Secure Transmission for Relay Wiretap Channels in the Presence of Spatially Random Eavesdroppers

We propose a secure transmission scheme for a relay wiretap channel, where a source communicates with a destination via a decode-and-forward relay in the presence of spatially random-distributed eavesdroppers. We assume that the source is equipped with multiple antennas, whereas the relay, the destination, and the eavesdroppers are equipped with a single antenna each. In the proposed scheme, in addition to information signals, the source transmits artificial noise signals in order to confuse the eavesdroppers. With the target of maximizing the secrecy throughput of the relay wiretap channel, we derive a closed-form expression for the transmission outage probability and an easy-to-compute expression for the secrecy outage probability. Using these expressions, we determine the optimal power allocation factor and wiretap code rates that guarantee the maximum secrecy throughput, while satisfying a secrecy outage probability constraint. Furthermore, we examine the impact of source antenna number on the secrecy throughput, showing that adding extra transmit antennas at the source brings about a significant increase in the secrecy throughput.Comment: 7 pages, 5 figures, accepted by IEEE Globecom 2015 Workshop on Trusted Communications with Physical Layer Securit

Imaging the transverse spin density of light via electromagnetically induced transparency

When a light beam is strongly laterally confined, its field vector spins in a plane not perpendicular to the propagation direction, leading to the presence of transverse spin angular momentum, which plays a crucial role in the field of chiral quantum optics. The existing techniques to measure the transverse spin density require complex setups and sophisticated time-consuming procedures. Here, we propose a scheme to measure the transverse spin density of an optical field in real time using a multi-level atomic medium. The susceptibility of the medium is spatially modulated by the transverse spin via electromagnetically induced transparency. The distribution of the transverse spin is then extracted by measuring the distributions of the Stokes parameters of another collimated probe field.Comment: 4 pages, 3 figure

Analysis of factors contributing to the severity of large truck crashes

Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree‐based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes