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Sound ranging is a method to locate sources of sound waves using microphones or other types of receivers at known reference positions. Nowadays, these receivers can be made both very small and powerful, lasting for a long time on a single battery charge, making them suitable for longtime outdoor purposes such as detecting artillery fire or other loud sounds. In this master thesis, we present a sou

A numerical simulation is performed to identify the material parameters of an elastic/visco-plastic material under tensile impact loading. The identification is based on an inverse analysis using the Kaiman filter technique and the finite element method. The input data to the inverse analysis are the time histories of the strains on specimens. The measurement errors can be treated reasonably by th

Pairs trading is a widely known investment strategy among hedge funds and institutional investors that focuses on profiting from the relative mispricing between assets. A vast majority of exisiting pairs trading algorithms rely on rather restrictive assumptions concerning the modeling of the so-called spread function and are unable to capture the effects of changing market conditions into the trad

The purpose of this thesis is to model and forecast Swedish government yields by using three classes of the Nelson Siegel Model Family. The three models considered are the Dynamic Nelson Siegel Model, Arbitrage-Free Nelson Siegel Model and Dynamic Nelson Siegel Svensson Model. A brief introduction to interest rate theory is given with emphasis on coupon bonds and yield curves. To introduce the con

In many applications involving mobile devices it is essential to track their position. Moreover, it is usually desired, to perform this localization in a distributed fashion without using a central processing unit. In this case, only distance measurements to reference nodes which are in range can be utilized. It is proposed in this thesis to also use distance measurements to other moving objects,

Inference and simulation in the context of high-dimensional dynamical systems remain computationally challenging problems. Some form of dimensionality reduction is required to make the problem tractable in general. In this paper, we propose a novel approximate Gaussian filtering and smoothing method which propagates low-rank approximations of the covariance matrices. This is accomplished by projec

Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samp

This paper is a comparative study of different approaches to using a Discrete Time Micro-structure model. By using the three filtering techniques Extended Kalman, Unscented Kalman and Bootstrap Particle, the hidden variables; excess demand and market liquidity, were estimated and used in an asset allocation strategy that invested in the asset when the excess demand as estimated as positive, due to

Land surface models have uncertainties due to their approximation of physical processes and the heterogeneity of the land surface. These can be compounded when key variables are inadequately represented. Land surface temperature (LST) is critical as it forms an integral component in the surface energy budget, water stress evaluation, fuel moisture derivation, and soil moisture-climate feedbacks. A