Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot’s environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors’ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Hydrodynamical investigations of liquid ventilation by means of advanced optical measurement techniques

Although liquid ventilation has been researched and studied for the last six decades, it did not achieve its expected optimal performance. Within this work, a deeper understanding of the fluid dynamics during liquid ventilation shall be gathered to extend the already available clinical knowledge about this ventilation strategy. In order to reach this goal, advanced optical flow measurement techniques are applied in different models of the human conductive airways to obtain global velocity fields, identifying prominent flow structures and to determine important dissolved oxygen transport paths. As the velocity measurements revealed, the evolving flow field is strongly dominated by secondary flow effects and is highly dependent on the local airway geometry. During the visualization experiments of the dissolved oxygen concentration fields, different transportation paths occur at inspirational and expirational flow. The initial concentration distribution can be linked to the underlying flow fields but decouples after the peak velocity phases. With higher flow rates/ tidal volumes, a more homogeneously distributed oxygen concentration can be reached.:List of Figures ....................................................................................... VII List of Tables ........................................................................................XIII Nomenclature ........................................................................................ XV 1 Introduction......................................................................................... 1 1.1 Motivation ........................................................................................1 1.2 Research objectives........................................................................... 3 1.3 Outline............................................................................................ 4 2 State of the art .................................................................................... 5 2.1 Liquid Ventilation............................................................................. 5 2.2 In vitro modeling.............................................................................. 8 2.3 Flow measurements ......................................................................... 11 2.4 Gas transport..................................................................................13 3 Flow field measurements ................................................................... 16 3.1 Hydrodynamic Model.......................................................................16 3.1.1 Lung replica ..........................................................................16 3.1.2 Flow parameter .....................................................................18 3.1.3 Limitations ...........................................................................22 3.2 Particle Tracking Velocimetry (PTV) ................................................24 3.2.1 Measurement principle ...........................................................24 3.2.2 Double-frame 2D-PTV ...........................................................25 3.2.3 Time-resolved 3D-PTV ..........................................................28 3.2.4 Phase-locked ensemble PTV ................................................... 31 3.3 Experimental set-up and measurement procedure ...............................33 3.3.1 Lung flow facility...................................................................33 3.3.2 2D-PTV configuration............................................................36 3.3.3 3D-PTV configuration............................................................36 3.4 Results & Discussion........................................................................38 3.4.1 Artificial lung........................................................................38 3.4.2 Realistic lung ........................................................................52 3.5 Conclusion ......................................................................................59 4 Oxygen transport ...............................................................................61 4.1 Hydrodynamic Model....................................................................... 61 4.1.1 Lung replica .......................................................................... 61 4.1.2 Flow parameter .....................................................................62 4.1.3 Limitations ...........................................................................65 4.2 Oxygen Sensitive Dye ......................................................................66 4.3 Experimental set-up......................................................................... 71 4.4 Results & Discussion........................................................................75 4.4.1 Constant flow rate .................................................................75 4.4.2 Oscillatory flow .....................................................................83 4.5 Conclusion ......................................................................................90 5 Summary............................................................................................ 92 6 Outlook .............................................................................................. 95 Bibliography ............................................................................................ 97Trotz intensiver Forschung in den letzten sechs Jahrzehnten, befindet sich die Flüssigkeitsbeatmung immernoch weit entfernt vom klinischen Alltag. Mit dieser Arbeit soll ein Beitrag geleistet werden, um das Wissen um die strömungsmechanischen Effekte während der Flüssigkeitsbeatmung zu vertiefen. Dazu werden verschiedene Modellexperimente durchgeführt, bei welchen moderne laseroptische Strömungsmessmethoden zum Einsatz kommen. Untersucht werden dabei unterschiedlich komplexe Geometrien der leitenden menschlichen Atemwege mit dem Ziel wesentliche Strömungsstrukturen, globale Geschwindigkeitsfelder und wichtige Transportwege des gelösten Sauerstoffs zu identifiziern. Die Geschwindigkeitsmessungen zeigen ein stark durch sekundäre Strömungseffekte dominiertes Geschwindigkeitsfeld, welches wesentlich von der lokalen Geometrie abhängig ist. Durch die qualitative und quantitative Erfassung der gelösten Sauerstoffkonzentrationsfelder können wichtige Transportwege aufgedeckt werden. Diese unterscheiden sich deutlich zwischen inspiratorischer und expiratorischer Strömungsrichtung. Die initialen Konzentrationsfelder stimmen mit den unterliegenden Geschwindigkeitsfeldern überein, unterscheiden sich ab der verzögernden Strömungsphase jedoch. Höhere Volumenströme/Tidalvolumen tragen dabei zu einer gleichmäßigeren Konzentrationsverteilung bei.:List of Figures ....................................................................................... VII List of Tables ........................................................................................XIII Nomenclature ........................................................................................ XV 1 Introduction......................................................................................... 1 1.1 Motivation ........................................................................................1 1.2 Research objectives........................................................................... 3 1.3 Outline............................................................................................ 4 2 State of the art .................................................................................... 5 2.1 Liquid Ventilation............................................................................. 5 2.2 In vitro modeling.............................................................................. 8 2.3 Flow measurements ......................................................................... 11 2.4 Gas transport..................................................................................13 3 Flow field measurements ................................................................... 16 3.1 Hydrodynamic Model.......................................................................16 3.1.1 Lung replica ..........................................................................16 3.1.2 Flow parameter .....................................................................18 3.1.3 Limitations ...........................................................................22 3.2 Particle Tracking Velocimetry (PTV) ................................................24 3.2.1 Measurement principle ...........................................................24 3.2.2 Double-frame 2D-PTV ...........................................................25 3.2.3 Time-resolved 3D-PTV ..........................................................28 3.2.4 Phase-locked ensemble PTV ................................................... 31 3.3 Experimental set-up and measurement procedure ...............................33 3.3.1 Lung flow facility...................................................................33 3.3.2 2D-PTV configuration............................................................36 3.3.3 3D-PTV configuration............................................................36 3.4 Results & Discussion........................................................................38 3.4.1 Artificial lung........................................................................38 3.4.2 Realistic lung ........................................................................52 3.5 Conclusion ......................................................................................59 4 Oxygen transport ...............................................................................61 4.1 Hydrodynamic Model....................................................................... 61 4.1.1 Lung replica .......................................................................... 61 4.1.2 Flow parameter .....................................................................62 4.1.3 Limitations ...........................................................................65 4.2 Oxygen Sensitive Dye ......................................................................66 4.3 Experimental set-up......................................................................... 71 4.4 Results & Discussion........................................................................75 4.4.1 Constant flow rate .................................................................75 4.4.2 Oscillatory flow .....................................................................83 4.5 Conclusion ......................................................................................90 5 Summary............................................................................................ 92 6 Outlook .............................................................................................. 95 Bibliography ............................................................................................ 9

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Proceedings of the 2010 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

On the annual Joint Workshop of the Fraunhofer IOSB and the Karlsruhe Institute of Technology (KIT), Vision and Fusion Laboratory, the students of both institutions present their latest research findings on image processing, visual inspection, pattern recognition, tracking, SLAM, information fusion, non-myopic planning, world modeling, security in surveillance, interoperability, and human-computer interaction. This book is a collection of 16 reviewed technical reports of the 2010 Joint Workshop

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

Modélisation formelle des systèmes de détection d'intrusions

L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity, and the complexity of cyber attacks. Generally, we have three types of Intrusion Detection System (IDS) : anomaly-based detection, signature-based detection, and hybrid detection. Anomaly detection is based on the usual behavior description of the system, typically in a static manner. It enables detecting known or unknown attacks but also generating a large number of false positives. Signature based detection enables detecting known attacks by defining rules that describe known attacker’s behavior. It needs a good knowledge of attacker behavior. Hybrid detection relies on several detection methods including the previous ones. It has the advantage of being more precise during detection. Tools like Snort and Zeek offer low level languages to represent rules for detecting attacks. The number of potential attacks being large, these rule bases become quickly hard to manage and maintain. Moreover, the representation of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular representation of a specification, that facilitates maintenance and understanding of rules. We extend the ASTD notation with new features to represent complex attacks. Next, we specify several attacks with the extended notation and run the resulting specifications on event streams using an interpreter to identify attacks. We also evaluate the performance of the interpreter with industrial tools such as Snort and Zeek. Then, we build a compiler in order to generate executable code from an ASTD specification, able to efficiently identify sequences of events

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic