It is shown that, in some case, the stability criteria can be easily checked. Several remarks are worked out to demonstrate the advantage of our results. (C) 2008 Elsevier B.V. All rights reserved.”
“The aim of this study was to evaluate a surface electromyography

(sEMG) signal and force model for the biceps brachii muscle during isotonic isometric contractions for an experimental set-up as well as for a simulation. The proposed model includes a new rate coding scheme and a new analytical formulation of the muscle force generation. The proposed rate coding scheme supposes varying minimum and peak firing frequencies according to motor unit (MU) type (I or II). Practically, the proposed analytical mechanogram allows us to tune the force contribution of each active MU according CAL-101 solubility dmso to its type and instantaneous firing rate. A subsequent sensitivity analysis using a Monte Carlo simulation allows deducing optimised input parameter ranges that guarantee a realistic behaviour of the proposed model according to two existing criteria and an additional one. In fact, this proposed new criterion evaluates the force generation efficiency according to neural intent. Experiments and simulations, at varying force levels and using the optimised parameter ranges, were performed to evaluate the proposed model. As

a result, our study showed that the proposed sEMG-force modelling can emulate the biceps brachii behaviour during isotonic isometric

contractions.”
“In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a VX-680 in vivo way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies ARN-509 nmr can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods.