Thesis (M.S.)--Boise State University, 2009. / Includes abstract. Includes bibliographical references (leaves 67-69).

Thesis (M.S.)--Boise State University, 2009. / Title from t.p. of PDF file (viewed Apr. 23, 2010). Includes abstract. Includes bibliographical references (leaves [67-69]).

Thesis (PH. D.)--Michigan State University. Electrical Engineering, 2008. / Title from PDF t.p. (viewed on Aug. 28, 2009) Includes bibliographical references (p. 91-99). Also issued in print.

Thesis (M.S. in Mechanical Engineering) Naval Postgraduate School, March 1997. / Thesis advisor(s): Healey, Anthony J. "March 1997." Includes bibliographical references (p. 151). Also available online.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 87-90).

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiii, 170 p.; also includes graphics (some col.). Includes bibliographical references (p. 162-170). Available online via OhioLINK's ETD Center

There has been an exponential increase in satellite image data availability. Image data are now collected with different spatial, spectral, and temporal resolutions. Image fusion techniques are used extensively to combine different images having complementary information into one single composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pansharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high resolution panchromatic image while preserving the spectral information in the multispectral image. Resolution merge, image integration, and multisensor data fusion are some of the equivalent terms used for pansharpening. Pansharpening techniques are applied for enhancing certain features not visible in either of the single data alone, change detection using temporal data sets, improving geometric correction, and enhancing classification. Various pansharpening algorithms are available in the literature, and some have been incorporated in commercial remote sensing software packages such as ERDAS Imagine® and ENVI®. The performance of these algorithms varies both spectrally and spatially. Hence evaluation of the spectral and spatial quality of the pansharpened images using objective quality metrics is necessary. In this thesis, quantitative metrics for evaluating the quality of pansharpened images have been developed. For this study, the Intensity-Hue-Saturation (IHS) based sharpening, Brovey sharpening, Principal Component Analysis (PCA) based sharpening and a Wavelet-based sharpening method is used.

Data fusion is the methodology of efficiently combining the relevant information from different sources. The goal is to achieve estimates and inferences with better confidence than those achievable by relying on a single source. Initial data fusion applications were predominantly in defense: target tracking, threat assessment and land mine detection. Nowadays, data fusion is applied to robotics (e.g., environment identification for navigation), medicine (e.g., medical diagnosis), geoscience (e.g., data integration from different sources) and industrial engineering (e.g., fault detection). This thesis focuses on data fusion for distributed multisensor tracking systems. In these systems, each sensor can provide the information as measurements or local estimates, i.e., tracks. The purpose of this thesis is to advance the research in the fusion of local estimates for multisensor multitarget tracking systems, namely, track fusion. This study also proposes new methods for track-to-track association, which is an implicit subproblem of track fusion. The first contribution is for the case where local sensors perform tracking using particle filters (Monte Carlo based methods). A method of associating tracks estimated through labeled particle clouds is developed and demonstrated with subsequent fusion. The cloud-to-cloud association cost is devised together with computation methods for the general and specialized cases. The cost introduced is proved to converge (with increasing clouds cardinality) toward the corresponding distance between the underlying distributions. In order to simulate the method introduced, a particle filter labeled at particle level was developed, based on the Probability Hypothesis Density (PHD) particle filter. The second contribution is for the case where local sensors produce tracks using Kalman filter-type estimators, in the form of track state estimate and track state covariance matrix. For this case the association and fusion is improved in both terms of accuracy and identity, by introducing at each fusion time the prior information (both estimate and identity) from the previous fusion time. The third contribution is for the case where local sensors produce track estimates under the form of MHT, therefore where each local sensor produces several hypotheses of estimates. A method to use the information from other sensors in propagating each sensor's internal hypotheses over time is developed. A practical fusion method for real world local tracking sensors, i.e., asynchronous and with incomplete information available, is also developed in this thesis. / Thesis / Doctor of Philosophy (PhD)

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / Sensor networks can demand large amounts of bandwidth if the raw sensor data is transferred to a central location. Feature recognition and sensor fusion algorithms can reduce this bandwidth. Unfortunately the designers of the system, having not yet seen the data which will be collected, may not know which algorithms should be used at the time the system is first installed. This paper describes a flexible architecture which allows the deployment of data reduction algorithms throughout the network while the system is in service. The network of sensors approach not only allows for signal processing to be pushed closer to the sensor, but helps accommodate extensions to the system in a very efficient and structured manner.

Vital signs can indicate patient deterioration prior to adverse events such as cardiac arrest, emergency admission to the intensive care unit (ICU), or death. However, many adverse events occur in wards outside the ICU where the level of care and the frequency of patient monitoring are lower than in the ICU. This thesis describes models for detection of deterioration in acutely ill patients in two environments: a step-down unit in which patients recovering from an ICU stay are continuously monitored, and a general ward where patients are intermittently monitored following upper gastrointestinal cancer surgery. Existing data fusion models for classification of vital signs depend on a threshold which defines a “region of normality”. Bradypnoea (low breathing rate) and bradycardia (low heart rate) are relatively rare, and so these two types of abnormalities tend to be misclassified by existing methods. In this thesis, techniques for selecting a threshold are described, such that the classification of vital-sign data is improved. In particular, the proposed approach reduces the misclassification of bradycardia and bradypnoea events, and indicates the type of abnormality associated with the deterioration in a patient’s vital signs. Patients recovering from upper gastrointestinal (GI) surgery have a high risk of emergency admission to the ICU. At present in the UK, most intermediate and general wards outside the ICU depend on intermittent, manual monitoring using track-and-trigger systems. Both manual and automated patient monitoring systems are reported to have high false alert rates. The models described in this thesis take into account the low monitoring frequency in the upper GI ward, such that the false alert rate is reduced. In addition to accuracy, early detection of deterioration is a highly desirable feature in patient monitoring systems. The models proposed in this thesis generate alerts for patients earlier than the early warning systems which are currently in use in hospitals in the UK. The improvements to existing models proposed in this thesis could be applied to continuous and intermittently acquired vital-sign data from other clinical environments.