Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

The evolution of innovation capability in multinational enterprise subsidiaries: dual network embeddedness and the divergence of subsidiary specialisation in Taiwan

This paper examines how innovation-related capabilities for production, design and marketing develop at the subsidiary level within multinational enterprises (MNEs). We focus on how subsidiary autonomy and changing opportunities to access internal (MNE) and external (host country) sources of capability contribute in a combined way to the accumulation of specialist capabilities in five Taiwan-based MNE subsidiaries in the semiconductor industry. Longitudinal analysis shows how the accumulation process is subject to discontinuities, as functional divisions are (re)opened and closed during the lifetime of the subsidiary. A composite set of innovation output measures also shows significant variations in within-function levels of capability across our sample. We conclude that subsidiary specialisation and unique subsidiary-specific advantages have evolved in a way that is strongly influenced by the above factors.

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.

Futures basis, inventory and commodity price volatility: an empirical analysis

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993–2011 to empirically analyze the behavior of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we analyze the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom.

A mesoscopic modelling approach to anaesthetic action on brain electrical activity

Relating the measurable, large scale, effects of anaesthetic agents to their molecular and cellular targets of action is necessary to better understand the principles by which they affect behavior, as well as enabling the design and evaluation of more effective agents and the better clinical monitoring of existing and future drugs. Volatile and intravenous general anaesthetic agents (GAs) are now known to exert their effects on a variety of protein targets, the most important of which seem to be the neuronal ion channels. It is hence unlikely that anaesthetic effect is the result of a unitary mechanism at the single cell level. However, by altering the behavior of ion channels GAs are believed to change the overall dynamics of distributed networks of neurons. This disruption of regular network activity can be hypothesized to cause the hypnotic and analgesic effects of GAs and may well present more stereotypical characteristics than its underlying microscopic causes. Nevertheless, there have been surprisingly few theories that have attempted to integrate, in a quantitative manner, the empirically well documented alterations in neuronal ion channel behavior with the corresponding macroscopic effects. Here we outline one such approach, and show that a range of well documented effects of anaesthetics on the electroencephalogram (EEG) may be putatively accounted for. In particular we parameterize, on the basis of detailed empirical data, the effects of halogenated volatile ethers (a clinically widely used class of general anaesthetic agent). The resulting model is able to provisionally account for a range of anaesthetically induced EEG phenomena that include EEG slowing, biphasic changes in EEG power, and the dose dependent appearance of anomalous ictal activity, as well as providing a basis for novel approaches to monitoring brain function in both health and disease.

Use of synthetic CaV2.2 Ca2+ channel peptides to study presynaptic function

Small, synthetic peptides based on specific regions of voltage-gated Ca2+ channels (VGCCs) have been widely used to study Ca2+ channel function and have been instrumental in confirming the contribution of specific amino acid sequences to interactions with putative binding partners. In particular, peptides based on the Ca2+ channel Alpha Interaction Domain (AID) on the intracellular region connecting domains I and II (the I-II loop) and the SYNaptic PRotein INTerction (synprint) site on the II-III loop have been widely used. Emerging evidence suggests that such peptides may themselves possess inherent functionality, a property that may be exploitable for future drug design. Here, we review our recent work using synthetic Ca2+ channel peptides based on sequences within the CaV2.2 amino terminal and I-II loop, originally identified as molecular determinates for G protein modulation, and their effects on VGCC function. These CaV2.2 peptides act as inhibitory modules to decrease Ca2+ influx with direct effects on VGCC gating, ultimately leading to a reduction of synaptic transmission. CaV2.2 peptides also attenuate G protein modulation of VGCCs. Amino acid substitutions generate CaV2.2 peptides with increased or decreased inhibitory effects suggesting that synthetic peptides can be used to further probe VGCC function and, potentially, form the basis for novel therapeutic development.

Flavonoid inhibitory pharmacodynamics on platelet function in physiological environments

The complex relationship between flavonoid-based nutrition and cardiovascular disease may be dissected by understanding the activities of these compounds in biological systems. The aim of the present study was to explore a hierarchy for the importance of dietary flavonoids on cardiovascular health by examining the structural basis for inhibitory effects of common, dietary flavonoids (quercetin, apigenin, and naringenin) and the plasma metabolite, tamarixetin. Understanding flavonoid effects on platelets in vivo can be informed by investigations of the ability of these compounds to attenuate the function of these cells. Inhibition of platelet function in whole blood and plasma was structure-dependent. The order of potency was apigenin > tamarixetin > quercetin = naringenin indicating that in vivo, important functional groups are potentially a methylated B ring, and a non-hydroxylated, planar C ring. Apigenin and the methylated metabolite of quercetin, tamarixetin significantly reduced thrombus volume at concentrations (5 μM) that suggested their reported physiological levels (0.1-1 μM) may exert low levels of inhibition. Flavonoid interactions with erythrocytes, leukocytes and human serum albumin in whole blood reduce their inhibitory activities against platelet function. The diminished inhibitory activity of flavonoids that we observed in whole blood and plasma indicated that these interactions do not overcome the attenuating effects of these compounds. Furthermore, inhibition of platelet aggregation by flavonoids was enhanced with increases in exposure time, indicating the potential for measurable inhibitory effects during resident plasma times. We conclude that flavonoid structures may be a major influence of their activities in vivo with methylated metabolites and those of flavones being more potent than those of flavonols and flavanones.

Impacts of plant-based foods in ancestral hominin diets on the metabolism and function of gut microbiota in vitro

Ancestral human populations had diets containing more indigestible plant material than present-day diets in industrialized countries. One hypothesis for the rise in prevalence of obesity is that physiological mechanisms for controlling appetite evolved to match a diet with plant fiber content higher than that of present-day diets. We investigated how diet affects gut microbiota and colon cells by comparing human microbial communities with those from a primate that has an extreme plant-based diet, namely, the gelada baboon, which is a grazer. The effects of potato (high starch) versus grass (high lignin and cellulose) diets on human-derived versus gelada-derived fecal communities were compared in vitro. We especially focused on the production of short-chain fatty acids, which are hypothesized to be key metabolites influencing appetite regulation pathways. The results confirmed that diet has a major effect on bacterial numbers, short-chain fatty acid production, and the release of hormones involved in appetite suppression. The potato diet yielded greater production of short-chain fatty acids and hormone release than the grass diet, even in the gelada cultures, which we had expected should be better adapted to the grass diet. The strong effects of diet on hormone release could not be explained, however, solely by short-chain fatty acid concentrations. Nuclear magnetic resonance spectroscopy found changes in additional metabolites, including betaine and isoleucine, that might play key roles in inhibiting and stimulating appetite suppression pathways. Our study results indicate that a broader array of metabolites might be involved in triggering gut hormone release in humans than previously thought. IMPORTANCE: One theory for rising levels of obesity in western populations is that the body's mechanisms for controlling appetite evolved to match ancestral diets with more low-energy plant foods. We investigated this idea by comparing the effects of diet on appetite suppression pathways via the use of gut bacterial communities from humans and gelada baboons, which are modern-day primates with an extreme diet of low-energy plant food, namely, grass. We found that diet does play a major role in affecting gut bacteria and the production of a hormone that suppresses appetite but not in the direction predicted by the ancestral diet hypothesis. Also, bacterial products were correlated with hormone release that were different from those normally thought to play this role. By comparing microbiota and diets outside the natural range for modern humans, we found a relationship between diet and appetite pathways that was more complex than previously hypothesized on the basis of more-controlled studies of the effects of single compounds.

The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.

Performing knowledge requirements analysis for public organisations in a Virtual Learning Environment: a social network analysis approach

This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was executed in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be extracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for applying knowledge management between organisations and creating communities of practice. Concepts of knowledge management and social network analysis provide the theoretical and methodological basis.

Observing wind, aerosol particles, clouds and precipitation: Finland's new ground-based remote-sensing network

The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.