No description available.

No description available.

<p>Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. </p><p>This Master's thesis has investigated the use of particle filters on radar measurements, in a TBD approach.</p><p>The work has been divided into two major problems, a time efficient implementation and new functional features, as estimating the radar cross section (RCS) and the extension of the target. The later is of great importance when the resolution of the radar is such, that specific features of the target can be distinguished. Results will be illustrated by means of realistic examples.</p>

Objective: Lower levels of anabolic hormones in older age are well documented. Several studies suggested that low insulin-like growth factor I (IGF-I) or testosterone levels were related to increased mortality. The aim of the present study was to investigate the combined influence of low IGF-I and low testosterone on all-cause mortality in men. Methods and results: From two German prospective cohort studies, the DETECT study and SHIP, 3942 men were available for analyses. During 21,838 person-years of follow-up, 8.4% (n = 330) of men died. Cox model analyses with age as timescale and adjusted for potential confounders revealed that men with levels below the 10th percentile of at least one hormone [hazard ratio (HR) 1.38 (95% confidence-interval (CI) 1.06–1.78), p = 0.02] and two hormones [HR 2.88 (95% CI 1.32–6.29), p < 0.01] showed a higher risk of all-cause mortality compared to men with non-low hormones. The associations became non-significant by using the 20th percentile as cut-off showing that the specificity increased with lower cut-offs for decreased hormone levels. The inclusion of both IGF-I and total testosterone in a mortality prediction model with common risk factors resulted in a significant integrated discrimination improvement of 0.5% (95% CI 0.3–0.7%, p = 0.03). Conclusions: Our results prove that multiple anabolic deficiencies have a higher impact on mortality than a single anabolic deficiency and suggest that assessment of more than one anabolic hormone as a biomarker improve the prediction of all-cause mortality.

Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. This Master's thesis has investigated the use of particle filters on radar measurements, in a TBD approach. The work has been divided into two major problems, a time efficient implementation and new functional features, as estimating the radar cross section (RCS) and the extension of the target. The later is of great importance when the resolution of the radar is such, that specific features of the target can be distinguished. Results will be illustrated by means of realistic examples.

abstract: Tracking a time-varying number of targets is a challenging dynamic state estimation problem whose complexity is intensified under low signal-to-noise ratio (SNR) or high clutter conditions. This is important, for example, when tracking multiple, closely spaced targets moving in the same direction such as a convoy of low observable vehicles moving through a forest or multiple targets moving in a crisscross pattern. The SNR in these applications is usually low as the reflected signals from the targets are weak or the noise level is very high. An effective approach for detecting and tracking a single target under low SNR conditions is the track-before-detect filter (TBDF) that uses unthresholded measurements. However, the TBDF has only been used to track a small fixed number of targets at low SNR. This work proposes a new multiple target TBDF approach to track a dynamically varying number of targets under the recursive Bayesian framework. For a given maximum number of targets, the state estimates are obtained by estimating the joint multiple target posterior probability density function under all possible target existence combinations. The estimation of the corresponding target existence combination probabilities and the target existence probabilities are also derived. A feasible sequential Monte Carlo (SMC) based implementation algorithm is proposed. The approximation accuracy of the SMC method with a reduced number of particles is improved by an efficient proposal density function that partitions the multiple target space into a single target space. The proposed multiple target TBDF method is extended to track targets in sea clutter using highly time-varying radar measurements. A generalized likelihood function for closely spaced multiple targets in compound Gaussian sea clutter is derived together with the maximum likelihood estimate of the model parameters using an iterative fixed point algorithm. The TBDF performance is improved by proposing a computationally feasible method to estimate the space-time covariance matrix of rapidly-varying sea clutter. The method applies the Kronecker product approximation to the covariance matrix and uses particle filtering to solve the resulting dynamic state space model formulation. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

Small unmanned aircraft systems (UAS) are rapidly gaining popularity. As the excitement surrounding small UAS has grown, the Federal Aviation Administration (FAA) has repeatedly stated that UAS must be capable of detecting and avoiding manned and unmanned aircraft. In developing detect-and-avoid (DAA) technology, one of the key challenges is identifying a suitable sensor. Automatic Dependent Surveillance-Broadcast (ADS-B) has gained much attention in both the research and consumer sectors as a promising solution. While ADS-B has many positive characteristics, further analysis is necessary to determine if it is suitable as a DAA sensor in environments with high-density small UAS operations. To further the understanding of ADS-B, we present a characterization of ADS-B measurement error that is derived from FAA regulations. Additionally, we analyze ADS-B by examining its strengths and weaknesses from the perspective of DAA on small UAS. To demonstrate the need and method for estimation of ADS-B measurements, we compare four dynamic filters for accuracy and computational speed. The result of the comparison is a recommendation for the best filter for ADS-B estimation. We then demonstrate this filter by estimating ADS-B measurements that have been recorded from the National Airspace System (NAS). We also present a novel long-range, convex optimization-based path planner for ADS-B-equipped small UAS in the presence of intruder aircraft. This optimizer is tested using a twelve-state simulation of the ownship and intruders.We also consider the effectiveness of ADS-B in high-density airspace. To do this we present a novel derivation of the probability of interference for ADS-B based on the number of transmitting aircraft. We then use this probability to document the need for limited transmit range for ADS-B on small UAS. We further leverage the probability of interference for ADS-B, by creating a tool that can be used to analyze self-separation threshold (SST) and well clear (WC) definitions based on ADS-B bandwidth limitations. This tool is then demonstrated by evaluating current SST and WC definitions and making regulations recommendations based on the analysis. Coupling this tool with minimum detection range equations, we make a recommendation for well clear for small UAS in ADS-B congested airspace. Overall these contributions expand the understanding of ADS-B as a DAA sensor, provide viable solutions for known and previously unknown ADS-B challenges, and advance the state of the art for small UAS.

Medicines and vaccines have revolutionized disease prevention and treatment, offering numerous benefits. However, they also raise concerns about Adverse Drug Reactions (ADRs), which can have severe consequences. Summaries of Product Characteristics (SmPCs), provided by the European Medicines Agency (EMA), and Structured Product Labelings (SPLs), provided by the Food and Drug Administration (FDA), are valuable sources of information on drug-ADR relations. Understanding these relations is crucial as it contributes to establishing labeled datasets for known ADRs and advancing statistical assessment methods. Uppsala Monitoring Centre (UMC) has developed a text mining pipeline to extract known ADRs from SPLs. While the pipeline works effectively with SPLs, it faces challenges with SmPCs provided in PDF format. This study explores extending the scanner component of the pipeline by utilizing Python PDF extraction libraries to extract text from SmPCs and fine-tuning domain-specific pre-trained BERT-based models for Named Entity Recognition (NER), which is a Natural Language Processing (NLP) task, aiming to identify known ADRs from SmPCs. The investigation finds pypdfium2 [1] to be the optimal Python PDF extraction library, and fine-tuned PubMedBERT—a domain-specific language model pre-training from scratch [2]—for the NER task achieves the best performance in identifying ADRs from SmPCs. The model's performance, evaluated using entity-level evaluation metrics including Exact, Covering, and Overlap match metrics, achieves F1-scores of 0.9138, 0.9268, and 0.9671, respectively, indicating significantly good performance. Consequently, the extension model investigated in this study will be integrated into the existing pipeline by UMC professionals.

Robust communication is required to provide a safe airspace for the large numbers of unmanned aerial systems that are coming to the National Airspace System (NAS). This thesis explores methods for providing robust communication to large numbers of vehicles in the NAS. Automatic dependent surveillance-broadcast (ASD-B) is a transmission system that can transmit to and is currently required on all manned aircraft. Unfortunately, ADS-B suffers connectivity problems when supporting large numbers of unmanned aerial systems (UAS). The 5G Cellular protocol can support large numbers of UAS, but connectivity suffers with an increase in distance and interference. Using a 5G cellular and an ADS-B simulator we evaluate the advantages of a combined ADS-B and 5G Cellular transmission system compared to a 5G or ADS-B exclusive system. We also offer hybrid system recommendations that clarify the appropriate operation strategies or triggers that should prompt transitions between transmission systems in different environmental situations. The simulation results show message success and vehicle collision rates, with each messaging method investigated to show the case for a combined communication system. This study shows that a hybrid transmission system is a possible communication solution for UAS operating in beyond visual line of sight (BVLOS) environments.

This dissertation considers radar detection and tracking of weak fluctuating targets using dynamic programming (DP) based track-before-detect (TBD). TBD combines target detection and tracking by integrating data over consecutive scans before making a decision on the presence of a target. A novel algorithm is proposed which employs order statistics in dynamic programming based TBD (OS-DP-TBD) to detect weak fluctuating targets. The well-known Swerling type 0, 1 and 3 targets are considered with non-Gaussian distributed clutter and complex Gaussian noise. The clutter is modeled using the Weibull, K and G0 distributions. The proposed algorithm is shown to provide better performance than well-known techniques in the literature. In addition, a novel expanding window multiframe (EW-TBD) technique is presented to improve the detection performance with reasonable computational complexity compared to batch processing. It is shown that EW-TBD has lower complexity than existing multiframe processing techniques. Simulation results are presented which confirm the superiority of the proposed expanding window technique in detecting targets even when they are not present in every scan in the window. Further, the throughput of the proposed technique is higher than with batch processing. Depending on the range and azimuth resolution of the radar system, the target may appear as a point in some radar systems and there will be target energy spillover in other systems. This dissertation considers both extended targets with different energy spillover levels and point targets. Simulation results are presented which confirm the superiority of the proposed algorithm in both cases. / Graduate