The Abbot Cell-Dyn Sapphire is a new generation haematology analyser. The system uses optical/fluorescence flow cytometry in combination with electronic impedance to produce a full blood count. Optical and impedance are the default methods for platelet counting while automated CD61-immunoplatelet analysis can be run as selectable test. The aim of this study was to determine the platelet count performance of the three counting methods available on the instrument and to compare the results with those provided by Becton Dickinson FACSCalibur flow cytometer used as reference method. A lipid interference experiment was also performed. Linearity, carryover and precision were good, and satisfactory agreement with reference method was found for the impedance, optical and CD61-immunoplatelet analysis, although this latter provided the closest results in comparison with flow cytometry. In the lipid interference experiment, a moderate inaccuracy of optical and immunoplatelet counts was observed starting from a very high lipid value.

The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine-learning techniques with no need to calculate the two abovementioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine-learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on F-114 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and random forest. The results show that random forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10x10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, random forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.

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This paper presents an experimental study using different projection strategies and techniques to improve the performance of Support Vector Machine (SVM) ensembles. The study has been made over 62 UCI datasets using Principal Component Analysis (PCA) and three types of Random Projections (RP), taking into account the size of the projected space and using linear SVMs as base classifiers. Random Projections are also combined with the sparse matrix strategy used by Rotation Forests, which is a method based in projections too. Experiments show that for SVMs ensembles (i) sparse matrix strategy leads to the best results, (ii) results improve when projected space dimension is bigger than the original one, and (iii) Random Projections also contribute to the results enhancement when used instead of PCA. Finally, random projected SVMs are tested as base classifiers of some state of the art ensembles, improving their performance.

Intracranial Pressure (ICP) measurements are of great importance for the diagnosis, monitoring and treatment of many vascular brain disturbances. The standard measurement of the ICP is performed invasively by the perforation of the cranial scalp in the presence of traumatic brain injury (TBI). Measuring the ICP in a noninvasive way is relevant for a great number of pathologies where the invasive measurement represents a high risk. The method proposed in this paper uses the Arterial Blood Pressure (ABP) and the Cerebral Blood Flow Velocity (CBFV) - which may be obtained by means of non-invasive methods - to estimate the ICP. A non-linear Support Vector Machine was used and reached a low error between the real ICP signal and the estimated one, allowing an on-line implementation of the ICP estimation, with an adequate temporal resolution.

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The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes. The aim of this work is to compare artificial neural networks (ANN) and support vector machines (SVM) when acting as fitness functions of a genetic algorithm (GA) that performs a feature selection process over some features extracted from the EGG signals. These features correspond to those that literature shows to be the most used in EGG analysis. The results show that the SVM classifier is faster, requires less memory and reached the same performance (86% of exactitude) than the ANN classifier when acting as the fitness function for the GA.

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