Abstract
Intelligent wheelchairs operating in dynamic environments need to sense its neighborhood and adapt the control signal, in real-time, to avoid collisions and protect the user. In this paper we propose a robust, real-time obstacle avoidance extension of the classic potential field methodology. Our algorithm is specially adapted to share the wheelchair's control with the user avoiding risky situations. This method relies on the idea of virtual forces, generated by the user command (attractive force) and by the objects detected on each ultrasonic sensor (repulsive forces), acting on the wheelchair. The resultant wheelchair's behavior is obtained by the sum of the attractive force and all the repulsive forces at a given position. Experimental results from drive tests in a cluttered office environment provided statistical evidence that the proposed algorithm is effective to reduce the number of collisions and still improve the user's safety perception. ©2010 IEEE.

Abstract
Many people with severe disabilities find it difficult or even impossible to use traditional powered wheelchairs independently by manually controlling these electrical devices. Intelligent wheelchairs are a very good solution to assist severely handicapped people who are unable to operate classical electrical wheelchair by themselves in their daily activities. This paper describes a development platform for intelligent wheelchairs called IntellWheels. The intelligent system developed may be added to commercial powered wheelchairs with minimal modifications in a very straightforward manner. The paper describes the concept and design of the platform, including the hardware and software, multimodal input interface and the intelligent wheelchair prototype developed to validate the approach. Preliminary results concerning automatic movement of the IntellWheels prototype are also described showing the autonomous movement capabilities of the prototype. © 2009 Springer-Verlag Berlin Heidelberg.

Abstract
Molecular dynamics simulations is a valuable tool to study protein unfolding in silico. Analyzing the relative spatial position of the residues during the simulation may indicate which residues are essential in determining the protein structure. We present a method, inspired by a popular data mining technique called Frequent Itemset Mining, that clusters sets of amino acid residues with a synchronized trajectory during the unfolding process. The proposed approach has several advantages over traditional hierarchical clustering. © 2009 Springer Berlin Heidelberg.

Abstract
Diabetic retinopathy (DR) is the leading cause of blindness in adults. In diabetes, there is activation of microglial cells and a concomitant release of inflammatory mediators. However, it remains unclear how diabetes triggers an inflammatory response in the retina. Activation of P2 purinergic receptors by adenosine triphosphate (ATP) may contribute to the inflammatory response in the retina, insofar as it has been shown to be associated with microglial activation and cytokine release. In this work, we evaluated how high glucose, used as a model of hyperglycemia, considered the main factor in the development of DR, affects the extracellular levels of ATP in retinal cell cultures. We found that basal extracellular ATP levels were not affected by high glucose or mannitol, but the extracellular elevation of ATP, after a depolarizing stimulus, was significantly higher in retinal cells cultured in high glucose compared with control or mannitol-treated cells. The increase in the extracellular ATP was prevented by application of botulinum neurotoxin A or by removal of extracellular calcium. In addition, degradation of exogenously added ATP was significantly lower in high-glucose-treated cells. It was also observed that, in retinal cells cultured under high-glucose conditions, the changes in the intracellular calcium concentrations were greater than those in control or mannitol-treated cells. In conclusion, in this work we have shown that high glucose alters the purinergic signaling system in the retina, by increasing the exocytotic release of ATP and decreasing its extracellular degradation. The resulting high levels of extracellular ATP may lead to inflammation involved in the pathogenesis of DR. (C) 2008 Wiley-Liss, Inc.

Abstract
In this technical brief, a new subspace state space system identification algorithm for multi-input multi-output bilinear systems driven by white noise inputs is introduced. The new algorithm is based on a uniformly convergent Picard sequence of linear deterministic-stochastic state space subsystems which are easily identifiable by any linear deterministic-stochastic subspace algorithm such as MOESP, N4SID, CVA, or CCA. The key to the proposed algorithm is the fact that the bilinear term is a second-order white noise process. Using a standard linear Kalman filter model, the bilinear term can be estimated and combined with the system inputs at each iteration, thus leading to a linear system with extended inputs of dimension m(n + 1), where n is the system order and m is the dimension of the inputs. It is also shown that the model parameters obtained with the new algorithm converge to those of the true bilinear model. Moreover, the proposed algorithm has the same consistency conditions as the linear subspace identification algorithms when i -> infinity, where i is the number of block rows in the past/future block Hankel data matrices. Typical bilinear subspace identification algorithms available in the literature cannot handle large values of i, thus leading to biased parameter estimates. Unlike existing bilinear subspace identification algorithms whose row dimensions in the data matrices grow exponentially, and hence suffer from the "curse of dimensionality," in the proposed algorithm the dimensions of the data matrices are comparable to those of a linear subspace identification algorithm. A case study is presented with data from a heat exchanger experiment.

Abstract
In this paper the Wiener-Hammerstein system proposed as a benchmark for the SYSID 2009 benchmark session is identified as a bilinear discrete system. The bilinear approximation relies on both facts that the Wiener-Hammerstein system can be described by a Volterra series which can be approximated by bilinear systems. The identification is performed with an iterative bilinear subspace identification algorithm previously proposed by the authors. In order to increase accuracy, polynomial static nonlinearities are added to the bilinear model input. These Hammerstein type bilinear models are then identified using the same iterative subspace identification algorithm. © 2009 IFAC.