Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities ...

Biometric systems have a variety of problems such as noisy data, non-universality, spoof attacks and unacceptable error rate. These limitations can be solved by deploying multimodal biometric systems. Multimodal biometric systems utilize two or more individual traits, like face, iris, retina and fingerprint. Multimodal biometric systems improve the recognition accuracy more than uni-modal metho...

A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameter...

This paper presents an application of a two-step finite element model updating methodology to a 3D truss bridge model, with the inertial moment and elastic modulus as variables. First, the sensitivity of the change in modal parameters to variations in the physical parameters were analyzed. Based on these sensitivity analyses, the parameters that can be updated were determined. Next, the optimiz...

Many events in different domains are characterized by a large number of individual moving elements, either in pursuit of a goal in groups (as in military operations), or subject to underlying physical forces that group elements with similar motion (as in weather phenomena). Visualizing and reasoning about happenings in such domains are often facilitated by abstracting the mass of spatiotemporal...