Since the invention of the automobile, engineers have been designing and making newer and newer improvements to them in order to provide customers with safer, faster, more reliable, and more comfortable vehicles. With each new generation, new technology can be seen being introduced into mainstream products, one of which that is currently being pushed is that of autonomy. Established brand manufacturers and small research teams have been dedicated for years to find a way to make the automobile autonomous with none of them being able to confidently answer that they have found a solution. Among the engineering community there are two schools of thought when solving this issue: camera and LiDAR; some believe that only cameras and computer vision are required while other believe that LiDAR is the solution. The most optimal case is to use both cameras and LiDAR’s together in order to increase reliability and ensure data confidence. Designers are reluctant to use LiDAR systems due to their massive weight, cost, and complexity; with too many moving components, these systems are very bulky and have multiple costly, moving parts that eventually need replacement due to their constant motion. The solution to this problem is to develop a solid-state LiDAR system which would solve all those issues previously stated and this research takes it one level further and looks into a potential prototype for a solid-state camera and Lidar package. Currently no manufacturer offers a system that contains a solid-state LiDAR system and a solid-state camera with computing capabilities, all manufacturers provided either just the camera, just the Lidar, or just the computation ability. This design will also use of the shelf COTS parts in order to increase reproducibility for open-source development and to reduce total manufacturing cost. While keeping costs low, this design is also able to keep its specs and performance on par with that of a well-used commercial product, the Velodyne VL50.

Machine learning has been increasingly integrated into several new areas, namely those related to vision processing and language learning models. These implementations of these processes in new products have demanded increasingly more expensive memory usage and computational requirements. Microcontrollers can lower this increasing cost. However, implementation of such a system on a microcontroller is difficult and has to be culled appropriately in order to find the right balance between optimization of the system and allocation of resources present in the system. A proof of concept that these algorithms can be implemented on such as system will be attempted in order to find points of contention of the construction of such a system on such limited hardware, as well as the steps taken to enable the usage of machine learning onto a limited system such as the general purpose MSP430 from Texas Instruments.

In-Band Full-Duplex (IBFD) can maximize the spectral resources and enable new types of technology, but generates self-interference (SI) that must be mitigated to enable practical applications. Analog domain SI cancellation (SIC), usually implemented as a digitally controlled adaptive filter, is one technique that is necessary to mitigate the interference below the noise floor. To maximize the efficiency and performance of the adaptive filter this thesis studies how key design choices impact the performance so that device designers can make better tradeoff decisions. Additionally, algorithms are introduced to maximize the SIC that incorporate the hardware constraints. The provided simulations show up to 45dB SIC with 7 bits of precision at 100MHz bandwidth.

This Honors Thesis describes the work done to implement an assembler for a MIPS-like processor. MIPS was a processor designed in the 1980s. While assemblers are available for the MIPS processor, the assembler described below was developed specifically for a MIPS-like processor designed as part of another project. This project was undertaken to improve the understanding of processor architecture, assembly language, machine language, and how to translate assembly instructions into machine language. Assembly language is a human readable language for writing computer programs. It is a low-level language that is processor specific. Modern languages such as C++ have to first be translated into assembly language and then translated into machine language. Machine language is the zeros and ones that the computer understands. While the original programs written in the mid 1900s were required to be written in machine language, that is no longer feasible since programs are much larger and the processors are more complex. Therefore, a means of translating from high-level languages to machine language is required. The work described here concerns the translation from assembly language to machine language.

This dissertation consists of four parts: design of antenna in lossy media, analysisof wire antennas using electric field integral equation (EFIE) and wavelets, modeling and measurement of grounded waveguide coplanar waveguide (GCPW) for automotive radar, and E-Band 3-D printed antenna and measurement using VNA. In the first part, the antenna is modeled and simulated in lossy media. First, the vector wave functions is solved in the fundamental mode. Next the energy flow velocity is plotted to show near-field energy distribution for both TM and TE in air and seawater environment. Finally the power relation in seawater is derived to calculate the source dipole moment and required power. In the second part, the current distribution on the antenna is derived by solving EFIE with moment of methods (MoM). Both triangle and Coifman wavelet (Coiflet) are used as basis and weight functions. Then Input impedance of the antenna is computed and results are compared with traditional sinusoid current distribution assumption. Finally the input impedance of designed antenna is computed and matching network is designed and show resonant at designed frequency. In the third part, GCPW is modeled and measured in E-band. Laboratory measurements are conducted in 75 to 84 GHz. The original system is embedded with error boxes due to misalignment and needed to be de-embedded. Then the measurement data is processed and the results is compared with raw data. In the fourth part, the horn antennas and slotted waveguide array antenna (SWA) are designed for automotive radar in 75GHz to 78GHz. The horn antennas are fabricated using 3D printing of ABS material, and electro-plating with copper. The analytic solution and HFSS simulation show good agreement with measurement.

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.

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

Advancements in technologies like the Internet of thing causes an increase in the presence of wireless transceivers. A cooperative communication between these transceivers opens a doorway for multiple novel applications. A mobile distributed transceiver architecture is a much more dynamic environment dictating the necessity of faster synchronization among the transceivers. A possibility of simultaneous synchronization in parallel with the communication will theoretically ensure a high-speed synchronization without affecting the data rate. One such system has been implemented using a Costas loop and an extension of such synchronization technique to the full-duplex model has also been addressed. The rise in spectral demand is hard to meet with the regular Time duplex and frequency duplex communication systems. A full-duplex system is theoretically expected to double the spectral efficiency. However it comes with tremendous challenges, This thesis works on one of those challenges in implementing full-duplex synchronization. A coherent full-duplex model is designed to overcome the issue of transmitter leakage modeled as injection pulling, A known solution for this effect has been used to resolve the issue and complete the coherent full-duplex model. This establishes the simultaneous synchronization and communication system.

Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management.

Machine learning is a powerful tool for processing and understanding the vast amounts of data produced by sensors every day. Machine learning has found use in a wide variety of fields, from making medical predictions through correlations invisible to the human eye to classifying images in computer vision applications. A wide range of machine learning algorithms have been developed to attempt to solve these problems, each with different metrics in accuracy, throughput, and energy efficiency. However, even after they are trained, these algorithms require substantial computations to make a prediction. General-purpose CPUs are not well-optimized to this task, so other hardware solutions have developed over time, including the use of a GPU, FPGA, or ASIC.

This project considers the FPGA implementations of MLP and CNN feedforward. While FPGAs provide significant performance improvements, they come at a substantial financial cost. We explore the options of implementing these algorithms on a smaller budget. We successfully implement a multilayer perceptron that identifies handwritten digits from the MNIST dataset on a student-level DE10-Lite FPGA with a test accuracy of 91.99%. We also apply our trained network to external image data loaded through a webcam and a Raspberry Pi, but we observe lower test accuracy in these images. Later, we consider the requirements necessary to implement a more elaborate convolutional neural network on the same FPGA. The study deems the CNN implementation feasible in the criteria of memory requirements and basic architecture. We suggest the CNN implementation on the same FPGA to be worthy of further exploration.