13,838 research outputs found
Broadband cloaking with volumetric structures composed of two-dimensional transmission-line networks
The cloaking performance of two microwave cloaks, both based on the recently
proposed transmission-line approach, are studied using commercial full-wave
simulation software. The cloaks are shown to be able to reduce the total
scattering cross sections of metallic objects of some restricted shapes and
sizes. One of the studied cloaks is electrically small in diameter, and the
other is electrically large, with the diameter equal to several wavelengths.Comment: 6 pages, 7 figure
Experimental realization of a broadband illusion optics device
We experimentally demonstrate the first metamaterial "illusion optics" device
- an "invisible gateway" by using a transmission-line medium. The device
contains an open channel that can block electromagnetic waves at a particular
frequency range. We also demonstrate that such a device can work in a broad
frequency range.Comment: 9 pages, 5 figure
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Automatic organization of personal photos is a problem with many real world
ap- plications, and can be divided into two main tasks: recognizing the event
type of the photo collection, and selecting interesting images from the
collection. In this paper, we attempt to simultaneously solve both tasks:
album-wise event recognition and image- wise importance prediction. We
collected an album dataset with both event type labels and image importance
labels, refined from an existing CUFED dataset. We propose a hybrid system
consisting of three parts: A siamese network-based event-specific image
importance prediction, a Convolutional Neural Network (CNN) that recognizes the
event type, and a Long Short-Term Memory (LSTM)-based sequence level event
recognizer. We propose an iterative updating procedure for event type and image
importance score prediction. We experimentally verified that image importance
score prediction and event type recognition can each help the performance of
the other.Comment: Accepted as oral in BMVC 201
Broadcast Coded Slotted ALOHA: A Finite Frame Length Analysis
We propose an uncoordinated medium access control (MAC) protocol, called
all-to-all broadcast coded slotted ALOHA (B-CSA) for reliable all-to-all
broadcast with strict latency constraints. In B-CSA, each user acts as both
transmitter and receiver in a half-duplex mode. The half-duplex mode gives rise
to a double unequal error protection (DUEP) phenomenon: the more a user repeats
its packet, the higher the probability that this packet is decoded by other
users, but the lower the probability for this user to decode packets from
others. We analyze the performance of B-CSA over the packet erasure channel for
a finite frame length. In particular, we provide a general analysis of stopping
sets for B-CSA and derive an analytical approximation of the performance in the
error floor (EF) region, which captures the DUEP feature of B-CSA. Simulation
results reveal that the proposed approximation predicts very well the
performance of B-CSA in the EF region. Finally, we consider the application of
B-CSA to vehicular communications and compare its performance with that of
carrier sense multiple access (CSMA), the current MAC protocol in vehicular
networks. The results show that B-CSA is able to support a much larger number
of users than CSMA with the same reliability.Comment: arXiv admin note: text overlap with arXiv:1501.0338
A Probabilistic Embedding Clustering Method for Urban Structure Detection
Urban structure detection is a basic task in urban geography. Clustering is a
core technology to detect the patterns of urban spatial structure, urban
functional region, and so on. In big data era, diverse urban sensing datasets
recording information like human behaviour and human social activity, suffer
from complexity in high dimension and high noise. And unfortunately, the
state-of-the-art clustering methods does not handle the problem with high
dimension and high noise issues concurrently. In this paper, a probabilistic
embedding clustering method is proposed. Firstly, we come up with a
Probabilistic Embedding Model (PEM) to find latent features from high
dimensional urban sensing data by learning via probabilistic model. By latent
features, we could catch essential features hidden in high dimensional data
known as patterns; with the probabilistic model, we can also reduce uncertainty
caused by high noise. Secondly, through tuning the parameters, our model could
discover two kinds of urban structure, the homophily and structural
equivalence, which means communities with intensive interaction or in the same
roles in urban structure. We evaluated the performance of our model by
conducting experiments on real-world data and experiments with real data in
Shanghai (China) proved that our method could discover two kinds of urban
structure, the homophily and structural equivalence, which means clustering
community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201
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