An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks

Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Carbon nanotubes on nanoporous alumina: From surface mats to conformal pore filling

Control over nucleation and growth of multi-walled carbon nanotubes in the nanochannels of porous alumina membranes by several combinations of posttreatments, namely exposing the membrane top surface to atmospheric plasma jet and application of standard S1813 photoresist as an additional carbon precursor, is demonstrated. The nanotubes grown after plasma treatment nucleated inside the channels and did not form fibrous mats on the surface. Thus, the nanotube growth mode can be controlled by surface treatment and application of additional precursor, and complex nanotube-based structures can be produced for various applications. A plausible mechanism of nanotube nucleation and growth in the channels is proposed, based on the estimated depth of ion flux penetration into the channels. PACS: 63.22.Np Layered systems; 68. Surfaces and interfaces; Thin films and nanosystems (structure and non-electronic properties); 81.07.-b Nanoscale materials and structures: fabrication and characterization © 2014 Fang et al.; licensee Springer

Large Arrays and Networks of Carbon Nanotubes: Morphology Control by Process Parameters

Large arrays and networks of carbon nanotubes, both single- and multi-walled, feature many superior properties which offer excellent opportunities for various modern applications ranging from nanoelectronics, supercapacitors, photovoltaic cells, energy storage and conversation devices, to gas- and biosensors, nanomechanical and biomedical devices etc. At present, arrays and networks of carbon nanotubes are mainly fabricated from the pre-fabricated separated nanotubes by solution-based techniques. However, the intrinsic structure of the nanotubes (mainly, the level of the structural defects) which are required for the best performance in the nanotube-based applications, are often damaged during the array/network fabrication by surfactants, chemicals, and sonication involved in the process. As a result, the performance of the functional devices may be significantly degraded. In contrast, directly synthesized nanotube arrays/networks can preclude the adverse effects of the solution-based process and largely preserve the excellent properties of the pristine nanotubes. Owing to its advantages of scale-up production and precise positioning of the grown nanotubes, catalytic and catalyst-free chemical vapor depositions (CVD), as well as plasma-enhanced chemical vapor deposition (PECVD) are the methods most promising for the direct synthesis of the nanotubes

Sequence-to-Sequence Imputation of Missing Sensor Data

Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data. The key challenge is that the missing sensor data problem typically comprises three sequences (a sequence of observed samples, followed by a sequence of missing samples, followed by another sequence of observed samples) whereas, the sequence-to-sequence model only considers two sequences (an input sequence and an output sequence). We address this problem by formulating a sequence-to-sequence in a novel way. A forward RNN encodes the data observed before the missing sequence and a backward RNN encodes the data observed after the missing sequence. A decoder decodes the two encoders in a novel way to predict the missing data. We demonstrate that this model produces the lowest errors in 12% more cases than the current state-of-the-art

In vivo imaging of the airway wall in asthma: fibered confocal fluorescence microscopy in relation to histology and lung function

<p>Abstract</p> <p>Background</p> <p>Airway remodelling is a feature of asthma including fragmentation of elastic fibres observed in the superficial elastin network of the airway wall. Fibered confocal fluorescence microscopy (FCFM) is a new and non-invasive imaging technique performed during bronchoscopy that may visualize elastic fibres, as shown by <it>in vitro </it>spectral analysis of elastin powder. We hypothesized that FCFM images capture <it>in vivo </it>elastic fibre patterns within the airway wall and that such patterns correspond with airway histology. We aimed to establish the concordance between the bronchial elastic fibre pattern in histology and FCFM. Second, we examined whether elastic fibre patterns in histology and FCFM were different between asthmatic subjects and healthy controls. Finally, the association between these patterns and lung function parameters was investigated.</p> <p>Methods</p> <p>In a cross-sectional study comprising 16 subjects (8 atopic asthmatic patients with controlled disease and 8 healthy controls) spirometry and bronchoscopy were performed, with recording of FCFM images followed by endobronchial biopsy at the airway main carina. Elastic fibre patterns in histological sections and FCFM images were scored semi-quantitatively. Agreement between histology and FCFM was analysed using linearly weighted kappa κ_w.</p> <p>Results</p> <p>The patterns observed in histological sections and FCFM images could be divided into 3 distinct groups. There was good agreement between elastic fibre patterns in histology and FCFM patterns (κ_w0.744). The semi-quantitative pattern scores were not different between asthmatic patients and controls. Notably, there was a significant difference in post-bronchodilator FEV₁%predicted between the different patterns by histology (p = 0.001) and FCFM (p = 0.048), regardless of asthma or atopy.</p> <p>Conclusion</p> <p>FCFM captures the elastic fibre pattern within the airway wall in humans <it>in vivo</it>. The association between post-bronchodilator FEV₁%predicted and both histological and FCFM elastic fibre patterns points towards a structure-function relationship between extracellular matrix in the airway wall and lung function.</p> <p>Trial registration</p> <p>Netherlands Trial Register <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=NTR1306">NTR1306</a></p

The effects of plasma treatment on bacterial biofilm formation on vertically-aligned carbon nanotube arrays

© The Royal Society of Chemistry 2015. Carbon nanotubes (CNTs) can be fabricated with an ordered microstructure by controlling their growth process. Unlike dispersed carbon nanotubes, these vertically-aligned arrays have the ability to support or inhibit bacteria biofilms. Here, we show that by treating the carbon nanotube arrays with plasma, different effects on biofilms of Gram-positive (Bacillus subtilis, Staphylococcus epidermidis) and Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa) can be observed

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Single-step ambient-air synthesis of graphene from renewable precursors as electrochemical genosensor

©The Author(s) 2017. Thermal chemical vapour deposition techniques for graphene fabrication, while promising, are thus far limited by resource-consuming and energy-intensive principles. In particular, purified gases and extensive vacuum processing are necessary for creating a highly controlled environment, isolated from ambient air, to enable the growth of graphene films. Here we exploit the ambient-air environment to enable the growth of graphene films, without the need for compressed gases. A renewable natural precursor, soybean oil, is transformed into continuous graphene films, composed of single-to-few layers, in a single step. The enabling parameters for controlled synthesis and tailored properties of the graphene film are discussed, and a mechanism for the ambient-air growth is proposed. Furthermore, the functionality of the graphene is demonstrated through direct utilization as an electrode to realize an effective electrochemical genosensor. Our method is applicable to other types of renewable precursors and may open a new avenue for low-cost synthesis of graphene films