4,266 research outputs found
A Compact Tri-band Printed Antenna for MIMO Applications
In this paper, a compact tri-band printed multi-input multi-output (MIMO) antenna with high isolation is presented to operate within WLAN and WiMAX frequency bands. By adopting a rectangular open-ended slot combined with a rectangular strip with an inverted L-shaped open-ended slot, three operating frequency bands can be obtained. The proposed compact MIMO antenna occupies an overall size of 19×33 mm2. Good port-to-port isolation is obtained. The simulated and measured results show that the presented antenna is suitable for multiband MIMO applications
Structure and Interpretation of Dual-Feasible Functions
We study two techniques to obtain new families of classical and general
Dual-Feasible Functions: A conversion from minimal Gomory--Johnson functions;
and computer-based search using polyhedral computation and an automatic
maximality and extremality test.Comment: 6 pages extended abstract to appear in Proc. LAGOS 2017, with 21
pages of appendi
An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection
Sophisticated automatic incident detection (AID) technology plays a key role
in contemporary transportation systems. Though many papers were devoted to
study incident classification algorithms, few study investigated how to enhance
feature representation of incidents to improve AID performance. In this paper,
we propose to use an unsupervised feature learning algorithm to generate higher
level features to represent incidents. We used real incident data in the
experiments and found that effective feature mapping function can be learnt
from the data crosses the test sites. With the enhanced features, detection
rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are
significantly improved in all of the three representative cases. This approach
also provides an alternative way to reduce the amount of labeled data, which is
expensive to obtain, required in training better incident classifiers since the
feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation
Systems (ITSC 2012
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