Energy harvesting from body motion using rotational micro-generation

Autonomous system applications are typically limited by the power supply operational lifetime when battery replacement is difficult or costly. A trade-off between battery size and battery life is usually calculated to determine the device capability and lifespan. As a result, energy harvesting research has gained importance as society searches for alternative energy sources for power generation. For instance, energy harvesting has been a proven alternative for powering solar-based calculators and self-winding wristwatches. Thus, the use of energy harvesting technology can make it possible to assist or replace batteries for portable, wearable, or surgically-implantable autonomous systems. Applications such as cardiac pacemakers or electrical stimulation applications can benefit from this approach since the number of surgeries for battery replacement can be reduced or eliminated. Research on energy scavenging from body motion has been investigated to evaluate the feasibility of powering wearable or implantable systems. Energy from walking has been previously extracted using generators placed on shoes, backpacks, and knee braces while producing power levels ranging from milliwatts to watts. The research presented in this paper examines the available power from walking and running at several body locations. The ankle, knee, hip, chest, wrist, elbow, upper arm, side of the head, and back of the head were the chosen target localizations. Joints were preferred since they experience the most drastic acceleration changes. For this, a motor-driven treadmill test was performed on 11 healthy individuals at several walking (1-4 mph) and running (2-5 mph) speeds. The treadmill test provided the acceleration magnitudes from the listed body locations. Power can be estimated from the treadmill evaluation since it is proportional to the acceleration and frequency of occurrence. Available power output from walking was determined to be greater than 1mW/cm³ for most body locations while being over 10mW/cm³ at the foot and ankle locations. Available power from running was found to be almost 10 times higher than that from walking. Most energy harvester topologies use linear generator approaches that are well suited to fixed-frequency vibrations with sub-millimeter amplitude oscillations. In contrast, body motion is characterized with a wide frequency spectrum and larger amplitudes. A generator prototype based on self-winding wristwatches is deemed to be appropriate for harvesting body motion since it is not limited to operate at fixed-frequencies or restricted displacements. Electromagnetic generation is typically favored because of its slightly higher power output per unit volume. Then, a nonharmonic oscillating rotational energy scavenger prototype is proposed to harness body motion. The electromagnetic generator follows the approach from small wind turbine designs that overcome the lack of a gearbox by using a larger number of coil and magnets arrangements. The device presented here is composed of a rotor with multiple-pole permanent magnets having an eccentric weight and a stator composed of stacked planar coils. The rotor oscillations induce a voltage on the planar coil due to the eccentric mass unbalance produced by body motion. A meso-scale prototype device was then built and evaluated for energy generation. The meso-scale casing and rotor were constructed on PMMA with the help of a CNC mill machine. Commercially available discrete magnets were encased in a 25mm rotor. Commercial copper-coated polyimide film was employed to manufacture the planar coils using MEMS fabrication processes. Jewel bearings were used to finalize the arrangement. The prototypes were also tested at the listed body locations. A meso-scale generator with a 2-layer coil was capable to extract up to 234 µW of power at the ankle while walking at 3mph with a 2cm³ prototype for a power density of 117 µW/cm³. This dissertation presents the analysis of available power from walking and running at different speeds and the development of an unobtrusive miniature energy harvesting generator for body motion. Power generation indicates the possibility of powering devices by extracting energy from body motion.

Robustness of speckle imaging techniques applied to horizontal imaging scenarios

Atmospheric turbulence near the ground severely limits the quality of imagery acquired over long horizontal paths. In defense, surveillance, and border security applications, there is interest in deploying man-portable, embedded systems incorporating image reconstruction methods to compensate turbulence effects. While many image reconstruction methods have been proposed, their suitability for use in man-portable embedded systems is uncertain. To be effective, these systems must operate over significant variations in turbulence conditions while subject to other variations due to operation by novice users. Systems that meet these requirements and are otherwise designed to be immune to the factors that cause variation in performance are considered robust. In addition robustness in design, the portable nature of these systems implies a preference for systems with a minimum level of computational complexity. Speckle imaging methods have recently been proposed as being well suited for use in man-portable horizontal imagers. In this work, the robustness of speckle imaging methods is established by identifying a subset of design parameters that provide immunity to the expected variations in operating conditions while minimizing the computation time necessary for image recovery. Design parameters are selected by parametric evaluation of system performance as factors external to the system are varied. The precise control necessary for such an evaluation is made possible using image sets of turbulence degraded imagery developed using a novel technique for simulating anisoplanatic image formation over long horizontal paths. System performance is statistically evaluated over multiple reconstruction using the Mean Squared Error (MSE) to evaluate reconstruction quality. In addition to more general design parameters, the relative performance the bispectrum and the Knox-Thompson phase recovery methods is also compared. As an outcome of this work it can be concluded that speckle-imaging techniques are robust to the variation in turbulence conditions and user controlled parameters expected when operating during the day over long horizontal paths. Speckle imaging systems that incorporate 15 or more image frames and 4 estimates of the object phase per reconstruction provide up to 45% reduction in MSE and 68% reduction in the deviation. In addition, Knox-Thompson phase recover method is shown to produce images in half the time required by the bispectrum. The quality of images reconstructed using Knox-Thompson and bispectrum methods are also found to be nearly identical. Finally, it is shown that certain blind image quality metrics can be used in place of the MSE to evaluate quality in field scenarios. Using blind metrics rather depending on user estimates allows for reconstruction quality that differs from the minimum MSE by as little as 1%, significantly reducing the deviation in performance due to user action.

Using a Prediction and Option Generation Paradigm to Understand Decision Making

In many complex and dynamic domains, the ability to generate and then select the appropriate course of action is based on the decision maker's "reading" of the situation--in other words, their ability to assess the situation and predict how it will evolve over the next few seconds. Current theories regarding option generation during the situation assessment and response phases of decision making offer contrasting views on the cognitive mechanisms that support superior performance. The Recognition-Primed Decision-making model (RPD; Klein, 1989) and Take-The-First heuristic (TTF; Johnson & Raab, 2003) suggest that superior decisions are made by generating few options, and then selecting the first option as the final one. Long-Term Working Memory theory (LTWM; Ericsson & Kintsch, 1995), on the other hand, posits that skilled decision makers construct rich, detailed situation models, and that as a result, skilled performers should have the ability to generate more of the available task-relevant options. The main goal of this dissertation was to use these theories about option generation as a way to further the understanding of how police officers anticipate a perpetrator's actions, and make decisions about how to respond, during dynamic law enforcement situations. An additional goal was to gather information that can be used, in the future, to design training based on the anticipation skills, decision strategies, and processes of experienced officers. Two studies were conducted to achieve these goals. Study 1 identified video-based law enforcement scenarios that could be used to discriminate between experienced and less-experienced police officers, in terms of their ability to anticipate the outcome. The discriminating scenarios were used as the stimuli in Study 2; 23 experienced and 26 less-experienced police officers observed temporally-occluded versions of the scenarios, and then completed assessment and response option-generation tasks. The results provided mixed support for the nature of option generation in these situations. Consistent with RPD and TTF, participants typically selected the first-generated option as their final one, and did so during both the assessment and response phases of decision making. Consistent with LTWM theory, participants--regardless of experience level--generated more task-relevant assessment options than task-irrelevant options. However, an expected interaction between experience level and option-relevance was not observed. Collectively, the two studies provide a deeper understanding of how police officers make decisions in dynamic situations. The methods developed and employed in the studies can be used to investigate anticipation and decision making in other critical domains (e.g., nursing, military). The results are discussed in relation to how they can inform future studies of option-generation performance, and how they could be applied to develop training for law enforcement officers.