6,557 research outputs found
Locally adaptive estimation methods with application to univariate time series
The paper offers a unified approach to the study of three locally adaptive
estimation methods in the context of univariate time series from both
theoretical and empirical points of view. A general procedure for the
computation of critical values is given. The underlying model encompasses all
distributions from the exponential family providing for great flexibility. The
procedures are applied to simulated and real financial data distributed
according to the Gaussian, volatility, Poisson, exponential and Bernoulli
models. Numerical results exhibit a very reasonable performance of the methods.Comment: Submitted to the Electronic Journal of Statistics
(http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Damage monitoring in sandwich beams by modal parameter shifts: a comparative study of burst random and sine dwell vibration testing
This paper presents an experimental study on the effects of multi-site damage on the vibration response of honeycomb sandwich beams, damaged by two different ways i.e., impact damage and core-only damage simulating damage due to bird or stone impact or due to mishandling during assembly and maintenance. The variation of the modal parameters with different levels of impact energy and density of damage is studied. Vibration tests have been carried out with both burst random and sine dwell testing in order to evaluate the damping estimation efficiency of these methods in the presence of damage. Sine dwell testing is done in both up and down frequency directions in order to detect structural non-linearities. Results show that damping ratio is a more sensitive parameter for damage detection than the natural frequency. Design of experiments (DOE) highlighted density of damage as the factor having a more significant effect on the modal parameters and also proved that sine dwell testing is more suitable for damping estimation in the presence of damage as compared to burst random testing
Sparse Distributed Learning Based on Diffusion Adaptation
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201
Robust detail-preserving signal extraction
We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --
Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing
Recent developments in quaternion-valued widely linear processing have
established that the exploitation of complete second-order statistics requires
consideration of both the standard covariance and the three complementary
covariance matrices. Although such matrices have a tremendous amount of
structure and their decomposition is a powerful tool in a variety of
applications, the non-commutative nature of the quaternion product has been
prohibitive to the development of quaternion uncorrelating transforms. To this
end, we introduce novel techniques for a simultaneous decomposition of the
covariance and complementary covariance matrices in the quaternion domain,
whereby the quaternion version of the Takagi factorisation is explored to
diagonalise symmetric quaternion-valued matrices. This gives new insights into
the quaternion uncorrelating transform (QUT) and forms a basis for the proposed
quaternion approximate uncorrelating transform (QAUT) which simultaneously
diagonalises all four covariance matrices associated with improper quaternion
signals. The effectiveness of the proposed uncorrelating transforms is
validated by simulations on both synthetic and real-world quaternion-valued
signals.Comment: 41 pages, single column, 10 figure
- …