Data fusion in wireless sensor networks can improve the performance of a network by eliminating redundancy and power consumption, ensuring fault-tolerance between sensors, and managing e®ectively the available com- munication bandwidth between network components. This thesis considers a data fusion approach applied to wireless sensor networks based on fuzzy logic theory. In particular, a cluster-based hierarchical design in wire- less sensor networks is explored combined with two data fusion methods based on fuzzy logic theory. A data fusion algorithm is presented and tested using Mamdani and Tsukamoto fuzzy inference methods. In addition, a concept related to the appropriate queuing models is presented based on classical queuing theory. Results show that the Mamdani method gives better results than the Tsukamoto approach for the two implementations considered. We noted that the proposed algorithm requires low processing and computational power. As a result, it can be applied to WSNs to provide optimal data fusion and ensures maximum sensor lifetime and minimum time delay.

This thesis investigates computer-based support tools to facilitate decision-making in civilian and military operations. As flexibility is essential when preparing for unknown threats to society, this support has to be general. Further motivations for flexible and general solutions include reduced costs for technical development and training, as well as faster and better informed decision-making. We use the term Effects-Based Management of Operations to denote the accomplishment of desired effects beyond traditional military goals by the deployment of all types of available capabilities. Supporting this work, DISCCO (Decision Support for Command and Control) is a set of network-based services including Command Support, helping commanders in the human, collaborative and continuous process of evolving, evaluating, and executing solutions to their tasks, Decision Support, improving the human process by integrating automatic and semi-automatic generation and evaluation of plans, and a Common Situation Model, capturing the hierarchical structure of the situation regarding own, allied, neutral, and hostile resources. The use of the DISCCO has been investigated in three different applications: planning for establishing surveillance of an operation area, planning for NBC defense, and executing a riot control operation. Together, these studies indicate that DISCCO is applicable in many different classes of Effects-Based Management of Operations. Hence, this generic concept will contribute to the work of both the civilian and military defense in dealing with a broad range of current and future threats to the society.

The Emergency Department (ED) provides the first line of care for anyone seeking treatment for an urgent problem caused by an accident or illness . Physiological observations in the ED are a required part of patient care, and are used to monitor a patient's condition. Manual observations are recorded regularly by nursing staff, using a Track and Trigger (T&T) system, in which higher scores indicate greater physiological abnormality. An observational study at the John Radcliffe Hospital, Oxford, was conducted to assess the effectiveness of T&T in the ED. Retrospective analysis showed that the effectivenessof T&T was limited by poor completion, and incorrect calculation of T&T scores. In response, we computed a retrospective, fully completed, scoring system which showedvery clear improvements in both sensitivity and specificity. In addition to nurse observations, higher acuity ED patients have their vital signs continuously monitored by bedside monitors. However, the alerts generated by the monitors are routinely ignored due to their high false alert rate. We investigated whether a baseline data fusion model and two alternative techniques, weighted Parzen windows and Support Vector Machines, could identify events relating to vital sign abnormality while keeping the number of false alerts to a minimum. The performance of each model was assessed by calculating its sensitivity and specificity. However, it was not possible to select an optimal model, due to the difficulty in assessing the relative importance of maximising true alertsand minimising false alerts. In the final part of this thesis, two limitations of the data fusion models are highlighted. Firstly, missing data is not handled coherently within the current models, and secondly the models do not make use of temporal information. One method of addressing both of these issues, Gaussian processes, was considered. Using this method, a novel framework was derived that allowed for alerts to be generated even when there is uncertainty in the vital sign values.

The new industry revolution creates a vast transformation in the manufacturing methods. Embedded Intelligence and communication technologies facilitate the execution of the smart factory. It can provide lots of features for strong customization of products. Assembly system is a critical segment of the smart factory. However, the complexity of production planning and the variety of products being manufactured, persuade the factories to use different methods to guide the workers for unfamiliar tasks in the assembly section. Motion tracking is the process of capturing the movement of human body or objects which has been used in different industrial systems. It can be integrated to a wide range of applications such as interacting with computers, games and entertainment, industry, etc. Motion tracking can be integrated to assembly systems and it has the potential to create an improvement in this industry as well. But the integration of motion tracking in industrial processes is still not widespread. This thesis work provides a fully automatic tracking solution for future systems in manufacturing industry and other fields. In general a configurable, flexible, and scalable motion tracking system is created in this thesis work to amend the tracking process. According to our environment, we have done a research between different motion tracking methods and technologies including Kinect and Leap Motion sensor, and finally the leap motion sensor is selected as the most appropriate method, because it fulfils our demands in this project. Multiple Leap motion sensors are used in this work to cover areas with different size. Data fusion between multiple leap motion sensors can be considered as another novel contribution of this thesis work. To achieve this goal data from multiple sensors are combined. This system can improve the lack of accuracy in order to creating a practical industrial application. By fusion of several sensors in order to achieve accuracies that allow implementation in practice, a motion tracking system with higher accuracy is created.

Approved for public release; distribution in unlimited. / Different sensors exploit different regions of the electromagnetic spectrum; therefore, a multi-sensor image fusion system can take full advantage of the complementary capabilities of individual sensors in the suit; to produce information that cannot be obtained by viewing the images separately. In this thesis, a framework for the multiresolution fusion of the night vision devices and thermal infrared imagery is presented. It encompasses a wavelet-based approach that supports both pixel-level and region-based fusion, and aims to maximize scene content by incorporating spectral information from both the source images. In pixel-level fusion, source images are decomposed into different scales, and salient directional features are extracted and selectively fused together by comparing the corresponding wavelet coefficients. To increase the degree of subject relevance in the fusion process, a region-based approach which uses a multiresolution segmentation algorithm to partition the image domain at different scales is proposed. The region's characteristics are then determined and used to guide the fusion process. The experimental results obtained demonstrate the feasibility of the approach. Potential applications of this development include improvements in night piloting (navigation and target discrimination), law enforcement etc. / Civilian, Republic of Singapore

Knowledge of the lane that a target is located in is of particular interest in on-road surveillance and target tracking systems. We formulate the problem and propose two approaches for on-road target estimation with lane tracking. The first approach for lane tracking is lane identification based ona Hidden Markov Model (HMM) framework. Two identifiers are developed according to different optimality goals of identification, i.e., the optimality for the whole lane sequence and the optimality of the current lane where the target is given the whole observation sequence. The second approach is on-road target tracking with lane estimation. We propose a 2D road representation which additionally allows to model the lateral motion of the target. For fusion of the radar and image sensor based measurement data we develop three, IMM-based, estimators that use different fusion schemes: centralized, distributed, and sequential. Simulation results show that the proposed two methods have new capabilities and achieve improved estimation accuracy for on-road target tracking.

Cette thèse s'inscrit dans le contexte de l'amélioration de la caractérisation de l'occupation du sol à partir d'observations de télédétection de natures très différentes : le radar polarimétrique et les images optiques multispectrales. Le radar polarimétrique permet la détermination de mécanismes de rétrodiffusion provenant de théorèmes de décomposition de l'information polarimétrique utiles à la classification des types d'occupation du sol. Cependant ces décompositions sont peu compréhensibles lorsque que plu- sieurs classes thématiques co-existent dans des proportions très variables au sein des cellules de résolution radar. Le problème est d'autant plus important que le speckle inhérent à l'imagerie radar nécessite l'estimation de ces paramètres sur des voisinages locaux. Nous nous interrogeons alors sur la capacité des données optiques multispectrales sensiblement plus résolues spatialement que le radar polarimétrique à améliorer la compréhension des mécanismes radar. Pour répondre à cette question, nous mettons en place une méthode de démélange des images radar polarimétrique par séparation thématique de sources. L'image optique peut être considérée comme un paramètre de réglage du radar fournissant une vue du mélange. L'idée générale est donc de commencer par un démélange thématique (décomposer l'information radar sur les types d'occupation du sol) avant de réaliser les décompositions polarimétriques (identifier des mécanismes de rétrodiffusion).Dans ce travail nous proposons d'utiliser un modèle linéaire et présentons un algorithme pour réaliser le démélange thématique. Nous déterminons ensuite la capacité de l'algorithme de démé- lange à reconstruire le signal radar observé. Enfin nous évaluons si l'information radar démélangée contient de l'information thématique pertinente. Cette évaluation est réalisée sur des données simulées que nous avons générées et sur des données Radarsat-2 complètement polarimétriques pour un cas d'application de mélange sol nu/forêt. Les résultats montrent que, malgré le speckle, la reconstruction est valable. Il est toujours possible d'estimer localement des bases thématiques permettant de décomposer l'information radar polarimétrique puis de reconstruire le signal observé. Cet algorithme de démélange permet aussi d'assimiler de l'information portée par les images optiques. L'évaluation de la pertinence thématique des bases de la décomposition est plus problématique. Les expériences sur des données simulées montrent que celles-ci représentent bien l'information thématique souhaitée, mais que cette bonne estimation est dépendante de la nature des types thématiques et de leurs proportions de mélange. Cette méthode nécessite donc des études complémentaires sur l'utilisation de méthodes d'estimation plus robustes aux statistiques des images radar. Son application à des images radar de longueur d'onde plus longue pourrait permettre, par exemple, une meilleure estimation du volume de végétation dans le contexte de forêts ouvertes / Land cover is a layer of information of significant interest for land management issues. In this context, combining remote sensing observations of different types is expected to produce more reliable results on land cover classification. The objective of this work is to explore the use of polarimetric radar images in association with co-registered higher resolution optical images. Extracting information from a polarimetric representation consists in decomposing it with target decomposition algorithms. Understanding these mechanisms is challenging as they are mixed inside the radar cell resolution but it is the key to producing a reliable land cover classification. The problem while using these target decomposition algorithms is that average physical parameters are obtained. As a result, each land cover type of a mixed pixel might not be well described by the average polarimetric parameters. The effect is all the more important as speckle affecting radar observations requires a local estimation of the polarimetric matrices. In this context, we chose to assess whether optical images can improve the understanding of radar images at the observation scale so as to retrieve more information. Spatial and spectral unmixing methods, traditionally designed for optical image fusion, were found to be an interesting framework. As a consequence, the idea of unmixing physical radar scattering mechanisms with the optical images is proposed. The original method developed is the decomposition of the polarimetric information, based on land cover type. This thematic decomposition is performed before applying usual target decomposition algorithms. A linear mixing model for radar images and an unmixing algorithm are proposed in this document. Having pointed out that the linear unmixing model is able to split off polarimetric information on a land cover type basis, the information contained in the unmixed matrices is evaluated. The assesment is carried out with generated simulated data and polarimetric radar images from the Radarsat-2 satellite. For this experiment, textit {Bare soil} and textit {Forested area} were considered for land cover types. It was found that despite speckle the reconstructed radar information after the unmixing is statically relevant with the observations. Moreover, the unmixing algorithm is capable of assimilating information from optical images. The question whether the unmixed radar images contain relevant thematic information is more challenging. Results on real and simulated data show that this capacity depends on the types of land cover considered and their respective proportions. Future work will be carried out to make the estimation step more robust to speckle and to test this unmixing algorithm on longer wavelength radar images. In this case, this method could be used to have a better estimation of vegetation biomass in the context of open forested areas

The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Sensors are shaping many activities in our society with an endless array of potential applications in military, civilian, and medical application. They support different real world applications ranging from common household appliances to complex systems. Technological advancement has enabled sensors to be used in medical applications, wherein they are deployed to monitor patients and assist disabled patients. Sensors have been invaluable in saving lives, be it a soldier's life in a remote battlefield or a civilian's life in a disaster area or natural calamities. In every application the sensors are deployed in a pre-defined manner to perform a specific function. Understanding the basic structure of a sensor node is essential as this would be helpful in using the sensors in devices and environments that have not been explored. In this research, patterns are used to present a more abstract view of the structure and architecture of sensor nodes and wireless sensor networks. This would help an application designer to choose from different types of sensor nodes and sensor network architectures for applications such as robotic landmine detection or remote patient monitoring systems. Moreover, it would also help the network designer to reuse, combine or modify the architectures to suit more complex needs. More importantly, they can be integrated with complete IT applications. One of the important applications of wireless sensor networks in the medical field is a remote patient monitoring system. In this work, patterns were developed to describe the architecture of patient monitoring system. / This pattern describes how to connect sensor nodes and other wireless devices with each other to form a network that aims to monitor the vital signs of a person and report it to a central system. This central system could be accessed by the patient's healthcare provider for treatment purposes. This system shows one of the most important applications of sensors and it application which needs to be integrated with medical records and the use of patterns makes this integration much simpler. / by Anupama Sahu. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

An ocean turbine extarcts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. MCM/PHM systems enable real time health assessment, prognostics and advisory generation by interpreting data from sensors installed on the machine being monitored. To effectively utilize sensor readings for determining the health of individual components, macro-components and the overall system, these measurements must somehow be combined or integrated to form a holistic picture. The process used to perform this combination is called data fusion. Data mining and machine learning techniques allow for the analysis of these sensor signals, any maintenance history and other available information (like expert knowledge) to automate decision making and other such processes within MCM/PHM systems. ... This dissertation proposes an MCM/PHM software architecture employing those techniques which were determined from the experiments to be ideal for this application. Our work also offers a data fusion framework applicable to ocean machinery MCM/PHM. Finally, it presents a software tool for monitoring ocean turbines and other submerged vessels, implemented according to industry standards. / by Janell A. Duhaney. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.