Quantum key distribution based on a Sagnac loop interferometer and polarization-insensitive phase modulators

We present a design for a quantum key distribution(QKD) system in a Sagnac loop configuration, employing a novel phase modulation scheme based on frequency shift, and demonstrate stable BB84 QKD operation with high interference visibility and low quantum bit error rate (QBER). The phase modulation is achieved by sending two light pulses with a fixed time delay (or a fixed optical path delay) through a frequency shift element and by modulating the amount of frequency shift. The relative phase between two light pulses upon leaving the frequency-shift element is determined by both the time delay (or the optical path delay) and the frequency shift, and can therefore be controlled by varying the amount of frequency shift. To demonstrate its operation, we used an acousto-optic modulator (AOM) as the frequency-shift element, and vary the driving frequency of the AOM to encode phase information.The interference visibility for a 40km and a 10km fiber loop is 96% and 99%, respectively, at single photon level. We ran BB84 protocol in a 40-km Sagnac loop setup continuously for one hour and the measured QBER remained within the 2%~5% range. A further advantage of our scheme is that both phase and amplitude modulation can be achieved simultaneously by frequency and amplitude modulation of the AOM's driving signal, allowing our QKD system the capability of implementing other protocols, such as the decoy-state QKD and the continuous- variable QKD. We also briefly discuss a new type of Eavesdropping strategy ("phaseremapping" attack) in bidirectional QKD system.Comment: 4 page

A New RSSI-based Centroid Localization Algorithm by Use of Virtual Reference Tags

A good design of node location is critical for efficient and effective wireless communications. This paper presents an improved algorithm, in order to solve the low localization accuracy caused by traditional centroid algorithm. The improved algorithm combined with VIRE system and traditional centroid algorithm. The VIRE algorithm is introduced and the signal propagation model is utilized to construct virtual reference tags in the location area. Simulation shows that this further developed algorithm has further improved the accuracy of positioning up to 35.12% compared to the traditional centroid algorithm. It is concluded that this algorithm can further improve the locating accuracy in comparison with the original centroid algorithm

AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on real-world networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-of-the-art approaches for network embedding.Comment: 8 pages, 5 figure