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With the increasing number of small Uncrewed Aerial Systems (sUAS) in the airspace, the need for robust Detect and Avoid (DAA) technologies is clear. This is especially true when considering the potential for non-cooperative aircraft with unknown intent. Many UAS use high resolution cameras to perform omnidirectional scans of their nearby airspace to localize traffic. These scans can be quite computationally expensive and often necessitate the use of costly and heavy hardware components. Ground-based solutions such as centralized, stationary towers are often expensive, difficult to proliferate, and have the disadvantage of not being onboard the aircraft and as such not always local to the airspace conflict. A feasibility exploration of acoustic detection and localization of non-cooperative aircraft using a low-cost microphone array, computationally inexpensive beamforming algorithms, and filtering techniques, is performed. The cost of the system is minimized by utilizing widely proliferated microphone hardware originally designed for short-range voice detection, as well as a small Uncrewed Aerial Systems (sUAS) from a developmental kit. Lastly, an exploration is conducted to maximize the detection range of the microphone system. A comparison of filtering techniques to try to filter sUAS self-noise is compared to alternative methods such as a ballistic sampling period where the motors of the sUAS are momentarily turned off to reduce noise. A final recommendation of a multi-sensor suite of microphones, cameras, along with other potential sensors, is determined. / Master of Science / As the number of drones increases throughout many industries, safe usage becomes very important. Industries such as search and rescue, infrastructure surveying, package delivery, and more, all have novel uses for drones that could change the way those industries operate. It is easy to imagine the benefit of same-day shipping with package-carrying drones, the quick location of a missing person by a search and rescue drone, and so on. However, obstacles such as buildings, trees, and other air traffic pose an obvious risk. Current methods to detect other aircraft often rely on cameras onboard the aircraft to spot nearby traffic. Other methods include using centralized stations on the ground to relay information about positioning between cooperating aircraft. These technologies provide functionality, but often can be expensive, heavy, require computers with large processing power, or assume the cooperation of the aircraft. An analysis of audio based detection of nearby drones is conducted. The microphones used were originally intended for use in home applications as a voice assistant. Programming techniques were used to listen and identify the sound of a nearby drone. Depending on the location of the drone, its sound would arrive to the microphones in unique time delays, providing a method of estimating the drone's position. Testing was performed on the ground and in the air to analyze the distance at which this microphone group could find a drone. Ultimately, a recommendation for the inclusion of microphones in a suite of sensors was made.

Ultra-wideband microwave imaging, with its advantages of absence of breast compression, nonionizing and noninvasive properties, is a complementary method to X-ray mammography for breast cancer detection that is safe and reasonably inexpensive. The motivation for employing microwave imaging techniques for detecting early-stage breast cancer stems from published research results showing the strong contrast in the dielectric properties at microwave frequencies between normal breast tissue and malignant lesion. This thesis contributes to development of novel techniques for the detection of early-stage breast cancer tumors well below a centimeter with specificity and high degree of accuracy, i.e., with minimum false negatives/positives. In our proposed approach, a modified Shannon entropy criterion (SEC) is formulated for determining when the time-reversed wave focuses back to the source target in the presence of an inhomogeneous lossy medium. It is demonstrated through two examples, the time-reversal mirror and cavity, that the SEC is found to be more robust than the inverse varimax norm. TR has been shown to be superior to other simple delay-based focusing techniques and here we have extended the TR algorithm by making it more robust in localizing small tumors. The importance of this finding becomes evident as the SEC allows for the detection of tumors that are sub-wavelength in size. Our novel sub-wavelength ultra-wide band (UWB) microwave radar imaging technique exploits the principle of phase-shifting mask (PSM) from optical lithography and is implemented using a time-reversal (TR) algorithm based on the transmission-line matrix (TLM) method. We incorporate the SEC in a TR algorithm to achieve a robust imaging algorithm exploiting the measurements acquired by our phase-shifting mask (PSM) experimental set-up. Unlike the FDTD TR algorithm by Kosmas et al., which excites one of the 23 antenna elements, we propose a different system where all antennas are stimulated simultaneously with their excitation based on the PSM principle. A 0.5-mm diameter tumor was detected and located using a 200-ps UWB pulse in a realistic inhomogeneous two dimensional breast model. The breast model was derived from magnetic resonance imaging data and simulated using the TLM method. The effect of the dielectric contrast and proximity of tumor to the antenna receivers are examined. A TLM-based TR algorithm employing two types of time reversal mirrors (TRMs) is proposed to improve the accuracy of localizing the sub-wavelength tumors. The final part of the thesis examines the feasibility and the design of a narrowbeam UWB antenna for microwave breast cancer detection focusing on the antenna feed structure and the printed TEM horn antenna. A feed structure of an UWB antenna for microwave radar imaging is designed. The UWB antennas are fundamental components of the UWB radar imaging hardware. The performance of the antenna is crucial for the resolution and the reliability of the whole imaging system. A TEM horn antenna is studied and suggestions are made regarding the hardware implementations of the experimental setup. We conclude by suggesting future work toward hardware and practical implementation of UWB microwave radar imaging with high resolution. / Thesis / Master of Applied Science (MASc)

Objective: Previous population-based studies provided conflicting results regarding the association of total serum insulin-like growth factor I (IGF-I) and mortality. The aim of the present study was to assess the relation of IGF-I levels with all-cause mortality in a prospective study. Design: DETECT (Diabetes Cardiovascular Risk-Evaluation: Targets and Essential Data for Commitment of Treatment) is a large, multistage, and nationally representative study of primary care patients in Germany. The study population included 2463 men and 3603 women. Death rates were recorded by the respective primary care physician. Serum total IGF-I levels were determined by chemiluminescence immunoassays and categorized into three groups (low, moderate, and high) according to the sex- and age-specific 10th and 90th percentiles. Results: Adjusted analyses revealed that men with low [hazard ratio (HR) 1.70 (95% confidence interval [CI] 1.05–2.73), p=0.03] and high [HR 1.76 (95% CI 1.09–2.85), p=0.02] IGF-I levels had higher risk of all-cause mortality compared to men with moderate IGF-I levels. The specificity of low IGF-I and high IGF-I levels increased with lower and higher cut-offs, respectively. No such association became apparent in women. Conclusions: The present study revealed a U-shaped relation between IGF-I and all-cause mortality in male primary care patients.

Cette thèse s'intéresse à l'étude et au développement de méthodes de pistage mono et multicible en contexte Track-Before-Detect (TBD) par filtrage particulaire. Contrairement à l'approche classique qui effectue un seuillage préalable sur les données avant le pistage, l'approche TBD considère directement les données brutes afin de réaliser conjointement la détection et le pistage des différentes cibles. Il existe plusieurs solutions à ce problème, néanmoins cette thèse se restreint au cadre bayésien des Modèles de Markov Cachés pour lesquels le problème TBD peut être résolu à l'aide d'approximations particulaires. Dans un premier temps, nous nous intéressons à des méthodes particulaires monocibles existantes pour lesquels nous proposons différentes lois instrumentales permettant l'amélioration des performances en détection et estimation. Puis nous proposons une approche alternative du problème monocible fondée sur les temps d'apparition et de disparition de la cible; cette approche permet notamment un gain significatif au niveau du temps de calcul. Dans un second temps, nous nous intéressons au calcul de la vraisemblance en TBD -- nécessaire au bon fonctionnement des filtres particulaires -- rendu difficile par la présence des paramètres d'amplitudes des cibles qui sont inconnus et fluctuants au cours du temps. En particulier, nous étendons les travaux de Rutten et al. pour le calcul de la vraisemblance au modèle de fluctuations Swerling et au cas multicible. Enfin, nous traitons le problème multicible en contexte TBD. Nous montrons qu'en tenant compte de la structure particulière de la vraisemblance quand les cibles sont éloignées, il est possible de développer une solution multicible permettant d'utiliser, dans cette situation, un seule filtre par cible. Nous développons également un filtre TBD multicible complet permettant l'apparition et la disparition des cibles ainsi que les croisements. / This thesis deals with the study and the development of mono and multitarget tracking methods in a Track-Before-Detect (TBD) context with particle filters. Contrary to the classic approach that performs before the tracking stage a pre-detection and extraction step, the TBD approach directly works on raw data in order to jointly perform detection and tracking. Several solutions to this problem exist, however this thesis is restricted to the particular Hidden Markov Models considered in the Bayesian framework for which the TBD problem can be solved using particle filter approximations.Initially, we consider existing monotarget particle solutions and we propose several instrumental densities that allow to improve the performance both in detection and in estimation. Then, we propose an alternative approach of the monotarget TBD problem based on the target appearance and disappearance times. This new approach, in particular, allows to gain in terms of computational resources. Secondly, we investigate the calculation of the measurement likelihood in a TBD context -- necessary for the derivation of the particle filters -- that is difficult due to the presence of the target amplitude parameters that are unknown and fluctuate over time. In particular, we extend the work of Rutten et al. for the likelihood calculation to several Swerling models and to the multitarget case. Lastly, we consider the multitarget TBD problem. By taking advantage of the specific structure of the likelihood when targets are far apart from each other, we show that it is possible to develop a particle solution that considers only a particle filter per target. Moreover, we develop a whole multitarget TBD solution able to manage the target appearances and disappearances and also the crossing between targets.

Bakgrund: Att upptäcka och stödja barn som far illa är ett stort och unikt ansvar. Sjuksköterskan är den enda utanför familjen som regelbundet träffar barn och följer deras hälsa, tillväxt och utveckling. Sjuksköterskan kan hamna i en svår situation då de misstänker att barn far illa och samtidigt vill behålla en god kontakt med föräldrarna. Alla föräldrar vill sina barns bästa men förutsättningarna varierar. Syfte: Syftet var att undersöka Skol- och BVC-sjuksköterskans erfarenheter av att möta familjer där barn far illa i hemmet. Metod: Datamaterialet utgörs av intervjuer med sju Skol- och BVC-sjuksköterskor med specialistexamen. Studien har analyserats med en kvalitativ innehållsanalys med induktiv ansats. Resultat: Ur analysen framträdde två huvudkategorier, Att samverka för barnet och Att vara en stödjande person. Resultatet visade att det var av betydelse att våga se och hjälpa familjer genom uppbyggnad av förtroende och stöd från sjuksköterskan. Konklusion: Av resultatet framkommer att sjuksköterskan såg en stor vikt av att bygga upp ett förtroende till barnet och familjen då det hade betydelse för hur sjuksköterskan sedan skulle kunna ge det stöd barnet och familjen behövde. Dock fanns en ständig rädsla för att familjen skulle tappa förtroendet. Det krävdes mod att våga se de barn som far eller riskerar att fara illa och vidta åtgärder.

abstract: In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted mean-squared error (MSE). As a result, the radar parameters are adaptively and optimally selected for superior performance. Based on previous work, this thesis highlights the applicability of the predicted covariance matrix to the lower SNR waveform-agile tracking problem. The adaptive waveform selection algorithm's MSE performance was compared against fixed waveforms using Monte Carlo simulations. It was found that the adaptive approach performed at least as well as the best fixed waveform when focusing on estimating only position or only velocity. When these estimates were weighted by different amounts, then the adaptive performance exceeded all fixed waveforms. This improvement in performance demonstrates the utility of the predicted covariance in waveform design, at low SNR conditions that are poorly handled with more traditional tracking algorithms. / Dissertation/Thesis / M.S. Electrical Engineering 2011

Nyckelord: Barnmisshandel, akutmottagning, sjuksköterska, upptäcka, svårigheter   SAMMANFATTNING Bakgrund: Barnmisshandel är ett hälsoproblem som förekommer över hela världen. Globalt sett är 7 % av barn med skador som söker vård på akutmottagningar utsatta för övergrepp. Forskning visar att många barn som är utsatta för övergrepp eller omsorgssvikt besöker barnakutmottagningar utan att deras utsatthet uppdagas och anmäls till myndigheter. En bidragande orsak anses vara sjuksköterskors bristande kunskap hur barnmisshandel upptäcks.  Syfte: Syftet var att undersöka sjuksköterskors upplevda svårigheter att upptäcka barn som far illa i samband med besök på barnakutmottagning och i pediatrisk slutenvård. Metod: Litteraturöversikt med en deduktiv ansats. Artiklar har sökts i databaserna: Psycinfo, Pubmed och Cinahl. I översikten har 14 vetenskapliga artiklar inkluderats.  Resultat: Resultatet visar på flera brister såsom tidsbrist och hög arbetsbelastning samt avsaknaden av rutiner och riktlinjer. Detta är försvårade för sjuksköterskans bedömningsprocess av barnet. Erfarenhet gjorde att sjuksköterskan vågade lita på sin intuition och samtidigt vara professionell i mötet med föräldrar. Närmare kontakt och samarbete med barnrättsinstitutioner för att få mer kunskap om barnmisshandel efterfrågades. Sjuksköterskor ville vara barnets advokat men oroades ofta över vem som skulle få betala priset vid en anmälan. Slutsats: Konsekvenserna av fysisk och/eller psykisk misshandel under barn och ungdomars uppväxt kan bli svåra och bestående. Trots att lagen om anmälningsskyldighet vid misstanke om att ett barn far illa är enkel att förstå upplever många barnsjuksköterskor att lagen kan vara svår att efterleva. Riktlinjer, utbildning och rutiner på arbetsplatsen om barn som far illa är avgörande för barnsjuksköterskan för att kunna upptäcka och förhindra förekomsten av barnmisshandel.

The traditional tracking approach of forming detections and then associating these detections together is known to perform poorly at low signal-to-noise ratios (SNR). Track-before-detect (TBD) approaches, where the sensor data is used directly as opposed to forming detections, has been shown to perform better than traditional approaches at low SNRs. One recently introduced TBD algorithm is the Quanta Tracking Algorithm that is formed by applying maximum likelihood estimation to the histogram probabilistic multi-target tracker (HPMHT). Quanta has shown promising performance for low SNR scenarios, but the body of literature is small and the evaluations that have been done so far do not consider several practical aspects of using the algorithm in real applications and are difficult to compare to other algorithms due to the SNR definitions used. This paper seeks to address these deficiencies in the literature. A re-derivation of Quanta that corrects some issues present in the original derivation while also integrating extensions from the HPMHT literature will also be presented. These extensions are shown to make Quanta able to correct for errors in the assumed size targets as well as improve estimating the SNR of fluctuating targets. / Thesis / Master of Applied Science (MASc)

In order to increase the safety of all air travel, technologies that continueto augment the pilot's ability to avoid collisions and stay clear of danger areneeded. But, before these can be certified and deployed, their performance andpotential failure cases have to be understood. This requires evaluating a modelof the system on simulated encounters, consisting of different trajectoriesthat should replicate the real world. This is commonly done using a statistical encounter model, which produces largeamounts of data but relies on the accuracy of the statistical model, thuslimited in its ability to produce realistic data. The goal with this project isto create an encounter dataset of real trajectories that would provide analternative to encounter models. This is done using an ADS-B dataset from The OpenSky Network (provided byDaedalean AI), consisting of 226 billion air traffic data points from 2019.First, a solution to efficiently query and reconstruct trajectories from thedataset is designed and implemented. Using it, a NMAC (Near Mid-Air Collision)dataset is created to demonstrate the viability of ADS-B as a source forcreating an encounter dataset, and to prove the capabilities of the designedsolution.