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

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--Michigan State University. Dept. of Electrical and Computer Engineering, 2006. / Title from PDF t.p. (viewed on Nov. 17, 2008) Includes bibliographical references (p. 181-187). Also issued in print.

Thesis (M.E.)--University of Canterbury, 2008. / Typescript (photocopy). "September 2008." Includes bibliographical references (p. 157-163). Also available via the World Wide Web.

Thèse (M.Eng.) -- Université du Québec à Chicoutimi, programme en extension à l'Université du Québec à Rimouski, 2006. / La p. de t. porte en outre: Mémoire de recherche présenté à l'Université du Québec à Rimouski comme exigence partielle du programme de maîtrise en ingénierie pour l'obtention du grade ès sciences appliquées (M.Sc.A.). CaQCU Bibliogr.: f. [125]. Document électronique également accessible en format PDF. CaQCU

Thesis (Ph. D.)--University of Texas at Austin, 2009. / Title from PDF title page (University of Texas Digital Repository, viewed on Sept. 9, 2009). Vita. Includes bibliographical references.

This dissertation is on sonic surveillance in the neoliberal context and its implication for privacy, agency, sovereignty, ownership and control. This research focuses on the social, political and ethical conceptions of privacy through musical consumption and sonic practices in the United States. I investigate non-creative recording practices in neoliberal life and identify the listening practices of surveillance capitalism to better understand how power circulates through sound. Through a multi-sited ethnography, I conduct three case studies on the recording and listening capacity of technological devices of everyday life in order to theorize what I term “the neoliberal ear”– a twenty-first century mode of listening to the world embedded into surveillance capitalism. I analyze three sonic tools of surveillance capitalism: streaming service Spotify, Smart Home device Amazon Echo, and Smart City communication hub LinkNYC. These technologies, I argue, embody and promote neoliberal ideology, and the companies that produces them operate within a neoliberal mode of governance allowed by public policies. This dissertation is interdisciplinary in scope and operates at theoretical crossings of sound and power, technology and cultural practices, and disciplinary crossings of music, law and computer science. I draw from, and build connections between; sound studies, ethnomusicology, legal literature and scholarship on copyright and privacy, surveillance studies, science and technology studies, and discourses on AI and ethics, to form theories of sound and power in surveillance capitalism.

No description available.

Thesis (M.Sc.)--City University of Hong Kong, 2005. / Title from title screen (viewed on Jan. 10, 2006) "A dissertation submitted in partial fulfillment of the requirements for the degree of Master of Science in Engineering Management." Includes bibliographical references.