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.

Modeling and Interpretation of Scattering Mechanisms in Polarimetric Synthetic Aperture Radar: Advances and perspectives

Abstract: Polarimetric target decomposition is a powerful technique to interpret scattering mechanisms in polarimetric synthetic aperture radar (PolSAR) data. Eigenvalue-?eigenvector-based and model-based methods are two main categories within the incoherent decomposition techniques. Eigenvalue-eigenvector-based decomposition becomes relatively mature since it has a clearer mathematical background and has only one decomposition solution. In contrast, model-based decompositions can obtain different decomposition solutions in terms of various scattering models. Meanwhile, conventional methods with models or assumptions that do not fit the observations may induce deficiencies. Thereby, the development of effective model-based decompositions has received considerable attention and many advances have been reported. This article aims to provide a review for these notable advances, mainly including the incorporation of orientation compensation processing, nonnegative eigenvalue constraint, generalized scattering models, complete information utilization, full-parameter inversion schemes, and fusion of polarimetry and interferometry. Airborne Pi-SAR data sets are used for demonstration. Besides, natural disaster damage evaluation using model-based decomposition is carried out based on advanced land-observing satellite/phased array type L-band synthetic aperture radar (ALOS/PALSAR) data. Finally, further development perspectives are presented and discussed.

Symmetry Hierarchy of Man‐Made Objects

Abstract: We introduce symmetry hierarchy of man-made objects, a high-level structural representation of a 3D model providing a symmetry-induced, hierarchical organization of the model’s constituent parts. Given an input mesh, we segment it into primitive parts and build an initial graph which encodes inter-part symmetries and connectivity relations, as well as self-symmetries in individual parts. The symmetry hierarchy is constructed from the initial graph via recursive graph contraction which either groups parts by symmetry or assembles connected sets of parts. The order of graph contraction is dictated by a set of precedence rules designed primarily to respect the law of symmetry in perceptual grouping and the principle of compactness of representation. We show that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations. We also develop an application of symmetry hierarchies for structural shape editing.

Adaptive Cache Management for Energy-Efficient GPU Computing

Abstract: With the SIMT execution model, GPUs can hidememory latency through massive multithreading for many applications that have regular memory access patterns. To support applications with irregular memory access patterns, cache hierarchies have been introduced to GPU architectures to capture temporal and spatial locality and mitigate the effect of irregular accesses. However, GPU caches exhibit poor efficiency due to the mismatch of the throughput-oriented execution model and its cache hierarchy design, which limits system performance and energy-efficiency. The massive amount of memory requests generated by GPU scause cache contention and resource congestion. Existing CPUcache management policies that are designed for multicoresystems, can be suboptimal when directly applied to GPUcaches. We propose a specialized cache management policy for GPGPUs. The cache hierarchy is protected from contention by the bypass policy based on reuse distance. Contention and resource congestion are detected at runtime. To avoid oversaturatingon-chip resources, the bypass policy is coordinated with warp throttling to dynamically control the active number of warps. We also propose a simple predictor to dynamically estimate the optimal number of active warps that can take full advantage of the cache space and on-chip resources. Experimental results show that cache efficiency is significantly improved and on-chip resources are better utilized for cache sensitive benchmarks. This results in a harmonic mean IPCimprovement of 74% and 17% (maximum 661% and 44% IPCimprovement), compared to the baseline GPU architecture and optimal static warp throttling, respectively.

Spectral Clustering on Multiple Manifolds

Abstract: Spectral clustering (SC) is a large family of grouping methods that partition data using eigenvectors of an affinity matrix derived from the data. Though SC methods have been successfully applied to a large number of challenging clustering scenarios, it is noteworthy that they will fail when there are significant intersections among different clusters. In this paper, based on the analysis that SC methods are able to work well when the affinity values of the points belonging to different clusters are relatively low, we propose a new method, called spectral multi-manifold clustering (SMMC), which is able to handle intersections. In our model, the data are assumed to lie on or close to multiple smooth low-dimensional manifolds, where some data manifolds are separated but some are intersecting. Then, local geometric information of the sampled data is incorporated to construct a suitable affinity matrix. Finally, spectral method is applied to this affinity matrix to group the data. Extensive experiments on synthetic as well as real datasets demonstrate the promising performance of SMMC.