Precise Position, Navigation, and Timing (PNT) is necessary for the functioning of many critical infrastructure sectors relied upon by millions every day. Specifically, precise timing is primarily provided through the Global Positioning System (GPS) and its system of satellites that each house multiple atomic clocks. Without precise timing, utilities such as the internet, the power grid, navigational systems, and financial systems would cease operation. Because oscillator devices experience frequency drift during operation, many systems rely on the precise time provided by GPS to maintain synchronization across the globe. However, GPS signals are particularly susceptible to disruption – both intentional and unintentional – due to their space-based, low-power, and unencrypted nature. It is for these reasons that there is a need to develop a system that can provide an accurate timing reference – one disciplined by a GPS signal – and can also maintain its nominal frequency in scenarios of intermittent GPS availability.
This project considers an accurate timing reference deployed via Field Programmable Gate Array (FPGA) and disciplined by a GPS module. The objective is to implement a timing reference on a DE10-Lite FPGA disciplined by the 1 Pulse-Per-Second (PPS) output of an MTK3333 GPS module. When a signal lock is achieved with GPS, the MTK3333 delivers a pulse input to the FPGA on the leading edge of every second. The FPGA aligns a digital oscillator to this PPS reference, providing a disciplined output signal at a 10 MHz frequency that is maintained in events of intermittent GPS availability. The developed solution is evaluated using a frequency counter disciplined by an atomic clock in addition to an oscilloscope. The findings deem the software solution acceptable with more work needed to debug the hardware solution

The stability of cheerleading stunts is crucial to athlete safety and team success. Consistency in stunt technique contributes to success in stunting skills, giving a team the tools to win competitions. Increased stunt technique reduces the chances of falls and the severity of those falls. Proper technique also prevents injuries caused by improper positions that place pressure on the lower back and shoulders. Bases must maintain strong technique with proper lines of support in order to maximize stunt stability. Through exploration of the EmbeddedML system, involving a neural network implemented using a SensorTile, cheerleading motions can be successfully classified. Using this system, it is possible to identify motions that result in both weak and injurious positions almost instantly. By alerting athletes to these incorrect motions, improper stunt technique can be corrected quickly and without the involvement of a coach. This automated technique correction would be incredibly beneficial to the sport of competitive cheerleading

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.

This paper discusses the process of creating and testing the haptic feedback wearable that utilizes a sweeping Light Detection and Ranging sensor. This design comes as an extension to the capstone project for electrical engineers. The design works by attaching a LiDAR sensor to a sweeping servo motor, and whenever an object is detected by the sensor, a motor will vibrate to notify the user that an object is nearby. The design incorporates four motors so that the user will have a sense of where an obstacle is coming from and be able to navigate around that obstacle. The design was tested for its accuracy in distance and angle measurement, its efficiency when it came to processing the data, and the uncertainty of the sensor due to beam spreading. Plotting the results for the distance and angle accuracy showed that the design is capable of accurate measurements. The implementation of the code was also very efficient and had no issues with latency when processing the data from the sensor. There was also uncertainty at the larger ranges for the sensor.

In wireless communication systems, the process of data transmission includes the estimation of channels. Implementing machine learning in this process can reduce the amount of time it takes to estimate channels, thus, resulting in an increase of the system’s transmission throughput. This maximizes the performance of applications relating to device-to-device communications and 5G systems. However, applying machine learning algorithms to multi-base-station systems is not well understood in literature, which is the focus of this thesis.

The ability of magnetic resonance imaging (MRI) to image any part of the human body without the effects of harmful radiation such as in CAT and PET scans established MRI as a clinical mainstay for a variety of different ailments and maladies. Short wavelengths accompany the high frequencies present in high-field MRI, and are on the same scale as the human body at a static magnetic field strength of 3 T (128 MHz). As a result of these shorter wavelengths, standing wave effects are produced in the MR bore where the patient is located. These standing waves generate bright and dark spots in the resulting MR image, which correspond to irregular regions of high and low clarity. Coil loading is also an inevitable byproduct of subject positioning inside the bore, which decreases the signal that the region of interest (ROI) receives for the same input power. Several remedies have been proposed in the literature to remedy the standing wave effect, including the placement of high permittivity dielectric pads (HPDPs) near the ROI. Despite the success of HPDPs at smoothing out image brightness, these pads are traditionally bulky and take up a large spatial volume inside the already small MR bore. In recent years, artificial periodic structures known as metamaterials have been designed to exhibit specific electromagnetic effects when placed inside the bore. Although typically thinner than HPDPs, many metamaterials in the literature are rigid and cannot conform to the shape of the patient, and some are still too bulky for practical use in clinical settings. The well-known antenna engineering concept of fractalization, or the introduction of self-similar patterns, may be introduced to the metamaterial to display a specific resonance curve as well as increase the metamaterial’s intrinsic capacitance. Proposed in this paper is a flexible fractal-inspired metamaterial for application in 3 T MR head imaging. To demonstrate the advantages of this flexibility, two different metamaterial configurations are compared to determine which produces a higher localized signal-to-noise ratio (SNR) and average signal measured in the image: in the first configuration, the metamaterial is kept rigid underneath a human head phantom to represent metamaterials in the literature (single-sided placement); and in the second, the metamaterial is wrapped around the phantom to utilize its flexibility (double-sided placement). The double-sided metamaterial setup was found to produce an increase in normalized SNR of over 5% increase in five of six chosen ROIs when compared to no metamaterial use and showed a 10.14% increase in the total average signal compared to the single-sided configuration.

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.

This project examines the dynamics and design of control systems for a rocket in propulsive ascent and descent using a simplified model with motion constrained to a vertical plane. The governing differential equations are analyzed. They are then linearized, after which transfer functions are derived relating controllable input variables to controlled output variables. The effect of changes in various parameters as well as other aspects of the system are examined. Methods for controller design based on the derived transfer functions are discussed. This will include the discussion of control of the final descent and landing of the rocket. Lastly, there is a brief discussion about both the successes and limitations of the model analyzed.