Reproduction de : Thèse d'exercice : Médecine : Nancy 1 : 2003. / Titre provenant de l'écran-titre.

Thesis (M.A. in Security Studies (Defense Decision Making and Planning))--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Dahl, Erik. Second Reader: Roberts, Nancy. "December 2009." Description based on title screen as viewed on January 28, 2010. Author(s) subject terms: Persistent surveillance. Includes bibliographical references (p. 61-66). Also available in print.

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

Thesis (M.S. in Information Systems and Operations)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Alex Bordetsky, Randy J. Hess. Includes bibliographical references (p. 101-105). Also available online.

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

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

While computers and composition researchers are concerned with the theoretical and pedagogical impacts of new technologies in our field, these researchers have only recently begun to consider the ramifications of the growing use of online social networking sites like MySpace and Facebook in academia. This dissertation fills a much-needed space in the field in its consideration of the pedagogical implications of social networking sites. Online social networking sites can provide teachable moments to talk with students about audience, discourse communities, intellectual property, and the tensions between public and private writing. Thus, if writing instructors ignore the growing conversation regarding online social networking sites, they may potentially miss out on familiar and accessible spaces for teaching rhetorical analysis.In this dissertation, through a qualitative analysis of undergraduate students and university writing instructors, I trace common threads in these individuals' attitudes and perceived beliefs about MySpace and Facebook. In chapters 1 and 2 I draw on Michel Foucault's theories of bio-power and confession to raise questions and concerns regarding pedagogical uses and abuses of online social networking sites, focusing specifically on issues of privacy and surveillance. In chapter 3, I outline the methods and methodologies that guided the qualitative portion of my study; the results of this study are reported in chapters 4 (students' views of social networking) and 5 (instructors' views), respectively. In chapter 5, I use technological literacy as a framework to argue that the immense popularity of online social networking sites coupled with the sheer amount of writing produced by students in these sites provides a compelling reason for rhetoric and composition instructors to begin paying attention to online social networking sites. To conclude chapter 5, I provide specific classroom activities that focus on MySpace and Facebook for instructors interested in bringing social networking back to the classroom. These classroom materials can be adapted to multiple classroom settings and can be modified based on a particular instructor's pedagogical needs.

Work-related asthma surveillance is needed to improve management of occupational exposures, clinical recognition/diagnosis, and worker compensation policies. This work investigated asthma in relation to work by evaluating the utility of existing Canadian surveillance data in providing useful information about the burden of work-related asthma; estimating the burden of work-related asthma among Canada's adult population; and evaluating the effect of job risk on asthma after considering other potential risk factors for asthma. The working population formed samples from two Statistics Canada surveillance programs: Canadian Community Health Survey (CCHS), 2002/03 Cycle 2.1 and National Population Health Survey (NPHS), Longitudinal Component (1994/95-2002/03). Both surveys enquired about health professional-diagnosed asthma; NPHS additionally asked age at time of diagnosis, so adult-onset versus childhood-onset asthma was determined. Both surveys enquired about current job held; corresponding job codes were linked to an asthma-specific job exposure matrix to judge job risk for occupational asthma. CCHS only provided current job information, in contrast, NPHS longitudinal data was used to determine job held at time of asthma-onset. Statistical measures for asthma in relation to job risk were estimated. CCHS results were likely biased by the healthy worker effect, as it showed the opposite effect of job risk on asthma than the NPHS; higher asthma prevalence was shown for NPHS men and women in high risk jobs. NPHS results indicated a large burden of adult-onset asthma among men (19,000) and childhood-onset asthma among women (17,000) attributed to working in high risk jobs for occupational asthma. Using NPHS, adjusted and crude prevalence odds ratio estimates were compared to further assess effect of job risk on asthma. For adult-onset asthma, there was no difference between estimates (men: 1.8, women: 1.1); for childhood-onset asthma, adjusted estimates were larger than crude, respectively (men: 1.3 v 1.2, women: 2.0 v 1.7). Age of asthma-onset and job held at time of asthma-onset is necessary surveillance information for estimating work-related asthma. There may be increased risk of work" caused" asthma among men and work "exacerbated" asthma among women in high risk jobs. Considering other risk factors for asthma did not reduce effect of job risk on asthma.

Video surveillance systems are playing an increasing role in preventing and investigating crime, protecting public safety, and safeguarding national security. In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors like human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features, this thesis improves a recently introduced approach that makes use of higher-level features of intentionality. Individuals in a scene are modelled as intentional agents instead of simply objects. Unusual behaviour detection then becomes a task of determining whether an agent's trajectory is explicable in terms of learned spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.