Game theory-based resource allocation for secure WPCN multiantenna multicasting systems

This paper investigates a secure wireless-powered multiantenna multicasting system, where multiple power beacons (PBs) supply power to a transmitter in order to establish a reliable communication link with multiple legitimate users in the presence of multiple eavesdroppers. The transmitter has to harvest radio frequency (RF) energy from multiple PBs due to the shortage of embedded power supply before establishing its secure com- munication. We exploit a novel and practical scenario that the PBs and the transmitter may belong to different operators and a hierarchical energy interaction between the PBs and the transmitter is considered. Specifically, the monetary incentives are required for the PBs to assist the transmitter for secure communications. This leads to the formulation of a Stackelberg game for the secure wireless-powered multiantenna multicasting system, where the transmitter and the PB are modelled as leader and follower, respectively, each maximizing their own utility function. The closed-form Stackelberg equilibrium of the formulated game is then derived where we study various scenarios of eavesdroppers and legitimate users that can have impact on the optimality of the derived solutions. Finally, numerical results are provided to validate our proposed schemes

Inflight estimation of gyro noise

A method is described and demonstrated for estimating single-axis gyro noise levels in terms of the Farrenkopf model parameters. This is accomplished for the Cosmic Background Explorer (COBE) by comparing gyro-propagated attitudes with less accurate single-frame solutions and fitting the squared differences to a third-order polynomial in time. Initial results are consistent with the gyro specifications, and these results are used to determine limits on the duration of batches used to determine attitude. Sources of error are discussed, and guidelines for a more elegant implementation, as part of a batch estimator or filter, are included for future work

Temperature-dependent optical response of phase-only nematic liquid crystal on silicon devices

Wavelength-dependent birefringence and dielectric anisotropy, two major optical properties of the nematic liquid crystal materials used in phase-only liquid crystal on silicon (LCOS) devices, are measured as a function of operating temperatures. The dynamic phase modulation depth and threshold voltage of these phase-only LCOS devices are also measured in the corresponding temperature range and compared with theoretical predictions. The results show that the dynamic response time can be reduced significantly by an appropriate increase of device operative temperature, while the necessary device elements, such as phase modulation depth and threshold voltage, can be maintained at the same time.This work was supported by the Beijing Natural Science Foundation (No. 4144076), the National Natural Science Foundation of China (Nos. 61307077 and 61376083), and the China Postdoctoral Science Foundation (Nos. 2013M530613 and 2015T80080). It is also supported by the EPSRC Platform Grant for Liquid Crystal Photonics (EP/F00897X/1)

Photoemission Spectroscopy of Magnetic and Non-magnetic Impurities on the Surface of the Bi2_2Se3_3 Topological Insulator

Dirac-like surface states on surfaces of topological insulators have a chiral spin structure that suppresses back-scattering and protects the coherence of these states in the presence of non-magnetic scatterers. In contrast, magnetic scatterers should open the back- scattering channel via the spin-flip processes and degrade the state's coherence. We present angle-resolved photoemission spectroscopy studies of the electronic structure and the scattering rates upon adsorption of various magnetic and non-magnetic impurities on the surface of Bi$_2$Se$_3$, a model topological insulator. We reveal a remarkable insensitivity of the topological surface state to both non-magnetic and magnetic impurities in the low impurity concentration regime. Scattering channels open up with the emergence of hexagonal warping in the high-doping regime, irrespective of the impurity's magnetic moment.Comment: 5 pages, 4 figure

Investigating the Use of Support Vector Machine Classification on Structural Brain Images of Preterm–Born Teenagers as a Biological Marker

Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM) classification methods we aimed to investigate whether MRI data, collected in adolescence, could be used to predict whether an individual had been born preterm or at term. To this end we collected T1–weighted anatomical MRI data from 143 individuals (69 controls, mean age 14.6y). The inclusion criteria for those born preterm were birth weight ≤ 1500g and gestational age < 37w. A linear SVM was trained on the grey matter segment of MR images in two different ways. First, all the individuals were used for training and classification was performed by the leave–one–out method, yielding 93% correct classification (sensitivity = 0.905, specificity = 0.942). Separately, a random half of the available data were used for training twice and each time the other, unseen, half of the data was classified, resulting 86% and 91% accurate classifications. Both gestational age (R = –0.24, p<0.04) and birth weight (R = –0.51, p < 0.001) correlated with the distance to decision boundary within the group of individuals born preterm. Statistically significant correlations were also found between IQ (R = –0.30, p < 0.001) and the distance to decision boundary. Those born small for gestational age did not form a separate subgroup in these analyses. The high rate of correct classification by the SVM motivates further investigation. The long–term goal is to automatically and non–invasively predict the outcome of preterm–born individuals on an individual basis using as early a scan as possible

Joint beamforming design for secure RIS-assisted IoT networks

This paper studies secure communication in an internet-of-things (IoT) network, where the confidential signal is sent by an active refracting reconfigurable intelligent surface (RIS)-based transmitter, and a passive reflective RIS is utilized to improve the secrecy performance of users in the presence of multiple eavesdroppers. Specifically, we aim to maximize the weighted sum secrecy rate by jointly designing the power allocation, transmit beamforming (BF) of the refracting RIS, and the phase shifts of the reflective RIS. To solve the non-convex optimization problem, we propose a linearization method to approximate the objective function into a linear form. Then, an alternating optimization (AO) scheme is proposed to jointly optimize the power allocation factors, BF vector and phase shifts, where the first one is found using the Lagrange dual method, while the latter two are obtained by utilizing the penalty dual decomposition method. Moreover, considering the demands of green and secure communications, by applying the Dinkelbach’s method, we extend our proposed scheme to solving a secrecy energy maximization problem. Finally, simulation results demonstrate the effectiveness of the proposed design