We address the sparse signal reconstruction problem over networked sensing system. Signal acquisition is performed as in compressive sensing (CS), hence the number of measurements is reduced. Majority of existing algorithms are developed based on p minimization in the framework of distributed convex optimization and thus whose performance is sensitive to the tuning of additional parameters. In ...

Settings. Our codes were written in Scala and compiled under Simple Build Tool (SBT). The programs were run on Mac OS. We test the effectiveness of our implementation in various aspects. If not mentioned explicitly, we adopt the following default settings. We report macroaveraged F1 measures, which were further averaged by ten-fold cross validations. We consider both “Bernoulli” and “Multinomia...

Imprecision is commonly encountered with respect to powers and predictive in clinical trials. In this article, we investigate the imprecision issues of four (Classical Power, Classical Conditional Bayesian Power) eight powers. To begin with, derive probabilities Control Superior (CS), Treatment (TS), Equivocal (E) powers, evaluate limits at point 0. Moreover, conduct extensive numerical experim...

Compressed Sensing based Terahertz imaging (CS-THz) is a computational imaging technique. It uses only one THz receiver to accumulate the random modulated image measurements where the original THz image is reconstruct from these measurements using compressed sensing solvers. The advantage of the CS-THz is its reduced acquisition time compared with the raster scan mode. However, when it applied ...

A computationally-efficient method for recovering sparse signals from a series of noisy observations, known as the problem of compressed sensing (CS), is presented. The theory of CS usually leads to a constrained convex minimization problem. In this work, an alternative outlook is proposed. Instead of solving the CS problem as an optimization problem, it is suggested to transform the optimizati...

Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA metho...

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...

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations e...

BACKGROUND Recently, an infant and child feeding index (ICFI) constructed on brief recalls of breastfeeding, feeding frequency and food diversification was assumed to provide long-term prediction about child feeding practices. The aim of this study was to investigate the association between the cross-sectional ICFI (CS-ICFI) or longitudinal ICFI (L-ICFI) and child anthropometric indices in down...