Thesis (Ph. D.)--Ohio State University, 2007. / Full text release at OhioLINK's ETD Center delayed at author's request

Thesis (Ph. D.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.

Thesis (doctoral)--University of Oulu, 2008. / Includes bibliographical references. Also available on the World Wide Web.

Thesis (M.Sc.)--York University, 2004. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 118-121). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ99317

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M.Ing. / Atmospheric turbulence (also referred to as optical or heat Scintillation, or heat shimmer) is a particular problem encountered in video surveillance, especially over distances where the target object focused on is over lkm in the distance. Images obtained from video surveillance are commonly required to be of a high quality for object identification and classification. Atmospheric turbulence causes degradation in the image quality through the blurring and a warping of the image, making object identification difficult. Algorithms have and still are being developed to suppress the image turbulence in digital video footage and enhance detail. There is a lack of reliable comparisons among algorithms to provide research direction, methods for identification of the best algorithms for particular applications, identification of useful image processing techniques and a full understanding of the problem. This need and lack of comparisons among the algorithms and atmospheric turbulence degraded videos is identified through the problem identification chapter. A literature study is undertaken in which the source of atmospheric turbulence and models are identified, image processing techniques discussed, filtering of electromagnetic waves reviewed, a review of some equipment, and a discussion of metrics. This is followed by the presentation of a number of atmospheric turbulence suppression algorithms developed by other authors. After a discussion of the algorithm implementations, the experimental design is described for algorithm image quality and performance investigation as well as the effect of optical filters. Experimental results are presented and discussed which provide repeatable results pertaining to the algorithms' image quality and processing requirements. The results allowed identification of the algorithms' strengths and weaknesses, how they compare, and their suitability for real and post processing environments. Efficient performing software components were also able to be identified, particularly Illuminance-Reflectance adjustment. The experiments and results provide a solution to this atmospheric turbulence comparison problem.

This dissertation investigates how the development and circulation of technologies of surveillance helped refashion institutional structures, systems of representation, and conceptions of nature and society in the Ottoman Empire and Syria throughout the transition from empire to nation-states. While militarization amplified the capabilities of modern states to discipline human perception through conscription and schooling, the notion of an all-seeing perspective - materialized in the aerial view - was incorporated in the apparatus of state power. The account moves between sites across the imperial territory and as the French mandate replaced Ottoman rule in Syria. These include ventures to propagate the map as an instrument of argumentation and discovery in the capital Istanbul, attempts by Jesuit geographers and earth scientists at inculcating new forms of sight upon the public in Beirut and the Beqaa Valley, the building of post-imperial scientific institutions in Damascus, and the optics of aerial warfare as devised by French strategists and subverted by Arab guerrilla commanders. Environmental representations such as maps and aerial photographs sustained competing social and institutional structures by inculcating upon their targeted audience concrete procedures for disciplining perception, which refashioned the subjectivity, social function, and epistemic virtues of the ideal commander and citizen. In the Middle East, as elsewhere, technologies of surveillance were integrated with existing political and philosophical currents such as Ottomanism, Arabism, and French colonialism, while simultaneously refashioning them from within. The coming of aerial warfare was an integral part of this ongoing process of cultural and technological transformation, which reconfigured political, epistemic, and ethical norms of war and peace.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2008. / Includes bibliographical references (p. 65-67).

Through-wall human monitoring within a highly cluttered environment is a problem of current interest. Example applications include law enforcement, disaster search-and-rescue, and urban military operations. The purpose is to clearly monitor humans through building walls using a radar system. Doppler-based sensors offer an inexpensive way to detect moving targets in the presence of stationary clutters. It also provides information regarding motions of the human by micro-Doppler returns. In this dissertation, the applications of data-driven model (DDM) are investigated for locating human subjects and classifying their activity using Doppler sensors. DDM is a mathematical model trained by a set of data that describe the input-output relationship. It is suitable for real-time applications. As DDM, an artificial neural network (ANN) and a support vector machine (SVM) are considered. A collection of Doppler sensors is studied to localize humans in two ways: the use of spatially distributed Doppler sensors and the use of a single-sensor array. Furthermore, the feasibility of classifying human activities is studied with the obtained Doppler information. First, an ANN is proposed to track humans using the Doppler information measured by a set of spatially distributed sensors. The ANN estimates the target position and velocity given the observed Doppler data from multiple sensors. A point-scatterer model is used for the training data generation. For the verification of the proposed method, a toy car and a human moving in a circular track are measured in line-of-sight and through-wall environments. Second, an array-processing algorithm is proposed to estimate the number of targets and their Direction-of-Arrival (DOA) based on ANN when the available number of sensor elements is small. Using software beamforming, a number of overlapping beams are simultaneously formed. The received signal strengths from all the beams produce a unique signature in accordance with the target locations, as well as the number of targets. The identification of the number of targets and their locations is carried out sequentially via ANNs. For the verification of the algorithm, both line-of-sight and through-wall measurements are performed using loudspeakers driven by audio tones and moving humans. Third, an SVM is proposed to classify activities of a human subject using the measured Doppler information. MicroDopplers from moving limbs of human subjects contain significant information regarding their activities. Seven different human activities of twelve human subjects are measured in the laboratory using a Doppler radar. Six microDoppler features are extracted from the resulting spectrograms. A decision-tree based SVM is used for the classification of seven activities based on the features. Diverse situations such as combination of different activities, oblique angle case, and throughwall case are also discussed. / text

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy