Open-set person identification based on mm-Wave Radar Point-clouds using Siamese Neural Networks.

openMillimeter-wave (mm-Wave) radar has been widely used in numerous applications in recent years, including drive-assistance system or short-range sensing due to its numerous advantages over other sensing technologies. The mm-Wave radar can measure the micro-Doppler phenomenon caused by moving objects in a scene, including people. The micro-Doppler effect induced by hunan gait has been proved to be a weak biometric identifier, due to the unique way of walking of each individual. In this work, we propose an open-set person identification based on the obtained mm-Wave radar point-clouds which intend to distinguish a new, unknown person from a known set of people. There are three main tasks studied: (1) extending a deep learning classification model to better distinguish unknown subjects in an open-set scenario; (2) applying Siamese Neural Network (SNN) for open-set identification to detect the new person in the recognized group of people; (3) evaluating the proposed method on our own measured data from a mm-Wave device on 20 subjects. We obtain useful experimental results to guide future work in this area.Millimeter-wave (mm-Wave) radar has been widely used in numerous applications in recent years, including drive-assistance system or short-range sensing due to its numerous advantages over other sensing technologies. The mm-Wave radar can measure the micro-Doppler phenomenon caused by moving objects in a scene, including people. The micro-Doppler effect induced by hunan gait has been proved to be a weak biometric identifier, due to the unique way of walking of each individual. In this work, we propose an open-set person identification based on the obtained mm-Wave radar point-clouds which intend to distinguish a new, unknown person from a known set of people. There are three main tasks studied: (1) extending a deep learning classification model to better distinguish unknown subjects in an open-set scenario; (2) applying Siamese Neural Network (SNN) for open-set identification to detect the new person in the recognized group of people; (3) evaluating the proposed method on our own measured data from a mm-Wave device on 20 subjects. We obtain useful experimental results to guide future work in this area

Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks

Distributed medium access control (MAC) protocols are essential for the proliferation of low cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this work, we propose a class of protocols that limit the performance gain which nodes can obtain through selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed protocol, and trigger a punishment if a departure from the protocol is perceived. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.Comment: 14 double-column pages, submitted to ACM/IEEE Trans Networkin

Lie Group Variational Collision Integrators for a Class of Hybrid Systems

The problem of 3-dimensional, convex rigid-body collision over a plane is fully investigated; this includes bodies with sharp corners that is resolved without the need for nonsmooth convex analysis of tangent and normal cones. In particular, using nonsmooth Lagrangian mechanics, the equations of motion and jump equations are derived, which are largely dependent on the collision detection function. Following the variational approach, a Lie group variational collision integrator (LGVCI) is systematically derived that is symplectic, momentum-preserving, and has excellent long-time, near energy conservation. Furthermore, systems with corner impacts are resolved adeptly using $\epsilon$-rounding on the sign distance function (SDF) of the body. Extensive numerical experiments are conducted to demonstrate the conservation properties of the LGVCI.Comment: 52 pages, 12 figure