41,499 research outputs found
Applications of sparse approximation in communications
Sparse approximation problems abound in many scientific, mathematical, and engineering applications. These problems are defined by two competing notions: we approximate a signal vector as a linear combination of elementary atoms and we require that the approximation be both as accurate and as concise as possible. We introduce two natural and direct applications of these problems and algorithmic solutions in communications. We do so by constructing enhanced codebooks from base codebooks. We show that we can decode these enhanced codebooks in the presence of Gaussian noise. For MIMO wireless communication channels, we construct simultaneous sparse approximation problems and demonstrate that our algorithms can both decode the transmitted signals and estimate the channel parameters
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
Simultaneous sparse approximation via greedy pursuit
A simple sparse approximation problem requests an approximation of a given input signal as a linear combination of T elementary signals drawn from a large, linearly dependent collection. An important generalization is simultaneous sparse approximation. Now one must approximate several input signals at once using different linear combinations of the same T elementary signals. This formulation appears, for example, when analyzing multiple observations of a sparse signal that have been contaminated with noise. A new approach to this problem is presented here: a greedy pursuit algorithm called simultaneous orthogonal matching pursuit. The paper proves that the algorithm calculates simultaneous approximations whose error is within a constant factor of the optimal simultaneous approximation error. This result requires that the collection of elementary signals be weakly correlated, a property that is also known as incoherence. Numerical experiments demonstrate that the algorithm often succeeds, even when the inputs do not meet the hypotheses of the proof
Modelling alternative strategies for delivering hepatitis B vaccine in prisons : the impact on the vaccination coverage of the injecting drug user population
Since 2001 hepatitis B vaccination has been offered to prisoners on reception into prisons in
England and Wales. However, short campaigns of vaccinating the entire population of individual
prisons have achieved high vaccination coverage for limited periods, suggesting that short
campaigns may be a preferable way of vaccinating prisoners. A model is used that describes the
flow of prisoners through prisons stratified by injecting status to compare a range of vaccination
scenarios that describe vaccination on prison reception or via regular short campaigns. Model
results suggest that vaccinating on prison reception can capture a greater proportion of the
injecting drug user (IDU) population than the comparable campaign scenarios (63% vs. 55 . 6%
respectively). Vaccination on prison reception is also more efficient at capturing IDUs for
vaccination than vaccination via a campaign, although vaccination via campaigns may have a
role with some infections for overall control
The Effect of Cooling Rate on the Ductile- Brittle Bend-transition Temperature of Chromium Wire
Effect of cooling rate on ductile-brittle bend-transition temperature of chromium wir
Investigation of mechanical properties of chromium, chromium-rhenium, and derived alloys Twenty-third quarterly progress report, Oct. 1, 1965 - Mar. 31, 1966
Mechanical properties of chromium-rhenium alloy wire
Improved sparse approximation over quasi-incoherent dictionaries
This paper discusses a new greedy algorithm for solving the sparse approximation problem over quasi-incoherent dictionaries. These dictionaries consist of waveforms that are uncorrelated "on average," and they provide a natural generalization of incoherent dictionaries. The algorithm provides strong guarantees on the quality of the approximations it produces, unlike most other methods for sparse approximation. Moreover, very efficient implementations are possible via approximate nearest-neighbor data structure
Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
This paper develops a new method for recovering m-sparse signals that is
simultaneously uniform and quick. We present a reconstruction algorithm whose
run time, O(m log^2(m) log^2(d)), is sublinear in the length d of the signal.
The reconstruction error is within a logarithmic factor (in m) of the optimal
m-term approximation error in l_1. In particular, the algorithm recovers
m-sparse signals perfectly and noisy signals are recovered with polylogarithmic
distortion. Our algorithm makes O(m log^2 (d)) measurements, which is within a
logarithmic factor of optimal. We also present a small-space implementation of
the algorithm. These sketching techniques and the corresponding reconstruction
algorithms provide an algorithmic dimension reduction in the l_1 norm. In
particular, vectors of support m in dimension d can be linearly embedded into
O(m log^2 d) dimensions with polylogarithmic distortion. We can reconstruct a
vector from its low-dimensional sketch in time O(m log^2(m) log^2(d)).
Furthermore, this reconstruction is stable and robust under small
perturbations
Direct use of linear time-domain aerodynamics in aeroservoelastic analysis: Aerodynamic model
The work presented here is the first part of a continuing effort to expand existing capabilities in aeroelasticity by developing the methodology which is necessary to utilize unsteady time-domain aerodynamics directly in aeroservoelastic design and analysis. The ultimate objective is to define a fully integrated state-space model of an aeroelastic vehicle's aerodynamics, structure and controls which may be used to efficiently determine the vehicle's aeroservoelastic stability. Here, the current status of developing a state-space model for linear or near-linear time-domain indicial aerodynamic forces is presented
- …