Mutual Solubilities of Ammonium-Based Ionic Liquids with Water and with Water/Methanol Mixture

Over the last two decades, ionic liquids have gained importance as alternative solvents to conventional VOCs in the field of homogeneous catalysis. This success is not only due to their ability to dissolve a large amount of metal catalysts, but it is also due to their potential to enhance yields of enantiopure products. The art of preparation of a specific enantiomer is a highly desired one and searched for in pharmaceutical industry. This work presents a study on solubility in water and in water/methanol mixture of a set of ILs composed of the bis (trifluoromethylsulfonyl) imide anion and of the N-alkyl-triethyl-ammonium cation (abbrev. [NR,222][NTf2]) with the alkyl chain R ranging from 6 to 12 carbons. Mutual solubilities between ILs and water, as well as between ILs and methanol/water mixture were investigated in detail. These solubilities were measured using two well-known and accurate experimental techniques based on a volumetric and a cloud-point methods. Both methods enabled us to measure the Tx diagrams reflecting the mutual solubilities between water (or water/methanol) and selected ILs in the temperature range from 293.15 to 338.15 K. The data were fitted by using the modified Flory-Huggins equation proposed by de Sousa and Rebelo and compared also with the prediction carried out by the Cosmo-RS methodology

Regularized Online Mixture of Gaussians for Background Subtraction

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

Gaussian mixture background modelling optimisation for micro-controllers

This paper proposes an optimisation of the adaptive Gaussian mixture background model that allows the deployment of the method on processors with low memory capacity. The effect of the granularity of the Gaussian mean-value and variance in an integer-based implementation is investigated and novel updating rules of the mixture weights are described. Based on the proposed framework, an implementation for a very low power consumption micro-controller is presented. Results show that the proposed method operates in real time on the micro-controller and has similar performance to the original model. © 2012 Springer-Verlag.

Heavier-than-water silicone oil mixture as a long-term tamponade agent:A pilot study

Purpose: To evaluate the tamponade effect on the retina of a heavier-than-water silicone oil mixture and to compare it with the effect of silicone oil. Methods: Prospective, non-randomised, comparative pilot study. Phakic/pseudophakic patients with retinal detachment undergoing vitrectomy with Densiron 68 or silicone oil were recruited. The 'separation volume', defined as the relative volume of the space between intraocular tamponade agent and retina, was estimated using magnetic resonance imaging in both groups and compared. Results: Nine participants were included; 4 received silicone oil and 5 Densiron 68. The mean separation volume was statistically significantly larger in the silicone oil group (0.477 ± 0.419 cm ) than in the Densiron group (0.042 ± 0.013 cm ; p = 0.014). Conclusions: In this study Densiron achieved an excellent tamponade effect in the retina. © 2011 S. Karger AG, Basel.

An optimisation of Gaussian mixture models for integer processing units

This paper investigates sub-integer implementations of the adaptive Gaussian mixture model (GMM) for background/foreground segmentation to allow the deployment of the method on low cost/low power processors that lack Floating Point Unit (FPU). We propose two novel integer computer arithmetic techniques to update Gaussian parameters. Specifically, the mean value and the variance of each Gaussian are updated by a redefined and generalised "round'' operation that emulates the original updating rules for a large set of learning rates. Weights are represented by counters that are updated following stochastic rules to allow a wider range of learning rates and the weight trend is approximated by a line or a staircase. We demonstrate that the memory footprint and computational cost of GMM are significantly reduced, without significantly affecting the performance of background/foreground segmentation.

Fast Convergence of Regularised Region-based Mixture of Gaussians for Dynamic Background Modelling

The momentum term has long been used in machine learning algorithms, especially back-propagation, to improve their speed of convergence. In this paper, we derive an expression to prove the O(1/k2) convergence rate of the online gradient method, with momentum type updates, when the individual gradients are constrained by a growth condition. We then apply these type of updates to video background modelling by using it in the update equations of the Region-based Mixture of Gaussians algorithm. Extensive evaluations are performed on both simulated data, as well as challenging real world scenarios with dynamic backgrounds, to show that these regularised updates help the mixtures converge faster than the conventional approach and consequently improve the algorithm’s performance.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

A Conditional Approach for Modelling Patient Re-admissions in Hospital using a mixture of Coxian phase-type distributions incorporating Bayes Theorem

The number of elderly patients requiring hospitalisation in Europe is rising. With a greater proportion of elderly people in the population comes a greater demand for health services and, in particular, hospital care. Thus, with a growing number of elderly patients requiring hospitalisation competing with non-elderly patients for a fixed (and in some cases, decreasing) number of hospital beds, this results in much longer waiting times for patients, often with a less satisfactory hospital experience. However, if a better understanding of the recurring nature of elderly patient movements between the community and hospital can be developed, then it may be possible for alternative provisions of care in the community to be put in place and thus prevent readmission to hospital. The research in this paper aims to model the multiple patient transitions between hospital and community by utilising a mixture of conditional Coxian phase-type distributions that incorporates Bayes' theorem. For the purpose of demonstration, the results of a simulation study are presented and the model is applied to hospital readmission data from the Lombardy region of Italy.