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.

Topological detection on wormholes in wireless ad hoc and sensor networks

Abstract: Wormhole attack is a severe threat to wireless ad hoc and sensor networks. Most existing countermeasures either require specialized hardware devices or make strong assumptions on the network in order to capture the specific (partial) symptom induced by wormholes. Those requirements and assumptions limit the applicability of previous approaches. In this paper, we present our attempt to understand the impact and inevitable symptom of wormholes and develop distributed detection methods by making as few restrictions and assumptions as possible. We fundamentally analyze the wormhole problem using a topology methodology and propose an effective distributed approach, which relies solely on network connectivity information, without any requirements on special hardware devices or any rigorous assumptions on network properties. We formally prove the correctness of this design in continuous geometric domains and extend it into discrete domains. We evaluate its performance through extensive simulations.

Polysulfide-containing Glyme-based Electrolytes for Lithium Sulfur Battery

Abstract: A new comparative investigation of lithium sulfur cells employing a tetraethylene glycol dimethyl ether–lithium trifluoromethanesulfonate (TEGDME-LiCF3SO3) electrolyte charged by various polysulfide species (Li2S2, Li2S4, Li2S6, and Li2S8) is here reported. We carefully detect the effects of lithium polysulfide addition by originally combining X-ray photoelectron spectroscopy (XPS) and electrochemical impedance spectroscopy (EIS). The measurements clearly reveal how the polysulfide addition affects the nature and composition of the solid electrolyte interphase (SEI) in terms of precipitated S-based species determined by XPS. The study demonstrates that the SEI layer formed on the Li anode decreases in impedance and stabilizes by the presence of polysulfide. This, together with a buffer effect strongly mitigating the sulfur-cathode dissolution and the shuttle reaction, significantly improves the stability of the lithium–sulfur cell. The data here reported clearly suggest the polysulfide as an effective add...

Photo-inspired model-driven 3D object modeling

Abstract: We introduce an algorithm for 3D object modeling where the user draws creative inspiration from an object captured in a single photograph. Our method leverages the rich source of photographs for creative 3D modeling. However, with only a photo as a guide, creating a 3D model from scratch is a daunting task. We support the modeling process by utilizing an available set of 3D candidate models. Specifically, the user creates a digital 3D model as a geometric variation from a 3D candidate. Our modeling technique consists of two major steps. The first step is a user-guided image-space object segmentation to reveal the structure of the photographed object. The core step is the second one, in which a 3D candidate is automatically deformed to fit the photographed target under the guidance of silhouette correspondence. The set of candidate models have been pre-analyzed to possess useful high-level structural information, which is heavily utilized in both steps to compensate for the ill-posedness of the analysis and modeling problems based only on content in a single image. Equally important, the structural information is preserved by the geometric variation so that the final product is coherent with its inherited structural information readily usable for subsequent model refinement or processing.

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

Abstract: The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.