In-network Collaborative Mobile Crowdsensing

International audienceOur work aims to make opportunistic crowdsensing a reliable means of detecting urban phenomena, as a component of smart city development. We believe that the optimal method for achieving this is by enforcing the cost-effective collection of high quality data. We then investigate a supporting middleware solution that reduces both the network traffic and computation at the cloud. To this end, our research focuses on defining a set of protocols that together implement "context-aware in-network collaborative mobile crowdsensing" by combining: (i) The inference of the crowdsensors' physical context so as to characterize the gathered data; (ii) The context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iii) Data aggregation at the edge to enhance the knowledge transferred to the cloud

THE EXTRACTION OF ACETIC ACID FROM \u3ci\u3eTHERMOTOGA NEAPOLITANA\u3c/i\u3e FERMENTATION SPENT MEDIUM USING FRESH/RECOVERED ALIQUAT 336 AND PRODUCE CMA

To offer an alternative pathway to produce calcium magnesium acetate (CMA) as an environmental friendly deicer to relieve the pressure on fossil fuels demand, a liquid-liquid extraction process was studied. This process includes using Aliquat 336 (tri-n-octylmethylammonium chloride) diluted in kerosene as an organic extractant to extract acetic acid from Thermotoga neapolitana fermentation spent medium, which contained 34 g/L of acetic acid and 10 g/L of NaCl. The extraction efficiency of Aliquat 336 was studied under several different extraction conditions. The initial pH of the spent medium was adjusted to a pH range of 2.25 to 5.75 with a 0.5 pH interval to study the initial pH effect on Aliquat 336 extraction efficiency. Two different Aliquat 336 concentrations in kerosene (20% and 50% v/v) were made for comparison. The highest extraction efficiency of Aliquat 336 mixture was nearly 50% for fresh Aliquat 336 mixture at 50% concentration. Na2CO3/Ca(OH)2 and Mg(OH)2 were used to recover the acetic acid from used Aliquat 336 mixture and produce NaCH3COOH and CMA at the same time. It was shown that the extraction efficiency of recovered Aliquat 336 was approximately 83% as high as the fresh one. The extraction efficiency of both fresh and recovered Aliquat 336 mixtures were greatly affected by the initial pH of the fermentation spent medium. Extraction efficiency were greatest in pH range of 2.25 to 3.75. A purity of 75% CMA was produced

Enrichment of Turbulence Field Using Wavelets

This thesis is composed of two parts. The first part presents a new turbulence generation method based on stochastic wavelets and tests various properties of the generated turbulence field in both the homogeneous and inhomogeneous cases. Numerical results indicate that turbulence fields can be generated with much smaller bases in comparison to synthetic Fourier methods while maintaining comparable accuracy. Adaptive generation of inhomogeneous turbulence is achieved by a scale reduction algorithm, which greatly reduces the computational cost and practically introduces no error. The generating formula proposed in this research could be adjusted to generate fully inhomogeneous and anisotropic turbulence with given RANS data under a divergence-free constraint, which was not achieved previously in similar research. Numerical examples show that the generated homogeneous and inhomogeneous turbulence are in good agreement with the input data and theoretical results. The second part presents a framework of solving turbulence deconvolution problems using optimization techniques on Riemannian manifolds. A filtered velocity field was deconvoluted without any information of the filter. The deconvolution results shows high accuracy compared with the original velocity field. The computational cost of the optimization problem was largely reduced using wavelet representation while still maintaining high accuracy. Utilization of divergence-free wavelets ensures the incompressible property of deconvolution results, which was barely achieved in previous research

MATREX: the DCU MT system for WMT 2009

In this paper, we describe the machine translation system in the evaluation campaign of the Fourth Workshop on Statistical Machine Translation at EACL 2009. We describe the modular design of our multi-engine MT system with particular focus on the components used in this participation. We participated in the translation task for the following translation directions: French–English and English–French, in which we employed our multi-engine architecture to translate. We also participated in the system combination task which was carried out by the MBR decoder and Confusion Network decoder. We report results on the provided development and test sets

Rebuilt the fading vicinity

Something is missing on the scale between me and the whole world. That’s also the dilemma of the Millennials. The giant gap between the ego and the world builds an invisible wall. This invisible barrier makes us nonchalant toward the trivial things happening just right in front of us. Ironically, the nonchalance makes our society more emotional and drastic. We get outraged more easily compare to the past. How should I invite people to appreciate and care more about our surroundings? How do I rebuild the fading vicinity? Those questions are the starting point of my thesis journey

The DCU dependency-based metric in WMT-MetricsMATR 2010

We describe DCU’s LFG dependencybased metric submitted to the shared evaluation task of WMT-MetricsMATR 2010. The metric is built on the LFG F-structurebased approach presented in (Owczarzak et al., 2007). We explore the following improvements on the original metric: 1) we replace the in-house LFG parser with an open source dependency parser that directly parses strings into LFG dependencies; 2) we add a stemming module and unigram paraphrases to strengthen the aligner; 3) we introduce a chunk penalty following the practice of METEOR to reward continuous matches; and 4) we introduce and tune parameters to maximize the correlation with human judgement. Experiments show that these enhancements improve the dependency-based metric's correlation with human judgement