1,376 research outputs found
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
The advent of large scale neural computational platforms has highlighted the
lack of algorithms for synthesis of neural structures to perform predefined
cognitive tasks. The Neural Engineering Framework offers one such synthesis,
but it is most effective for a spike rate representation of neural information,
and it requires a large number of neurons to implement simple functions. We
describe a neural network synthesis method that generates synaptic connectivity
for neurons which process time-encoded neural signals, and which makes very
sparse use of neurons. The method allows the user to specify, arbitrarily,
neuronal characteristics such as axonal and dendritic delays, and synaptic
transfer functions, and then solves for the optimal input-output relationship
using computed dendritic weights. The method may be used for batch or online
learning and has an extremely fast optimization process. We demonstrate its use
in generating a network to recognize speech which is sparsely encoded as spike
times.Comment: In submission to Frontiers in Neuromorphic Engineerin
QuickCSG: Fast Arbitrary Boolean Combinations of N Solids
QuickCSG computes the result for general N-polyhedron boolean expressions
without an intermediate tree of solids. We propose a vertex-centric view of the
problem, which simplifies the identification of final geometric contributions,
and facilitates its spatial decomposition. The problem is then cast in a single
KD-tree exploration, geared toward the result by early pruning of any region of
space not contributing to the final surface. We assume strong regularity
properties on the input meshes and that they are in general position. This
simplifying assumption, in combination with our vertex-centric approach,
improves the speed of the approach. Complemented with a task-stealing
parallelization, the algorithm achieves breakthrough performance, one to two
orders of magnitude speedups with respect to state-of-the-art CPU algorithms,
on boolean operations over two to dozens of polyhedra. The algorithm also
outperforms GPU implementations with approximate discretizations, while
producing an output without redundant facets. Despite the restrictive
assumptions on the input, we show the usefulness of QuickCSG for applications
with large CSG problems and strong temporal constraints, e.g. modeling for 3D
printers, reconstruction from visual hulls and collision detection
QuickCSG: Fast Arbitrary Boolean Combinations of N Solids
QuickCSG computes the result for general N-polyhedron boolean expressions
without an intermediate tree of solids. We propose a vertex-centric view of the
problem, which simplifies the identification of final geometric contributions,
and facilitates its spatial decomposition. The problem is then cast in a single
KD-tree exploration, geared toward the result by early pruning of any region of
space not contributing to the final surface. We assume strong regularity
properties on the input meshes and that they are in general position. This
simplifying assumption, in combination with our vertex-centric approach,
improves the speed of the approach. Complemented with a task-stealing
parallelization, the algorithm achieves breakthrough performance, one to two
orders of magnitude speedups with respect to state-of-the-art CPU algorithms,
on boolean operations over two to dozens of polyhedra. The algorithm also
outperforms GPU implementations with approximate discretizations, while
producing an output without redundant facets. Despite the restrictive
assumptions on the input, we show the usefulness of QuickCSG for applications
with large CSG problems and strong temporal constraints, e.g. modeling for 3D
printers, reconstruction from visual hulls and collision detection
Analytical representation of architectural built heritage. A Sketch-to-Bim approach
HBIM methodology is increasingly used for the management of all aspects of architectural heritage, from survey and analysis to
conservation, management and restoration issues.
Application of HBIM are the so-called Scan-to-BIM processes in which the artifact is surveyed with digital techniques of laser
scanning and photogrammetry. These techniques result in point clouds, the basis of the subsequent process of informative and
geometric modelling of the artifact. The resulting "smart models" are composed of parametric objects rich in information that can be
easily updated at any time.
The proposed methodology aims at integrating a study of architectural orders, whose results become preparatory to the subsequent
phases of survey and modeling, to the classic Scan-to-BIM workflows. In particular, in the modeling these results have allowed a
more targeted choice of techniques used.
The method has been applied to the atrium of the former Jesuit College of Santa Croce in Cagliari, which today hosts one of the seats
of the Faculty of Engineering and Architecture of the University of Cagliari; in particular, the atrium of the east body of the former
Jesuit College, designed by the Piedmontese architect Antonio Felice De Vincenti, has been modelled
Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: an overview
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Cloth manipulation by robots is gaining popularity among researchers because of its relevance, mainly (but not only) in domestic and assistive robotics. The required science and technologies begin to be ripe for the challenges posed by the manipulation of soft materials, and many contributions have appeared in the last years. This survey provides a systematic review of existing techniques for the basic perceptual tasks of grasp point localization, state estimation and classification of cloth items, from the perspective of their manipulation by robots. This choice is grounded on the fact that any manipulative action requires to instruct the robot where to grasp, and most garment handling activities depend on the correct recognition of the type to which the particular cloth item belongs and its state. The high inter- and intraclass variability of garments, the continuous nature of the possible deformations of cloth and the evident difficulties in predicting their localization and extension on the garment piece are challenges that have encouraged the researchers to provide a plethora of methods to confront such problems, with some promising results. The present review constitutes for the first time an effort in furnishing a structured framework of these works, with the aim of helping future contributors to gain both insight and perspective on the subjectPeer ReviewedPostprint (author's final draft
Tessellations and Pattern Formation in Plant Growth and Development
The shoot apical meristem (SAM) is a dome-shaped collection of cells at the
apex of growing plants from which all above-ground tissue ultimately derives.
In Arabidopsis thaliana (thale cress), a small flowering weed of the
Brassicaceae family (related to mustard and cabbage), the SAM typically
contains some three to five hundred cells that range from five to ten microns
in diameter. These cells are organized into several distinct zones that
maintain their topological and functional relationships throughout the life of
the plant. As the plant grows, organs (primordia) form on its surface flanks in
a phyllotactic pattern that develop into new shoots, leaves, and flowers.
Cross-sections through the meristem reveal a pattern of polygonal tessellation
that is suggestive of Voronoi diagrams derived from the centroids of cellular
nuclei. In this chapter we explore some of the properties of these patterns
within the meristem and explore the applicability of simple, standard
mathematical models of their geometry.Comment: Originally presented at: "The World is a Jigsaw: Tessellations in the
Sciences," Lorentz Center, Leiden, The Netherlands, March 200
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