35,563 research outputs found
Channel estimation and symbol detection for block transmission using data-dependent superimposed training
We address the problem of frequency-selective
channel estimation and symbol detection using superimposed
training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)
This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance
Learning phase transitions from dynamics
We propose the use of recurrent neural networks for classifying phases of
matter based on the dynamics of experimentally accessible observables. We
demonstrate this approach by training recurrent networks on the magnetization
traces of two distinct models of one-dimensional disordered and interacting
spin chains. The obtained phase diagram for a well-studied model of the
many-body localization transition shows excellent agreement with previously
known results obtained from time-independent entanglement spectra. For a
periodically-driven model featuring an inherently dynamical time-crystalline
phase, the phase diagram that our network traces in a previously-unexplored
regime coincides with an order parameter for its expected phases.Comment: 5 pages + 3 fig, appendix + 5 fi
A population-based approach to background discrimination in particle physics
Background properties in experimental particle physics are typically
estimated using control samples corresponding to large numbers of events. This
can provide precise knowledge of average background distributions, but
typically does not consider the effect of fluctuations in a data set of
interest. A novel approach based on mixture model decomposition is presented as
a way to estimate the effect of fluctuations on the shapes of probability
distributions in a given data set, with a view to improving on the knowledge of
background distributions obtained from control samples. Events are treated as
heterogeneous populations comprising particles originating from different
processes, and individual particles are mapped to a process of interest on a
probabilistic basis. The proposed approach makes it possible to extract from
the data information about the effect of fluctuations that would otherwise be
lost using traditional methods based on high-statistics control samples. A
feasibility study on Monte Carlo is presented, together with a comparison with
existing techniques. Finally, the prospects for the development of tools for
intensive offline analysis of individual events at the Large Hadron Collider
are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser.
Minor changes have been made to the text with respect to the published
article with a view to improving readabilit
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
A binary neural k-nearest neighbour technique
K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record in turn. This paper evaluates a novel k-NN classifier with linear growth and faster run-time built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and real-valued data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations
Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis
The aim of this thesis is to develop automated methods for the analysis of the
spatial patterns, and the functional behaviour of endothelial cells, viewed under
microscopy, with applications to the understanding of atherosclerosis.
Initially, a radial search approach to segmentation was attempted in order to
trace the cell and nuclei boundaries using a maximum likelihood algorithm; it
was found inadequate to detect the weak cell boundaries present in the available
data. A parametric cell shape model was then introduced to fit an equivalent
ellipse to the cell boundary by matching phase-invariant orientation fields of the
image and a candidate cell shape. This approach succeeded on good quality
images, but failed on images with weak cell boundaries. Finally, a support
vector machines based method, relying on a rich set of visual features, and a
small but high quality training dataset, was found to work well on large numbers
of cells even in the presence of strong intensity variations and imaging noise.
Using the segmentation results, several standard shear-stress dependent parameters
of cell morphology were studied, and evidence for similar behaviour
in some cell shape parameters was obtained in in-vivo cells and their nuclei.
Nuclear and cell orientations around immature and mature aortas were broadly
similar, suggesting that the pattern of flow direction near the wall stayed approximately
constant with age. The relation was less strong for the cell and
nuclear length-to-width ratios.
Two novel shape analysis approaches were attempted to find other properties
of cell shape which could be used to annotate or characterise patterns, since a
wide variability in cell and nuclear shapes was observed which did not appear
to fit the standard parameterisations. Although no firm conclusions can yet be
drawn, the work lays the foundation for future studies of cell morphology.
To draw inferences about patterns in the functional response of cells to flow,
which may play a role in the progression of disease, single-cell analysis was performed
using calcium sensitive florescence probes. Calcium transient rates were
found to change with flow, but more importantly, local patterns of synchronisation
in multi-cellular groups were discernable and appear to change with flow.
The patterns suggest a new functional mechanism in flow-mediation of cell-cell
calcium signalling
A high performance k-NN approach using binary neural networks
This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations. (C) 2003 Elsevier Ltd. All rights reserved
- …