Exploiting Sparse Structures in Source Localization and Tracking

This thesis deals with the modeling of structured signals under different sparsity constraints. Many phenomena exhibit an inherent structure that may be exploited when setting up models, examples include audio waves, radar, sonar, and image objects. These structures allow us to model, identify, and classify the processes, enabling parameter estimation for, e.g., identification, localisation, and tracking.In this work, such structures are exploited, with the goal to achieve efficient localisation and tracking of a structured source signal. Specifically, two scenarios are considered. In papers A and B, the aim is to find a sparse subset of a structured signal such that the signal parameters and source locations maybe estimated in an optimal way. For the sparse subset selection, a combinatorial optimization problem is approximately solved by means of convex relaxation, with the results of allowing for different types of a priori information to be incorporated in the optimization. In paper C, a sparse subset of data is provided, and a generative model is used to find the location of an unknown number of jammers in a wireless network, with the jammers’ movement in the network being tracked as additional observations become available

Sparse Chroma Estimation for Harmonic Audio

This work treats the estimation of the chromagram for harmonic audio signals using a block sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a Fourier-based framework that maps the fundamental frequency of a musical tone to its corresponding chroma. Such an approach often leads to problems with tone ambiguity, which we avoid by taking into account the harmonic structure and perceptional attributes in music. The performance of the proposed method is evaluated using real audio files, clearly showing preferable performance as compared to other commonly used methods

Midlife risk factor exposure and incidence of cardiac arrest depending on cardiac or non-cardiac origin

Objective: Little is known about midlife risk factors of future cardiac arrest. Our objective was to evaluate cardiovascular risk factors in midlife in relation to the risk of cardiac arrest (CA) of cardiac and non-cardiac origin later in life. Methods: We cross-matched individuals of the population based Malmö Diet and Cancer study (n = 30,447) with the local CA registry of the city of Malmö. Baseline exposures were related to incident CA. Results: During a mean follow-up of 17.6. ±. 4.6. years, 378 CA occurred, of whom 17.2% survived to discharge. Independent midlife risk factors for CA of cardiac origin included coronary artery disease (HR 2.84 (1.86-4.34) (p 30kg/m2) (HR 2.37 (1.51-3.71) (p <0.001)), smoking (HR 2.05 (1.33-3.15) (p <0.001)) and being on antihypertensive treatment (HR 2.25 (1.46-3.46) (p <0.001)). Conclusion: Apart from smoking, which increases the risk of CA in general, the midlife risk factor pattern differs between CA of cardiac and non-cardiac origin. Whereas CA of cardiac origin is predicted by history of cardiovascular disease, dyslipidemia and diabetes mellitus, the main risk factors for CA of non-cardiac origin are obesity and hypertension. In addition to control of classical cardiovascular risk factors for prevention of CA, our results suggest that prevention of midlife obesity may reduce the risk of CA of non-cardiac origin

Specialpedagogens uppdrag i skolans verksamhet för elever i förskoleklass till årskurs sex

Sammanfattning: Vi har under vår utbildning mött en osäkerhet ute i verksamheterna vad det gäller specialpedagogens uppdrag. Vi har känt att gränsen mellan speciallärare och specialpedagoger har varit otydlig. Syftet med denna studie är därför att kartlägga specialpedagogens uppdrag i skolans verksamhet, i arbetet mot eleverna i förskoleklass till årskurs sex. Våra frågeställningar är: Vad säger det specialpedagogiska programmet på Göteborgs universitet om specialpedagogens uppdrag? Finns det några övergripande styrdokument som beskriver specialpedagogens uppdrag? Hur beskriver specialpedagogen sitt uppdrag i verksamheten? Hur använder sig rektorerna/skolledarna av den specialpedagogiska kompetensen? Som metod har vi valt kvalitativ intervju då vi vill ha en djupare förståelse för specialpedagogens uppdrag i verksamheten. Vi har intervjuat sex specialpedagoger samt deras rektorer. Vi har kommit fram till att specialpedagogens uppdrag är mer omfattande än vad högskoleförordningens examensförordning för specialpedagoger uppger. En stor del av sin tid avsätter exempelvis specialpedagogen till specialpedagogisk undervisning. Detta examensarbete ger en kunskap om specialpedagogens uppdrag och vilken betydelse specialpedagogen har för hela skolans verksamhet för att uppfylla målet "en skola för alla"

Optimal sensor placement for localizing structured signal sources

This work is concerned with determining optimal sensor placements that allow for an accurate location estimate of structured signal sources, taking into account the expected location areas and the typical range of the parameters detailing the signals. In the presentation, we illustrate the technique for tonal sound signals, exploiting the expected harmonic structure of such signals. To determine preferable sensor placements, we propose a computationally efficient scheme that minimizes theoretical lower bounds on the variance of the location estimate over the possible sensor placements, while taking into account the expected variability in the impinging signals, and introducing various forms of constraints on the optimization. Numerical examples and real measurements illustrate the performance of the proposed scheme

Identifying Decaying Sinusoidal Modes Using Signed Measurements

In this paper, we generalize the one-bit quantized low resolution RELAX algorithm to allow for exponentially decaying modes. The resulting greedy algorithm exploits a time-varying threshold to allow for the estimation of the parameters detailing the modes. The improvement offered by the proposed algorithm as compared to its predecessor, and the corresponding Cramér-Rao Lower Bound, is illustrated using numerical examples