Noninvasive brain-actuated control of a mobile robot by human EEG

Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74.     

Sensitivity to punishment and sensitivity to reward and traffic violations

The aim of our study consists of contributing information on the relationship between the personality variables derived from Gray’s model and the conduct that accompanies the infringement of the road traffic rules. Seven hundred and ninety-two adults of both sexes took part in the study (389 men and 403 women), all of whom had driving licences and drove frequently. The subjects answered “The Sensitivity to Punishment and Sensitivity to Reward Questionnaire”, a scale of monotony avoidance, and two Likert scales of attitude and behaviour in connection with traffic violations.

We found a high positive relationship between attitude and behaviour, with the men infringing the rules more than the women. Hypotheses regarding a relationship between traffic offences and sensibility to reward and monotony avoidance were confirmed. Those people with high scores in sensitivity to punishment and low ones in sensitivity to reward were those who drove within the law, while those with low sensitivity to punishment and high sensitivity to reward were those who broke it more. Sensitivity to reward was a stronger determinant in encouraging infringement of the rules than was sensitivity to punishment in discouraging the subjects to do so.     

Identification of protein ligands in complex biological samples using intensity-fading MALDI-TOF mass spectrometry

The easy detection of biomolecular interactions in complex mixtures using a minimum amount of material is of prime interest in molecular and cellular biology research. In this work, a mass spectrometry MALDI-TOF based approach, which we call intensity-fading (IF MALDI-TOFMS), and which was designed for just such a purpose, is reported. This methodology is based on the use of the MALDI ion intensities to detect quickly the formation of complexes between nonimmobilized biomolecules in which a protein is one of the partners (protein-protein, protein-peptide, protein-organic molecule, and protein-nucleic acid complexes). The complex is detected through the decrease (fading) of the molecular ion intensities of the partners as directly compared to the MALDI mass spectrum of the mixture (problem and control molecules) following the addition of the target molecule. The potential of the approach is examined in several examples of model interactions, mainly involving small nonprotein and protein inhibitors of proteases, at both the qualitative and semiquantitative levels. Using this method, different protein ligands of proteolytic enzymes in total extracts of invertebrate organisms have been identified in a simple way. The proposed procedure should be easily applied to the high-throughput screening of biomolecules, opening a new experimental strategy in functional proteomics.     

Orthogonal experimental design applied for wear characterization of aluminum/C<Subscript>sf</Subscript> metal composite fabricated by the thixomixing method

High silicon content aluminum alloy (hypereutectic) possess good tribological characteristics with low coefficients of friction, when embedded with short carbon fiber (C_sf), making this composite a good material choice where good wear and high strength properties are required in light weight components. There is no previously published information available, to the knowledge of the authors, regarding the influence of wear parameters and their interactions on the tribological behavior of C_sf reinforced metal matrix composites. In this study a Taguchi design of experiment (DoE) was conducted to optimize and analyze the effects of the wear parameters on the tribological properties of Al/C_sf metal matrix composite. A novel thixomixing method which was used to process the metal within the semisolid state was employed to embed short carbon fibers homogenously into the metal matrix. The influences of the sliding speed, applied load and volume fraction, of C_sf on the specific wear rate and coefficient of friction were examined, with each of these input parameters tested at three levels(0, 4.2, 8.1%vol.). The results were indicated that Al/C_sf composite had better tribological properties than Al alloy due to which contains carbon as solid lubricant. According to the statistical analysis, the influence of volume fraction of carbon fiber on wear parameters was ranked first; so the load and sliding speed are at the following rankings. The contribution percentage for each parameter was determined by the analysis of variance. The relatively good interfacial adherence of carbon fiber and matrix alloy were demonstrated. The coherent and adherent graphite-rich layer on the worn surface was characterized using scanning electron microscopy (SEM).     

Global and local distance-based generalized linear models

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models first to the generalized linear model framework. Then, a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore, they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distance-based prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.     

On the selection of variables for qualitative modelling of dynamical systems

Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.     

State-based predictions with self-correction on Enterprise Desktop Grid environments

The abundant computing resources in current organizations provide new opportunities for executing parallel scientific applications and using resources. The Enterprise Desktop Grid Computing (EDGC) paradigm addresses the potential for harvesting the idle computing resources of an organization’s desktop PCs to support the execution of the company’s large-scale applications. In these environments, the accuracy of response-time predictions is essential for effective metascheduling that maximizes resource usage without harming the performance of the parallel and local applications. However, this accuracy is a major challenge due to the heterogeneity and non-dedicated nature of EDGC resources. In this paper, two new prediction techniques are presented based on the state of resources. A thorough analysis by linear regression demonstrated that the proposed techniques capture the real behavior of the parallel applications better than other common techniques in the literature. Moreover, it is possible to reduce deviations with a proper modeling of prediction errors, and thus, a Self-adjustable Correction method (SAC) for detecting and correcting the prediction deviations was proposed with the ability to adapt to the changes in load conditions. An extensive evaluation in a real environment was conducted to validate the SAC method. The results show that the use of SAC increases the accuracy of response-time predictions by 35%. The cost of predictions with self-correction and its accuracy in a real environment was analyzed using a combination of the proposed techniques. The results demonstrate that the cost of predictions is negligible and the combined use of the prediction techniques is preferable.     

Experimental validation of the discrete-time MCS adaptive strategy

This paper is concerned with the implementation and experimental validation of a discrete-time model reference adaptive control strategy, known as Minimal Control Synthesis (MCS) algorithm. After discussing the proof of stability of the algorithm when applied to discretized models of continuous-time plants, the problem of controlling a highly nonlinear electro-mechanical device is taken as a representative case of study. It is shown that the discrete-time MCS is an effective strategy to solve the problem while guaranteeing robustness to unmodeled nonlinear dynamics over a wide range of test manoeuvres.     

Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI

Various aspects of global environmental change affect plant photosynthesis, the primary carbon input in ecosystems. Thus, accurate methods of measuring plant photosynthesis are important. Remotely sensed spectral indices can monitor in detail the green biomass of ecosystems, which provides a measure of potential photosynthetic capacity. In evergreen vegetation types, however, such as Mediterranean forests, the amount of green biomass changes little during the growing season and, therefore, changes in green biomass are not responsible for changes in photosynthetic rates in those forests. This study examined the net photosynthetic rates and the diametric increment of stems in a Mediterranean forest dominated by Quercus ilex using three spectral indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Average annual EVI accounted for 83% of the variability of the diametric increment of Q. ilex stems over a 10 year period. NDVI was marginally correlated with the diametric increment of stems. This study was the first to identify a significant correlation between net photosynthetic rates and radiation use efficiency at the leaf level using PRI derived from satellite data analysed at the ecosystem level. These results suggest that each spectral index provided different and complementary information about ecosystem carbon uptake in a Mediterranean Q. ilex forest.     

Winter and spring surface velocity fields in the Cape Blanc region as deduced with the maximum cross-correlation technique

The ocean surface velocity field in the Cape Blanc region, off Northwest Africa, is investigated with the maximum cross-correlation (MCC) method applied to channel-4 Advanced Very High Resolution Radiometer satellite images. An initial sensitivity analysis allows us to select the four parameters that provide maximum area coverage and the best velocity resolution, while limiting the standard deviation for each velocity component within reasonable values. These are (m, n, MV, CT)?=?(22, 32, 50, 0.6), where m and n are the number of pixels of the search window (SW) and reference window (RW), respectively, MV is the maximum possible velocity (in cm s^?1), and CT is a correlation threshold for a feature to be tracked. A total of 489 images, for years 2005 and 2006, are analysed, and 106 velocity maps are generated with good coverage of the coastal transition zone (CTZ), most of them for the winter (34) and spring (59) seasons. We remove spurious data using the method's own filters (MV, CT, and a neighbour-vector comparison), requesting the velocity components to have Gaussian distributions and smoothing the resulting velocity fields with a median-vector filter. The instantaneous velocity maps illustrate the response of the alongshore coastal jet north of Cape Blanc (and its extension along the Cape Verde frontal region) to wind forcing, as well as the presence of numerous mesoscalar features (100–300 km wide) superposed on a westward offshore transport south of Cape Blanc. We also produce mean and standard deviation winter and spring velocity maps, which are compared with the corresponding mean sea surface temperature fields. The along-shore and offshore flow is better defined and is more intense in spring than in winter, in concordance with cross-slope sharper temperature gradients during this season, and brings about a cooling of the whole region. We identify five different ubiquitous currents: a southwestward jet north of Cape Blanc, a northwestward jet off Banc d'Argin, an offshore convergent jet, a spring jet-like feature at 18° N, and a southward flow in the southwestern CTZ.