The operation speed of power delivery system of packages has been upgraded to GHz. The instant current will pass to the power plane of the mother board by way of the IC pins and result in electromagnetic wave propagation between the power plane and the ground plane, then to produce the programs of electromagnetic interference. In this thesis, we will analyze the EMI of power delivery system of packages by finite-difference time-domain in two dimensions structure in three sections. In firist section, to computing EMI in finite-difference time-domain in two dimensions structure. In second section, to analyze more complicated power delivery plane, ex: EBG, in finite-difference time-domain in two dimensions structure. In three section, to add property of capacitors on power plane to reduce EMI in two dimensions structure. Above all, we hope to built a fast computing method to compute EMI to solve the time-consuming problems of full-wave simulated software. And to supply the engineer to deal with the similar problems in packages efficiently.

Most recorded data are rounded to the nearest decimal place due to the precision of the recording mechanism. This rounding entails errors in estimation and measurement. In this paper, we compare the performances of three types of estimators based on rounded data from time series models, namely A-K corrected estimator, approximate MLE and the SOS estimator. In order to perform the comparison, the A-K corrected estimators for the MA(1) model are derived theoretically. To improve the efficiency of the estimation, two types of variance-reduction estimators are further proposed, which are based on linear combinations of aforementioned three estimators. Simulation results show the proposed variance reduction estimators significantly improve the estimation efficiency.

Great progress has been made in sleep medical research in recent years and sleep medicine has thus evolved into a specialized medical field. Sleep apnea syndrome is one of the mostly commonly seen sleep disorders. It is now clear that sleep apnea has adverse effects on the heart and is a risk factor for several cardiovascular diseases. Studies have found that decreased heart rate variability (HRV) is a prognostic factor for cardiovascular disease and it also associated with higher mortality rate. Considering the confounding effect of BMI and sleep apnea severity, this work investigates the correlation between heart rate variability and AHI (apnea-hypopnea index which is used to characterize the severity of sleep apnea) by dividing patients into different BMI subgroups. This work includes 1068 male subjects with complete overnight ECG recordings. The low-frequency (LF), the high-frequency (HF) component and the LF/HF ratio of HRV are computed for the 10 BMI subgroups. The Bootstrap method and the BCa technique for confidence interval estimation are employed to verify the linear association between the HRV measures and the severity of sleep apnea. The experimental results show that statically significant correlation exist between LF/HF ratio and AHI for BMI ¡Ù28 patient groups. Statically significant correlation between LF and AHI also exists for BMI ¡Ù27 patient groups. These results demonstrate that the associations between some of the HRV measures and AHI are clearly BMI dependent.

abstract: Integrated oxide/semiconductor heterostructures have attracted intense interest for device applications which require sharp interfaces and controlled defects. The research of this dissertation has focused on the characterization of perovskite oxide/oxide and oxide/semiconductor heterostructures, and the analysis of interfaces and defect structures, using scanning transmission electrom microscopy (STEM) and related techniques. The SrTiO3/Si system was initially studied to develop a basic understanding of the integration of perovskite oxides with semiconductors, and successful integration with abrupt interfaces was demonstrated. Defect analysis showed no misfit dislocations but only anti-phase boundaries (APBs) in the SrTiO3 (STO) films. Similar defects were later observed in other perovskite oxide heterostructures. Ferroelectric BaTiO3 (BTO) thin films deposited directly onto STO substrates, or STO buffer layers with Ge substrates, were grown by molecular beam epitaxy (MBE) in order to control the polarization orientation for field-effect transistors (FETs). STEM imaging and elemental mapping by electron energy-loss spectroscopy (EELS) showed structurally and chemically abrupt interfaces, and the BTO films retained the c-axis-oriented tetragonal structure for both BTO/STO and BTO/STO/Ge heterostructures. The polarization displacement in the BTO films of TiN/BTO/STO heterostructures was investigated. The Ti4+ atomic column displacements and lattice parameters were measured directly using HAADF images. A polarization gradient, which switched from upwards to downwards, was observed in the BTO thin film, and evidence was found for positively-charged oxygen vacancies. Heterostructures grown on Ge substrates by atomic layer deposition (ALD) were characterized and compared with MBE-grown samples. A two-step process was needed to overcome interlayer reaction at the beginning of ALD growth. A-site-rich oxide films with thicknesses of at least 2-nm had to be deposited and then crystallized before initiating deposition of the following perovskite oxide layer in order to suppress the formation of amorphous oxide layers on the Ge surface. BTO/STO/Ge, BTO/Ge, SrHfTiO3/Ge and SrZrO3/Ge thin films with excellent crystallinity were grown using this process. Metal-insulator-metal (MIM) heterostructures were fabricated as ferroelectric capacitors and then electrically stressed to the point of breakdown to correlate structural changes with electrical and physical properties. BaTiO3 on Nb:STO was patterned with different top metal electrodes by focused-ion-beam milling, Au/Ni liftoff, and an isolation-defined approach. / Dissertation/Thesis / Doctoral Dissertation Materials Science and Engineering 2018

The ever-expanding need for energy, including those related to fossil fuels, is behind the drive to explore more complicated regions, such as salt and subsalt provinces. This exploration quest relies heavily on recorded surface seismic data to provide precise and detailed subsurface properties. However, conventional seismic processing algorithms including the state-of-the-art full-waveform inversion (FWI) fail to recover those features in many areas of salt provinces. Even the industrial solution with substantial involvement of manual human-interpretation has faced challenges in many regions. In this thesis, I attempt to replace those manual, and somewhat erroneous, steps to the velocity building in salt provinces with a mathematically robust algorithm under the FWI machinery. I, specifically, regularize FWI by penalizing the velocity drops with depth with a new more flexible function. Although promising, FWI is computationally very expensive, especially for large 3D seismic data. It updates an initial guess of the model iteratively using the gradient of the misfit function, which requires lengthy computations and large memory space/disc storage. Based on the adjoint state method, gradient computation usually requires us to store the source wavefield, or include an additional extrapolation step to propagate the source wavefield from its temporary storage at the boundary. To mitigate this computational overburden, I propose an amplitude excitation gradient calculation based on representing the source wavefield history by a single, specifically the most energetic arrival. In this thesis, I also propose a novel-multiscale scheme based on ux-corrected transport (FCT) to reduce artifacts in the gradient direction due to the noise present in seismic data. FCT comprises of two finite difference schemes: a transport and a diffusion to compute the flux at a grid point. I observe a couple of benefits in FCT-based FWI. First, it yields a smooth gradient at the earlier iterations of FWI by promoting the lower frequency content of the seismic data. Second, it is easily compatible with the existing FWI code, and with any objective function. The multiscale strategy starts with a large smoothing parameter and relaxes it progressively to zero to achieve the final inverted model from traditional FWI.

Testing the equality between two high-dimensional covariance matrices is challenging. As the most efficient way to measure evidential discrepancies in observed data, the likelihood ratio test is expected to be powerful when the null hypothesis is violated. However, when the data dimensionality becomes large and potentially exceeds the sample size by a substantial margin, likelihood ratio based approaches face practical and theoretical challenges. To solve this problem, this study proposes a method by which we first randomly project the original high-dimensional data into lower-dimensional space, and then apply the corrected likelihood ratio tests developed with random matrix theory. We show that testing with a single random projection is consistent under the null hypothesis. Through evaluating the power function, which is challenging in this context, we provide evidence that the test with a single random projection based on a random projection matrix with reasonable column sizes is more powerful when the two covariance matrices are unequal but component-wise discrepancy could be small -- a weak and dense signal setting. To more efficiently utilize this data information, we propose combined tests from multiple random projections from the class of meta-analyses. We establish the foundation of the combined tests from our theoretical analysis that the p-values from multiple random projections are asymptotically independent in the high-dimensional covariance matrices testing problem. Then, we show that combined tests from multiple random projections are consistent under the null hypothesis. In addition, our theory presents the merit of certain meta-analysis approaches over testing with a single random projection. Numerical evaluation of the power function of the combined tests from multiple random projections is also provided based on numerical evaluation of power function of testing with a single random projection. Extensive simulations and two real genetic data analyses confirm the merits and potential applications of our test. / Statistics

Assessment of chemical mutagenicity is essential to protecting human health from genetic disease. Current assays are limited in their ability to provide mechanistic insight into the endogenous and exogenous processes involved in mutagenesis. Duplex Sequencing (DS), a novel error-corrected sequencing technology, overcomes many of the limitations faced by conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. Furthermore, customizable panels enable assessment of the endogenous genomic features that drive mutagenesis. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. The objectives of this study were to demonstrate the potential of DS as a robust in vivo mutagenicity test and to explore its rich data to gain a better understanding of spontaneous and chemically-induced mutagenicity in somatic and germ cells. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) and germ cells of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and tissues were sampled at least 28 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in MF and distinct spectra consistent with the known mutagenic mechanisms of PRC in both tissues. Mouse PRC doses at which significant effects were observed are in range with those used for chemotherapy, suggesting that similar effects may be observed in human patients. This supports the contribution of PRC towards secondary cancers following treatment. DS results were comparable to those obtained using the gold-standard lacZ TGR assay, with DS showing greater sensitivity to detect smaller changes in MF. Analysis of mutation spectra and the genomic features that drive the mutational response revealed intrinsic differences between BM and germ cells that may underlie differences in endogenous mutagenic mechanisms and/or DNA repair pathways. The results suggest that germ cells may have intrinsic mechanisms to reduce mutation burden relative to somatic cells. While historically analysis of germ cell mutagenicity has been neglected in favour of somatic cells, our work supports the independent assessment of germ cell mutagenicity during regulatory testing. Finally, we conducted power analyses to inform the optimal DS study designs for the two tissues. We found that low intra-group variability within BM samples allows a reduction in sample size to three animals per group whilst still maintaining 80% power to detect an effect. In contrast, the relatively high intra-group variability and low background MF in germ cells suggests a minimum of eight animals per group to detect an effect. Overall, our results support the use of DS as a mutagenicity test and highlight many of the advantages it holds over conventional assays. Moreover, our study reveals the potential for mutagenic effects in PRC-treated cancer patients. Further work to test DS with more chemicals and across a wider range of tissues is recommended for future implementation as a mutagenicity test.

Many real-world network data can be formulated as graphs, where a binary relation exists between nodes. One of the fundamental problems in network data analysis is community detection, clustering the nodes into different groups. Statistically, this problem can be formulated as hypothesis testing: under the null hypothesis, there is no community structure, while under the alternative hypothesis, community structure exists. One is of the method is to use the largest eigenvalues of the scaled adjacency matrix proposed by Bickel and Sarkar (2016), which works for dense graph. Another one is the subgraph counting method proposed by Gao and Lafferty (2017a), valid for sparse network. In this paper, firstly, we empirically study the BS or GL methods to see whether either of them works for moderately sparse network; secondly, we propose a subsampling method to reduce the computation of the BS method and run simulations to evaluate the performance.

When categorical data are misplaced into the wrong category, we say the data is affected by misclassification. This is common for data collection. It is well-known that naive estimators of category probabilities and coefficients for regression that ignore misclassification can be biased. In this dissertation, we develop methods to provide improved estimators and confidence intervals for a proportion when only a misclassified proxy is observed, and provide improved estimators and confidence intervals for regression coefficients when only misclassified covariates are observed. Following the introduction and literature review, we develop two estimators for a proportion , one which reduces the bias, and one with smaller mean square error. Then we will give two methods to find a confidence interval for a proportion, one using optimization techniques, and the other one using Fieller's method. After that, we will focus on developing methods to find corrected estimators for coefficients of regression with misclassified covariates, with or without perfectly measured covariates, and with a known estimated misclassification/reclassification model. These correction methods use the score function approach, regression calibration and a mixture model. We also use Fieller's method to find a confidence interval for the slope of simple regression with misclassified binary covariates. Finally, we use simulation to demonstrate the performance of our proposed methods.

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