A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

A multi-level probabilistic neural network

Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

An agent based approach to distributed task modeling for co-operative task support

This paper describes the design and implementation of an agent based network for the support of collaborative switching tasks within the control room environment of the National Grid Company plc. This work includes aspects from several research disciplines, including operational analysis, human computer interaction, finite state modelling techniques, intelligent agents and computer supported co-operative work. Aspects of these procedures have been used in the analysis of collaborative tasks to produce distributed local models for all involved users. These models have been used as the basis for the production of local finite state automata. These automata have then been embedded within an agent network together with behavioural information extracted from the task and user analysis phase. The resulting support system is capable of task and communication management within the transmission despatch environment.

The distributed work of local action: interaction amongst virtually collocated research teams

Existing research on synchronous remote working in CSCW has highlighted the troubles that can arise because actions at one site are (partially) unavailable to remote colleagues. Such ‘local action’ is routinely characterised as a nuisance, a distraction, subordinate and the like. This paper explores interconnections between ‘local action’ and ‘distributed work’ in the case of a research team virtually collocated through ‘MiMeG’. MiMeG is an e-Social Science tool that facilitates ‘distributed data sessions’ in which social scientists are able to remotely collaborate on the real-time analysis of video data. The data are visible and controllable in a shared workspace and participants are additionally connected via audio conferencing. The findings reveal that whilst the (partial) unavailability of local action is at times problematic, it is also used as a resource for coordinating work. The paper considers how local action is interactionally managed in distributed data sessions and concludes by outlining implications of the analysis for the design and study of technologies to support group-to-group collaboration.

Three dimensional picture of the local structure of 1,4-Polybutadiene from a complete atomistic model and neutron scattering data

An efficient method of combining neutron diffraction data over an extended Q range with detailed atomistic models is presented. A quantitative and qualitative mapping of the organization of the chain conformation in both glass and liquid phase has been performed. The proposed structural refinement method is based on the exploitation of the intrachain features of the diffraction pattern by the use of internal coordinates for bond lengths, valence angles and torsion rotations. Models are built stochastically by assignment of these internal coordinates from probability distributions with limited variable parameters. Variation of these parameters is used in the construction of models that minimize the differences between the observed and calculated structure factors. A series of neutron scattering data of 1,4-polybutadiene at the region 20320 K is presented. Analysis of the experimental data yield bond lengths for C-C and C=C of 1.54 and 1.35 Å respectively. Valence angles of the backbone were found to be at 112 and 122.8 for the CCC and CC=C respectively. Three torsion angles corresponding to the double bond and the adjacent R and β bonds were found to occupy cis and trans, s(, trans and g( and trans states, respectively. We compare our results with theoretical predictions, computer simulations, RIS models, and previously reported experimental results.

Exploring the market potential of 'local' in food systems

Local food initiatives create a niche market in many developed countries where consumer choice is being met with an expanding offering in both conventional as well as complementary retail outlets. Supermarkets in conjunction with the food service sector currently dominate food sales and consumption, and are likely to do so for the foreseeable future. However, the local food sector offers an opportunity for implementing niche marketing strategies for many businesses. Local food activities tend to be relatively independent activities and a clearer definition for “local” food would assist in consolidating this important component of the food system. Related to this, consumers would benefit from the establishment of some form of assurance system for the ‘localness’ of food. In the UK, with its well established local food market, farmers’ markets, farm shops and box schemes are currently having the largest impact in terms of total sales. Hence further research is required to confirm that support for similar business ventures in Australia would be a viable strategy for strengthening its local food systems.

Online modeling with tunable RBF network

In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A multi-innovation recursive least square (MRLS) algorithm is applied to update the weights of RBF online, while the modeling performance is monitored. When the modeling residual of the RBF network becomes large in spite of the weight adaptation, a node identified as insignificant is replaced with a new node, for which the tunable center vector and diagonal covariance matrix are optimized using the quantum particle swarm optimization (QPSO) algorithm. The major contribution is to combine the MRLS weight adaptation and QPSO node structure optimization in an innovative way so that it can track well the local characteristic in the nonstationary system with a very sparse model. Simulation results show that the proposed algorithm has significantly better performance than existing approaches.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Energy-efficient hybrid system for Wireless Body Area Network Applications

Wireless Body Area Networks (WBANs) consist of a number of miniaturized wearable or implanted sensor nodes that are employed to monitor vital parameters of a patient over long duration of time. These sensors capture physiological data and wirelessly transfer the collected data to a local base station in order to be further processed. Almost all of these body sensors are expected to have low data-rate and to run on a battery. Since recharging or replacing the battery is not a simple task specifically in the case of implanted devices such as pacemakers, extending the lifetime of sensor nodes in WBANs is one of the greatest challenges. To achieve this goal, WBAN systems employ low-power communication transceivers and low duty cycle Medium Access Control (MAC) protocols. Although, currently used MAC protocols are able to reduce the energy consumption of devices for transmission and reception, yet they are still unable to offer an ultimate energy self-sustaining solution for low-power MAC protocols. This paper proposes to utilize energy harvesting technologies in low-power MAC protocols. This novel approach can further reduce energy consumption of devices in WBAN systems.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Coordinated ground-based, low altitude satellite and Cluster observations on global and local scales during a transient post-noon sector excursion of the magnetospheric cusp

On 14 January 2001, the four Cluster spacecraft passed through the northern magnetospheric mantle in close conjunction to the EISCAT Svalbard Radar (ESR) and approached the post-noon dayside magnetopause over Greenland between 13:00 and 14:00 UT During that interval, a sudden reorganisation of the high-latitude dayside convection pattern accurred after 13:20 UT most likely caused by a direction change of the Solar wind magnetic field. The result was an eastward and poleward directed flow-channel, as monitored by the SuperDARN radar network and also by arrays of ground-based magnetometers in Canada, Greenland and Scandinavia. After an initial eastward and later poleward expansion of the flow-channel between 13:20 and 13:40 UT, the four Cluster spacecraft, and the field line footprints covered by the eastward looking scan cycle of the Sondre Stromfjord incoherent scatter radar were engulfed by cusp-like precipitation with transient magnetic and electric field signatures. In addition, the EISCAT Svalbard Radar detected strong transient effects of the convection reorganisation, a poleward moving precipitation, and a fast ion flow-channel in association with the auroral structures that suddenly formed to the west and north of the radar. From a detailed analysis of the coordinated Cluster and ground-based data, it was found that this extraordinary transient convection pattern, indeed, had moved the cusp precipitation from its former pre-noon position into the late post-noon sector, allowing for the first and quite unexpected encounter of the cusp by the Cluster spacecraft. Our findings illustrate the large amplitude of cusp dynamics even in response to moderate solar wind forcing. The global ground-based data proves to be an invaluable tool to monitor the dynamics and width of the affected magnetospheric regions.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.