A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

The Journal of Supercomputing - With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining...     

Classifying and Summarizing Information from Microblogs During Epidemics

During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.     

Robust object tracking with crow search optimized multi-cue particle filter

Pattern Analysis and Applications - Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of...     

Centered inclined slot coupling between waveguides with coplanar axes

The article presents the method of finding the complete equivalent circuit of two waveguides with coplanar axes coupled through a centered inclined slot in the common broad wall. The variational method based on dyadic Green's function is used for finding the parameters of the equivalent circuit. A cosinusoidal aperture field distribution is assumed. Considerable mathematical simplification is resulted from replacing the centered inclined slot by an equivalent magnetic dipole. Coupling slot characteristics are deduced, including resonant length, dominant mode scattering in both the waveguides. Numerical and experimental results for resonant lengths and scattering parameters are presented over a range of tilt angles, frequencies, and waveguide dimensions. These results have significant applications in linear waveguide arrays and coupler design. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks

State-of-the-art molecular dynamics (MD) simulations generate massive datasets involving billion-vertex chemical bond networks, which makes data mining based on graph algorithms such as K-ring analysis a challenge. This paper proposes an algorithm to improve the efficiency of ring analysis of large graphs, exploiting properties of K-rings and spatial correlations of vertices in the graph. The algorithm uses dual-tree expansion (DTE) and spatial hash-function tagging (SHAFT) to optimize computation and memory access. Numerical tests show nearly perfect linear scaling of the algorithm. Also a parallel implementation of the DTE + SHAFT algorithm achieves high scalability. The algorithm has been successfully employed to analyze large MD simulations involving up to 500 million atoms.