Biomedical signal filtering for noisy environments

 Luke\u27s work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface

On the feasibility of utilising gearing to extend the rotational workspace of a class of parallel robots

Parallel manipulators provide several benefits compared to serial manipulators of similar size. These advantages typically include higher speed and acceleration, improved position accuracy and increased stiffness. However, parallel manipulators also suffer from several disadvantages. These drawbacks commonly include a small ratio of the positional workspace relative to the manipulator footprint and a limited rotational capability of the manipulated platform. A few parallel manipulators featuring a large ratio of the positional workspace relative to the footprint have been proposed. This paper investigates the feasibility of employing gearing to extend the range of the end-effector rotation of such mechanisms. The objective is to achieve parallel manipulators where both the positional and rotational workspace are comparable to that of serial manipulators

An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis

Profiles of a Heritage : Images of Wildlife by British Columbia Artists

Davison documents the work of thirty B.C. artists who have used realism to depict animals in nature. Includes artist profiles and statements. 23 bibl. ref