Induced Parametrisation and its Applications in Geometric Computation

The paper describes a concept of induced rational parametrisation for curves. Parametrisations of curves are defined in terms of rational parametrisations of simpler or `primitive' curves. The technique has applications in computer graphics and geometric modeling. A range of examples is given

Illumination modelling of a mobile device environment for effective use in driving mobile apps

The present generation of Ambient Light Sensors (ALS) of a mobile handheld device suffer from two practical shortcomings. The ALSs are narrow angle, i.e. they respond effectively only within a narrow angle of operation and there is a latency of operation. As a result mobile applications that operate based on the ALS readings could perform sub-optimally especially when operated in environments with non-uniform illumination. The applications will either adopt with unacceptable levels of latency or/and may demonstrate a discrete nature of operation. In this paper we propose a framework to predict the ambient illumination of an environment in which a mobile device is present. The predictions are based on an illumination model that is developed based on a small number of readings taken during an application calibration stage. We use a machine learning based approach in developing the models. Five different regression models were developed, implemented and compared based on Polynomial, Gaussian, Sum of Sine, Fourier and Smoothing Spline functions. Approaches to remove noisy data, missing values and outliers were used prior to the modelling stage to remove their negative effects on modelling. The prediction accuracy for all models were found to be above 0.99 when measured using R-Squared test with the best performance being from Smoothing Spline. In this paper we will discuss mathematical complexity of each model and investigate how to make compromises in finding the best mode

Case based reasoning approach for transaction outcomes prediction on currency markets

This paper presents a case based reasoning approach for making profit in the foreign exchange (forex) market with controlled risk using k nearest neighbour (kNN) and improving on the results with neural networks (NNs) and a combination of both. Although many professionals have proven that exchange rates can be forecast using neural networks for example, poor trading strategies and unpredictable market fluctuation can inevitably still result in substantial loss. As a result, the method proposed in this paper will focus on predicting the outcome of potential trades with fixed stop loss (ST) and take profit (TP) positions1, in terms of a win or loss. With the help of the Monte Carlo method, randomly generated trades together with different traditional technical indicators are fed into the models, resulting in a win or lose output. This is clearly a case based reasoning approach, in terms of searching similar past trade setups for selecting successful trades. There are several advantages over classical forecasting associated with such an approach, and the technique presented in this paper brings a novel perspective to problem of exchange trades predictability. The strategies implemented have not been empirically investigated with such wide a range of time granularities as is done in this paper, in any to the authors known academic literature. The profitability of this approach is back-tested at the end of this paper and highly encouraging results are reported

A comparative study in ultrasound breast imaging classification

Revue Européenne d'Ethnographie de l'Education / Revista europeia de etnografia da educação Sujet : Fondée en 1999 au colloque de Lecce (Italie), la Société européenne d'ethnographie de l'éducation (SEEE) s'était donnée pour tâche, à coté de la promotion de la démarche ethnographique, du développement de la recherche et du renforcement des contacts entre étudiants dans les différents pays d'Europe, une dimension éditoriale où figurent à la fois le projet de publications de recherches prop..

Out of focus ultrafast processing of metals for reduced secondary electron yield

We have demonstrated out-of-focus ultrafast pulsed laser processing of copper with a variable working distance, without the need for mechanical movement. This was achieved by employing a diffractive optical element. The method has been demonstrated in a practical application to reduce the secondary electron yield (SEY) of copper to below 1.3. We show that using an extended focus element not only increases the consistency of processing across a range of working distances, but also changes the topography of the produced structures, reducing the SEY. This presented approach shows promise in facilitating the Large Hadron Collider’s (LHC’s) upcoming high luminosity upgrade by preventing electron clouds

Wear Minimization for Cuckoo Hashing: How Not to Throw a Lot of Eggs into One Basket

We study wear-leveling techniques for cuckoo hashing, showing that it is possible to achieve a memory wear bound of $\log\log n+O(1)$ after the insertion of $n$ items into a table of size $Cn$ for a suitable constant $C$ using cuckoo hashing. Moreover, we study our cuckoo hashing method empirically, showing that it significantly improves on the memory wear performance for classic cuckoo hashing and linear probing in practice.Comment: 13 pages, 1 table, 7 figures; to appear at the 13th Symposium on Experimental Algorithms (SEA 2014

Formulation, pilot-scale preparation, physicochemical characterization and digestibility of a lentil protein-based model infant formula powder

Background: Infant formula is a human milk substitute for consumption during the first months of life. The protein component of such products is generally of dairy origin. Alternative sources of protein, such as those of plant origin, are of interest due to dairy allergies, intolerances, and ethical and environmental considerations. Lentils have high levels of protein (20–30%) with a good amino acid profile and functional properties. In this study, a model lentil protein-based formula (LF), in powder format, was produced and compared to two commercial plant-based infant formulae (i.e., soy; SF and rice; RF) in terms of physicochemical properties and digestibility. Results: The macronutrient composition was similar between all the samples; however, RF and SF had larger volume-weighted mean particle diameters (D[4,3] of 121–134 ∼m) than LF (31.9 ∼m), which was confirmed using scanning electron and confocal laser microscopy. The larger particle sizes of the commercial powders were attributed to their agglomeration during the drying process. Regarding functional properties, the LF showed higher D[4,3] values (17.8 ∼m) after 18 h reconstitution in water, compared with the SF and RF (5.82 and 4.55 ∼m, respectively), which could be partially attributed to hydrophobic protein–protein interactions. Regarding viscosity at 95 °C and physical stability, LF was more stable than RF. The digestibility analysis showed LF to have similar values (P <0.05) to the standard SF. Conclusion: These results demonstrated that, from the nutritional and physicochemical perspectives, lentil proteins represent a good alternative to other sources of plant proteins (e.g., soy and rice) in infant nutritional products