Minimum redundancy array structure for interference cancellation

Adaptive antenna arrays are widely used in many advanced radar, sonar, and communication systems because of their effectiveness in cancelling intentional or unintentional interferers. A uniformly spaced linear array, referred to as a Uniform Regular Array (URA), is the usual structure used for interference cancellation. The Minimum Redundancy Array (MRA) structure proposed in this work is a special kind of thinned array whose application was limited in the past to direction finding. MRAs with the same number of array elements can resolve directions of much more closely spaced signals than URAs. The URA structure is customarily utilized for interference cancellation, and the Minimum Noise Variance (MNV) criterion is a common performance measure for deriving optimum weights, provided that the desired signal is absent during adaptation. The MNV criterion is to minimize the combined sum of the interference and background noise power. Another approach to interference cancellation using the URA structure is the eigencanceling method. This method, which is based on the eigenstructure of the spatial autocorrelation matrix, when compared to the conventional beamforming method, has the following advantages: 1) deeper interference cancellation 2) independence of the interfers\u27 power, and 3) faster optimum weight convergence. In this work, both the conventional beamforming and eigencanceling methods were applied to the MRA structure and investigated analytically. Performance of the MRAs were studied and compared to that of the URAs. For uncorrelated interferers, the cancellation depth of the MRA in the main beam region was almost the same as that of the URA with the same aperture and many more elements. When the eigencanceling technique was applied, it was found that the convergence rate of the MRA was about four times faster than that of the URA. This work also contains other topics, such as the relation between the eigenspaces of the MRA structure and its corresponding URA. Preliminary results on planar MRA structures are also included. For an array application with a large aperture requirement in terms of the number of array elements, the MRA proved to be a much better choice than the URA in achieving interference cancellation

Regression Models for Dynamic Treatment Regimens and Quantile Association of Bivariate Survival Data

In this dissertation we propose two new regression models under different types of survival data, including regression analysis for cumulative incidence functions (CIFs) under two-stage randomization, and quantile association regression for bivariate survival data. The first topic concerns dynamic treatment regimens (DTRs) which are sets of rules for choosing effective treatments for individual patients based on their characteristics and intermediate responses, and have drawn considerable attention in the field of personalized medicine. Sequential Multiple Assignment Randomized Trial (SMART) design is often used to gather data on different DTRs. In this dissertation, we focus on finding personalized optimal DTRs from a two-stage SMART by regressing covariates on CIFs for competing risks outcomes. To our best knowledge no regression is readily available for analyzing competing risks outcome data from a SMART. Thus, we extend existing CIF regression models to handle covariate effects for DTRs. Asymptotic properties are established for our proposed estimators. We show the improvement provided by our proposed methods through simulation studies, and illustrate its practical utility through an analysis of a two-stage neuroblastoma study, where disease progression is subject to competing-risk censoring by death. In the second project, we focus on local association in bivariate survival times, which is often of scientific importance. The local association measures capture the dynamic pattern of association over time, and it is desirable to quantify local association for different characteristics of the population. In this work, we adopt a novel quanitle-based local association measure, which is free of marginal distributions, and propose a quanitle association regression model to allow covariate effects on the local association under the copula framework. Estimating equations for the quantile association coefficients are constructed via the relationship between this quanitle-based measure and the copula model. To avoid estimating density functions in variance estimation, we extend the induced smoothing idea to our proposed estimators in obtaining the covariance matrix. The asymptotic properties for the resulting estimators are studied. The proposed estimators and inference procedure are evaluated through simulation, and applied to an age-related macular degeneration (AMD) dataset in studying risk factors on the association between AMD progression in two eyes

Determinants of the Location of FDI: Evidence from Fujian

Abstract: By choosing the following variables: labor costs, market size, economic openness, basic labor quality, regional labor supply size, infrastructure development, capital output ratio and the total cumulative FDI stocks, we established a panel data model and estimated the effects of the determinants of foreign direct investment (FDI) in 9 areas of Fujian Province from 1999 to 2007. We found that the total regional foreign trade volume, road density and cumulative FDI stocks had a positive effect, but the average wage levels and the number of primary school students had a negative effect on FDI

Entanglement Entropy of Topological Orders with Boundaries

In this paper we explore how non trivial boundary conditions could influence the entanglement entropy in a topological order in 2+1 dimensions. Specifically we consider the special class of topological orders describable by the quantum double. We will find very interesting dependence of the entanglement entropy on the boundary conditions particularly when the system is non-Abelian. Along the way, we demonstrate a streamlined procedure to compute the entanglement entropy, which is particularly efficient when dealing with systems with boundaries. We also show how this method efficiently reproduces all the known results in the presence of anyonic excitations.Comment: 29 pages, 11 figure

An overview of out-of-step protection in power systems

Power system is subjected to an extensive variety of little or bigger disturbance to the system during the operation. The power system that designed as one of the main requirement is to survive from the larger type of disturbances like faults. The power swing in certain system is the variation in three phase power flow in the power system. This paper mainly discussed the power swing and distance relay and the effect of the power swing on the distance relay and demonstrate about the basic power system stability and power swing phenomena. Moreover, out of step protection and detection applications are revised as well. At the end, the paper also demonstrated the past study of out of step application of TNB 275 KV network

Retrieval of phase memory in two independent atomic ensembles by Raman process

In spontaneous Raman process in atomic cell at high gain, both the Stokes field and the accompanying collective atomic excitation (atomic spin wave) are coherent. We find that, due to the spontaneous nature of the process, the phases of the Stokes field and the atomic spin wave change randomly from one realization to another but are anti-correlated. The phases of the atomic ensembles are read out via another Raman process at a later time, thus realizing phase memory in atoms. The observation of phase correlation between the Stokes field and the collective atomic excitations is an important step towards macroscopic EPR-type entanglement of continuous variables between light and atoms