13,553 research outputs found
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
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