Enhancement of Antenna Array Performance Using Reconfigurable Slot-Ring Antennas and Integrated Filter/Antennas

As modern communication system technology develops, the demand for devices with smaller size, higher efficiency, and more functionality has increased dramatically. In addition, highly integrated RF-front-end modules with a reduced footprint and less transition loss between cascaded devices are desirable in most advanced wireless communication systems. Antenna arrays are widely used in wireless communication systems due to their high directivity and beam steering capability. Moreover, antenna arrays are preferred in mobile communication systems for diversity reception to reduce signal fading effects. In order to meet the various requirements of rapidly developing wireless communication systems, low cost, compact, multifunctional integrated antenna arrays are in high demand. Reconfigurable antennas that can flexibly adapt to different applications by dynamically changing their frequency and radiation properties have attracted a lot of attention. Frequency, radiation pattern, polarization, or a combination of two or more of these parameters in the reconfiguration of antennas was studied and presented in recent years. A single reconfigurable antenna is able to replace multiple traditional antennas and accomplish different tasks. Thus, the complexity of wireless communication systems can be greatly reduced with a smaller device size. On the other hand, the integration of antennas with other devices in wireless communication systems that can improve the efficiency and shrink the device size is a growing trend in antenna technology. Compact and highly efficient integrated filters and antennas were studied previously; the studies show that by seamlessly co-designing filters with patch antennas, the fractional bandwidth (FBW) of the antennas can be enhanced as compared to stand-alone antennas. However, the advantages of both the reconfigurable antenna and integrated filter/antenna technology have not been fully applied to antenna array applications. Therefore, this dissertation explores how to maximize the antenna array performance using reconfigurable antennas and integrated filter/antennas. A continuously frequency reconfigurable slot-ring antenna/array with switches and varactors is presented first. By changing the state of the loaded switches, the reconfigurable slot-ring antenna/array is able to operate as an L-band slot-ring antenna or a 2x2 S-band slot-ring antenna array. In each frequency band, the operation frequency of the antenna/array can be continuously tuned with the loaded varactors. To further enhance the functionality of the reconfigurable slot-ring antenna array, a dual-polarized fractal-shaped reconfigurable slot-ring antenna/array is developed with a reduced number of switches and an increased FBW. Additionally, ground plane solutions are explored to achieve single-sided radiation. The benefits of filter/antenna integration are also investigated in both linearly polarized patch phased arrays and circularly polarized patch antenna arrays. Finally, a preliminary study of a tunable integrated evanescent mode filter/antenna is conducted to validate the concept of combining reconfigurable antennas and integrated filter/antennas

A simple uniformly optimal method without line search for convex optimization

Line search (or backtracking) procedures have been widely employed into first-order methods for solving convex optimization problems, especially those with unknown problem parameters (e.g., Lipschitz constant). In this paper, we show that line search is superfluous in attaining the optimal rate of convergence for solving a convex optimization problem whose parameters are not given a priori. In particular, we present a novel accelerated gradient descent type algorithm called auto-conditioned fast gradient method (AC-FGM) that can achieve an optimal $\mathcal{O}(1/k^2)$ rate of convergence for smooth convex optimization without requiring the estimate of a global Lipschitz constant or the employment of line search procedures. We then extend AC-FGM to solve convex optimization problems with H\"{o}lder continuous gradients and show that it automatically achieves the optimal rates of convergence uniformly for all problem classes with the desired accuracy of the solution as the only input. Finally, we report some encouraging numerical results that demonstrate the advantages of AC-FGM over the previously developed parameter-free methods for convex optimization

Split Households, Family Migration and Urban Settlement: Findings from China’s 2015 National Floating Population Survey

For decades, China’s rural migrants have split their households between their rural origins and urban work locations. While the hukou system continues to be a barrier to urban settlement, research has also underscored split households as a migrant strategy that spans the rural and urban boundary, questioning if sustained migration will eventually result in permanent urban settlement. Common split-household arrangements include sole migration, where the spouse and children are left behind, and couple migration, where both spouses are migrants, leaving behind their children. More recently, nuclear family migration involving both the spouse and children has been on the rise. Based on a 2015 nationally representative “floating population” survey, this article compares sole migrants, couple migrants, and family migrants in order to examine which migrants choose which household arrangements, including whether specific household arrangements are more associated with settlement intention than others. Our analysis also reveals differences between work-related migrants and family-related migrants. The findings highlight demographic, gender, economic, employment, and destination differences among the different types of migrant household arrangements, pointing to family migration as a likely indicator of permanent settlement. The increase of family migration over time signals to urban governments an increased urgency to address their needs as not only temporary dwellers but more permanent residents

Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation

In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE updates one single search sequence by solving a single projection subproblem in each iteration. We show that OE can achieve the optimal rate of convergence for solving a variety of VI problems in a much simpler way than existing approaches. We then introduce the stochastic operator extrapolation (SOE) method and establish its optimal convergence behavior for solving different stochastic VI problems. In particular, SOE achieves the optimal complexity for solving a fundamental problem, i.e., stochastic smooth and strongly monotone VI, for the first time in the literature. We also present a stochastic block operator extrapolations (SBOE) method to further reduce the iteration cost for the OE method applied to large-scale deterministic VIs with a certain block structure. Numerical experiments have been conducted to demonstrate the potential advantages of the proposed algorithms. In fact, all these algorithms are applied to solve generalized monotone variational inequality (GMVI) problems whose operator is not necessarily monotone. We will also discuss optimal OE-based policy evaluation methods for reinforcement learning in a companion paper

Who Teaches in Rural Schools in Underdeveloped Areas? An Investigation Based on a Survey of 5,554 Teachers from 117 Towns in H Province in Wuling Mountains Zone, China

Teacher shortage is a major hindrance to China’s rural education growth in underdeveloped areas, as well as one of the main causes of educational injustice. We conducted a survey of 5,554 teachers from 117 towns in H province in the Wuling Mountains Zone to investigate the issue of rural school teacher supply. From geographical, emotional, and institutional perspectives, we used a polynomial logit model to examine the validity of the “hometown effects” hypothesis. The findings showed that hometown effects exist in China in all three dimensions. The institutional hometown effects are the most pronounced; when compared to open recruitment, teachers sourced through teacher supply augmentation programs (such as the Secondary Normal Graduates Program, Special Position Program, and Targeted Position Program) are more likely to teach in rural schools, particularly more disadvantaged village primary schools or teaching sites. China’s policy of increasing teacher supply has had a considerable positive influence on rural school staffing. Students from rural areas make better teacher candidates; feelings for hometowns should be encouraged among normal school or university students in pre-service education; and the implementation of teacher support policies should be emphasized to retain rural teachers and improve their teaching quality

Accelerated stochastic approximation with state-dependent noise

We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classical problem setting in which the variance of noise is assumed to be uniformly bounded, herein we assume that the variance of stochastic gradients is related to the "sub-optimality" of the approximate solutions delivered by the algorithm. Such problems naturally arise in a variety of applications, in particular, in the well-known generalized linear regression problem in statistics. However, to the best of our knowledge, none of the existing stochastic approximation algorithms for solving this class of problems attain optimality in terms of the dependence on accuracy, problem parameters, and mini-batch size. We discuss two non-Euclidean accelerated stochastic approximation routines--stochastic accelerated gradient descent (SAGD) and stochastic gradient extrapolation (SGE)--which carry a particular duality relationship. We show that both SAGD and SGE, under appropriate conditions, achieve the optimal convergence rate, attaining the optimal iteration and sample complexities simultaneously. However, corresponding assumptions for the SGE algorithm are more general; they allow, for instance, for efficient application of the SGE to statistical estimation problems under heavy tail noises and discontinuous score functions. We also discuss the application of the SGE to problems satisfying quadratic growth conditions, and show how it can be used to recover sparse solutions. Finally, we report on some simulation experiments to illustrate numerical performance of our proposed algorithms in high-dimensional settings

Noise-induced stochastic Nash equilibrium

In order to better understand the impact of environmental stochastic fluctuations on the evolution of animal behavior, we introduce the concept of a stochastic Nash equilibrium (SNE) that extends the classical concept of a Nash equilibrium (NE). Based on a stochastic stability analysis of a linear evolutionary game with temporally varying payoffs, we address the question of the existence of a SNE, either weak when the geometric mean payoff against it is the same for all other strategies or strong when it is strictly smaller for all other strategies, and its relationship with a stochastically evolutionarily stable (SES) strategy. While a strong SNE is always SES, this is not necessarily the case for a weak SNE. We give conditions for a completely mixed weak SNE not to be SES and to coexist with at least two strong SNE. More importantly, we show that a pair of two completely mixed strong SNE can emerge as the noise level increases. This not only indicates that a noise-induced SNE may possess some properties that a NE cannot possess, such as being completely mixed and strong, but also illustrates the complexity of evolutionary game dynamics in a stochastic environment

The Influence of an Interlayer on Dual Hydraulic Fractures Propagation

Multi-cluster hydraulic fracturing of long-range horizontal wells is an approach for enhancing the productivity of low-permeability shale reservoirs. In this study, RFPA-Petrol (rock failure process analysis on petroleum problems) is applied for modeling hydraulic fracture propagation in multilayered formations. RFPA-Petrol based on coupled hydraulic-mechanical-damage (HMD) modeling was first tested by modeling a laboratory scale experiment on a physical (cement) model with a single completion. The modeling demonstrated the capability of RFPA-Petrol for simulating hydraulic fracture propagation. Then, we used RFPA-Petrol to investigate how the difference in material properties between oil-bearing layers and interlayers and the fracturing fluid properties influence the propagation of dual fractures in multilayered laboratory-scale models. In this case, the models with geological discontinuities in the vertical direction are strongly heterogeneous and RFPA-Petrol simulations successfully modeled the fracture configurations