57,224 research outputs found
Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting
We present an interactive perception model for
object sorting based on Gaussian Process (GP) classification
that is capable of recognizing objects categories from point
cloud data. In our approach, FPFH features are extracted from
point clouds to describe the local 3D shape of objects and
a Bag-of-Words coding method is used to obtain an object-level
vocabulary representation. Multi-class Gaussian Process
classification is employed to provide and probable estimation of
the identity of the object and serves a key role in the interactive
perception cycle – modelling perception confidence. We show
results from simulated input data on both SVM and GP based
multi-class classifiers to validate the recognition accuracy of our
proposed perception model. Our results demonstrate that by
using a GP-based classifier, we obtain true positive classification
rates of up to 80%. Our semi-autonomous object sorting
experiments show that the proposed GP based interactive
sorting approach outperforms random sorting by up to 30%
when applied to scenes comprising configurations of household
objects
Edge- and Node-Disjoint Paths in P Systems
In this paper, we continue our development of algorithms used for topological
network discovery. We present native P system versions of two fundamental
problems in graph theory: finding the maximum number of edge- and node-disjoint
paths between a source node and target node. We start from the standard
depth-first-search maximum flow algorithms, but our approach is totally
distributed, when initially no structural information is available and each P
system cell has to even learn its immediate neighbors. For the node-disjoint
version, our P system rules are designed to enforce node weight capacities (of
one), in addition to edge capacities (of one), which are not readily available
in the standard network flow algorithms.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
The solvability of consensus in iterated models extended with safe-consensus
The safe-consensus task was introduced by Afek, Gafni and Lieber (DISC'09) as
a weakening of the classic consensus. When there is concurrency, the consensus
output can be arbitrary, not even the input of any process. They showed that
safe-consensus is equivalent to consensus, in a wait-free system. We study the
solvability of consensus in three shared memory iterated models extended with
the power of safe-consensus black boxes. In the first model, for the -th
iteration, processes write to the memory, invoke safe-consensus boxes and
finally they snapshot the memory. We show that in this model, any wait-free
implementation of consensus requires safe-consensus black-boxes
and this bound is tight. In a second iterated model, the processes write to
memory, then they snapshot it and finally they invoke safe-consensus boxes. We
prove that in this model, consensus cannot be implemented. In the last iterated
model, processes first invoke safe-consensus, then they write to memory and
finally they snapshot it. We show that this model is equivalent to the previous
model and thus consensus cannot be implemented.Comment: 49 pages, A preliminar version of the main results appeared in the
SIROCCO 2014 proceeding
A Duality-Based Approach for Distributed Optimization with Coupling Constraints
In this paper we consider a distributed optimization scenario in which a set
of agents has to solve a convex optimization problem with separable cost
function, local constraint sets and a coupling inequality constraint. We
propose a novel distributed algorithm based on a relaxation of the primal
problem and an elegant exploration of duality theory. Despite its complex
derivation based on several duality steps, the distributed algorithm has a very
simple and intuitive structure. That is, each node solves a local version of
the original problem relaxation, and updates suitable dual variables. We prove
the algorithm correctness and show its effectiveness via numerical
computations
On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract
In this extended abstract a view on the role of Formal Methods in System
Engineering is briefly presented. Then two examples of useful analysis
techniques based on solid mathematical theories are discussed as well as the
software tools which have been built for supporting such techniques. The first
technique is Scalable Approximated Population DTMC Model-checking. The second
one is Spatial Model-checking for Closure Spaces. Both techniques have been
developed in the context of the EU funded project QUANTICOL.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector
We describe an efficient and fault-tolerant algorithm for distributed cyclic
garbage collection. The algorithm imposes few requirements on the local
machines and allows for flexibility in the choice of local collector and
distributed acyclic garbage collector to use with it. We have emphasized
reducing the number and size of network messages without sacrificing the
promptness of collection throughout the algorithm. Our proposed collector is a
variant of back tracing to avoid extensive synchronization between machines. We
have added an explicit forward tracing stage to the standard back tracing stage
and designed a tuned heuristic to reduce the total amount of work done by the
collector. Of particular note is the development of fault-tolerant cooperation
between traces and a heuristic that aggressively reduces the set of suspect
objects.Comment: 47 pages, LaTe
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