Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

Classification of EEG signals using a genetic-based machine learning classifier

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

A study of cavitation induced surface erosion in abrasive waterjet cutting systems

Current jet cutting systems experience severe nozzle erosion and associated maintenance and downtime costs. An experimental investigation was conducted to qualitatively and quantitatively analyse the generation of cavitation in a high pressure water jet cutting system, and to characterise cavitation induced accelerated surface erosion by slurries. The analysis of surface morphology indicates that the shearing induced by cavitation played a major role in the erosion process. The results promise a feasible solution to reduce nozzle wear, and to enhance material removal in the jet cutting process

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study

Background Jumping mechanography is a technology for quantitatively assessing muscular function and balance in older adults. This study sought to define the association between jumping mechanography parameters and fall risk in Vietnamese individuals. Methods The study involved 375 women and 244 men aged 50 years and older, who were recruited from the general population in Ho Chi Minh City (Vietnam). The individuals had been followed for 2 years. At baseline, Esslinger Fitness index (EFI), jumping power, force, velocity of lower limbs, and the ability to maintain balance were measured by a Leonardo Mechanograph Ground Reaction Force system (Novotec Medical, Pforxheim, Germany). The incidence of falls during the follow-up period was ascertained from self-report. Logistic regression analysis was used to analyse the association between jumping mechanography parameters and fall risk. Results The average age of participants at baseline was 56.7 years (SD 5.85). During the 2 year follow-up, 92 falls were reported, making the incidence of fall at ~15% [95% confidence interval (CI), 12.1 to 18.2]. The incidence of fall increased with advancing age, and women had a higher incidence than men (17.6% vs. 10.7%; P = 0.024). In univariate analysis, maximal velocity [odds ratio (OR) 0.65; 95% CI, 0.52 to 0.82], maximal force (OR 0.83; 95% CI, 0.65 to 1.04), and maximal power (OR 0.68; 95% CI, 0.52 to 0.88) were each significantly associated with fall risk. EFI was not significantly associated with fall risk (OR 1.09; 95% CI, 0.86 to 1.39). However, in a multiple logistic regression model, greater maximum velocity was associated with lower odds of fall (OR 0.38; 95% CI, 0.16 to 0.92). Conclusions These data suggest that jumping mechanography is a useful tool for assessing fall risk in older adults of Vietnamese background

Roboteye technology for thermal target tracking using predictive control

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Thermal cameras are widely used in the fatigue analysis of mechanical structures using the thermoelastic effect. Nevertheless, such analysis is hampered due to blurry images resulting from the motion of structure-under-test. To address the issue this paper presents a system that utilizes robotic vision and predictive control. The system comprises of a thermal camera, a vision camera, a RobotEye, and a fiducial detection system. A marker is attached to a thermal target in order to estimate its position and orientation using the proposed detection system. To predict the future position of the thermal moving object, a Kalman filter is used. Finally, the Model Predictive Control (MPC) approach is applied to generate commands for the robot to follow the target. Results of the tracking by MPC are included in this paper along with the performance evaluation of the whole system. The evaluation clearly shows the improvement in the tracking performance of the development for thermal structural analysis

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Computer Simulations Provide Guidance for Molecular Medicine through Insights on Dynamics and Mechanisms at the Atomic Scale

International audienceComputer simulations provide crucial insights and rationales for the design of molecular approaches in medicine. Several case studies illustrate how molecular model building and molecular dynamics simulations of complex molecular assemblies such as membrane proteins help in that process. Important aspects relate to build relevant molecular models with and without a crystal structure, to model membrane aggregates, then to link (dynamic) models to function, and finally to understand key disease-triggering phenomena such as aggregation. Through selected examples-including key signaling pathways in neurotransmission-the links between a molecular-level understanding of biological mechanisms and original approaches to treat disease conditions will be illuminated. Such treatments may be symptomatic, e.g. by better understanding the function and pharmacology of macromolecular key players, or curative, e.g. through molecular inhibition of disease-inducing molecular processes

The impact of signal-to-noise ratio, diffusion-weighted directions and image resolution in cardiac diffusion tensor imaging - insights from the ex-vivo rat heart

Background: Cardiac diffusion tensor imaging (DTI) is limited by scan time and signal-to-noise (SNR) restrictions. This invariably leads to a trade-off between the number of averages, diffusion-weighted directions (ND), and image resolution. Systematic evaluation of these parameters is therefore important for adoption of cardiac DTI in clinical routine where time is a key constraint. Methods: High quality reference DTI data were acquired in five ex-vivo rat hearts. We then retrospectively set 2 ≤ SNR ≤ 97, 7 ≤ ND ≤ 61, varied the voxel volume by up to 192-fold and investigated the impact on the accuracy and precision of commonly derived parameters. Results: For maximal scan efficiency, the accuracy and precision of the mean diffusivity is optimised when SNR is maximised at the expense of ND. With typical parameter settings used clinically, we estimate that fractional anisotropy may be overestimated by up to 13% with an uncertainty of ±30%, while the precision of the sheetlet angles may be as poor as ±31°. Although the helix angle has better precision of ±14°, the transmural range of helix angles may be under-estimated by up to 30° in apical and basal slices, due to partial volume and tapering myocardial geometry. Conclusions: These findings inform a baseline of understanding upon which further issues inherent to in-vivo cardiac DTI, such as motion, strain and perfusion, can be considered. Furthermore, the reported bias and reproducibility provides a context in which to assess cardiac DTI biomarkers