The measurements of radio signals are commonly used for localization purposes where the goal is to determine the spatial position of one or multiple objects. In realistic scenarios, any transmitted radio signal will be affected by the environment through reflections, diffraction at edges and corners etc. This causes a phenomenon known as multipath propagation, by which multiple instances of the transmitted signal having traversed different paths are heard by the receiver. These are known as Multi-Path Components (MPCs). The direct path (DP) between transmitter and receiver may also be occluded, causing what is referred to as non-Line-of-Sight (non-LOS) conditions. As a consequence of these effects, the estimated position of the object(s) may often be erroneous. This thesis focuses on how to achieve better localization accuracy by accounting for the above-mentioned multipath propagation and non-LOS effects. It is proposed how to mitigate these in the context of positioning based on estimation of the DP between transmitter and receiver. It is also proposed how to constructively utilize the additional information about the environment which they implicitly provide. This is all done in a framework wherein a given signal model and a map of the surroundings are used to build a mathematical model of the radio environment, from which the resulting MPCs are estimated. First, methods to mitigate the adverse effects of multipath propagation and non-LOS conditions for positioning based on estimation of the DP between transmitter and receiver are presented. This is initially done by using robust statistical measurement error models based on aggregated error statistics, where significant improvements are obtained without the need to provide detailed received signal information. The gains are seen to be even larger with up-to-date real-time information based on the estimated MPCs. Second, the association of the estimated MPCs with the signal paths predicted by the environmental model is addressed. This leads to a combinatorial problem which is approached with tools from multi-target tracking theory. A rich radio environment in terms of many MPCs gives better localization accuracy but causes the problem size to grow large—something which can be remedied by excluding less probable paths. Simulations indicate that in such environments, the single best association hypothesis may be a reasonable approximation which avoids the calculation of a vast number of possible hypotheses. Accounting for erroneous measurements is crucial but may have drawbacks if no such are occurring. Finally, theoretical localization performance bounds when utilizing all or a subset of the available MPCs are derived. A rich radio environment allows for good positioning accuracy using only a few transmitters/receivers, assuming that these are used in the localization process. In contrast, in a less rich environment where basically only the DP/LOS components are measurable, more transmitters/receivers and/or the combination of downlink and uplink measurements are required to achieve the same accuracy. The receiver’s capability of distinguishing between multiple MPCs arriving approximately at the same time also affects the localization accuracy.

This master thesis investigates the subsystems required to create a combined residual-current device, power manager and phase-switcher for electric vehicles charging, controlled by an FPGA. The purpose of this task is to create a prototype design for Chargestorm, a company that manufacture charging stations for electrical vehicles and provides a portal for payment. Each subsystem will be separately investigated to see the available alternatives and evaluate which solutions fit this design best. The system is designed to handle currents of 32 A on three phases. The design consists of a hall sensor to detect the residual current, switches to meet the switching requirements and to break the circuit when needed, current transformers to measure current and differential amplifiers to measure voltage. All logic and communication is controlled by an FPGA. Specific isolation requirements are set to prevent the power grid from arcing to the low voltage components. Optocouplers are used to allow communication between the components on the high voltage and the low voltage sides. The final design is placed on a six layer printed circuit board. This is mainly to allow for more copper to conduct the high current and thermal management. Theoretically, the work is complete and all requirements are fulfilled. In practice however, the prototype have not been fully tested and evaluated to see if the theory matches the real world.

In recent years the use of graphics processing units for general purpose computation has been increasing. This provides a relatively cheap and easy way of optimizing computation intensive tasks. Although a lot of research has been done on this subject the power aspect of this is not very clear. This thesis treats the implementation and benchmarking of three radar signal processing algorithms for the CPU and GPU of the Jetson Tegra X2 module. The objective was to measure the power consumption and speed of the GPU versus CPU implementations. All three algorithms were most efficiently executed on the GPU both in terms of power consumption and speed. The Space Time Adaptive Processing algorithm presented the biggest speedup and the Corner Turn the smallest. It was found that the both the computation and power efficiency of the GPU implementations was lower for sufficiently small input matrices.

Transport of proteins to the nucleus through a nuclear envelope is controlled mostly via nuclear localization signal (NLS). Nuclear localization signal is rich in positively charged amino acids arginine and lysine. It was observed that activity of this NLS could be regulated through a phosphorylation of serine in its close proximity. Either a phosphorylation of serine or phosphomimetic changes of these "presequences" could represent an important mechanism regulating a localization of protein in cells in relation to a cellular activation. In our laboratory was identified protein - Fragile X mental retardation syndrome 1 neighbor (Fmr1nb), whose cellular localization could be driven by this posttranslational modification.

Learning to perceive scenes and objects from 2D images as 3D models is atrivial task for a human but very challenging for a computer. Being ableto retrieve a 3D model from a scene just by taking a picture of it canbe of great use in many fields, for example when making 3D blueprintsfor buildings or working with animations in the game or film industry.Novel view synthesis is a field within deep learning where generativemodels are trained to construct 3D models of scenes or objects from 2Dimages. In this work, the generative model HoloGAN is combined together with aU-net segmentation network. The solution is able to, given an imagecontaining a single object as input, swap that object to another oneand then perform a rotation of the scene, generating new images fromunobserved view points. The segmentation network is trained with pairedsegmentation masks while HoloGAN is able to in an unsupervised mannerlearn 3D metrics of scenes from unlabeled 2D images. The system as awhole is trained on one dataset containing images of cars while theperformance of HoloGAN was evaluated on four additionaldatasets. The chosen method proved to be successful but came with somedrawbacks such as requiring large dataset sizes and being computationalexpensive to train.

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The practical considerations in ultrawideband (UWB) synthetic aperture radar (SAR) data processing in general and UWB SAR imaging in particular are clarified and presented in detail in this thesis. They are imaging algorithm, impulse response function in SAR imaging (IRF-SAR), apodization, RF interference (RFI) and SAR image quality measurement. Different algorithms in both time- and frequency domain and their suitability to process UWB SAR data are investigated and evaluated. The necessary modifications for these algorithms are proposed to fulfill the requirements of UWB SAR data processing. The time-domain imaging algorithms are highly recommended for UWB SAR data processing due to their characteristics such as integrated motion error compensation, unlimited scene size and local processing. A new IRF-SAR, which is a function of fractional bandwidth and antenna beamwidth, is derived. The function allows us to investigate different UWB SAR systems. Such investigations are not facilitated by currently used IRF-SAR, Sinc functions. The derived IRF-SAR is totally valid to investigate narrowband (NB) SAR systems. A discussion about the apodization techniques and possibilities to apply to UWB SAR data processing is given in this thesis. Handling orthogonal and non-orthogonal sidelobe in UWB SAR imaging is shown to be challenging with the currently used apodization approaches. The linear apodization approaches always result in the loss in resolutions while the phase information can be destroyed by the nonlinear apodization approaches. A new approach to suppress RFI in UWB SAR signal, which is easy to be disturbed by RFI sources, is suggested. The advantages of the approach compared to the others can be found in adaptive and real time processing characteristics. A new definition of SAR image quality measurement is also presented in this thesis. The complicated behavior of IRF-SAR over fractional bandwidth and antenna beamwidth results in the unsuitability of the currently used definition for UWB SAR image quality measurements. The unsuitability is mainly caused by the inappropriate delimitation of mainlobe and sidelobe areas, the fixed broadening factors and the fixed spreading factor of the orthogonal and non-orthogonal sidelobes. Based on these practical considerations, the thesis also presents some possibilities to propose a definition of UWB SAR which is still not available. The beginning investigated results show that these possibilities comply with the UWB definition proposed by Federal Communications Commission (FCC) in 2002.

Localization is essential in a variety of applications such as navigation systems, aerospace and surface surveillance, robotics and animal migration studies to mention a few. There are many standard techniques available, where the most common are based on information from satellite or terrestrial radio beacons, radar networks or vision systems. In this thesis, two alternative techniques are investigated.The first localization technique is based on one or more magnetometers measuring the induced magnetic field from a magnetic object. These measurements depend on the position and the magnetic signature of the object and can be described with models derived from the electromagnetic theory. For this technology, two applications have been analyzed. The first application is traffic surveillance, which has a high need for robust localization systems. By deploying one or more magnetometer in the vicinity of the traffic lane, vehicles can be detected and classified. These systems can be used for safety purposes, such as detecting wrong-way drivers on highways, as well as for statistical purposes by monitoring the traffic flow. The second application is indoor localization, where a mobile magnetometer measures the stationary magnetic field induced by magnetic structures in indoor environments. In this work, models for such magnetic environments are proposed and evaluated.The second localization technique uses light sensors measuring light intensity during day and night. After registering the time of sunrise and sunset from this data, basic formulas from astronomy can be used to locate the sensor. The main application is localization of small migrating animals. In this work, a framework for localizing migrating birds using light sensors is proposed. The framework has been evaluated on data from a common swift, which during a period of ten months was equipped with a light sensor. / Förmågan att kunna bestämma var ett objekt befinner sig är viktigt inom många olika tillämpningar, till exempel inom flyg- och sjöbevakning, robotik och studier av djurs flyttvägar, för att nämna några.  Det är speciellt önskvärt att kunna utföra denna positionering utan mänsklig inblandning, antingen för att kunna positionerna objekt som en människa inte skulle klara av att göra, eller för att effektivisera arbetet. För att automatiskt bestämma en position behövs sensorer, som mäter olika saker i dess omgivning och omvandlar detta till en elektrisk signal. Med ett datorprogram kan denna elektriska signal i sin tur sedan omvandlas till en position. Det finns många standardteknologier tillgängliga som använder sig av olika typer av sensorer som mäter olika saker. De vanligaste är baserade på satelliternavigering (GPS), radiovågor, radar och kameror.  I denna avhandling har två alternativa teknologier undersökts som i vissa tillämpningar har olika fördelar gentemot standardteknologierna.Den första teknologin för att positionera ett objekt är baserad på en eller flera sensorer som känner av magnetfältet från objekt som innehåller mycket metall, till exempel fordon. Från detta magnetfält kan man bestämma position och även storlek på objektet. Med denna teknologi som grund har två tillämpningar analyserats. Den första tillämpningen är trafikövervakning, där det finns ett stort behov av teknologi som kan bestämma position på bilar. Genom att placera ut en eller flera sensorer längs vägrenen kan man känna av bilar som kommer i närheten. Dessa system kan användas för säkerhetsändamål, som att varna för bilar som kör i fel riktning på motorvägar, eller för statistiska ändamål genom att övervaka trafikflödet. Den andra tillämpningen handlar om att bestämma position för ett objekt i en inomhusmiljö. I många byggnader finns det många objekt som innehåller metall. Dessa objekt omges av ett magnetfält. Genom att i en inomhusmiljö vandra runt med en sensor, så kommer den att känna av olika starka magnetfält beroende på var i byggnaden man befinner sig. I denna avhandling kommer vi undersöka matematiska modeller för att beskriva sådana magnetiska objekt. Den andra teknologin använder ljussensorer för att studera till vilka områden som flyttfåglar flyger. Fågeln utrustas med en ljussensor som mäter ljusstyrka under hela dygnet. Därefter släpps fågeln iväg och förhoppningsvis hittar man den ett år senare igen så att all information från sensorn kan analyseras. Från dessa mätningar kan man i efterhand beräkna vid vilken tidpunkt som soluppgången och solnedgången har inträffat. Därefter kan fågels flyttväg bestämmas med hjälp av formler från astronomin. I detta arbete föreslås en metod för hur denna information kan analyseras. Metoden har utvärderats på data från en tornseglare som under en period på tio månader flyttat till Afrika och sedan tillbaka till Sverige igen.

Image filtering methods are designed to enhance noisy images captured in situations that are problematic for the camera sensor. Such noisy images originate from unfavourable illumination conditions, camera motion, or the desire to use only a low dose of ionising radiation in medical imaging. Therefore, in this thesis work I have investigated the theory of partial differential equations (PDE) to design filtering methods that attempt to remove noise from images. This is achieved by modeling and deriving energy functionals which in turn are minimized to attain a state of minimum energy. This state is obtained by solving the so called Euler-Lagrange equation. An important theoretical contribution of this work is that conditions are put forward determining when a PDE has a corresponding energy functional. This is in particular described in the case of the structure tensor, a commonly used tensor in computer vision.A primary component of this thesis work is to model adaptive image filtering such that any modification of the image is structure preserving, but yet is noise suppressing. In color image filtering this is a particular challenge since artifacts may be introduced at color discontinuities. For this purpose a non-Euclidian color opponent transformation has been analysed and used to separate the standard RGB color space into uncorrelated components.A common approach to achieve adaptive image filtering is to select an edge stopping function from a set of functions that have proven to work well in the past. The purpose of the edge stopping function is to inhibit smoothing of image features that are desired to be retained, such as lines, edges or other application dependent characteristics. Thus, a step from ad-hoc filtering based on experience towards an application-driven filtering is taken, such that only desired image features are processed. This improves what is characterised as visually relevant features, a topic which this thesis covers, in particular for medical imaging.The notion of what are relevant features is a subjective measure may be different from a layman's opinion compared to a professional's. Therefore, we advocate that any image filtering method should yield an improvement not only in numerical measures but also a visual improvement should be experienced by the respective end-user / NACIP, VIDI, GARNICS

During environmental research projects in the Arctic region AFRY has come across an unproportionally high number of cases where the navigation of drones have not worked as intended, compared to other regions. The main objective of this thesis is to investigate the cause of these navigational problems and determine the main cause. A second objective is to design a solution that can mitigate these observed errors and improve the navigation. To establish the main error sources flight logs from flight tests performed at Svalbard are analyzed. The drone considered in this project is a quadcopter with a Pixhawk Cube flight controller and the Ardupilot software. A Pixhawk Here+ module is used for external sensors. The data logs show several cases of drones having troubles flying along a straight line. Analyzing the sensor data for the flights show that many of the flights suffer from the gyroscope drifting around the z-axis. The data show that the varying temperature on the IMU board is the cause of the drifting gyroscope. The on-board heaters did not manage to keep the temperature constant due to a too high target temperature and low outside temperatures. The system is aided with information from the magnetometer to estimate the drift around the z-axis. Results show that the estimation system is having trouble correctly estimating large drifts. To investigate why the magnetometer cannot properly compensate for the gyroscope, simulations of the magnetometer and estimation system are made. The results show that an increasing angle of inclination increases the gyroscope bias estimation errors. The large angle of inclination causes the horizontal components of the magnetic field to become too small for the magnetometer to measure correctly. The solution consists of instructions on how to operate the drone to properly use the on-board heaters, as well as an external module consisting of multiple magnetometers. Multiple magnetometers reduced the variance in the readings, but did not reach the accuracy needed to replace the external magnetometers on the drone. A better calibration method could be explored in the future, or another solution such as an improved magnetometer, a gyro compass, a GPS compass or dual GNSS antennas.