A 2X2 Polarization Switchable Patch Antenna Array For Polarization Modulation

This thesis introduced and investigated a new concept in the development of a 2X2 polarization switchable patch antenna array for polarization modulation. Polarization modulation is a technique to modulate digital information using different polarization of the electromagnetic wave. The existing waveform based modulation techniques are well developed and start approaching to their performance limits. Polarization modulation provides a new modulation dimension for wireless communications and can further increase data throughput. A four-port circular patch antenna is presented and investigated for the implementation of polarization modulation. It has four ports, each excites radiation of a different polarization. A single pole four throw RF switch is used to switch between the ports depending on the information symbol to be transmitted. Then, the antenna is transformed to a 2X2 antenna array to increase the directivity and gain. The antenna is studied numerically and it shows good performance

Ab initio molecular dynamics study of nanoscale heat transfer and energy conversion

Title from PDF of title page (University of Missouri--Columbia, viewed on September 10, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Yuwen ZhangIncludes bibliographical references.M.S. University of Missouri--Columbia 2013.Dissertations, Academic -- University of Missouri--Columbia -- Mechanical and aerospace engineering."May 2013"In this thesis, ab initio molecular dynamics simulation based on a plane wave/pseudopotential implementation of density functional theory was adopted to investigate nanoscale heat transfer and energy conversions for semiconductors. The first one investigates the heat conduction process occurring in Si/Ge superlattices at selected stages from the initial point of nonzero temperature gradient to the final state of thermal equilibrium. The second one studies the thermal energy transportation phenomena spanning from heat conduction of thermal radiation with the modeling of variable gap distances in different thin layer systems. The third one presents an ab initio molecular dynamics study of femtosecond laser processing of germanium. As the first work of studying the nanoscale energy transport spanning from heat conduction to thermal radiation and the femtosecond laser material interaction in mechanical engineering, the simulation results highlight the promising application of the first-principles molecular dynamics in thermal engineering. We believe our results and the conclusion drawn will be quite useful in helping to resolving the heat transfer and energy conversion problem during the miniaturization of integrated circuits and molecular electronics

Multiscale modeling and simulation of laser interaction with metals

Abstract from short.pdf file.Dissertation supervisor: Dr. Yuwen Zhang.Includes vita.The electron temperature dependent electron heat capacity, electron thermal conductivity and effective electron-phonon coupling factor are modeled and computed on the basis of ab initio quantum mechanics (QM) calculation. The obtained electron thermophysical parameters are implemented into energy equation of electron subsystem in two temperature model (TTM). Upon laser irradiation on metal, energy transfer from the electron subsystem to the lattice subsystem is modeled by including electron thermophysical parameters in molecular dynamics (MD) and TTM coupled simulation. Phenomena, such as melting, layer-ablation, vaporization are found in the simulation results. In addition, bond hardening of femtosecond laser irradiated gold is observed. As the first work studying the laser interaction with metallic materials ranging from atomic scale to continuum scale, the successful construction of the QM-MD-TTM integrated simulation provides a general way that is accessible to other metals in laser heating. The simulation results highlight the promising application of the QM-MD-TTM integrated simulation. Obtained results from pure ab initio MD provide a better relation between microscopic processes and material response detected in experiments and serve for improved interpretation of experimental results on ultrafast laser-metal interactions. The results simulated and conclusion drawn will empower the multi-scale modeling of laser material interaction and be quite useful in helping to resolving the heat transfer and energy conversion problem during ultrashort laser processing of metals.Includes bibliographical references (pages 111-124)

Self Information Update for Large Language Models through Mitigating Exposure Bias

Current LLMs have demonstrated remarkable capabilities in addressing users' requests for various types of information. However, these models are limited by the most recent data available in their pretraining corpora, rendering them incapable of providing up-to-date information. Retraining LLMs from scratch is cost-prohibitive, and the effectiveness of continual fine-tuning on new corpora has not been thoroughly examined. Additionally, current update procedures typically demand significant human input to prepare the information into more structured format, such as knowledge triples, conversational data or responses with human feedback. In this study, we conduct a comprehensive examination of a novel self information update task in LLMs, which only requires the provision of informative text corpora. For instance, we can use the latest news articles to update the LLMs' existing knowledge. We define the self information update task and assess the continual fine-tuning approach for this purpose. We observe that the naive method of continual fine-tuning can be problematic due to LLMs' exposure bias, which prioritizes existing information over new information we aim to integrate and leads to incorrect reasoning chains that ultimately diminish the efficacy of information updates. Based on our analysis, we propose an effective method to mitigate exposure bias by incorporating the selection of relevant facts into training losses. Furthermore, we develop a dataset to evaluate information updates, derived from news articles published after March 2023. Experimental results demonstrate that our proposed approach significantly increases the factual consistency score (0 to 1) by 0.16 while having minimal impact on performance for instructions not directly related to the new information