National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Temperature-dependent excitonic photoluminescence excited by two-photon absorption in perovskite CsPbBr 3 quantum dots.

Abstract: Recently, lead halide perovskite quantum dots have been reported with potential for photovoltaic and optoelectronic applications due to their excellent luminescent properties. Herein excitonic photoluminescence (PL) excited by two-photon absorption in perovskite CsPbBr3 quantum dots (QDs) has been studied at a broad temperature range, from 80 to 380 K. Two-photon absorption has been investigated and the absorption coefficient is up to 0.085 cm/GW at room temperature. Moreover, the PL spectrum excited by two-photon absorption shows a linear blue-shift (0.32 meV/K) below the temperature of 220 K. However, for higher temperatures, the PL peak approaches a roughly constant value and shows temperature-independent chromaticity up to 380 K. This behavior is distinct from the general red-shift for semiconductors and can be attributed to the result of thermal expansion, electron–phonon interaction and structural phase transition around 360 K. The strong nonlinear absorption and temperature-independent chromaticity of CsPbBr3 QDs observed in temperature range from 220 to 380 K will offer new opportunities in nonlinear photonics, light-harvesting, and light-emitting devices.

Electron-Doping-Enhanced Trion Formation in Monolayer Molybdenum Disulfide Functionalized with Cesium Carbonate

Abstract: We report effective and stable electron doping of monolayer molybdenum disulfide (MoS2) by cesium carbonate (Cs2CO3) surface functionalization. The electron charge carrier concentration in exfoliated monolayer MoS2 can be increased by about 9 times after Cs2CO3 functionalization. The n-type doping effect was evaluated by in situ transport measurements of MoS2 field-effect transistors (FETs) and further corroborated by in situ ultraviolet photoelectron spectroscopy, X-ray photoelectron spectroscopy, and Raman scattering measurements. The electron doping enhances the formation of negative trions (i.e., a quasiparticle comprising two electrons and one hole) in monolayer MoS2 under light irradiation and significantly reduces the charge recombination of photoexcited electron–hole pairs. This results in large photoluminescence suppression and an obvious photocurrent enhancement in monolayer MoS2 FETs.

Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds

Abstract: Energy conservation is a major concern in cloud computing systems because it can bring several important benefits such as reducing operating costs, increasing system reliability, and prompting environmental protection. Meanwhile, power-aware scheduling approach is a promising way to achieve that goal. At the same time, many real-time applications, e.g., signal processing, scientific computing have been deployed in clouds. Unfortunately, existing energy-aware scheduling algorithms developed for clouds are not real-time task oriented, thus lacking the ability of guaranteeing system schedulability. To address this issue, we first propose in this paper a novel rolling-horizon scheduling architecture for real-time task scheduling in virtualized clouds. Then a task-oriented energy consumption model is given and analyzed. Based on our scheduling architecture, we develop a novel energy-aware scheduling algorithm named EARH for real-time, aperiodic, independent tasks. The EARH employs a rolling-horizon optimization policy and can also be extended to integrate other energy-aware scheduling algorithms. Furthermore, we propose two strategies in terms of resource scaling up and scaling down to make a good trade-off between task's schedulability and energy conservation. Extensive simulation experiments injecting random synthetic tasks as well as tasks following the last version of the Google cloud tracelogs are conducted to validate the superiority of our EARH by comparing it with some baselines. The experimental results show that EARH significantly improves the scheduling quality of others and it is suitable for real-time task scheduling in virtualized clouds.

A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images

Abstract: Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to overfitting and achieves better accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows that our depth predictions are competitive with state-of-the-art and lead to faithful 3D projections.