Recently, the “big dat” emerged as a hot topic because of the tremendous growth of the Information and Communication Technology (ICT). One of the highly anticipated key contributors of the big data in the future networks is the distributed Wireless Sensor Networks (WSNs). Although the data generated by an individual sensor may not appear to be significant, the overall data generated across nume...

Longitudinal infant studies offer a unique opportunity for revealing the dynamics of rapid human brain development in the first year of life. To this end, it is important to develop tissue segmentation and registration techniques for facilitating the detection of global and local morphological changes of brain structures in an infant population. However, there are two inherent challenges involv...

Feature selection on high-dimensional networks plays an important role in understanding of biological mechanisms and disease pathologies. It has a broad range of applications. Recently, a Bayesian nonparametric mixture model (Zhao, Kang, and Yu 2014) has been successfully applied for selecting gene and gene sub-networks. We extend this method to a unified approach for feature selection on gener...

A maximum likelihood (ML) acoustic source location estimation method is presented. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers th...

This paper presents a framework for object-oriented scene segmentation in video, which uses motion as the major characteristic to distinguish different moving objects and then to segment the scene into object regions. From the feature block (FB) correspondences through at least two frames obtained via a tracking algorithm, the reference feature measurement matrix and feature displacement matrix...

We present an approach for dense estimation of motion and depth of a scene containing a multiple number of differently moving objects with the camera system itself being in motion. The estimates are used to segregate the image sequence into a number of independently moving objects by assigning the object hypothesis with maximum a posteriori (MAP) probability to each image point. Different to pr...

A framework for probabilistic segmentation of Magnetic Resonance (MR) images is proposed which utilizes three types of models: intensity models to capture the graylevel appearance of a structure, relative-spatial models which describe the spatial relationships between structures in a subject-specific reference frame, and shape models to describe the shape of structures in a subjectindependent r...

Continuous-time Bayesian networks (CTBNs) (Nodelman, Shelton, & Koller 2002; 2003), are an elegant modeling language for structured stochastic processes that evolve over continuous time. The CTBN framework is based on homogeneous Markov processes, and defines two distributions with respect to each local variable in the system, given its parents: an exponential distribution over when the variabl...

-We present a fully automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters – WM and GM), cerebral spinal fluid (CSF) and brain lesion to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise...

With case-parent triads, one can estimate genotype relative risks by measuring the apparent overtransmission of susceptibility genotypes from parents to affected offspring. Results obtained using such designs, properly analyzed, resist both bias due to population structure and bias due to self-selection. Most diseases are not purely genetic, and environmental cofactors can also be important. In...