Blar i utgivelsesdato Mastergradsoppgaver i teknologi - anvendt fysikk

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    • A compact portable resonance probe system for in situ measurements of snow conditions 

      Reistad, Jakop (Master thesis; Mastergradsoppgave, 2018-01-31)
      A resonance probe for measuring the dielectric properties of snow in terms of the resonance frequency is described and tested. The dielectric properties of snow are closely related to the density and the water content in snow. Having a probe capable of measuring the dielectric properties is therefore a useful tool for anyone working with quantitative descriptions of snow properties. In situ measurements ...

    • Estimering av egenskaper til naturlige klasser i radarbilder med en blandingsmodell 

      Moe, Åse Mari (Master thesis; Mastergradsoppgave, 2018-05-31)
      I løpet av de siste årene har isdekket i polhavet gått fra å være flerårsis til å bli en sesongbasert istype. Hvordan isen utvikler seg på sommeren vil avhenge av smeltevannandelen, blant annet på grunn av at smeltevannet har mindre albedo enn isen. For å estimere andelen smeltevann er det i denne oppgaven utviklet flere metoder som kan brukes på satellittbilder. Disse metodene bruker en statistisk ...

    • Analyzing Behavioral Biometrics of Handwriting Using Myo Gesture Control Armband 

      Tveit, Brynjulv (Master thesis; Mastergradsoppgave, 2018-06-05)
      Through the last few decades, computer technology has gradually merged into our everyday lives. Computers and sensors are embedded in an increasing amount of household items, enabling us to monitor and remotely control our connected devices from apps on our smartphones. The technology interfaces are also evolving along with new technologies. Among the up and coming digital interfaces are wearable ...

    • Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives 

      Trosten, Daniel Johansen (Master thesis; Mastergradsoppgave, 2019-05-31)
      Deep image clustering is a rapidly growing branch of machine learning and computer vision, in which deep neural networks are trained to discover groups within a set of images, in an unsupervised manner. Deep neural networks have proven to be immensely successful in several machine learning tasks, but the majority of these advances have been in supervised settings. The process of labeling data for ...

    • Correction Strategies for Breathing Induced Motion in PET Images 

      Bakkevoll, Stian (Master thesis; Mastergradsoppgave, 2019-06-03)
      Breathing induced motion highly impacts the quality of PET images. Motion blur impacts important parameters such as standardised uptake value (SUV) and lesion volume. To be able to stage lung cancer correctly, and provide correct treatment, the impact of motion needs to be reduced. There exists techniques to combat the breathing motion, such as elastic motion correction and gating strategies. ...

    • An investigation of the robustness of distance measure-based supervised labelling of segmented remote sensing images 

      Kiærbech, Åshild (Master thesis; Mastergradsoppgave, 2019-06-03)
      Unsupervised clustering methods on remote sensing images have shown good results. However, this type of machine learning needs additional labelling to be an end-to-end classification in the same manner as traditional supervised classification. The automation of the labelling needs further exploration. We want to investigate the robustness of a supervised automatic labelling scheme by comparing a ...

    • Segmentation and Unsupervised Adversarial Domain Adaptation Between Medical Imaging Modalities 

      Strauman, Andreas Storvik (Master thesis; Mastergradsoppgave, 2019-07-13)
      Segmenting and labelling tumors in multimodal medical imaging are often vital parts of diagnostics and can in many cases be very labor intensive for clinicians. The effort in advancing time-saving methods in the medical health sector might be of great help for busy clinicians and can maybe even save lives. Furthermore, creating methods that generically, accurately and successfully process unlabelled ...

    • Investigating the effect of class-imbalance on convolutional neural networks for angiodysplasia detection 

      Kaspersen, Oskar Anstein (Master thesis; Mastergradsoppgave, 2019-10-31)
      In this thesis, we look at a deep learning approach to AD detection and focus specifically on the problem of class imbalance, which arises from the fact that lesions only occupy a small part of the images, by analyzing how weighting of the loss function can help address this issue. Balancing the weights of the foreground and background class in the cost-function was found to be crucial to achieve ...

    • Comparison of the Ice Watch Database and Sea Ice Classification from Sentinel-1 Imagery 

      Pedersen, Joakim Lillehaug (Master thesis; Mastergradsoppgave, 2019-12-13)
      In this thesis,we investigate the potential use of in-situ sea ice observations from the Ice Watch database as ground truth data for an automated classification algorithm of sea ice types from Sentinel-1 SAR data. The Ice Watch database and the Sentinel-1 data archive are searched for in-situ observations and satellite data acquisitions in Extra Wide swath mode overlapping in both space and time. ...

    • Affinity-Guided Image-to-Image Translation for Unsupervised Heterogeneous Change Detection 

      Hansen, Mads Adrian (Master thesis; Mastergradsoppgave, 2019-12-16)
      Change detection in earth observation remote sensing images can be used to describe the extent of natural disasters, e.g., forest fires and floods. When time is of the essence, the ability to utilize heterogeneous images is fundamental, i.e., images that are not directly comparable due to the sensors used or the capturing conditions. The recent advances in machine learning have dispersed into the ...

    • Deep Learning of Oriented Bounding Box Regression Networks for Ship Detection in Optical Satellite Images 

      Sandland, Åsmund Mikael (Master thesis; Mastergradsoppgave, 2020-05-31)
      Maritime surveillance is important for management of maritime traffic and to prevent activities like illegal fishing, hazardous cargo transportation, piracy, and smuggling of goods and humans. Remote sensing is frequently used for positioning vessels that are not transmitting via the Automatic Identification System (AIS). Modern optical remote sensing instruments provide high-resolutional imagery, ...

    • MIR-based in-situ measurement of Silicon crystal-melt interface 

      Jensen, Mathias N. (Master thesis; Mastergradsoppgave, 2020-06-29)
      The project explores the a proposed MIR-based measurement system for measuring the deflection of the interface between the crystal and melt during production of mono-crystalline Silicon in the Czochralski process. The absorption spectrum is modeled and the specific absorption for a select set of wavelengths is estimated for temperatures approching 1687K. It was estimated that the intrinsic absorption ...

    • Numerical and experimental investigation of absorbing polymer films suitable for boundary photoacoustic imaging 

      Salmi, Marte Helene Skogdahl (Master thesis; Mastergradsoppgave, 2020-07-13)
      One of the main challenges in conventional photoacoustic methods, is that thin biological samples typically have low optical absorption in the visible region. Therefore, it is often necessary to stain or label the sample with a color which provide sufficient absorption for the laser wavelength used in the scanning system. Unfortunately, the labeling often introduce unwanted properties to the biological ...

    • Introducing Soft Option-Critic for Blood Glucose Control in Type 1 Diabetes : Exploiting Abstraction of Actions for Automated Insulin Administration 

      Jenssen, Christian (Master thesis; Mastergradsoppgave, 2020-07-15)
      Type 1 Diabetes (T1D) is an autoimmune disease where the insulin-producing cells are damaged and unable to produce sufficient amounts of insulin, causing an inability to regulate the body's blood sugar levels. Administrating insulin is necessary for blood glucose regulation, requiring diligent and continuous care from the patient to avoid critical health risks. The dynamics governing insulin-glucose ...

    • Wideband Self-Interference Cancellation Using Multi-Tap Filter in Radar Front End 

      Heiskel, Bendik (Mastergradsoppgave; Master thesis, 2020-12-14)
      The largest hurdle in full duplex wireless systems is the self-interference introduced by the transmitted signal into the received signal. In multi antenna systems this interference is caused by the direct coupling between the transmitting and receiving antennas. In systems where the transmitter and receiver uses the same antenna the interference is caused by inadequate isolation between the ...

    • Investigating the Impact of Susceptibility Artifacts on Adjacent Tumors in PET/MRI through Simulated Tomography Experiments 

      Olsen, Erlend Bredal (Mastergradsoppgave; Master thesis, 2021-06-01)
      For quantitative PET imaging, attenuation correction (AC) is mandatory. Currently, all main vendors of hybrid PET/MRI systems apply a segmentation-based approach to compute a Dixon AC-map based on fat and water images derived from in- and opposed-phase MR-images. Changes in magnetic susceptibility pose major problems for MRI, which may lead to artifacts resulting in tissue misclassification in the ...

    • Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization 

      Nilsen, Torjus (Mastergradsoppgave; Master thesis, 2021-06-01)
      Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in the Arctic. A current challenge however is that most sensory equipment can only capture one or fewer ...

    • Imputation and classification of time series with missing data using machine learning 

      Dretvik, Vilde Fonn (Mastergradsoppgave; Master thesis, 2021-06-21)
      This work is about classifying time series with missing data with the help of imputation and selected machine learning algorithms and methods. The author has used imputation to replace missing values in two data sets, one containing surgical site infection (SSI) data of 11 types of blood samples of patients over 20 days, and another data set called uwave which contain 3D accelerometer data of several ...

    • ConvMixerSeg: Weakly Supervised Semantic Segmentation for CT Liver Images 

      Joakimsen, Harald Lykke (Mastergradsoppgave; Master thesis, 2021-12-17)
      The predictive power of modern deep learning approaches is posed to revolutionize the medical imaging field, however, their usefulness and applicability are severely limited by the lack of well annotated data. Liver segmentation in CT images is an application that could benefit particularly well from less data hungry methods and potentially lead to better liver volume estimation and tumor detection. ...

    • Validating Uncertainty-Aware Virtual Sensors For Industry 4.0 

      Mohammad, Gutama Ibrahim (Mastergradsoppgave; Master thesis, 2022-01-26)
      In industry 4.0 manufacturing, sensors provide information about the state, behavior, and performance of processes. Therefore, one of the main goals of Industry 4.0 is to collect high-quality data to realize its business goal, namely zero-defect manufacturing, and high-quality products. However, hardware sensors cannot always gather quality data due to several factors. First, industrial 4.0 deploys ...