Oak Ridge National Laboratory

About: Oak Ridge National Laboratory is a facility organization based out in Oak Ridge, Tennessee, United States. It is known for research contribution in the topics: Neutron & Ion. The organization has 31868 authors who have published 73724 publications receiving 2633689 citations. The organization is also known as: ORNL.

High Mobility WSe2 p- and n-Type Field-Effect Transistors Contacted by Highly Doped Graphene for Low-Resistance Contacts

Abstract: We report the fabrication of both n-type and p-type WSe2 field-effect transistors with hexagonal boron nitride passivated channels and ionic-liquid (IL)-gated graphene contacts. Our transport measurements reveal intrinsic channel properties including a metal–insulator transition at a characteristic conductivity close to the quantum conductance e2/h, a high ON/OFF ratio of >107 at 170 K, and large electron and hole mobility of μ ≈ 200 cm2 V–1 s–1 at 160 K. Decreasing the temperature to 77 K increases mobility of electrons to ∼330 cm2 V–1 s–1 and that of holes to ∼270 cm2 V–1 s–1. We attribute our ability to observe the intrinsic, phonon-limited conduction in both the electron and hole channels to the drastic reduction of the Schottky barriers between the channel and the graphene contact electrodes using IL gating. We elucidate this process by studying a Schottky diode consisting of a single graphene/WSe2 Schottky junction. Our results indicate the possibility to utilize chemically or electrostatically high...

Unsupervised Feature Learning for Aerial Scene Classification

Abstract: The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall.

A Survey of CPU-GPU Heterogeneous Computing Techniques

Abstract: As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.

Superhydrophobic materials and coatings: a review

Abstract: Over the past few years, the scientific community, as well as the world's coatings industry has seen the introduction of oxide/polymer-based superhydrophobic surfaces and coatings with exceptional water repellency. Online videos have caught the public's imagination by showing people walking through mud puddles without getting their tennis shoes wet or muddy, and water literally flying off coated surfaces. This article attempts to explain the basics of this behavior and to discuss and explain the latest superhydrophobic technological breakthroughs. Since superhydrophobic surfaces and coatings can fundamentally change how water interacts with surfaces, and the fact that earth is a water world, it can legitimately be said that this technology has the potential to literally change the world.