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Thesis (M.S.)--Ohio University, November, 2001. / Title from PDF t.p.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2008. / Adviser: Lewis A. Friedland. Includes bibliographical references.

Thesis (M. Eng.)--Carleton University, 1988. / Also available in electronic format on the Internet.

Thesis (M. Eng.)--Carleton University, 1997. / Includes bibliographical references. Also available in electronic format on the Internet.

Thesis (M.A.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 103-105). Also available in print.

Baseband digital communication in electro-magnetic measurement while drilling (EM-MWD) systems is often corrupted by non-white surface noise. The inability to reliably decode the transmitted signals in a noisy environment limits the depth at which EM-MWD systems can operate. Correlation receivers, which are optimal in the presence of additive white Gaussian noise, can be sub-optimal in the presence of various types of field noise at different drilling sites. This thesis investigates the application of artificial neural networks (ANN) as communication receivers in EM-MWD baseband digital communication systems. The performances of various ANN architectures and training algorithms are studied and compared with conventional correlation receivers via computer simulations. Standard symbol error rate (SER) test results show that the NN receiver is able to adapt to site-specific noise and thus outperforms the traditional correlation receiver.

Limitations of current eldercare monitoring systems leave a need for new solutions. A monitoring system based on a frequency modulated continuous wave ultra-wideband short-range radar is proposed for this application. The complete proposed monitoring system is comprised of four blocks: boundary detection, silhouette capture, human identification, and data transmission. This paper develops the RF front end hardware for the silhouette capture subsystem. System requirements are derived for the silhouette capture subsystem. An architecture for the RF front end is designed, and required individual component specifications are determined. Components are selected off the shelf or custom designed for each socket. Full transmitter and receiver level plans are calculated to ensure expected system performance meets system requirements. A component library and full system schematic is created, PCB layout is completed, and PCB files are generated and sent for fabrication. PCB traces and individual components are characterized over frequency, and methods that improve inadequate performance are documented and discussed.

This paper introduces a system which enables visually impaired users to detect objects and landmarks within the line of sight. The system works in two modes: landmark mode, which detects predefined landmarks, and object mode, which detects objects for everyday use. Users can get audio announcement for the name of the detected object or landmark as well as its estimated distances. Landmark detection helps visually impaired users explore an unfamiliar environment and build a mental map. The proposed system utilizes a deep learning system for detection, which is deployed on the mobile phone and optimized to run in real-time. Unlike many other existing deep-learning systems that require an Internet connection or specific accessories. Our system works offline and only requires a smart phone with camera, which gives the advantage to avoid the cost for data services, reduce delay to access the cloud server, and increase the system reliability in all environments.

The network architecture of the current Internet cannot accommodate the deployment of novel network-layer protocols. To address this fundamental problem, network virtualization has been proposed, where a single physical infrastructure is shared among different virtual network slices. A key operational problem in network virtualization is the need to allocate physical node and link resources to virtual network requests. While several different virtual network mapping algorithms have been proposed in literature, it is difficult to compare their performance due to differences in the evaluation methods used. In this thesis work, we proposed VNMBench, a virtual network mapping benchmark that provides a set of standardized inputs and evaluation metrics. Using this benchmark, different algorithms can be evaluated and compared objectively. The benchmark model separate into two parts: static model and dynamic model, which operated in fixed and changed mapping process. We present such an evaluation using three existing virtual network mapping algorithms. We compare the evaluation results of our synthetic benchmark with those of actual Emulab requests to show that VNMBench is sufficiently realistic. We believe this work provides an important foundation to quantitatively evaluating the performance of a critical component in the operation of virtual networks.