53 research outputs found
A Greedy Link Scheduler for Wireless Networks with Fading Channels
We consider the problem of link scheduling for wireless networks with fading
channels, where the link rates are varying with time. Due to the high
computational complexity of the throughput optimal scheduler, we provide a low
complexity greedy link scheduler GFS, with provable performance guarantees. We
show that the performance of our greedy scheduler can be analyzed using the
Local Pooling Factor (LPF) of a network graph, which has been previously used
to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for
networks with static channels. We conjecture that the performance of GFS is a
lower bound on the performance of GMS for wireless networks with fading
channel
Method for fabricating zig-zag slabs for solid state lasers
A method for batch manufacturing of slabs for zig-zag lasers including steps of bonding two non-active media to either side of an active medium to form a sandwich, dicing the sandwich to provide slices, rendering two surfaces of each slice into total-internal-reflection (TIR) surfaces, and then dicing the slices perpendicular to the TIR surfaces to provide a plurality of zig-zag slabs
Complex-valued Iris Recognition Network
In this work, we design a complex-valued neural network for the task of iris
recognition. Unlike the problem of general object recognition, where
real-valued neural networks can be used to extract pertinent features, iris
recognition depends on the extraction of both phase and amplitude information
from the input iris texture in order to better represent its stochastic
content. This necessitates the extraction and processing of phase information
that cannot be effectively handled by a real-valued neural network. In this
regard, we design a complex-valued neural network that can better capture the
multi-scale, multi-resolution, and multi-orientation phase and amplitude
features of the iris texture. We show a strong correspondence of the proposed
complex-valued iris recognition network with Gabor wavelets that are used to
generate the classical IrisCode; however, the proposed method enables automatic
complex-valued feature learning that is tailored for iris recognition.
Experiments conducted on three benchmark datasets - ND-CrossSensor-2013,
CASIA-Iris-Thousand and UBIRIS.v2 - show the benefit of the proposed network
for the task of iris recognition. Further, the generalization capability of the
proposed network is demonstrated by training and testing it across different
datasets. Finally, visualization schemes are used to convey the type of
features being extracted by the complex-valued network in comparison to
classical real-valued networks. The results of this work are likely to be
applicable in other domains, where complex Gabor filters are used for texture
modeling
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