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This thesis explores the use of machine learning (ML) and smartphone sensors to improve indoordistance estimation, a critical aspect of healthcare tests like the 6-minute walk test (6MWT). In order to make tests like the 6MWT more available, and lower the barrier for a patient toget tested, there are multiple problems which need to be solved: How can the distance data needed for these tests be collected reliably and remotely, and without having to rely on the patient reporting correct data; How can these tests be performed indoors, without relying on GPS or other GNSS, which are unreliable indoors. To tackle these challenges a convolutional neural network (CNN) trained on a dataset containing continuous ground truth was employed. An enhancement of an existing CNN model was done by collecting more training data, tuning hyper parameters, and testing it on a diverse dataset. The results of this thesis shows that when predicting distance walked on data from participants the CNN model has seen before, the precision meets clinical minimum for being able to show a change in the health condition. On real world data the performance suffers. Despite limitations due to the scope of data collection, the results still underscores the potential of ML for accurate and efficient indoor distance estimation and points to future research directions. / <p></p><p></p><p></p>

This thesis considers statistical issues in source localization from the received signal strength (RSS) measurements at sensor locations, under the practical assumption of log-normal shadowing. Distance information of source from sensor locations can be estimated from RSS measurements and many algorithms directly use powers of distances to localize the source, even though distance measurements are not directly available. The first part of the thesis considers the statistical analysis of distance estimation from RSS measurments. We show that the underlying problem is inefficient and there is only one unbiased estimator for this problem and its mean square error (MSE) grows exponentially with noise power. Later, we provide the linear minimum mean square error (MMSE) estimator whose bias and MSE are bounded in noise power. The second part of the thesis establishes an isomorphism between estimates of differences between squares of distances and the source location. This is used to completely characterize the class of unbiased estimates of the source location and to show that their MSEs grow exponentially with noise powers. Later, we propose an estimate based on the linear MMSE estimate of distances that has error variance and bias that is bounded in the noise variance.

<p>Two methods for evaluating and improving plane wave beamforming have beendeveloped. The methods estimate the shape of the wavefront and use theinformation in the beamforming. One of the methods uses estimates of the timedelays between the sensors to approximate the shape of the wavefront, and theother estimates the wavefront by matching the received wavefront to sphericalwavefronts of different radii. The methods are compared to a third more commonmethod of beamforming, which assumes that the impinging wave is planar. Themethods’ passive ranging abilities are also evaluated, and compared to a referencemethod based on triangulation.Both methods were evaluated with both real and simulated data. The simulateddata was obtained using Raylab, which is a simulation program based on ray-tracing. The real data was obtained through a field-test performed in the Balticsea using a towed array sonar and a stationary source emitted tones.The performance of the matched beamformers depends on the distance to the tar-get. At a distance of 600 m near broadside the power received by the beamformerincreases by 0.5-1 dB compared to the plane wave beamformer. At a distance of300 m near broadside the improvement is approximately 2 dB. In general, obtain-ing an accurate distance estimation proved to be difficult, and highly dependenton the noise present in the environment. A moving target at a distance of 600 mat broadside can be estimated with a maximum error of 150 m, when recursiveupdating of the covariance matrix with a updating constant of 0.25 is used. Whenrecursive updating is not used the margin of error increases to 400 m.</p>

Two methods for evaluating and improving plane wave beamforming have beendeveloped. The methods estimate the shape of the wavefront and use theinformation in the beamforming. One of the methods uses estimates of the timedelays between the sensors to approximate the shape of the wavefront, and theother estimates the wavefront by matching the received wavefront to sphericalwavefronts of different radii. The methods are compared to a third more commonmethod of beamforming, which assumes that the impinging wave is planar. Themethods’ passive ranging abilities are also evaluated, and compared to a referencemethod based on triangulation.Both methods were evaluated with both real and simulated data. The simulateddata was obtained using Raylab, which is a simulation program based on ray-tracing. The real data was obtained through a field-test performed in the Balticsea using a towed array sonar and a stationary source emitted tones.The performance of the matched beamformers depends on the distance to the tar-get. At a distance of 600 m near broadside the power received by the beamformerincreases by 0.5-1 dB compared to the plane wave beamformer. At a distance of300 m near broadside the improvement is approximately 2 dB. In general, obtain-ing an accurate distance estimation proved to be difficult, and highly dependenton the noise present in the environment. A moving target at a distance of 600 mat broadside can be estimated with a maximum error of 150 m, when recursiveupdating of the covariance matrix with a updating constant of 0.25 is used. Whenrecursive updating is not used the margin of error increases to 400 m.

The purpose of this thsis is to develop a vision perception based Intelligent Vehicle Driving Assistant System ( IVDAS ), which utilizes CCD camera to capture the movement of vehicle and road image on DSP-Based . According to daytime and night time, we analyzed the full information in the image to acquire the important and proper characteristics about lane mark and vehicle. There are two sub-systems in our system , including Lane Mark Detection and Vehicle Detection. The main goal is to identify if there are existing vehicles in the front of or near our vehicle. This system can provide information for the Intelligent Vehicle to make decision to avoid accident happening and assisted driver in driving safely.

This thesis describes the possibilities of using smartphones and their cameras in combination with modern computer vision algorithms to track and measure the speed of a floorball. Previous research within the area is described and an explanation is given as to why an implementation using three-frame temporal differencing to detect objects in motion works best to detect and track the ball. 100 floorball shots were recorded and measured using a speedometer radar and two different smartphones, one running the application and the other recording each shot. The video recording for each shot was then used to manually create a baseline for speed comparison. A second experiment was later conducted to analyse the sensitivity and effect on the determined ball size in the floorball shot analysis. The results from the first experiment show that the speedometer radar results in average deviate by 12% from the speed baseline. The speedshooting application however has results that, on average, deviate from the speed baseline by 6%. Furthermore, the results show that a faulty ball size detection is the major cause of error in the speedshooting application. The main conclusion that can be drawn from this is that it is possible to use a smartphone and computer vision methodologies to determine the speed of a floorball shot. In fact, it is even possible to do so with greater accuracy than the radar used in the experiments in this thesis. However, to prove the accuracy of the application for normal use, further testing needs to be conducted in new experiment conditions, for example by recording shots at higher speeds than those recorded in the experiments in this thesis.

Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácie jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase pracovať na uvedenom probléme. Implementačná časť spočíva vo využití akcelerátoru Intel NCS v spojení s Raspberry Pi priamo ako súčasť drona. Výsledný systém je schopný generovať výstup o polohe osôb v zábere kamery a príslušne ho prezentovať.

Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty v grafe. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácia jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase pracovať na uvedenom probléme. Implementačná časť spočíva vo využití akcelerátoru Intel NCS v spojení s Raspberry Pi priamo ako súčasť drona. Výsledný systém je schopný generovať výstup o polohe detekovaných osôb v zábere kamery a príslušne ho prezentovať.

Tato disertační práce se věnuje lokalizaci v bezdrátových sítích se zaměřením na odhad vzdálenosti. Lokalizace je v bezdrátových sítích s mobilními ale i statickými uzly důležitým procesem, neboť znalost pozice uzlů může být během provozu sítě dále s výhodou využita. V práci je prezentována nová metoda odhadu vzdálenosti na základě měření síly přijatého signálu. Navržená metoda je postavena tak, aby s co nejnižšími energetickými náklady dosáhla požadovaného stupně přesnosti i ve značně odlišných rádiových podmínkách. Před návrhem vlastní metody byla provedena experimentální analýza spotřeby anergie a šíření signálu s jeho využitím pro lokalizační účely. Na základě provedené analýzy byla navržena nová metoda (Adaptabilní energeticky nenáročná metoda odhadu vzdálenosti), která byla následně ověřena v simulátoru a experimentální síti za reálných podmínek.