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.

Coherent Beam Combining of Fiber Amplifiers Using Stochastic Parallel Gradient Descent Algorithm and Its Application

Abstract: We present theoretical and experimental research on coherent beam combining of fiber amplifiers using stochastic parallel gradient descent (SPGD) algorithm. The feasibility of coherent beam combining using SPGD algorithm is detailed analytically. Numerical simulation is accomplished to explore the scaling potential to higher number of laser beams. Experimental investigation on coherent beam combining of two and three W-level fiber amplifiers is demonstrated. Several application fields, i.e., atmosphere distortion compensating, beam steering, and beam shaping based on coherent beam combining using SPGD algorithm are proposed.

Generation of OAM Beams Using Phased Array in the Microwave Band

Abstract: A system is proposed to generate vortex electromagnetic (EM) beams in the microwave band, which generates high-order vortex beams at the X-frequency band for the first time. First, the orbital angular momentum (OAM)-generating system is designed and the signal model based on the uniform circular array is presented. Subsequently, the mathematical model with array error contributions is established and, comprehensively, numerical simulations are conducted to analyze how amplitude and phase errors affect the radiation field and the EM vortex imaging. The experimental results validate that the proposed system can readily generate vortex beams of high quality, which are in agreement with the simulated results. The work paves the way to applications of OAM-carrying beams as well as a novel information-rich radar paradigm.

Global, regional, and national causes of under-5 mortality in 2000–19: an updated systematic analysis with implications for the Sustainable Development Goals

Abstract: Causes of mortality are a crucial input for health systems for identifying appropriate interventions for child survival. We present an updated series of cause-specific mortality for neonates and children younger than 5 years from 2000 to 2019.We updated cause-specific mortality estimates for neonates and children aged 1-59 months, stratified by level (low, moderate, or high) of mortality. We made a substantial change in the statistical methods used for previous estimates, transitioning to a Bayesian framework that includes a structure to account for unreported causes in verbal autopsy studies. We also used systematic covariate selection in the multinomial framework, gave more weight to nationally representative verbal autopsy studies using a random effects model, and included mortality due to tuberculosis.In 2019, there were 5·30 million deaths (95% uncertainty range 4·92-5·68) among children younger than 5 years, primarily due to preterm birth complications (17·7%, 16·1-19·5), lower respiratory infections (13·9%, 12·0-15·1), intrapartum-related events (11·6%, 10·6-12·5), and diarrhoea (9·1%, 7·9-9·9), with 49·2% (47·3-51·9) due to infectious causes. Vaccine-preventable deaths, such as for lower respiratory infections, meningitis, and measles, constituted 21·7% (20·4-25·6) of under-5 deaths, and many other causes, such as diarrhoea, were preventable with low-cost interventions. Under-5 mortality has declined substantially since 2000, primarily because of a decrease in mortality due to lower respiratory infections, diarrhoea, preterm birth complications, intrapartum-related events, malaria, and measles. There is considerable variation in the extent and trends in cause-specific mortality across regions and for different strata of all-cause under-5 mortality.Progress is needed to improve child health and end preventable deaths among children younger than 5 years. Countries should strategize how to reduce mortality among this age group using interventions that are relevant to their specific causes of death.Bill & Melinda Gates Foundation; WHO.

An Efficient Maximum Likelihood Method for Direction-of-Arrival Estimation via Sparse Bayesian Learning

Abstract: The computationally prohibitive multi-dimensional searching procedure greatly restricts the application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in practical systems. In this paper, we propose an efficient ML DOA estimator based on a spatially overcomplete array output formulation. The new method first reconstructs the array output on a predefined spatial discrete grid under the sparsity constraint via sparse Bayesian learning (SBL), thus obtaining a spatial power spectrum estimate that also indicates the coarse locations of the sources. Then a refined 1-D searching procedure is introduced to estimate the signal directions one by one based on the reconstruction result. The new method is able to estimate the incident signal number simultaneously. Numerical results show that the proposed method surpasses state-of-the-art methods largely in performance, especially in demanding scenarios such as low signal-to-noise ratio (SNR), limited snapshots and spatially adjacent signals.