiNbO3 (MgO:SLN) and LiTaO3 (MgO:SLT), and non-doped stoichiometric LiTaO3 (SLT), 2) KTiOPO4 (KTP) and its isomorphs RbTiOPO4 (RTP), and 3) KNbO3 (KN). The focus in our investigations have been put on the spontaneous polarization switching phenomena, optimization of the periodic poling conditions, and the photochromic optical damage properties which were characterized by the help of blue light-induced infrared absorption (BLIIRA) measurements. With electrical studies of the spontaneous polarization switching, we were able to determine quantitatively, and compare, the coercive field values of different materials by applying triangularly shaped electric fields. We found that the values of the coercive fields depended on the increase rate of the applied electric field. The coercive field of KN was the lowest (less than 0.5 kV/mm) followed by the ones of KTP, SLT, and MgO:SLT (1.5 to 2.5 kV/mm). MgO:SLN, and RTP had relatively high coercive fields, approximately 5.0 to 6.0 kV/mm, respectively. Based on the domain switching characteristics we found, we successfully fabricated periodically-poled devices in all of the investigated materials with 30 μm periodicities and sample thickness of 1 mm. Blue light-induced infrared absorption (BLIIRA) has been characterized for unpoled bulk and periodically-poled samples using a high-sensitivity, thermal-lens spectroscopy technique. SLT showed a large photorefraction effect and the BLIIRA signal could not be properly measured because of the large distortion of the probe beam. The rise and relaxation time of BLIIRA, after switching the blue light on and off was in a time span of 10 to 30 sec except for KTP and its isomorphs, which needed minutes to hours in order to saturate at a fixed value. KN and MgO:SLN showed the lowest susceptibility to the induced absorption. Periodic poling slightly increased the susceptibility of KTP, MgO:SLT, and KN. Relatively high thresholds were observed in MgO:SLT and KN. By increasing the peak-power intensity of the blue light, the induced absorption for MgO:SLN, KTP and KN saturated at a constant value while that of MgO:SLT increase in a constant fashion. This trend is critical issue for the device reliability at high-power applications. / QC 20100830

Denna rapport redogör för skapandet av ett API som kan extrahera information från bilder på kvitton. Informationen som APIet skulle kunna ta fram var organisationsnummer, datum, tid, summa och moms. Här ingår även en fördjupning om tekniken OCR (optical character recognition) som omvandlar bilder och dokument till text. Examensarbetet utfördes åt Flex Applications AB. Examensarbetet utfördes åt Flex Applications AB. / This report describes the creation of an API that can extract information from pictures of receipts. Registration number, date, time, sum and tax are the information that the API was going to be able to deliver. In this thesis there is also a deepening of the technology OCR (optical character recognition) that transforms pictures and documents to text. The thesis was performed for Flex Applications AB.

Traumatic brain injury (TBI) affects millions of people worldwide, leading to significant mortality and disability rates. Elevated intracranial pressure (ICP) resulting from TBI can cause severe complications and requires early detection to improve patient outcomes. While invasive methods are commonly used to measure ICP accurately, non-invasive techniques such as optic nerve sheath diameter (ONSD) measurement show promise. This study aims at the creation of a tool that can automatically perform a segmentation of the ONS from a head computed tomography (CT) scan, and extracts meaningful measures from the segmentation mask, that can be used by radiologists and medics when treating people affected by TBI. This has been achieved using a deep learning model called ”nnU-Net”, commonly adopted for semantic segmentation in medical contexts. The project makes use of manually labeled head CT scans from a public dataset named CQ500, to train the aforementioned segmentation model, using an iterative approach. The initial training using 33 manually segmented samples demonstrated highly satisfactory segmentations, with good performance indicated by Dice scores. A subsequent training, combined with manual corrections of 44 unseen samples, further improved the segmentation quality. The segmentation masks enabled the development of an automatic tool to extract and straighten optic nerve volumes, facilitating the extraction of relevant measures. Correlation analysis with a binary label indicating potential raised ICP showed a stronger correlation when measurements were taken closer to the eyeball. Additionally, a comparison between manual and automated measures of optic nerve sheath diameter (ONSD), taken at a 3mm distance from the eyeball, revealed similarity between the two methods. Overall, this thesis lays the foundation for the creation of an automatic tool whose purpose is to make faster and more accurate diagnosis, by automatically segmenting the optic nerve and extracting useful prognostic predictors. / Traumatisk hjärnskada (TBI) drabbar miljontals människor över hela världen, vilket leder till betydande dödlighet och funktionshinder. Förhöjt intrakraniellt tryck (ICP) till följd av TBI kan orsaka allvarliga komplikationer och kräver tidig upptäckt för att förbättra patientens resultat. Medan invasiva metoder vanligtvis används för att mäta ICP exakt, icke-invasiva tekniker som synnervens höljediameter (ONSD) mätning ser lovande ut. Denna studie syftar till att skapa ett verktyg som automatiskt kan utföra en segmentering av ONS från en datortomografi skanning av huvudet, och extraherar meningsfulla åtgärder från segmenteringsmasken, som kan användas av radiologer och läkare vid behandling av personer som drabbats av TBI. Detta har uppnåtts med hjälp av en deep learning modell som kallas ”nnU-Net”, som vanligtvis används för semantisk segmentering i medicinska sammanhang. Projektet använder sig av manuellt märkta datortomografi skanningar från en offentlig datauppsättning som heter CQ500, för att träna den tidigare nämnda segmenteringsmodellen, med hjälp av en iterativ metod. Den inledande träningen med 33 manuellt segmenterade prov visade tillfredsställande segmentering, med god prestation indikerad av Dice-poäng. En efterföljande utbildning, i kombination med manuella korrigeringar av 44 osedda prover, förbättrade segmenteringskvaliteten ytterligare. Segmenteringsmaskerna möjliggjorde utvecklingen av ett automatiskt verktyg för att extrahera och räta ut optiska nervvolymer, vilket underlättade utvinningen av relevanta mått. Korrelationsanalys med en binär märkning som indikerar potentiellt förhöjd ICP visade en starkare korrelation när mätningar gjordes närmare ögongloben. Dessutom avslöjade en jämförelse mellan manuella och automatiserade mätningar av optisk nervmanteldiameter (ONSD), tagna på ett avstånd på 3 mm från ögongloben, likheten mellan de två metoderna. Sammantaget lägger denna avhandling grunden för skapandet av ett automatiskt verktyg vars syfte är att göra snabbare och mer exakta diagnoser, genom att automatiskt segmentera synnerven och extrahera användbara prognostiska prediktorer.

The Arlanda area is a construction intensive area facing problems with risk of leaching of arsenic (As) from the bedrock to surface- and groundwater. Construction projects in the area have had problems with high levels of As in the bedrock and the risk of leaching increases through processing of aggregates and blasting of the bedrock. Additionally, there are high concentrations of As in potable water and elevated concentrations are correlated with occurrences of metasedimentary rock, but may also be related to other rock types. The existing geological information of the area was collected in the 1960´s and modern petrographic information as well as modelling of P-T and redox conditions are needed to understand the As mineralogy of the bedrock. Methods used in this project include field work, optical microscopy, electron microprobe analyses, geothermometry calculations, pseudosection modelling in Perple_X and geochemical modelling in PHREEQC. The results indicate that the As-rich bedrock domain include rocks of both igneous and sedimentary origin. As-bearing minerals löllingite and arsenopyrite were found in the matrix of two of the metasedimentary rock samples, while no As-minerals were found in metavolcanic samples. P-T estimates from several geothermobarometry models all suggest amphibolite facies metamorphism for the area, with pressure of 3.0-5.5 kbar and temperature of 490-640 °C. Simple modelling of equilibration of löllingite and arsenopyrite in pure water indicate that As(III) is the dominating oxidation state of As and that the molality of As increases with increasing T and decreasing pH. The results of this thesis provide new information on the petrography and P-T conditions for metamorphism of As-bearingsupracrustal rocks in the Arlanda area, but future research is needed to be able to predict the spatial occurrence of As in the bedrock. / Arlandaområdet är ett av de mest expansiva områdena i Sverige där en stor mängd infrastrukturprojekt är planerade under de närmaste 5-20 åren. Tidigare byggnadsprojekt i området har dock stött på problem med höga bakgrundshalter av arsenik (As) i berggrunden och det finns även en risk för urlakning av As från berggrunden till både yt-och grundvatten. Denna risk ökar under byggnadsarbeten i och med till exempel sprängning av berg. Ytterligare ett problem är att det i området runtomkring Arlanda ofta är höga halter av arsenik i dricksvattenbrunnar. Från data över As-halter i bergborrade brunnar har man kunnat se att höga halter av As ofta förekommer i metasedimentära bergarter, men även kan uppträda i andra bergarter. Den tillgängliga geologiska informationen över området är insamlad på 60-talet och ny petrografisk information, samt modellering av tryck- och temperaturförhållanden är nödvändig för att förstå förekomst av As i berggrunden. Målet med detta projekt är att med hjälp av fältarbete, optisk- och elektronmikroskopering, samt termodynamisk modellering få djupare kunskap kring ytbergarterna i området och utvärdera förekomsten av arsenik i dessa. Under vilka tryck- och temperaturförhållanden som de metamorfa bergarterna omvandlats studeras genom beräkningar från mineralsammansättningar samt modellering i programmet Perple_X. Resultatet från projektet visar att bergarter i As-anrikade zoner är av både magmatiskt och sedimentärt ursprung. Arsenikmineralen löllingit och arsenikkis dokumenterades endast i bergarter av sedimentärt ursprung. Bergarternas kemiska sammansättning tyder också på att de högsta As-halterna finns i de metasedimentära bergarterna. Modellering i PHREEQC visar att As(III) är den dominerande formen av As när löllingit och arsenikkis reagerar med vatten. Tryck- och temperaturberäkningar samt tryck- och termodynamisk modellering tyder på metamorfos under amfibolitfacies, med tryck omkring 3.0-5.5 kbar och temperatur omkring 490-640 °C. Resultaten från detta projekt ger ny information om de metamorfa bergarterna i Arlanda området och förekomst av As i dessa. Vidare studier är nödvändiga för att kunnaförutse i vilken form och i vilka bergarter As förekommer.

Automatic document processing has been a subject of interest in the industry for the past few years, especially with the recent technological advances in Machine Learning and Computer Vision. This project investigates in-depth a major component used in Document Image Processing known as Optical Character Recognition (OCR). First, an improvement upon existing shallow CNN+LSTM is proposed, using domain-specific data synthesis. We demonstrate that this model can achieve an accuracy of up to 97% on non-handwritten text, with an accuracy improvement of 24% when using synthetic data. Furthermore, we deal with handwritten text that presents more challenges including the variance of writing style, slanting, and character ambiguity. A CNN+Transformer architecture is validated to recognize handwriting extracted from real-world insurance statements data. This model achieves a maximal accuracy of 92% on real-world data. Moreover, we demonstrate how a data pipeline relying on synthetic data can be a scalable and affordable solution for modern OCR needs. / Automatisk dokumenthantering har varit ett ämne av intresse i branschen under de senaste åren, särskilt med de senaste tekniska framstegen inom maskininlärning och datorseende. I detta projekt kommer man att på djupet undersöka en viktig komponent som används vid bildbehandling av dokument och som kallas optisk teckenigenkänning (OCR). Först kommer en förbättring av befintlig ytlig CNN+LSTM att föreslås, med hjälp av domänspecifik datasyntes. Vi kommer att visa att denna modell kan uppnå en noggrannhet på upp till 97% på icke handskriven text, med en förbättring av noggrannheten på 24% när syntetiska data används. Dessutom kommer vi att behandla handskriven text som innebär fler utmaningar, t.ex. variationer i skrivstilen, snedställningar och tvetydiga tecken. En CNN+Transformer-arkitektur kommer att valideras för att känna igen handskrift från verkliga data om försäkringsbesked. Denna modell uppnår en maximal noggrannhet på 92% på verkliga data. Dessutom kommer vi att visa hur en datapipeline som bygger på syntetiska data är en skalbar och prisvärd lösning för moderna OCR-behov.

Large corporations receive and send large volumes of invoices containing various fields detailing a transaction. Such fields include VAT, due date, total amount, etc. One common way to automatize invoice processing is optical character recognition (OCR). This technology entails automatic reading of characters from scanned images. One problem with invoices is that there is no universal layout standard. This creates difficulties when processing data from invoices with different layouts. This thesis aims to examine common errors in the output from Azure's Form Recognizer general document model and the ways in which machine learning (ML) can be used to solve the aforementioned problem, by providing error detection as a first step when classifying OCR output as correct or incorrect. To examine this, an analysis of common errors was made based on OCR output from 70 real invoices, and a Bidirectional Encoder Representations from Transformers (BERT) model was fine-tuned for invoice classification. The results show that the two most common OCR errors are: (i) extra words showing up in a field and (ii) words missing from a field. Together these two types of errors account for 51% of OCR errors. For correctness classification, a BERT type Transformer model yielded an F-score of 0.982 on fabricated data. On real invoice data, the initial model yielded an F-score of 0.596. After additional fine-tuning, the F-score was raised to 0.832. The results of this thesis show that ML, while not entirely reliable, may be a viable first step in assessment and correction of OCR errors for invoices. / Stora företag tar emot och skickar ut stora volymer fakturor innehållande olika fält med transaktionsdetaljer. Dessa fält inkluderar skattesats, förfallodatum, totalbelopp, osv. Ett vanligt sätt att automatisera fakturahantering är optisk teckenläsning. Denna teknologi innebär automatisk läsning av tecken från inskannade bilder. Ett problem med fakturor är att det saknas standardmall. Detta försvårar hanteringen av inläst data från fakturor med olika gränssnitt. Denna uppsats söker utforska vanliga fel i utmatningen från Azure's Form Recognizer general document model och sätten på vilka maskininlärning kan användas för att lösa nämnda problem, genom att förse feldetektering som ett första steg genom att klassificera optisk teckenläsningsutmatning som korrekt eller inkorrekt. För att undersöka detta gjordes en analys av vanligt förkommande fel i teckenläsningsutdata från 70 verkliga fakturor, och en BERT-modell finjusterades för klassificering av fakturor. Resultaten visar att de två vanligast förekommande optiska teckenläsningsfelen är:(i) att ovidkommande ord upptäcks i ett inläst värdefält och (ii) avsaknaden av ord i ett värdefält, vilka svarar för 51% av de optiska teckenläsningsfelen. För korrekthetsklassificeringen användes Transformermodellen BERT vilket gav ett F-värde på 0.98 för fabrikerad data. För data från verkliga fakturor var F-värdet 0.596 för den ursprungliga modellen. Efter ytterligare finjustering hamnade F-värdet på 0.832. Resultaten i denna uppsats visar att maskininlärning, om än inte fullt tillförlitligt, är ett gångbart första steg vid bedömning och korrigering av optiska teckenläsningsfel.