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This thesis investigates the combination of pose and radio channel estimation. Pose is the knowledge of the position and orientation of a device whereas the radio channel describes the transmission medium between radio transmitters and receivers. The two subjects are both active research topics with a long history of applications but there has to the author's knowledge been very little work publis
In this paper, we revisit the problem of estimating the trifocal tensor from image line measurements. With measurements of corresponding lines in three views, a linear method [1] requiring 13 lines was developed to estimate the trifocal tensor from which projective reconstruction of the scene is made possible. By further utilizing the nonlinear constraints on the trifocal tensor, we propose severa
Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive contro
The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding aplications are needed. Unfortunately existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to efficient CPU allocation for ti
Imaging and inverse scattering of seismic reflection data can be formulated in terms of a certain class of Fourier integral operators. We present an approximation and discretization of such operators following a multiscale approach, based on wave packets as the quanta for representing seismic data. One of the key ingredients in our approach is the coupling of dyadic parabolic decomposition and pro
Image warping for spatial forecast verification is applied to the test cases employed by the Spatial Forecast Verification Intercomparison Project (ICP), which includes both real and contrived cases. A larger set of cases is also used to investigate aggregating results for summarizing forecast performance over a long record of forecasts. The technique handles the geometric and perturbed cases with
A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermor
In this paper a controller reduction method for discrete-time linear time-invariant systems is described. Using the bounded-real lemma, the proposed method generates reduced controllers with closed loop stability and H-infinity norm performance guarantee. Information of the full controller is used as a basis for reduction using singular value decomposition. This is different from traditional model
In this paper, we study the problems of estimating relative pose between two cameras in the presence of radial distortion. Specifically, we consider minimal problems where one of the cameras has no or known radial distortion. There are three useful cases for this setup with a single unknown distortion: (i) fundamental matrix estimation where the two cameras are uncalibrated, (ii) essential matrix
A numerical model that combines a random wave transformation and a wave-induced current model was developed in order to predict the wave and current fields around a detached breakwater. The wave field was determined using the EBED model, as reported by Mase in 2001, with a modified energy dissipation term. The surface roller associated with wave breaking was modeled based on a modification of the
The thesis covers different topics related to model predictive control (MPC) and particularly distributed model predictive control (DMPC). One topic of the thesis is gradient-based optimization algorithms for solving the optimization problem arising in DMPC in a distributed manner. The underlying idea is to solve the optimization problem in distributed fashion using dual decomposition, which is a
Feedback is an ancient idea, but feedback control is a young field. Nature long ago discovered feedback since it is essential for homeostasis and life. It was the key for harnessing power in the industrial revolution and is today found everywhere around us. Its development as a field involved contributions from engineers, mathematicians, economists and physicists. It is the first systems disciplin
Aquifers and groundwater-dependent ecosystems (GDEs) are facing increasing pressure from water consumption, irrigation and climate change. These pressures modify groundwater levels and their temporal patterns and threaten vital ecosystem services such as arable land irrigation and ecosystem water requirements, especially during droughts. This review examines climate change effects on groundwater a
This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we model the network interconnections with integral quadratic constraints. This approach yields a sparse linear matrix inequal- ity which can be decomposed into a set of smaller, coupled linear matrix inequalities. This allow
This paper reviews the 25-plus year history of significant developments of the GENESIS shoreline response model. Topics discussed are line sources and sinks of sand, representation of shore-normal structures including natural sand bypassing, wave transmission by and shoreline response to shore-parallel structures, seawalls, migrating longshore sand waves, seasonal variation by cross-shore sand tra
This thesis presents an improved version of the classic relay autotuner. The proposed autotuner uses an asymmetric relay function to better excite the process in the experiment phase. The improved excitation provides the possibility to obtain better models and hence better tuning, without making the autotuner more complicated or time consuming.Some processes demand more accurate modeling and tunin
The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resourc
