14 research outputs found
A Class of Nonconvex Penalties Preserving Overall Convexity in Optimization-Based Mean Filtering
mean filtering is a conventional, optimization-based method to
estimate the positions of jumps in a piecewise constant signal perturbed by
additive noise. In this method, the norm penalizes sparsity of the
first-order derivative of the signal. Theoretical results, however, show that
in some situations, which can occur frequently in practice, even when the jump
amplitudes tend to , the conventional method identifies false change
points. This issue is referred to as stair-casing problem and restricts
practical importance of mean filtering. In this paper, sparsity is
penalized more tightly than the norm by exploiting a certain class of
nonconvex functions, while the strict convexity of the consequent optimization
problem is preserved. This results in a higher performance in detecting change
points. To theoretically justify the performance improvements over
mean filtering, deterministic and stochastic sufficient conditions for exact
change point recovery are derived. In particular, theoretical results show that
in the stair-casing problem, our approach might be able to exclude the false
change points, while mean filtering may fail. A number of numerical
simulations assist to show superiority of our method over mean
filtering and another state-of-the-art algorithm that promotes sparsity tighter
than the norm. Specifically, it is shown that our approach can
consistently detect change points when the jump amplitudes become sufficiently
large, while the two other competitors cannot.Comment: Submitted to IEEE Transactions on Signal Processin
Recovery of Low-Rank Matrices under Affine Constraints via a Smoothed Rank Function
In this paper, the problem of matrix rank minimization under affine
constraints is addressed. The state-of-the-art algorithms can recover matrices
with a rank much less than what is sufficient for the uniqueness of the
solution of this optimization problem. We propose an algorithm based on a
smooth approximation of the rank function, which practically improves recovery
limits on the rank of the solution. This approximation leads to a non-convex
program; thus, to avoid getting trapped in local solutions, we use the
following scheme. Initially, a rough approximation of the rank function subject
to the affine constraints is optimized. As the algorithm proceeds, finer
approximations of the rank are optimized and the solver is initialized with the
solution of the previous approximation until reaching the desired accuracy.
On the theoretical side, benefiting from the spherical section property, we
will show that the sequence of the solutions of the approximating function
converges to the minimum rank solution. On the experimental side, it will be
shown that the proposed algorithm, termed SRF standing for Smoothed Rank
Function, can recover matrices which are unique solutions of the rank
minimization problem and yet not recoverable by nuclear norm minimization.
Furthermore, it will be demonstrated that, in completing partially observed
matrices, the accuracy of SRF is considerably and consistently better than some
famous algorithms when the number of revealed entries is close to the minimum
number of parameters that uniquely represent a low-rank matrix.Comment: Accepted in IEEE TSP on December 4th, 201
DOA Estimation in Partially Correlated Noise Using Low-Rank/Sparse Matrix Decomposition
We consider the problem of direction-of-arrival (DOA) estimation in unknown
partially correlated noise environments where the noise covariance matrix is
sparse. A sparse noise covariance matrix is a common model for a sparse array
of sensors consisted of several widely separated subarrays. Since interelement
spacing among sensors in a subarray is small, the noise in the subarray is in
general spatially correlated, while, due to large distances between subarrays,
the noise between them is uncorrelated. Consequently, the noise covariance
matrix of such an array has a block diagonal structure which is indeed sparse.
Moreover, in an ordinary nonsparse array, because of small distance between
adjacent sensors, there is noise coupling between neighboring sensors, whereas
one can assume that nonadjacent sensors have spatially uncorrelated noise which
makes again the array noise covariance matrix sparse. Utilizing some recently
available tools in low-rank/sparse matrix decomposition, matrix completion, and
sparse representation, we propose a novel method which can resolve possibly
correlated or even coherent sources in the aforementioned partly correlated
noise. In particular, when the sources are uncorrelated, our approach involves
solving a second-order cone programming (SOCP), and if they are correlated or
coherent, one needs to solve a computationally harder convex program. We
demonstrate the effectiveness of the proposed algorithm by numerical
simulations and comparison to the Cramer-Rao bound (CRB).Comment: in IEEE Sensor Array and Multichannel signal processing workshop
(SAM), 201
Successive Concave Sparsity Approximation for Compressed Sensing
In this paper, based on a successively accuracy-increasing approximation of
the norm, we propose a new algorithm for recovery of sparse vectors
from underdetermined measurements. The approximations are realized with a
certain class of concave functions that aggressively induce sparsity and their
closeness to the norm can be controlled. We prove that the series of
the approximations asymptotically coincides with the and
norms when the approximation accuracy changes from the worst fitting to the
best fitting. When measurements are noise-free, an optimization scheme is
proposed which leads to a number of weighted minimization programs,
whereas, in the presence of noise, we propose two iterative thresholding
methods that are computationally appealing. A convergence guarantee for the
iterative thresholding method is provided, and, for a particular function in
the class of the approximating functions, we derive the closed-form
thresholding operator. We further present some theoretical analyses via the
restricted isometry, null space, and spherical section properties. Our
extensive numerical simulations indicate that the proposed algorithm closely
follows the performance of the oracle estimator for a range of sparsity levels
wider than those of the state-of-the-art algorithms.Comment: Submitted to IEEE Trans. on Signal Processin
Sewer Pipes Condition Prediction Models: A State-of-the-Art Review
Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models
A Class of Nonconvex Penalties Preserving Overall Convexity in Optimization-Based Mean Filtering
Consistent Change Point Detection for Piecewise Constant Signals With Normalized Fused LASSO
We consider the problem of offline change point detection from noisy piecewise constant signals. We propose normalized fused LASSO (FL), an extension of the FL, obtained by normalizing the columns of the sensing matrix of the LASSO equivalent. We analyze the performance of the proposed method, and in particular, we show that it is consistent in detecting change points as the noise variance tends to zero. Numerical experiments support our theoretical findings.QC 20170614</p
Comparison of Diagnostic Markers of Aortic Coarctation in Prenatal and Postnatal Echocardiography
Background: The prenatal diagnosis of coarctation of aorta (CoA) remains a major challenge, as the false-positive diagnosis is fairly common. The purpose of this study was to find some useful prenatal sonographic markers compatible with the postnatal diagnosis of CoA.
Methods: The study included fetuses suspected of CoA in the second and third-trimester ultrasound tests. All cases were examined by fetal echocardiography at a single ultrasound clinic between 2019 and 2020. The proportion of right and left ventricular size was assessed and ductal/isthmus diameter ratio was measured. A comparison was drawn between the results of neonates with neonatal CoA and neonates without CoA.
Results: Of 20 fetuses with suspected prenatal CoA, 3 (15%) had neonatal CoA. The mean ductal/isthmus ratio was significantly higher in the neonates with CoA (1.96 vs. 1.46; p< 0.001). The diagnostic power of ductal/isthmus ratio to detect CoA with a cut point of 1.53 had a sensitivity and specificity of 100% and 70.6%, respectively, a positive and negative predictive value of 37.5%Â and 100%, respectively, and an overall accuracy of 75%.
Conclusion: The ductal/isthmus ratio diameter and ventricular disproportion are significant sonographic markers for the prenatal diagnosis of CoA, and the ductal/isthmus ratio has high sensitivity and specificity compared to postnatal findings