Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Acceleration and visualization of Dynamic Network Optimization

With the emerging prevalence of smart phones and 4G LTE networks, the demand for faster-better-cheaper mobile services anytime and anywhere is ever growing. The Dynamic Network Optimization (DNO) concept emerged as a solution that optimally and continuously tunes the network settings, in response to varying network conditions and subscriber needs. Yet, the DNO realization is still at infancy, largely hindered by the bottleneck of the lengthy optimization runtime. This paper presents the design and prototype of a novel cloud based parallel solution that further enhances the scalability of our prior work on various parallel solutions that accelerate network optimization algorithms. The solution aims to satisfy the high performance required by DNO, preliminarily on a sub-hourly basis. The paper subsequently visualizes a design and a full cycle of a DNO system. A set of potential solutions to large network and real-time DNO are also proposed. Overall, this work creates a breakthrough towards the realization of DNO.

Interventions in social networks: impact on mood and network dynamics

Results from two studies on longitudinal friendship networks are presented, exploring the impact of a gratitude intervention on positive and negative affect dynamics in a social network. The gratitude intervention had been previously shown to increase positive affect and decrease negative affect in an individual but dynamic group effects have not been considered. In the first study the intervention was administered to the whole network. In the second study two social networks are considered and in each only a subset of individuals, initially low/high in negative affect respectively received the intervention as `agents of change'. Data was analyzed using stochastic actor based modelling techniques to identify resulting network changes, impact on positive and negative affect and potential contagion of mood within the group. The first study found a group level increase in positive and a decrease in negative affect. Homophily was detected with regard to positive and negative affect but no evidence of contagion was found. The network itself became more volatile along with a fall in rate of change of negative affect. Centrality measures indicated that the best broadcasters were the individuals with the least negative affect levels at the beginning of the study. In the second study, the positive and negative affect levels for the whole group depended on the initial levels of negative affect of the intervention recipients. There was evidence of positive affect contagion in the group where intervention recipients had low initial level of negative affect and contagion in negative affect for the group where recipients had initially high level of negative affect.

Optimizing mixing during the sponge cake manufacturing process

Sponge cakes have traditionally been manufactured using multistage mixing methods to enhance potential foam formation by the eggs. Today, use of all-in (single-stage) mixing methods is superseding multistage methods for large-scale batter preparation to reduce costs and production time. In this study, multistage and all-in mixing procedures and three final high-speed mixing times (3, 5, and 15 min) for sponge cake production were tested to optimize a mixing method for pilot-scale research. Mixing for 3 min produced batters with higher relative density values than did longer mixing times. These batters generated well-aerated cakes with high volume and low hardness. In contrast, after 5 and 15 min of high-speed mixing, batters with lower relative density and higher viscosity values were produced. Although higher bubble incorporation and retention were observed, longer mixing times produced better developed gluten networks, which stiffened the batters and inhibited bubble expansion during mixing. As a result, these batters did not expand properly and produced cakes with low volume, dense crumb, and high hardness values. Results for all-in mixing were similar to those for the multistage mixing procedure in terms of the physical properties of batters and cakes (i.e., relative density, elastic moduli, volume, total cell area, hardness, etc.). These results suggest the all-in mixing procedure with a final high-speed mixing time of 3 min is an appropriate mixing method for pilot-scale sponge cake production. The advantages of this method are reduced energy costs and production time.

The Italian academies 1525-1700: networks of culture, innovation and dissent

The intellectual societies known as Academies played a vital role in the development of culture, and scholarly debate throughout Italy between 1525-1700. They were fundamental in establishing the intellectual networks later defined as the ‘République des Lettres’, and in the dissemination of ideas in early modern Europe, through print, manuscript, oral debate and performance. This volume surveys the social and cultural role of Academies, challenging received ideas and incorporating recent archival findings on individuals, networks and texts. Ranging over Academies in both major and smaller or peripheral centres, these collected studies explore the interrelationships of Academies with other cultural forums. Individual essays examine the fluid nature of academies and their changing relationships to the political authorities; their role in the promotion of literature, the visual arts and theatre; and the diverse membership recorded for many academies, which included scientists, writers, printers, artists, political and religious thinkers, and, unusually, a number of talented women. Contributions by established international scholars together with studies by younger scholars active in this developing field of research map out new perspectives on the dynamic place of the Academies in early modern Italy. The publication results from the research collaboration ‘The Italian Academies 1525-1700: the first intellectual networks of early modern Europe’ funded by the Arts and Humanities Research Council and is edited by the senior investigators.

A structure-dynamic approach to cortical organization: Number of paths and accessibility

A structure-dynamic approach to cortical systems is reported which is based on the number of paths and the accessibility of each node. The latter measurement is obtained by performing self-avoiding random walks in the respective networks, so as to simulate dynamics, and then calculating the entropies of the transition probabilities for walks starting from each node. Cortical networks of three species, namely cat, macaque and humans, are studied considering structural and dynamical aspects. It is verified that the human cortical network presents the highest accessibility and number of paths (in terms of z-scores). The correlation between the number of paths and accessibility is also investigated as a mean to quantify the level of independence between paths connecting pairs of nodes in cortical networks. By comparing the cortical networks of cat, macaque and humans, it is verified that the human cortical network tends to present the largest number of independent paths of length larger than four. These results suggest that the human cortical network is potentially the most resilient to brain injures. (C) 2009 Elsevier B.V. All rights reserved.

Storing quantum states in bosonic dissipative networks

By considering a network of dissipative quantum harmonic oscillators, we deduce and analyse the optimum topologies which are able to store quantum superposition states, protecting them from decoherence, for the longest period of time. The storage is made dynamically, in that the states to be protected evolve through the network before being retrieved back in the oscillator where they were prepared. The decoherence time during the dynamic storage process is computed and we demonstrate that it is proportional to the number of oscillators in the network for a particular regime of parameters.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

Effect of the manufacturing process on the interlaminar fracture toughness of 2/2 twill weave fabric carbon/epoxy composites

A 2/2 twill weave fabric carbon fibre reinforced epoxy matrix composite MTM56/CF0300 was used to investigate the effect of different manufacturing processes on the interlaminar fracture toughness. Double cantilever beam tests were performed on composites manufactured by hot press, autoclave and 'Quickstep' processes. The 'Quickstep' process was recently developed in Perth, Western Australia for the manufacture of advanced composite components. The values of the mode I critical strain energy release rate (G1d were compared and the results showed that the composite specimens manufactured by the autoclave and the 'Quickstep' process had much higher interlaminar fracture toughness than the specimen produced by the hot press. When compared to specimens manufactured by the hot press, the interlaminar fracture toughness values of the Quickstep and autoclave samples were 38% and 49% higher respectively. The 'Quickstep' process produced composite specimens that had comparable interlaminar fracture toughness to autoclave manufactured composites. Scanning electron microscopy (SEM) was employed to study the topography of the mode I interlaminar fracture surface and dynamic mechanical analysis (DMA) was performed to investigate the fibre/matrix interphase. SEM micrography and DMA spectra indicated that autoclave and 'Quickstep' produced composites with stronger fibre/matrix adhesion than hot press.

Prediction based location aided routing for mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P-LAR) protocol. Simulation study and analytical results of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Mobility prediction based routing for minimizing control overhead in mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided routing (P-LAR) protocol. Simulation study and analytical results of the of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Address reuse in wireless sensor networks

Sensor Networks have applications in diverse fields. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks, it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose a dynamic addressing mechanism for wireless sensor networks. The scheme enables successful reuse of addresses in event-driven wireless sensor networks. It also eliminates the need for network-wide Duplicate Address Detection (DAD) to ensure uniqueness of network level addresses.

Using autonomous mobile agents for efficient data collection in sensor networks

Sensor networks are emerging as the new frontier in sensing technology, however there are still issues that need to be addressed. Two such issues are data collection and energy conservation. We consider a mobile robot, or a mobile agent, traveling the network collecting information from the sensors themselves before their onboard memory storage buffers are full. A novel algorithm is presented that is an adaptation of a local search algorithm for a special case of the Asymmetric Traveling Salesman Problem with Time-windows (ATSPTW) for solving the dynamic scheduling problem of what nodes are to be visited so that the information collected is not lost. Our algorithms are given and compared to other work.

Novel data management algorithms in peer-to-peer content distribution networks

The peer-to-peer content distribution network (PCDN) is a hot topic recently, and it has a huge potential for massive data intensive applications on the Internet. One of the challenges in PCDN is routing for data sources and data deliveries. In this paper, we studied a type of network model which is formed by dynamic autonomy area, structured source servers and proxy servers. Based on this network model, we proposed a number of algorithms to address the routing and data delivery issues. According to the highly dynamics of the autonomy area, we established dynamic tree structure proliferation system routing, proxy routing and resource searching algorithms. The simulations results showed that the performance of the proposed network model and the algorithms are stable.