746 research outputs found
Self-Stabilizing TDMA Algorithms for Dynamic Wireless Ad-hoc Networks
In dynamic wireless ad-hoc networks (DynWANs), autonomous computing devices
set up a network for the communication needs of the moment. These networks
require the implementation of a medium access control (MAC) layer. We consider
MAC protocols for DynWANs that need to be autonomous and robust as well as have
high bandwidth utilization, high predictability degree of bandwidth allocation,
and low communication delay in the presence of frequent topological changes to
the communication network. Recent studies have shown that existing
implementations cannot guarantee the necessary satisfaction of these timing
requirements. We propose a self-stabilizing MAC algorithm for DynWANs that
guarantees a short convergence period, and by that, it can facilitate the
satisfaction of severe timing requirements, such as the above. Besides the
contribution in the algorithmic front of research, we expect that our proposal
can enable quicker adoption by practitioners and faster deployment of DynWANs
that are subject changes in the network topology
Structure-Aware Classification using Supervised Dictionary Learning
In this paper, we propose a supervised dictionary learning algorithm that
aims to preserve the local geometry in both dimensions of the data. A
graph-based regularization explicitly takes into account the local manifold
structure of the data points. A second graph regularization gives similar
treatment to the feature domain and helps in learning a more robust dictionary.
Both graphs can be constructed from the training data or learned and adapted
along the dictionary learning process. The combination of these two terms
promotes the discriminative power of the learned sparse representations and
leads to improved classification accuracy. The proposed method was evaluated on
several different datasets, representing both single-label and multi-label
classification problems, and demonstrated better performance compared with
other dictionary based approaches
Sparsity Based Poisson Denoising with Dictionary Learning
The problem of Poisson denoising appears in various imaging applications,
such as low-light photography, medical imaging and microscopy. In cases of high
SNR, several transformations exist so as to convert the Poisson noise into an
additive i.i.d. Gaussian noise, for which many effective algorithms are
available. However, in a low SNR regime, these transformations are
significantly less accurate, and a strategy that relies directly on the true
noise statistics is required. A recent work by Salmon et al. took this route,
proposing a patch-based exponential image representation model based on GMM
(Gaussian mixture model), leading to state-of-the-art results. In this paper,
we propose to harness sparse-representation modeling to the image patches,
adopting the same exponential idea. Our scheme uses a greedy pursuit with
boot-strapping based stopping condition and dictionary learning within the
denoising process. The reconstruction performance of the proposed scheme is
competitive with leading methods in high SNR, and achieving state-of-the-art
results in cases of low SNR.Comment: 13 pages, 9 figure
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