University of Arkansas

About: University of Arkansas is a education organization based out in Fayetteville, Arkansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17225 authors who have published 33329 publications receiving 941102 citations. The organization is also known as: Arkansas & UA.

Temporal and Spatial Interference between Four-Wave Mixing and Six-Wave Mixing Channels.

Abstract: Using phase control between four-wave mixing (FWM) and six-wave mixing (SWM) channels in a four-level atomic system, we demonstrate temporal and spatial interferences between these two nonlinear optical processes. Efficient and coexisting FWM and SWM signals are produced in the same electromagnetically induced transparency window via atomic coherence. The temporal interference has a femtosecond time scale corresponding to the optical transition frequency. Such studies of intermixing between different order nonlinear optical processes with a controllable phase delay can have important applications in high-precision measurements, coherence quantum control, and quantum information processing.

Batch Analysis of Digital Images to Evaluate Turfgrass Characteristics

Abstract: Techniques using digital image analysis have been recently developed to evaluate turfgrass stands for percent green cover and average color. Manually analyzing digital images may become cumbersome and tedious if turf field trials contain many plots or if images are collected at frequent intervals for analysis. The objective of the following work was to develop a user-friendly macro capable of automated batch analysis of an unlimited number of digital images. A macro named "Turf Analysis" was written to batch analyze images using SigmaScan Pro software. The macro, which requires less than 1 min of manual execution, performs percent cover and/or color analysis on an unlimited number of images and automatically saves the results into a spreadsheet file. The macro is freely available to download.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Abstract: Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In ihis study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.