Reconstruction of Lamb wave dispersion curves by sparse representation with continuity constraints.

Ultrasonic Lamb waves are a widely used research tool for nondestructive structural health monitoring. They travel long distances with little attenuation, enabling the interrogation of large areas. To analyze Lamb wave propagation data, it is often important to know precisely how they propagate.

Geodesic distance on a Grassmannian for monitoring the progression of Alzheimer's disease.

We propose a geodesic distance on a Grassmannian manifold that can be used to quantify the shape progression patterns of the bilateral hippocampi, amygdalas, and lateral ventricles in healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Longitudinal magnetic resonance imaging (MRI) scans of 754 subjects (3092 scans in total) were used in this study. Longitudinally, the geodesic distance was found to be proportional to the elapsed time separating the two scans in question.

Data-driven and calibration-free Lamb wave source localization with sparse sensor arrays.

Most Lamb wave localization techniques require that we know the wave's velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system's reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media.

An efficient method to detect periodic behavior in botnet traffic by analyzing control plane traffic.

Botnets are large networks of bots (compromised machines) that are under the control of a small number of bot masters. They pose a significant threat to Internet's communications and applications. A botnet relies on command and control (C2) communications channels traffic between its members for its attack execution.

Dispersion curve recovery with orthogonal matching pursuit.

Dispersion curves characterize many propagation mediums. When known, many methods use these curves to analyze waves. Yet, in many scenarios, their exact values are unknown due to material and environmental uncertainty.

Data-driven matched field processing for Lamb wave structural health monitoring.

Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments.

Sparse recovery of the multimodal and dispersive characteristics of Lamb waves.

Guided waves in plates, known as Lamb waves, are characterized by complex, multimodal, and frequency dispersive wave propagation, which distort signals and make their analysis difficult. Estimating these multimodal and dispersive characteristics from experimental data becomes a difficult, underdetermined inverse problem. To accurately and robustly recover these multimodal and dispersive properties, this paper presents a methodology referred to as sparse wavenumber analysis based on sparse recovery methods.

Scale transform signal processing for optimal ultrasonic temperature compensation.

In structural health monitoring, temperature compensation is an important step to reduce systemic errors and avoid false-positive results. Several methods have been developed to accomplish temperature compensation in guided wave systems, but these techniques are often limited in computational speed. In this paper, we present a new methodology for optimal, stretch-based temperature compensation that operates on signals in the stretch factor and scale-transform domains.

Automatic detection of regional heart rejection in USPIO-enhanced MRI.

Contrast-enhanced magnetic resonance imaging (MRI) is useful to study the infiltration of cells in vivo. This research adopts ultrasmall superparamagnetic iron oxide (USPIO) particles as contrast agents. USPIO particles administered intravenously can be endocytosed by circulating immune cells, in particular, macrophages.

Immune cells detection of the in vivo rejecting heart in USPIO-enhanced magnetic resonance imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is useful to study the infiltration of immune cells, in particular macrophages. Contrast agents, for example ultra-small superparamagnetic iron oxide (USPIO) particles, administered intravenously into the blood stream will be engulfed by macrophages circulating in the circulation system. When a transplanted heart rejects, macrophages and other immune cells will infiltrate the rejecting tissue.

Affine-permutation invariance of 2-D shapes.

Shapes provide a rich set of clues on the identity and topological properties of an object. In many imaging environments, however, the same object appears to have different shapes due to distortions such as translation, rotation, reflection, scaling, or skewing. Further, the order by which the object's feature points are scanned changes, i.

Figure-ground segmentation from occlusion.

Layered video representations are increasingly popular; see [2] for a recent review. Segmentation of moving objects is a key step for automating such representations. Current motion segmentation methods either fail to segment moving objects in low-textured regions or are computationally very expensive.

STACS: new active contour scheme for cardiac MR image segmentation.

The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution.

In vivo detection of acute rat renal allograft rejection by MRI with USPIO particles.

Background: Magnetic resonance imaging (MRI) for non-invasively detecting renal rejection was developed by monitoring the accumulation of macrophages labeled with dextran-coated ultrasmall superparamagnetic iron oxide (USPIO) particles at the rat renal allografts during acute rejection.

Methods: Five groups of male rats with DA-->BN renal allografts and one group with BN-->BN renal isografts were investigated by MRI before, immediately after, and 24 hr after intravenous infusion with different doses of USPIO particles. All infusions were done on post-operative day 4.