In this thesis, multiple ground target tracking algorithms are studied. From different aspects of the ground target tracking, three different types of tracking algorithms are proposed according to the specialties of the ground target motion and sensors employed. Firstly, the dependent target tracking for ground targets is studied. State dependency is a common assumption in traditional target tracking algorithms, while this may not be the true in ground target tracking as the motion of targets are constraint to certain path. To enhance the tracking algorithm for ground targets, starting with the dependency assumption, Markov Random Field (MRF) based Probabilistic Data Association (PDA) approach is derived to associate motion dependent targets. The driving behavior model is introduced to describe motion relationship among targets. The Posterior Cramer-Rao Lower Bound (PCRLB) is derived for this new motion model. Experiments and simulations show that the proposed algorithm can reduce the false associations and improve the predictions. Eventually, the proposed approach alleviates issues like the track impurity and coalescence problem and achieves better performance comparing to standard trackers assuming state independence. Ground target tracking using cameras is then studied. To build an efficient multi- target visual tracking algorithm, fast single target visual tracking is an important component. A novel visual tracking algorithm that has high speed and better or comparable performance to state-of-the-art trackers is proposed. The proposed approach solves the tracking task by using a mixed-motion proposal based particle filter with Ridge Regression observation likelihood calculation. This approach largely reduces the exhaustive searching in common state-of-art trackers while maintains efficient representation of the target appearance change. Experiments on 100 public benchmark videos, as well as a high frame rate benchmark, are carried out to compare the performance with the state-of-art published algorithms. The results of the experiment show the proposed tracker achieves good performance while beats other algorithms in speed with a large margin. The proposed visual target tracker is integrated into a new multiple ground tar- get tracking algorithm using a single camera. The multi-target tracker addresses the issues in the target detection, data association and track management aside from the single target tracker. A perspective aware detection algorithm utilizing the re- cent advanced Convolutional Neural Networks (CNN) based detector is proposed to detect multiple ground targets and alleviate the weakness of CNN detectors in detecting small objects. A hierarchical class tree based multi-class data association is presented to solve the multi-class association problem with potential misclassified detections. Track management is also improved utilizing the high efficiency detectors and a Support Vector Machine (SVM) based track deletion is proposed to correctly remove the dead tracks. Benchmarking is presented in experiments and results are analyzed. A case study of applying the proposed algorithm is provided demonstrating the usefulness in real applications. / Thesis / Doctor of Philosophy (PhD)

This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the target state is corrected sequentially to the robot correction in the Recursive Bayesian Estimation. In the second stage, with the target being closer, the target state is corrected simultaneously with the robot and the landmarks. The process allows the robot's state uncertainty to be propagated into the estimated target's state, bridging the gap between tracking only methods where the target is estimated assuming known observer state and SLAM methods where only landmarks are considered. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. Also, target belief is always maintained in comparison to temporary tracking methods such as image-tracking. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map entropies in the absence of other landmarks. / M.S. / This thesis presents a generalized framework with the goal of allowing a robot to localize itself and a static target, while building a map of the environment. This map is used as in the Simultaneous Localization and Mapping (SLAM) framework to enhance robot accuracy and with close features. Target, here, is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be continuously observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the robot and close landmarks are estimated simultaneously and they are both corrected. Using the robot's uncertainty in its estimate, the target state is then estimated sequentially, considering known robot state. That decouples the target estimation from the rest of the process. In the second stage, with the target being closer, target, robot and landmarks are estimated simultaneously. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map uncertainties in the absence of other landmarks.

Universiẗat, Diss., 1999--Tübingen.

This dissertation concerns with the formulation of an improved multi-target multi-Bernoulli (MeMBer) filter and the use of the joint multi-target (JoM) estimator in an effective and efficient manner for a specific implementation of MeMBer filters. After reviewing random finite set (RFS) formalism for multi-target tracking problems and the related Bayes estimators the major contributions of this dissertation are explained in detail. The second chapter of this dissertation is dedicated to the analysis of the relationship between the multi-Bernoulli RFS distribution and the MeMBer corrector. This analysis leads to the formulation of an unbiased MeMBer filter without making any limiting assumption. Hence, as opposed to the popular cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter, the proposed MeMBer filter can be employed under the cases when sensor detection probability is moderate to low. Furthermore, a statistical refinement process is introduced to improve the stability of the estimated cardinality of targets obtained from the proposed MeMBer filter. The results from simulations demonstrate the effectiveness of the improved MeMBer filter. In Chapters III and IV, the Bayesian optimal estimators proposed for the RFS based multi-target tracking filters are examined in detail. First, an optimal solution to the unknown constant in the definition of the JoM estimator is determined by solving a multi-objective optimization problem. Thus, the JoM estimator can be implemented for tracking of a Bernoulli target using the optimal joint target detection and tracking (JoTT) filter. The results from simulations confirm assertions about its performance obtained by theoretical analysis in the literature. Finally, in the third chapter of this dissertation, the proposed JoM estimator is reformulated for RFS multi-Bernoulli distributions. Hence, an effective and efficient implementation of the JoM estimator is proposed for the Gaussian mixture implementations of the MeMBer filters. Simulation results demonstrate the robustness of the proposed JoM estimator under low-observable conditions. / Thesis / Doctor of Philosophy (PhD)

<p>The PANDA experiment is a part of the future FAIR accelerator facility and will study the strong interaction by detecting the reaction products from antiproton-proton annihilations in a near full solid-angle configuration. One option for the internal proton target in PANDA is frozen micro-spheres of hydrogen, so-called pellets.</p><p>Such a pellet target is interesting because of the unique characteristics it offers; the high target thickness, the small interaction volume, the minimal gas load on the vacuum system, and the possibility of tracking individual pellets. Nevertheless, it is possible to allocate the bulky equipment needed to produce the pellets at a few meters away from the beam. This way particle detectors can be located close and almost fully around the interaction point.</p><p>This thesis is devoted to the optimization of a pellet target. To perform measurements, a Pellet-Test Station was built at The Svedberg Laboratory, Uppsala. For the first time, experimental results show the pellet distribution in space and time, and in addition, the vacuum along the pellet pipes. Furthermore, dedicated measurements carried out at CELSIUS/WASA demonstrate the existence of pellet heating as a result of beam-target interactions.</p><p>In performing calculations, the potential problems with pellet heating at PANDA are outlined. Moreover, to look at the consequences for the desired physics, a reaction involving short-lived D-mesons has been used to show the advantages of pellets compared to a more spacious target.</p><p>In conclusion, these studies lead to a deeper understanding of the pellet properties, which makes it possible to suggest future improvements, such as cooling with no vibrations.</p>

The PANDA experiment is a part of the future FAIR accelerator facility and will study the strong interaction by detecting the reaction products from antiproton-proton annihilations in a near full solid-angle configuration. One option for the internal proton target in PANDA is frozen micro-spheres of hydrogen, so-called pellets. Such a pellet target is interesting because of the unique characteristics it offers; the high target thickness, the small interaction volume, the minimal gas load on the vacuum system, and the possibility of tracking individual pellets. Nevertheless, it is possible to allocate the bulky equipment needed to produce the pellets at a few meters away from the beam. This way particle detectors can be located close and almost fully around the interaction point. This thesis is devoted to the optimization of a pellet target. To perform measurements, a Pellet-Test Station was built at The Svedberg Laboratory, Uppsala. For the first time, experimental results show the pellet distribution in space and time, and in addition, the vacuum along the pellet pipes. Furthermore, dedicated measurements carried out at CELSIUS/WASA demonstrate the existence of pellet heating as a result of beam-target interactions. In performing calculations, the potential problems with pellet heating at PANDA are outlined. Moreover, to look at the consequences for the desired physics, a reaction involving short-lived D-mesons has been used to show the advantages of pellets compared to a more spacious target. In conclusion, these studies lead to a deeper understanding of the pellet properties, which makes it possible to suggest future improvements, such as cooling with no vibrations.

Target culture is a concept regularly used in Translation Studies but it is not a concept which is routinely defined any further than the geographical location of the target language. In English translation this can be problematic because some translations published in English are produced in one English-speaking country which are then sold to other English-speaking domains and this process of migration might not be obvious from the edition notice of the book. The underlying principle for the production of these translations could be that one translation can fit all English target cultures. Yet, in contrast, some anglophone translations are published separately e.g. as a British translation or an American translation. There has been, so far, minimal investigation into the different ways in which English translations come into existence and, therefore, this thesis aims to address the theoretical gap by creating a taxonomy of translation. The thesis presents new terminology for the various translation types within the anglophone world: for example, a translation can be separate when published independently by both Britain or America, or it can be transatlantic when it is shared by both countries. The existence of transatlantic translation challenges preconceived ideas regarding the concept of target culture within Descriptive Translation Studies. Through textual, paratextual and metatextual analysis of several case studies of each translation type the thesis explores the possible refinement of the concept of target culture per se. The thesis is underpinned by analysis of the work of two prominent Swedish children’s authors: Astrid Lindgren and Sven Nordqvist. Swedish children’s literature was selected because of its proven perennial contribution to the genre of children’s literature and its exceptional success in translation. Furthermore, children’s literature itself presents its own unique challenges in translation because, for this particular genre, the target culture introduces powerful constraints based upon the educational, social and cultural expectations of the receiving language community. However, in the case of the transatlantic translation, it is the initial target culture constraints which will be present within the text. In the second country to receive the translation, expectations regarding educational, social and cultural ideals may vary from the first target culture. Ultimately, the thesis argues that there are powerful constraining ideological forces within target cultures which are visible in separate translation; those same forces may present themselves in transatlantic translation also, but the origin of the ideology behind them may not be obvious. Thus, the thesis aims to change the way we label translation within newly delineated English-speaking target cultures.

The rapidly increasing number of existing drugs with genomic, biomedical, and pharmacological data make computational analyses possible, which reduces the search space for drugs and facilitates drug repositioning (DR). Thus, artificial intelligence, machine learning, and data mining have been used to identify biological interactions such as drug-target interactions (DTI), drug-disease associations, and drug-response. The prediction of these biological interactions is seen as a critical phase needed to make drug development more sustainable. Furthermore, late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed. In this dissertation, we tried to address three crucial problems associated with the DR pipeline and presents several novel computational methods developed for DR. First, we developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. Second, because it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). We discussed how to develop more robust DTBA methods and subsequently developed Affinity2Vec, the first regression-based method that formulates the entire task as a graph-based method and combines several computational techniques from feature representation learning, graph mining, and machine learning with no 3D structural data of proteins. Affinity2Vec outperforms the state-of-the-art methods. Finally, since drug development failure is associated with sub-optimal target identification, we developed the first DL-based computational method (OncoRTT) to identify cancer-specific therapeutic targets for the ten most common cancers worldwide. Implementing our approach required creating a suitable dataset that could be used by the computational method to identify oncology-related DTIs. Thus, we created the OncologyTT datasets to build and evaluate our OncoRTT method. Our methods demonstrated their efficiency by achieving high prediction performance and identifying therapeutic targets for several cancer types. Overall, in this dissertation, we developed several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities.

Even though English is a subject where Swedish pupils do well compared to pupils in other countries, research indicates that pupils are not always motivated to learn in the English classroom. Therefore, the aim of this study is to find research relating to the use of the target language in classrooms for pupils at the upper elementary level, particularly language learners in Sweden. The focus of this thesis is to find out what benefits and challenges accompany the use of the target language during English lessons, as well as what pupils’ opinions are on the consistent use of the target language in the classroom. This literature review of five research articles shows that it is beneficial for pupils’ language development to have lessons where mainly the target language is used. It is for example beneficial for pupils’ ability to speak, their pronunciation, vocabulary and ability to use language strategies. The results show that there are challenges as well, especially for the teachers, since use of the target language presumes that the teacher has good language skills and is capable of scaffolding each pupil at their individual level and in their zone of proximal development. Furthermore, there are challenges like differences in pupils’ skill level, creating tasks that both motivate and stimulate, and creating a safe learning environment. Even though the results in this thesis are limited, it is still obvious that it is an important area, where more research is necessary in order to assist teachers in how to teach English as effectively as possible. / <p>Engelska</p>

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Real-time transmission of airborne images to a ground station is highly desirable in many telemetering applications. Such transmission is often through an error prone, time varying wireless channel, possibly under jamming conditions. Hence, a fast, efficient, scalable, and error resilient image compression scheme is vital to realize the full potential of airborne reconnaisance. JPEG2000, the current international standard for image compression, offers most of these features. However, the computational complexity of JPEG2000 limits its use in some applications. Thus, we present a scalable low complexity coder (SLCC) that possesses many desirable features of JPEG2000, yet having high throughput. Continuous radio-wave telemetry is required during planned tests of directed-energy weapons systems in order to characterize in situ the effects of laser irradiation on different target materials. Unfortunately, the incident radiation can cause disruption of the radio signal during the directed-energy testing. Several phenomena associated with directed-energy impact can lead to communication path losses, such as ablation, charged particle emission, charring, and chemical changes in the target materials. Directed-energy impact on the target material leads to target heating and consequent ablation. In this paper, a numerical model has been developed to describe the laser induced ablation of metal surfaces. The model describes the absorption of the laser energy by the metal and the resulting temperature rise in the surface. This temperature rise then induces ablation of the target material. Results for an aluminum target irradiated with a KrF laser were obtained. Temperature profiles in the target material and surface temperature changes are presented along with the ablation rate as a function of time as the aluminum target is irradiated. This report presents results for cases when laser energy absorption by the plasma plume created above the surface is not significant.