yes / This paper describes the characterization and design of energy efficient user terminal transceiver power amplifier. To reduce the design of bulky external circuitry, the load modulation technique is employed. The design core is based on the combination of Class B and Class C that includes quarter wavelength transformer at the output to perform the load modulation. The handset transceiver for this power amplifier is designed to operate over the frequency range of 3.4GHz to 3.6GHz mobile WiMAX band. The performances of the load modulation amplifier are compared with conventional Class B amplifier. The results of 30dBm output power and 53% power added efficiency are achieved.

Yes / The biggest challenge for future 4G systems is the need to limit the energy consumptions of battery-powered and base station devices, with the aim to prolong their operational time and avoid active cooling in the base station. The green wireless communications requires research in areas such as energy efficient RF front end, MAC protocol, networking, deployment, operation, and also the integration of base station with renewable power supply. In this paper, the design concept of energy efficient RF front end is considered in terms of RF power amplifiers at which it represents the workhorse of modern wireless communication systems and inherently nonlinear. The approach of output power back off is to amplify the signal at the linear region to avoid distortion, but this approach suffers from significant reduction in efficiency and power output. To boost the efficiency at wide range of output power and keep the same margin for signal with high crest factor, the load modulation technique with new offset line are employed to operate over the frequency range of 3.4GHz to 3.6GHz band. The performances of load modulation power amplifier are compared with balanced amplifier. The results of 42dBm output power and 62% power added efficiency are achieved.

The modern day expeditionary warfighter faces extraordinary challenges in the battle field and being a beast of burden should not be one of them. Currently the dismounted warfighter is impeded with carrying over 100lbs of tactical gear and supplies for multiday missions in remote territory. Expeditionary forces are also facing an energy and logistical crisis getting water, fuel, and batteries to the tip of the spear. Finding ways to enable self-sufficiency and reducing resupply tethers for small unit operations is a high priority for the armed forces. The Hybrid Electric Autonomous Tactical Support System directly and efficiently tackles both problems head on by synergizing efforts to lighten the load and self sustaining base power by combining the capabilities of the Ground Unmanned Support Surrogate (GUSS) and the Experimental Forward Operating Base projects. Hybridization of the drivetrain of the GUSS vehicle will provide the reliable power for onboard autonomous systems and also enable silent operation modes. The hybrid onboard generator can efficiently provide generous amounts of exportable DC and AC power on demand and is an ideally sized backup/primary power system for small unit bases and forward command posts. The vehicle's onboard energy storage and generator system can also be linked with renewable energy sources to demonstrate the tactical smart mini grid concept. This thesis develops the power requirements for an autonomous system, GUSS mission derived hybrid electric drivetrain specifications, and Marine Corps small echelon bases for the development of the multifunction Hybrid Electric Autonomous Tactical Support System. / Master of Science

Powered exoskeletons have the potential to revolutionize the labor workplace across many disciplines, from manufacturing to agriculture. However, there are still many barriers to adoption and widespread implementation of exoskeletons. One major research gap of powered exoskeletons currently is the development of a control framework to best cooperate with the user. This limitation is first in understanding the physical and cognitive interaction between the user and exoskeleton, and then in designing a controller that addresses this interaction in a way that provides both physical assistance towards completing a task, and a decrease in the cognitive demand of operating the device. This work demonstrates that multi-objective, optimization-based control can be used to provide a coincident implementation of autonomous robot control, and human-input driven control. A parameter called 'acceptance' can be added to the weights of the cost functions to allow for an automatic trade-off in control priority between the user and robot objectives. This is paired with an update function that allows for the exoskeleton control objectives to track the user objectives over time. This results in a cooperative, powered exoskeleton controller that is responsive to user input, dynamically adjusting control autonomy to allow the user to act to complete a task, learn the control objective, and then offload all effort required to complete the task to the autonomous controller. This reduction in effort is physical assistance directly towards completing the task, and should reduce the cognitive load the user experiences when completing the task. To test the hypothesis of whether high task assistance lowers the cognitive load of the user, a study is designed and conducted to test the effect of the shared autonomy controller on the user's experience operating the robot. The user operates the robot under zero-, full-, and shared-autonomy control cases. Physical workload, measured through the force they exert to complete the task, and cognitive workload, measured through pupil dilation, are evaluated to significantly show that high-assistance operation can lower the cognitive load experienced by a user alongside the physical assistance provided. Automatic adjustment in autonomy works to allow this assistance while allowing the user to be responsive to changing objectives and disturbances. The controller does not remove all mental effort from operation, but shows that high acceptance does lead to less mental effort. When implementing this control beyond the simple reaching task used in the study, however, the controller must be able to both track to the user's desired objective and converge to a high-assistance state to lead to the reduction in cognitive load. To achieve this behavior, first is presented a method to design and enforce Lyapunov stability conditions of individual tasks within a multi-objective controller. Then, with an assumption on the form of the input the user will provide to accomplish their intended task, it is shown that the exoskeleton can stably track an acceptance-weighted combination of the user and robot desired objectives. This guarantee of following the proper trajectory at corresponding autonomy levels results in comparable accuracy in tracking a simulated objective as the base shared autonomy approach, but with a much higher acceptance level, indicating a better match between the user and exoskeleton control objectives, as well as a greater decrease in cognitive load. This process of enforcing stability conditions to shape human-exoskeleton system behavior is shown to be applicable to more tasks, and is in preparation for validation with further user studies. / Doctor of Philosophy / Powered exoskeletons are robots that can be worn by users to physically aid them in accomplishing tasks. These robots differ in scale, from single-joint devices like powered ankle supports or lower-back braces for lifting, to large, multi-joint devices with a broad range of capabilities and potential applications. These multi-joint exoskeletons have been used in many applications such as medical rehabilitation robots, and labor-assisting devices for enhancing strength and avoiding injury. Broader use and adoption in industry could have a great positive impact on the experience of workers performing any heavy-labor tasks. There are still barriers to widespread adoption, however. When closely interacting with machinery like a powered exoskeleton, workers want guarantees of saftey, trust, and cooperation that current exoskeletons have not been able to provide. In fact, studies have shown that industrial devices capable of providing significant assistive force when accomplishing a task, also tend to impart additional, uncomfortable disturbance forces on the user. For example, a lower-body exoskeleton meant to help in lifting tasks might make the simple act of walking more difficult, both physically and mentally. There is a need for exoskeletons that are intuitively cooperative, and can provide both physical assistance towards completing a task and cognitive assistance that makes coordinating with the human user easier. In this dissertation we examine the control problem of powered exoskeletons. In the past, many powered exoskeleton controllers are direct, scripted controllers with exact objectives, or actions tied only to human input. To go beyond this, we leverage "multi-objective-control", originally designed for humanoid robots, which is capable of controlling the robot to accomplish multiple goals at the same time. This approach is the base on which a more complex controller can be created. We show first that the multi-objective control can be used to achieve human desired actions and robot autonomous control tasks at the same time, with a parameter to trade-off which actor, the human or the robot, has the priority control at that time. This framework has the capacity to allow the human to instruct the robot in tasks to accomplish, and then robot can fully mimic the user, offloading the physical effort required to accomplish the task. It is proposed that this offloading of effort from the user will also lower the cognitive load the user is under when actively commanding the exoskeleton. To test this hypothesis, a user study is conducted where human operators work with an upper-body powered exoskeleton to complete a simple reaching task. This study shows that on average, the more assistance the exoskeleton provides to the user, the lower their mental demand is. Additionally, when responding to new challenges or sudden disturbances, the robot can easily cooperate, balancing its own autonomy with the user's to allow the user to respond as they need to their changing environment, then resume active assistance when the change is resolved. Finally, to guarantee that the exoskeleton responds quickly and accurately to the user's intentions, a new strategy is derived to update the robot's internal objectives to match the users' goals. This strategy is based on the assumption that the exoskeleton knows what type of task the user is trying to complete. If this is true, then the exoskeleton can estimate the users objectives from the actions they task, and ensure assistance towards completing the task. This control design is proven in simulation, and in preparation for followup studies to evaluate the user experience of this improved strategy.

Master of Science

The use of aerodynamic forces to assist in certain orbital transfers can greatly reduce the fuel consumption as compared with corresponding all-propulsive transfers. Therefore, in seeking minimum fuel trajectories, aeroassisted transfers need to be investigated. A review of the current literature indicates that such problems have been solved almost exclusively via optimal control theory formulations that result in continuously varying control laws. The use of a piecewise-constant strategy allows the controls to vary to a degree necessary to affect changes in the desired state dependent parameters while simplifying the optimization process. In the process of searching for a tool to produce numerical results, the current research investigates three candidate methods of solving the parameter optimization problem of minimum fuel aeroassisted orbital transfer with piecewise-constant controls. A method based on implicitly integrating the state trajectory is chosen over methods which analytically and explicitly integrate the state trajectory. The implicit method offers improved performance over the explicit method while presenting a more correct solution than the analytic method. The analytic method is shown to suffer from approximations that lead to undesirable solutions. Analytic expressions for the characteristic velocities of Hohmann and idealized aeroassisted transfers are presented and compared. For a large number of transfers from high Earth orbit to low Earth orbit, the aeroassisted mode requires less fuel. Numerical results are presented for minimum fuel transfer from geosynchronous Earth orbit to low earth orbit for a variety of control strategies. The piecewise-constant strategies are seen to provide solutions which are comparable to those found via optimal control theory. / Master of Science

This research provides a thorough flow characterization study to compare the functionality of two types of load-leveling valves that are commonly used for air suspension systems of commercial trucks. The first valve features a simple disk/slot design and is relatively compact for installation. The second type is larger and has a sophisticated, chambered design, which allows for considerably quicker fill and exhaust response times in the transient region. A new approach is introduced to estimate the transient mass flow rate of a load-leveling valve under different suspension pressures, without requiring a mass flow meter. An extensive series of dynamic tests are conducted to characterize and compare the two load-leveling valves. A generic heavy-truck pneumatic suspension, consisting of load-leveling valves, airspring, air tank, and air-hose fittings, is configured for testing. The test setup is used to evaluate the transient performance of each type of load-leveling valve in a typical truck suspension. The flow behavior of the system is validated by the force/pressure responses of the air spring due to various displacement excitations. The experimental results describe the detailed flow behavior of both valves. The flow characterization results can be incorporated as one of the most critical parameters for future model development of pneumatic systems. The tests indicate that the leveling valve with chambered design has a far faster transient flow response than the disk valve, although it is more complicated in its mechanical design and therefore costs more. To take advantage of the design simplicity of the disk valve, while also enabling it to have a faster transient response (compared with the chambered design), it is re-designed with larger flow openings and other elements to match the performance of the chambered valve for transient flow. A comparison of the experimental results and simulations validates that the re-designed rotary disk valve performs nearly the same as the chambered valve, but is simpler and costs less. The study's results are directly applicable to improving the transient dynamics of heavy truck air suspensions by providing a better understanding of how load-leveling valves can be used not only to provide ride-height control, but also to influence the roll and pitch dynamics of heavy trucks. / Master of Science

This study focuses on the interaction of wash load particles with gravel bed rivers. The effects of excess fine sediment loading to streams on general water quality, contaminant transport, and benthic organism mortality has been well examined. A fundamental assumption in fluvial geomorphology and river engineering is that wash load particles ($d<63mu m$) do not deposit to stream beds, but are instead transported downstream until they deposit in reservoirs or estuaries. The goal of this study is to determine if wash load sized particles can deposit to gravel beds, where within the bed substrate deposition occurs, under what hydraulic conditions it occurs, and how the composition of the bed affects the spatial and temporal deposition pattern. Further, this study attempts to quantify the mass flux of wash load to the bed based on a simple mass conservation model using the aforementioned conditions as model parameters. This was accomplished through a series of experiments in which a mixture of pure kaolinite clay was allowed to deposit at constant shear over an acrylic, gravel, or sand-gravel mixture. Discharge was then increased to determine the effects of increased bed shear stress on deposited material and further wash load interaction with the bed. Results indicate that wash load will deposit to acrylic, gravel, and sand-gravel beds during conditions where no bedload movement is occurring. Bed composition is the primary factor controlling the mass flux of wash load from the water column to the bed. Deposition on acrylic beds forms clay ripples which translate downstream, while deposition in porous beds occurs primarily within the bed substrate. Shear stress also affects mass flux and the magnitude of its effects are related to the bed composition. Discharge increases below the threshold of bedload movement only cause large scale entrainment of deposited particles over non-porous beds. Periods of higher discharge over porous beds result in continued deposition within the bed substrates. This research enhances not only our knowledge of sediment processes within fluvial systems, but also allows for the quantification of the wash load portion of those processes given minimal initial condition information. The model developed here may be used within larger hydrologic models when examining contaminant spills or mass loading of stream networks with wash load to estimate the mass deposition to the bed. Instances where wash load is contaminated the mass of contaminated sediment retained by the bed is of great importance to local communities given a reliance of residents on that water source for water, livelihood, and recreation. / Master of Science

Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model. / Master of Science / Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at only a single home or an aggregate load of a collection of homes. This study aims to address this gap and serve as an analysis on short term load forecasting on 3 communities of residential buildings. Detailed analysis on the model performances across all homes have been studied. Deep learning models have been used in this study and their efficacy is measured compared to a simple ANN model.

Quantification of total phosphorus (TP) loads entering a lake or reservoir is important because phosphorus is most often the limiting nutrient in terms of algae growth, thus phosphorus can control the extent of eutrophication. Four methods for assessing the annual tributary phosphorus loads to two different Virginia reservoirs were analyzed, three methods that use tributary monitoring program data and one that uses land-use and rainfall data. In this project, one tributary has been extensively monitored for many years and served as a control on which the other methods were tested. The key difference between this research and previous studies is the inclusion of flow-weighted composite storm sampling instead of simple grab sample analyses of storm flow. Three of the methods employed flow stratification, and the impact of the base flow separation point was examined. It was found that the Regression Method developed in this research was the least sensitive to the base flow separation point, which is a valuable attribute because a wrong choice will not significantly affect the estimate. The Monte Carlo Method was found to underestimate the TP loads. The amount of rainfall impacted the accuracy of the methods, with more error occurring in a year with lower precipitation. / Master of Science