DESIGN OF CLASS F-BASED DOHERTY POWER AMPLIFIER FOR S-BAND APPLICATIONS

Modern RF and millimeter-wave communication links call for high-efficiency front end systems with high output power and high linearity to meet minimum transmission requirements. Advanced modulation techniques, such as orthogonal frequency-division multiplexing (OFDM) require a large power amplifier (PA) dynamic range due to the high peak-to-average power ratio (PAPR). This thesis provides the analysis, design, and experimental verification of a high-efficiency, high-linearity S-band Doherty power amplifier (DPA) based on the Class F PA. Traditional Class F PAs use harmonically tuned output matching networks to obtain up to 88.4% power-added efficiency (PAE) theoretically, however the amplifier experiences poor linearity performance due to switched mode operation, typically yielding less than 30dB C/I ratio [1]. The DPA overcomes this linearity limitation by using an auxiliary amplifier to boost output power when the amplifier is subject to a high input power due to its limited conduction cycle. The DPA also provides improved saturated output power back-off performance to maintain high PAE during operation. The DPA presented in this thesis optimizes PAE while maintaining linearity by employing harmonically tuned Class F amplifier topology on a primary and an auxiliary amplifier. A Class F PA is first designed and fabricated to optimize output network linearity – this is followed by a DPA design based on the fabricated Class F PA. A GaN HEMT Class F PA and DPA operating at 2.2GHz are implemented with the PAs measuring 40% and 45% PAE respectively while maintaining a 30dB carrier-to-intermodulation (C/I) ratio on a two-tone test. The PAE is characterized at maximum 21dBm input power per tone and 20MHz tone spacing. When subject to a single 24dBm continuous wave input tone, the Class F PA and DPA output 37dBm and 35.5dBm respectively. The PAs presented in the thesis provide over 30dB C/I ratio up to 21dBm input tones while maintaining over 40% PAE suitable for base station applications

Carrier Card for Nvidia Jetson AGX Xavier

This project prototypes and implements custom carrier cards for interfacing with the Nvidia Jetson AGX Javier within the Zendar Processing Unit (ZPU). The Jetson module is an AI computer used as a computing system for autonomous machines, including robots, drones, and self-driving cars. The carrier card functions as an I/O interface with the Jetson module, and it is designed for and sponsored by Zendar, a start-up designing high-resolution radar imaging systems. The purpose of the carrier card is to function as a collection of multiple I/O interfaces, including HDMI, USB, and PCIE, for users to control the Jetson, to input radar data, and to output a video feed from the GPU. It is also capable of supplying up to 40W from an external source to power the Jetson under full computing conditions. The carrier card features one power switch, one HDMI port, one micro-USB port, two USB-A ports, one Ethernet port, one PCIe Gen4 connector, and debug interfaces. The carrier card has the same form factor as the Jetson module and features a low profile to seamlessly slot into current ZPU enclosures

GENOMIC AND TRANSCRIPTOMIC LANDSCAPE OF COLORECTAL PREMALIGNANCY

Colorectal cancer (CRC) is the third most commonly diagnosed cancer among men and women in the United States, with 3 to 5 percent of the cases diagnosed in the background of a hereditary form of the disease. Biologically, CRC is divided into two groups: microsatellite instable (MSI) and chromosomally unstable (CIN). Genomic and transcriptomic characterization of CRC has emerged from large-scale studies in recent years due to the advancement of next-generation sequencing technologies. These studies have identified key genes and pathways altered in CRC and provided insights to the discovery of therapeutic targets. Despite the wealth of knowledge acquired in the carcinoma stage, there have been insufficient efforts to systematically characterize premalignant lesions at the molecular level, which could lead to a better understanding of neoplastic initiation, risk prediction, and the development of targeted chemoprevention strategies. The challenge in characterizing premalignancy has always been the limited availability of sample material. This challenge is tackled by getting more samples, integrating public datasets, deploying better technology that use less amount of nucleic acids and in-silico tools to extract multi-layer information from the same experiment. My genomic study consisted of whole exome sequencing (WES) and high-depth targeted sequencing on 80 premalignant lesions bulk tissue and crypts to assess clonality and mutational heterogeneity. WES results showed the presence of multiple clone in premalignancy based on clustering somatic mutation allele frequency. In addition, I determined that multiple clones originate from independent crypts harboring distinct APC and KRAS alterations. In my second study, I performed immune expression profiling and assessment of mutation and neoantigen rate of 28 premalignant lesions with DNA mismatch repair (MMR) deficient and proficient background using RNAseq. My results showed an activated immune profile despite low mutational and neoantigen rate, which challenges the canonical view in MMR-deficient carcinoma stage that immune activation is largely due to high mutation and neoantigen rate. In the last study, I performed transcriptomic sub-classifications of 398 premalignant lesions that associate them with different carcinomas subtypes, and clinical and histopathological features. My results revealed two major findings: prominent immune activation and WNT and MYC activation in premalignancy. In summary, my large-scale genomic and transcriptomic analyses of colorectal adenomas have identified key molecular characteristics in early colorectal tumorigenesis and provide a foundation for discovering novel preventive strategies