Wi-Fi Indoor Positioning System

Location tracking services are more attractive technologies in today’s world. These services make the use of wireless networks and broadband multimedia wireless networks to provide the location tracking services inside the buildings and campus areas. In this services determining the user’s current location or position accurately is the most important phenomena. Wi-Fi enabled indoor positioning technique is widely used in the outdoor environment to locate the persons moving inside the building and this technique is gaining popularity as all the android smart phones have this application. This technique is efficient in improving the positioning techniques. The aim of this project is to create an application to locate the position of the user inside thebuilding with more accuracy of the position of the user

Context-Specific Requirement of Forty-Four Two-Component Loci in Pseudomonas aeruginosa Swarming

Summary: Swarming in Pseudomonas aeruginosa is a coordinated movement of bacteria over semisolid surfaces (0.5%–0.7% agar). On soft agar, P. aeruginosa exhibits a dendritic swarm pattern, with multiple levels of branching. However, the swarm patterns typically vary depending upon the experimental design. In the present study, we show that the pattern characteristics of P. aeruginosa swarm are highly environment dependent. We define several quantifiable, macroscale features of the swarm to study the plasticity of the swarm, observed across different nutrient formulations. Furthermore, through a targeted screen of 113 two-component system (TCS) loci of the P. aeruginosa strain PA14, we show that forty-four TCS genes regulate swarming in PA14 in a contextual fashion. However, only four TCS genes—fleR, fleS, gacS, and PA14_59770—were found essential for swarming. Notably, many swarming-defective TCS mutants were found highly efficient in biofilm formation, indicating opposing roles for many TCS loci. : Pathogenic Organism; Biological Sciences; Microbiology Subject Areas: Pathogenic Organism, Biological Sciences, Microbiolog

Active modulation of surfactant-driven flow instabilities by swarming bacteria

Models based on surfactant-driven instabilities have been employed to describe pattern formation by swarming bacteria. However, by definition, such models cannot account for the effect of bacterial sensing and decision making. Here we present a more complete model for bacterial pattern formation which accounts for these effects by coupling active bacterial motility to the passive fluid dynamics. We experimentally identify behaviors which cannot be captured by previous models based on passive population dispersal and show that a more accurate description is provided by our model. It is seen that the coupling of bacterial motility to the fluid dynamics significantly alters the phase space of surfactant-driven pattern formation. We also show that our formalism is applicable across bacterial species.</p