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

Acute Respiratory Distress Syndrome: Response

The epistolic response to other letters published in The New England Journal of Medicine 337 (2017), no. 19, pp. 1903-1905, as a result of the Review Article: B. Taylor Thompson, Rachel C. Chambers, Kathleen D. Liu, Acute Respiratory Distress Syndrome, The New England of Medicine 377, no. 6, (2017), pp. 562-572

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

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

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode

Acute Respiratory Distress Syndrome.

Fifty years ago, Ashbaugh and colleagues described 12 patients with tachypnea, refractory hypoxemia, and diffuse opacities on chest radiographs after infection or trauma.1 Prominent hyaline membranes were seen lining the alveolar spaces of the lungs in 6 of the 7 patients who died, findings previously thought to be specific for the respiratory distress syndrome of the newborn. Thus, the term adult (later changed to acute) respiratory distress syndrome (ARDS) was proposed. Since ARDS was last reviewed in the Journal, 17 years ago,2 substantial progress has been made in the care of affected patients and those at risk for the disorder, with reductions in both incidence and mortality. However, ARDS remains a relatively common and lethal or disabling syndrome. In a recent international study involving 29,144 patients,3 10% of all patients admitted to the intensive care unit (ICU) and 23% of mechanically ventilated patients had ARDS. Mortality in the subgroup of patients with severe ARDS was 46%.3 Patients who survive this disorder are at high risk for cognitive decline, depression, post-traumatic stress disorder, and persistent skeletal-muscle weakness

Noninvasive Instrument-based Tests for Detecting and Measuring Vitreous Inflammation in Uveitis: A Systematic Review

PURPOSE: This systematic review aims to identify instrument-based tests for quantifying vitreous inflammation in uveitis, report the test reliability and the level of correlation with clinician grading. METHODS: Studies describing instrument-based tests for detecting vitreous inflammation were identified by searching bibliographic databases and trials registers. Test reliability measures and level of correlation with clinician vitreous haze grading are extracted. RESULTS: Twelve studies describing ultrasound, optical coherence tomography (OCT), and retinal photography for detecting vitreous inflammation were included: Ultrasound was used for detection of disease features, whereas OCT and retinal photography provided quantifiable measurements. Correlation with clinician grading for OCT was 0.53-0.60 (three studies) and for retinal photography was 0.51 (1 study). Both instruments showed high inter- and intra-observer reliability (>0.70 intraclass correlation and Cohen's kappa), where reported in four studies. CONCLUSION: Retinal photography and OCT are able to detect and measure vitreous inflammation. Both techniques are reliable, automatable, and warrant further evaluation

Mathematical modelling of container transfers for a fleet of autonomous straddle carriers

The main contribution of this paper is a mathematical model describing performance metrics for coordinating multiple mobile robots in a seaport container terminal. The scenario described here requires dealing with many difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. Furthermore, in contrast to the automatically guided vehicle planning problem in a manufacturing environment, the container carriers described here are free ranging. Although, the port structure imposes a set of "virtual" roadways along which the vehicles are allowed to travel, path planning is essential in preventing contention and collisions. A performance metric which minimises total yard-vehicle usage, while producing robust traffic plans by encouraging both early starting and finishing of jobs is presented for different vehicle fleet sizes and job allocation scenarios. ©2010 IEEE