The thesis starts with a careful review of existing signal processing techniques and state of the art methods possible for vital signs monitoring using UWB impulse systems. Then an in-depth analysis of various approaches is presented.
Robust heart-rate monitoring methods are proposed based on a novel result: spectrally the fundamental heartbeat frequency is respiration-interference-limited while its higher-order harmonics are noise-limited. The higher-order statistics related to heartbeat can be a robust indication when the fundamental heartbeat is masked by the strong lower-order harmonics of respiration or when phase calibration is not accurate if phase-based method is used. Analytical spectral analysis is performed to validate that the higher-order harmonics of heartbeat is almost respiration-interference free. Extensive experiments have been conducted to justify an adaptive heart-rate monitoring algorithm. The scenarios of interest are, 1) single subject, 2) multiple subjects at different ranges, 3) multiple subjects at same range, and 4) through wall monitoring.
A remote sensing radar system implemented using the proposed adaptive heart-rate estimation algorithm is compared to the competing remote sensing technology, a remote imaging photoplethysmography system, showing promising results.
State of the art methods for vital signs monitoring are fundamentally related to process the phase variation due to vital signs motions. Their performance are determined by a phase calibration procedure. Existing methods fail to consider the time-varying nature of phase noise. There is no prior knowledge about which of the corrupted complex signals, in-phase component (I) and quadrature component (Q), need to be corrected. A precise phase calibration routine is proposed based on the respiration pattern. The I/Q samples from every breath are more likely to experience similar motion noise and therefore they should be corrected independently. High slow-time sampling rate is used to ensure phase calibration accuracy. Occasionally, a 180-degree phase shift error occurs after the initial calibration step and should be corrected as well. All phase trajectories in the I/Q plot are only allowed in certain angular spaces. This precise phase calibration routine is validated through computer simulations incorporating a time-varying phase noise model, controlled mechanic system, and human subject experiment.
This project explores the optimization of HVAC and renewable energy systems of new, modular and portable off grid systems like the Recycling Microfactory, a joint project between Arizona State University and the Department of Defense (DOD). There has been a growing push for innovative solutions to address the underlying deficiencies in United States supply chains and energy infrastructure. This paper seeks to elaborate on the proposed solutions of portable and modular infrastructure to support neglected sectors of the economy: energy grid modernization and waste management specifically. This will be done by analyzing the Microfactory’s operations and optimizing the site’s energy efficiency. Background knowledge and context behind the current state of supply chains and of both energy and waste management sectors are briefly explained in the introduction followed by a high-level overview of the concept of modular infrastructure such as the Recycling Microfactory. The body of the thesis is organized into two sections. The first section focuses on the methods for planning the structure, layout, and workflow of the Recycling Microfactory for when it is out for transport and organized for operation. A series of 3D parametric models were used for the high-fidelity layouts of the Microfactory and was developed in conjunction with user experience gained from evaluating the custom-built processing equipment. The second section further expands the initial energy simulation models of the Microfactory generated from the first simulations of the project. Utilizing the building energy modeling (BEM) software EnergyPlus/OpenStudio, more advanced models accounting for HVAC sizing requirements, climate building standards (i.e., building insulation), and human comfort standards for workspaces are generated. A more realistic simulation of the energy requirements of the Microfactory to maintain temperature and humidity standards is presented through a comprehensive review of the OpenStudio building model design flow.
A modification to the first stage of the two-stage detector is proposed in this study, which significantly simplifies the analysis of the this detector. Cha et al. have used a heuristic approach to determine the thresholds for this two-stage detector. In this study, the probability density function for the modified two-stage detector is derived, and using this probability density function, an approach for determining the thresholds for this two-dimensional detection problem has been proposed. The proposed method of threshold selection reveals an interesting behavior shown by the two-stage detector. With the help of theoretical receiver operating characteristic analysis, it is shown that the two-stage detector gives a better detection performance as compared to the other three detectors. However, the Berger's estimator proves to be a simpler alternative, since it gives only a slightly poorer performance as compared to the two-stage detector. All the four detectors have also been implemented on a SAR data set, and it is shown that the two-stage detector and the Berger's estimator generate images where the areas showing change are easily visible.