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.

Secure Communication via Sending Artificial Noise by the Receiver: Outage Secrecy Capacity/Region Analysis

Abstract: A novel approach for ensuring confidential wireless communication is proposed and analyzed from an information-theoretic standpoint. In this approach, the legitimate receiver generates artificial noise (AN) to impair the intruder's channel. This method is robust because it does not need the feedback of channel state information (CSI) to the transmitter and does not assume that the number of Eve's antennas should be smaller than that of Bob. Furthermore, we propose a new concept of outage secrecy region to evaluate the secrecy performance from a geometrical perspective. This should be useful if we need to know what zone should be protected (or militarized). Analysis and simulation results in practical environments show that the proposed method has a good performance.

Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

Abstract: Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: Local Reference Frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and 96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets.