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.

Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing

Abstract: In this paper, we describe our experiment developing an implementation of the Linpack benchmark for TianHe-1, a petascale CPU/GPU supercomputer system, the largest GPU-accelerated system ever attempted before. An adaptive optimization framework is presented to balance the workload distribution across the GPUs and CPUs with the negligible runtime overhead, resulting in the better performance than the static or the training partitioning methods. The CPU-GPU communication overhead is effectively hidden by a software pipelining technique, which is particularly useful for large memory-bound applications. Combined with other traditional optimizations, the Linpack we optimized using the adaptive optimization framework achieved 196.7 GFLOPS on a single compute element of TianHe-1. This result is 70.1% of the peak compute capability and 3.3 times faster than the result using the vendor’s library. On the full configuration of TianHe-1 our optimizations resulted in a Linpack performance of 0.563PFLOPS, which made TianHe-1 the 5th fastest supercomputer on the Top500 list released in November 2009.

Deformable 3D Convolution for Video Super-Resolution

Abstract: The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate deformable convolution with 3D convolution, obtaining both superior spatio-temporal modeling capability and motion-aware modeling flexibility. Extensive experiments have demonstrated the effectiveness of D3D in exploiting spatio-temporal information. Comparative results show that our network achieves state-of-the-art SR performance. Code is available at: this https URL.

Printed aerogels: chemistry, processing, and applications.

Abstract: As an extraordinarily lightweight and porous functional nanomaterial family, aerogels have attracted considerable interest in academia and industry in recent decades. Despite the application scopes, the modest mechanical durability of aerogels makes their processing and operation challenging, in particular, for situations demanding intricate physical structures. “Bottom-up” additive manufacturing technology has the potential to address this drawback. Indeed, since the first report of 3D printed aerogels in 2015, a new interdisciplinary research area combining aerogel and printing technology has emerged to push the boundaries of structure and performance, further broadening their application scope. This review summarizes the state-of-the-art of printed aerogels and presents a comprehensive view of their developments in the past 5 years, and highlights the key near- and mid-term challenges.

Strong field enhancement and light-matter interactions with all-dielectric metamaterials based on split bar resonators

Abstract: Strong subwavelength field enhancement has often been assumed to be unique to plasmonic nanostructures. Here we propose a type of all-dielectric metamaterials based on split bar resonators. The nano gap at the centre of the resonant elements results in large local field enhancement and light localization in the surrounding medium, which can be employed for strong light-matter interactions. In a Fano-resonant dielectric metamaterial comprising pairs of asymmetric split silicon bars, the enhancement of electric field amplitude in the gap exceeds 120 while the averaged electromagnetic energy density is enhanced by more than 7000 times. An optical refractive index sensor with a potential sensitivity of 525 nm/RIU is designed based on the proposed metamaterials. The proposed concept can be applied to other types of dielectric nanostructures and may stimulate further research of dielectric metamaterials for applications ranging from nonlinear optics and sensing to the realization of new types of active lasing devices.

Component-Based License Plate Detection Using Conditional Random Field Model

Abstract: This paper presents a novel algorithm for license plate detection in complex scenes, particularly for the all-day traffic surveillance environment. Unlike low-level feature-based methods, our work is motivated by component-based models for object detection. The detection process is divided into three steps, namely, decomposition, modeling, and inference. First, observing that one license plate is decomposed into several constituent characters, the maximally stable extremal region detector is used to extract candidate characters in images. Then, conditional random field (CRF) models are constructed on the candidate characters in neighborhoods. This way, the spatial and visual relationships among the characters is integrated in CRF in the form of probability distribution. Finally, the exact bounding boxes of license plates are estimated through the belief propagation inference on CRF. Both visual and structural features of license plates are fully exploited during detection. Hence, our approach can adapt to various environmental factors, such as cluttered background and illumination variation. A series of experiments are conducted on images that are collected from the actual road surveillance environment. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.