Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signal...

Compressive sensing (CS) refers to the process of reconstructing a signal that is supposed to be sparse or compressible. CS has wide applications, such as in cognitive radio networks. In this paper, we investigate effective CS schemes for the trade-off between energy efficiency and estimation error. We propose an enhancement to a Bayesian estimation approach and an enhancement to the isotonic r...

Random regression models (RRM) have become common for the analysis of longitudinal data or repeated records on individual over time. The goal of this paper was to explore the use of random regression models with orthogonal / Legendre polynomials (RRL) to analyze new repeated measures called clutch size (CS) as a meristic trait for Iranian native fowl. Legendre polynomial functions of increasing...

Background: We aimed to compare the efficacy of local injection therapies for lateral epicondylitis in a Bayesian framework. Methods: We searched the Embase, PubMed, Cochrane Central Register of Controlled Trials, Web of Science, Scopus, and ProQuest, for randomized controlled trials published from inception to February 2021 in any languages. The injection therapies included corticosteroids (C...

In the spring of 2005 our department offered a new course entitled “Intelligent Systems” (IS). This course, which will be required for future CS majors, provides an introduction to a number of AI topics including predicate logic, frames, rule-based systems, neural networks, Bayesian networks, decision trees, and fuzzy logic. The theme of agents is woven throughout the course. Each section of th...

Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS. Se...

We present a novel approach to inverse problems in imaging based on a hierarchical Bayesian-MAP (HB-MAP) formulation. In this paper we specifically focus on the difficult and basic inverse problem of multi-sensor (tomographic) imaging wherein the source object of interest is viewed from multiple directions by independent sensors. Given the measurements recorded by these sensors, the problem is ...

Submitted by admin on Mon, 09/08/2014 3:08pm Title Is Bayesian analysis ready for use in phase III randomized clinical trials? Beware the sound of the sirens. Publication Type Journal Article Year of Publication 2005 Authors Howard, G, Coffey, CS, Cutter, GR Journal Stroke Volume 36 Issue 7 Pagination 1622-3 Date Published 2005 Jul ISSN 1524-4628

Compressive Sensing (CS) is an emerging compression technique that takes advantage of a signal’s sparsity to sample and compress this signal at the same time. Its many advantages as well as its satisfactory compression ratios (CR) makes it a very desirable technique in telemonitoring where the bandwidth available is very small and needs to be efficiently used. In the case of electroencephalogra...

Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete models. The reasons for this is that one needs to provide constructs succinctly model the independencies in such models, also efficient inference.
 Three types of are important represent exploit scalable inference hybrid models: conditional elegantly modeled Bayesian networks, context-speci...