15,883 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
Self-stabilizing K-out-of-L exclusion on tree network
In this paper, we address the problem of K-out-of-L exclusion, a
generalization of the mutual exclusion problem, in which there are units
of a shared resource, and any process can request up to units
(). We propose the first deterministic self-stabilizing
distributed K-out-of-L exclusion protocol in message-passing systems for
asynchronous oriented tree networks which assumes bounded local memory for each
process.Comment: 15 page
A review of the field of artificial intelligence and its possible applications to nasa objectives final report
Artificial intelligence - control, data gathering, and data analyzing systems desig
Self-Stabilization in the Distributed Systems of Finite State Machines
The notion of self-stabilization was first proposed by Dijkstra in 1974 in his classic paper. The paper defines a system as self-stabilizing if, starting at any, possibly illegitimate, state the system can automatically adjust itself to eventually converge to a legitimate state in finite amount of time and once in a legitimate state it will remain so unless it incurs a subsequent transient fault. Dijkstra limited his attention to a ring of finite-state machines and provided its solution for self-stabilization. In the years following his introduction, very few papers were published in this area. Once his proposal was recognized as a milestone in work on fault tolerance, the notion propagated among the researchers rapidly and many researchers in the distributed systems diverted their attention to it. The investigation and use of self-stabilization as an approach to fault-tolerant behavior under a model of transient failures for distributed systems is now undergoing a renaissance. A good number of works pertaining to self-stabilization in the distributed systems were proposed in the yesteryears most of which are very recent. This report surveys all previous works available in the literature of self-stabilizing systems
Learning how to be robust: Deep polynomial regression
Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the problem is even harder when outliers have strong structure.
Departing from problem-tailored heuristics for robust estimation of parametric
models, we explore deep convolutional neural networks. Our work aims to find a
generic approach for training deep regression models without the explicit need
of supervised annotation. We bypass the need for a tailored loss function on
the regression parameters by attaching to our model a differentiable hard-wired
decoder corresponding to the polynomial operation at hand. We demonstrate the
value of our findings by comparing with standard robust regression methods.
Furthermore, we demonstrate how to use such models for a real computer vision
problem, i.e., video stabilization. The qualitative and quantitative
experiments show that neural networks are able to learn robustness for general
polynomial regression, with results that well overpass scores of traditional
robust estimation methods.Comment: 18 pages, conferenc
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