High-Isolation Dual-Polarized Microstrip Antenna via Substrate Integrated Waveguide Technology

A dual-polarized microstrip antenna with high-isolation is proposed by the utilization of the substrate-integrated waveguide (SIW) technology. According to the SIW technology, the metalized holes (MHs) are inserted into the substrate for the proposed antenna and the electric fields of the feeding parts are enclosed, so the isolation of the antenna is enhanced. The bandwidth is improved due to the MHs in the four sides of the antenna. A prototype of the proposed antenna has been fabricated and measured. Experimental results indicate that the antenna obtains the isolation more than 40 dB and achieves the impedance bandwidth of 21.9% and 23.8%(11.8-14.6 GHz and 11.65-14.8 GHz for two ports) of the reflection coefficients less than -20 dB. The cross polarization with the main lobe remains less than -30 dB and the half-power beam width is about 70° for the proposed antenna. Meanwhile, the front-to-back ratio remains to be better than 20 dB. A good agreement between the measured and simulated results validates the proposed design

Characterization of wake effects and loading status of wind turbine arrays under different inflow conditions

The objective of the present work is to improve the accuracy of Actuator Line Modeling (ALM) in predicting the unsteady aerodynamic loadings on turbine blades and turbine wake by assessing different methods used to determine the relative velocity between the rotating blades and wind. ALM is incorporated into a Large Eddy Simulation (LES) solver in OpenFOAM (Open Field Operations and Manipulations). The aerodynamic loadings are validated by experiment results from National Renewable Energy Laboratory (NREL). Turbine wakes are validated by predictions of large eddy simulation using exact 3D blade geometries from a two-blade NREL Phase VI turbine. Three different relative velocity calculation methods are presented: iterative process in Blade Element Momentum (BEM) theory, local velocity sampling, and Lagrange-Euler Interpolation (LEI). Loadings and wakes obtained from these three methods are compared. It is discovered that LEI functions better than the conventional BEM with iterative process in both loading and wake prediction. Then LES-ALM with LEI is performed on a small wind farm deploying five NREL Phase VI turbines in full wake setting. The power outputs and force coefficients of downstream turbines are evaluated. The LES-ALM with LEI is also performed on a small wind farm deploying 25 NREL Phase VI turbines with different inflow angles (from full wake setting to partial wake setting). The power outputs and force coefficients of each turbine are evaluated under different inflow angles (the angle the rotor has to turn to make the rotor plane face the incoming wind) (0, 5, 15, 30 and 45 degree). The power coefficient distributions and thrust coefficient distributions of the wind farm under each inflow angle are compared. The range of inflow angle which is best for power generation is also discussed. The results demonstrate that the LES-ALM with LEI has the potential to optimize wind farm arrangement and pitch angle of individual turbines

Fractal Metamaterial Absorber with Three-Order Oblique Cross Dipole Slot Structure and its Application for In-band RCS Reduction of Array Antennas

To miniaturize the perfect metamaterial absorber, a fractal three-order oblique cross dipole slot structure is proposed and investigated in this paper. The fractal perfect metamaterial absorber (FPMA) consists of two metallic layers separated by a lossy dielectric substrate. The top layer etched a three-order oblique fractal-shaped cross dipole slot set in a square patch and the bottom one is a solid metal. The parametric study is performed for providing practical design guidelines. A prototype with a thickness of 0.0106λ (λ is the wavelength at 3.18 GHz) of the FPMA was designed, fabricated, measured, and is loaded on a 1×10 guidewave slot array antennas to reduce the in-band radar cross section (RCS) based on their surface current distribution. Experiments are carried out to verify the simulation results, and the experimental results show that the absorption at normal incidence is above 90% from 3.17 to 3.22GHz, the size for the absorber is 0.1λ×0.1λ, the three-order FPMA is miniaturized 60% compared with the zero-order ones, and the array antennas significantly obtain the RCS reduction without the radiation deterioration

Limited Attention Allocation in a Stochastic Linear Quadratic System with Multiplicative Noise

This study addresses limited attention allocation in a stochastic linear quadratic system with multiplicative noise. Our approach enables strategic resource allocation to enhance noise estimation and improve control decisions. We provide analytical optimal control and propose a numerical method for optimal attention allocation. Additionally, we apply our ffndings to dynamic mean-variance portfolio selection, showing effective resource allocation across time periods and factors, providing valuable insights for investors

Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain

A significant challenge in autonomous driving systems lies in image understanding within complex environments, particularly dense traffic scenarios. An effective solution to this challenge involves removing the background or static objects from the scene, so as to enhance the detection of moving targets as key component of improving overall system performance. In this paper, we present an efficient algorithm for background removal in automotive radar applications, specifically utilizing a frequency-modulated continuous wave (FMCW) radar. Our proposed algorithm follows a three-step approach, encompassing radar signal preprocessing, three-dimensional (3D) ego-motion estimation, and notch filter-based background removal in the azimuth-elevation-Doppler domain. To begin, we model the received signal of the FMCW multiple-input multiple-output (MIMO) radar and develop a signal processing framework for extracting four-dimensional (4D) point clouds. Subsequently, we introduce a robust 3D ego-motion estimation algorithm that accurately estimates radar ego-motion speed, accounting for Doppler ambiguity, by processing the point clouds. Additionally, our algorithm leverages the relationship between Doppler velocity, azimuth angle, elevation angle, and radar ego-motion speed to identify the spectrum belonging to background clutter. Subsequently, we employ notch filters to effectively filter out the background clutter. The performance of our algorithm is evaluated using both simulated data and extensive experiments with real-world data. The results demonstrate its effectiveness in efficiently removing background clutter and enhacing perception within complex environments. By offering a fast and computationally efficient solution, our approach effectively addresses challenges posed by non-homogeneous environments and real-time processing requirements

Computing All Restricted Skyline Probabilities on Uncertain Datasets

Restricted skyline (rskyline) query is widely used in multi-criteria decision making. It generalizes the skyline query by additionally considering a set of personalized scoring functions F. Since uncertainty is inherent in datasets for multi-criteria decision making, we study rskyline queries on uncertain datasets from both complexity and algorithm perspective. We formalize the problem of computing rskyline probabilities of all data items and show that no algorithm can solve this problem in truly subquadratic-time, unless the orthogonal vectors conjecture fails. Considering that linear scoring functions are widely used in practical applications, we propose two efficient algorithms for the case where \calF is a set of linear scoring functions whose weights are described by linear constraints, one with near-optimal time complexity and the other with better expected time complexity. For special linear constraints involving a series of weight ratios, we further devise an algorithm with sublinear query time and polynomial preprocessing time. Extensive experiments demonstrate the effectiveness, efficiency, scalability, and usefulness of our proposed algorithms.Comment: Full version, a shorter version to appear in ICDE 202

TRUSTING AND FEELING TRUSTED: TWO DISTINCT ASPECTS OF LEADER-FOLLOWER TRUST RELATIONSHIPS

Ph.DDOCTOR OF PHILOSOPH

Stochastic optimization with decisions truncated by random variables and its applications in operations

We study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem