Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV

Temporal interval production and short-term memory

Interference with time estimation from concurrent nontemporal processing has been shown to depend on the short-term memory requirements of the concurrent task (Fortin Breton, 1995; Fortin, Rousseau, Bourque, & Kirouac, 1993). In particular, it has been claimed that active processing of information in short-term memory produces interference, whereas simply maintaining information does not. Here, four experiments are reported in which subjects were trained to produce a 2,500-msec interval and then perform concurrent memory tasks. Interference with timing was demonstrated for concurrent memory tasks involving only maintenance. In one experiment, increasing set size in a pitch memory task systematically lengthened temporal production. Two further experiments suggested that this was due to a specific interaction between the short-term memory requirements of the pitch task and those of temporal production. In the final experiment, subjects performed temporal production while concurrently remembering the durations of a set of tones. Interference with interval production was comparable to that produced by the pitch memory task. Results are discussed in terms of a pacemaker-counter model of temporal processing, in which the counter component is supported by short-term memory.

UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures

A better understanding of the systemic processes by which innovation occurs is useful, both conceptually and to inform policymaking in support of innovation in more sustainable technologies. This paper analyses current innovation systems in the UK for a range of new and renewable energy technologies, and generates policy recommendations for improving the effectiveness of these innovation systems. Although incentives are in place in the UK to encourage innovation in these technologies, system failures—or ‘gaps’—are identified in moving technologies along the innovation chain, preventing their successful commercialisation. Sustained investment will be needed for these technologies to achieve their potential. It is argued that a stable and consistent policy framework is required to help create the conditions for this. In particular, such a framework should be aimed at improving risk/reward ratios for demonstration and pre-commercial stage technologies. This would enhance positive expectations, stimulate learning effects leading to cost reductions, and increase the likelihood of successful commercialisation.

The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function.

Platelets in the circulation are triggered by vascular damage to activate, aggregate and form a thrombus that prevents excessive blood loss. Platelet activation is stringently regulated by intracellular signalling cascades, which when activated inappropriately lead to myocardial infarction and stroke. Strategies to address platelet dysfunction have included proteomics approaches which have lead to the discovery of a number of novel regulatory proteins of potential therapeutic value. Global analysis of platelet proteomes may enhance the outcome of these studies by arranging this information in a contextual manner that recapitulates established signalling complexes and predicts novel regulatory processes. Platelet signalling networks have already begun to be exploited with interrogation of protein datasets using in silico methodologies that locate functionally feasible protein clusters for subsequent biochemical validation. Characterization of these biological systems through analysis of spatial and temporal organization of component proteins is developing alongside advances in the proteomics field. This focused review highlights advances in platelet proteomics data mining approaches that complement the emerging systems biology field. We have also highlighted nucleated cell types as key examples that can inform platelet research. Therapeutic translation of these modern approaches to understanding platelet regulatory mechanisms will enable the development of novel anti-thrombotic strategies.

Planning rigid body motions using elastic curves

This paper tackles the problem of computing smooth, optimal trajectories on the Euclidean group of motions SE(3). The problem is formulated as an optimal control problem where the cost function to be minimized is equal to the integral of the classical curvature squared. This problem is analogous to the elastic problem from differential geometry and thus the resulting rigid body motions will trace elastic curves. An application of the Maximum Principle to this optimal control problem shifts the emphasis to the language of symplectic geometry and to the associated Hamiltonian formalism. This results in a system of first order differential equations that yield coordinate free necessary conditions for optimality for these curves. From these necessary conditions we identify an integrable case and these particular set of curves are solved analytically. These analytic solutions provide interpolating curves between an initial given position and orientation and a desired position and orientation that would be useful in motion planning for systems such as robotic manipulators and autonomous-oriented vehicles.

Cumulant-based deconvolution and identification: several new families of linear equations

This paper presents several new families of cumulant-based linear equations with respect to the inverse filter coefficients for deconvolution (equalisation) and identification of nonminimum phase systems. Based on noncausal autoregressive (AR) modeling of the output signals and three theorems, these equations are derived for the cases of 2nd-, 3rd and 4th-order cumulants, respectively, and can be expressed as identical or similar forms. The algorithms constructed from these equations are simpler in form, but can offer more accurate results than the existing methods. Since the inverse filter coefficients are simply the solution of a set of linear equations, their uniqueness can normally be guaranteed. Simulations are presented for the cases of skewed series, unskewed continuous series and unskewed discrete series. The results of these simulations confirm the feasibility and efficiency of the algorithms.

A novel scheme for human-friendly and time-delays robust neuropredictive teleoperation

A novel Neuropredictive Teleoperation (NPT) Scheme is presented. The design results from two key ideas: the exploitation of the measured or estimated neural input to the human arm or its electromyograph (EMG) as the system input and the employment of a predictor of the arm movement, based on this neural signal and an arm model, to compensate for time delays in the system. Although a multitude of such models, as well as measuring devices for the neural signals and the EMG, have been proposed, current telemanipulator research has only been considering highly simplified arm models. In the present design, the bilateral constraint that the master and slave are simultaneously compliant to each other's state (equal positions and forces) is abandoned, thus obtaining a simple to analyzesuccession of only locally controlled modules, and a robustness to time delays of up to 500 ms. The proposed designs were inspired by well established physiological evidence that the brain, rather than controlling the movement on-line, programs the arm with an action plan of a complete movement, which is then executed largely in open loop, regulated only by local reflex loops. As a model of the human arm the well-established Stark model is employed, whose mathematical representation is modified to make it suitable for an engineering application. The proposed scheme is however valid for any arm model. BIBO-stability and passivity results for a variety of local control laws are reported. Simulation results and comparisons with traditional designs also highlight the advantages of the proposed design.