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

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.

Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks

Single-carrier (SC) block transmission with frequency-domain equalisation (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high bandwidthefficiency and high power-efficiency systems, the channel can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, We model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

The effects of different forms of advice on the control of a simulated complex system

Three experiments examine the effect of different forms of computer-generated advice on concurrent and subsequent performance of individuals controlling a simulated intensive-care task. Experiment 1 investigates the effect of optional and compulsory advice and shows that both result in an improvement in subjects' performance while receiving the advice, and also in an improvement in subsequent unaided performance. However, although the advice compliance displayed by the optional advice group shows a strong correlation with subsequent unaided performance, compulsory advice has no extra benefit over the optional use of advice. Experiment 2 examines the effect of providing users with on-line explanations of the advice, as well as providing less specific advice. The results show that both groups perform at the same level on the task as the advice groups from Experiment 1, although subjects receiving explanations scored significantly higher on a written post-task questionnaire. Experiment 3 investigates in more detail the relationship between advice compliance and performance. The results reveal a complex relationship between natural ability on the task and the following of advice, in that people who use the advice more tend to perform either better or worse than the more moderate users. The theoretical and practical implications of these experiments are discussed.

Mutational activation of niche-specific genes provides insight into regulatory networks and bacterial function in a complex environment

The genome of the plant-colonizing bacterium Pseudomonas fluorescens SBW25 harbors a subset of genes that are expressed specifically on plant surfaces. The function of these genes is central to the ecological success of SBW25, but their study poses significant challenges because no phenotype is discernable in vitro. Here, we describe a genetic strategy with general utility that combines suppressor analysis with IVET (SPyVET) and provides a means of identifying regulators of niche-specific genes. Central to this strategy are strains carrying operon fusions between plant environment-induced loci (EIL) and promoterless 'dapB. These strains are prototrophic in the plant environment but auxotrophic on laboratory minimal medium. Regulatory elements were identified by transposon mutagenesis and selection for prototrophs on minimal medium. Approximately 106 mutants were screened for each of 27 strains carrying 'dapB fusions to plant EIL and the insertion point for the transposon determined in approximately 2,000 putative regulator mutants. Regulators were functionally characterized and used to provide insight into EIL phenotypes. For one strain carrying a fusion to the cellulose-encoding wss operon, five different regulators were identified including a diguanylate cyclase, the flagella activator, FleQ, and alginate activator, AmrZ (AlgZ). Further rounds of suppressor analysis, possible by virtue of the SPyVET strategy, revealed an additional two regulators including the activator AlgR, and allowed the regulatory connections to be determined.

A tungsten complex containing a highly delocalized metal-ligand system

Nucleophilic attack of (triphenylphosphonio) cyclopentadienide on the dichlorodiazomethane-tungsten complex trans[ BrW(dppe)(2)(N2CCl2)]PF6 [dppe is 1,2-bis(diphenylphosphino) ethane] results in C-C bond formation and affords the title compound, trans-[W(C24H18ClN2P)Br(C26H24P2)(2)]PF6 center dot 0.6CH(2)Cl(2). This complex, bis[1,2- bis(diphenylphosphino)ethane] bromido{chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethanediido} tungsten hexafluorophosphate dichloromethane 0.6-solvate, contains the previously unknown ligand chloro[3-(triphenylphosphonio) cyclopentadienylidene] diazomethane. Evidence from bond lengths and torsion angles indicates significant through-ligand delocalization of electron density from tungsten to the nominally cationic phosphorus(V) centre. This structural analysis clearly demonstrates that the tungsten-dinitrogen unit is a powerful pi-electron donor with the ability to transfer electron density from the metal to a distant acceptor centre through an extended conjugated ligand system. As a consequence, complexes of this type could have potential applications as nonlinear optical materials and molecular semiconductors.

Concomitant polymorphism of an antiferromagnetically coupled dicopper(II,II) complex with single strand helical assembly: synthesis, structure, DSC, magnetic and heterogeneous catalytic studies

Two concomitant polymorphic coordination complexes (dark blue - I and black - II) with the formula (Cu2C44H60N4O4) have been synthesized and characterized crystallographically. Magnetic measurements show the presence of a strong antiferromagnetic interaction and the 2J value corresponds extremely well to the theoretically calculated one, indicating the fact that it follows nicely the magneto-structural relationship. Immobilization of the copper(II) complex I on a 2D-hexagonal mesoporous silica showed good catalytic efficiency in the liquid phase partial oxidation of olefins in the presence of TBHP as an oxidant. (C) 2009 Elsevier Ltd. All rights reserved.

Synthesis, structure and properties of cobalt(II/III) seleno-bisphenolate complexes containing NN bidentate co-ligands: kinetic studies on the reduction of a cobalt(III) complex by ascorbic acid in 80% MeCN-20% water

Two cobalt complexes, [Co(L-Se)(phen)]center dot CH2Cl2 (1) and [Co(L-Se)(N,N-Me(2)en)(CH3COO-)] (2) have been synthesized and characterized by elemental analyses, magnetic measurements, i.r. studies etc. Single crystal X- ray studies reveal that in complex (1) cobalt atom is in +2 oxidation state with trigonal bipyramidal geometry, while in complex (2) it is in +3 oxidation state and surrounded octahedrally. The asymmetric unit of complex (2) contains two crystallographically independent discrete molecules. Complex (1) was found to be paramagnetic with mu(eff) = 2.19 BM indicating a low spin cobalt(II) d(7) system, whereas complex (2) is found to be diamagnetic with cobalt(III) in low spin d(6) state. The kinetic studies on the reduction of (2) by ascorbic acid in 80% MeCN-20% H2O (v/v) at 25 degrees C reveal that the reaction proceeds through the rapid formation of inner-sphere adduct, probably by replacing the loosely coordinated AcO- group, followed by electron transfer in a slow step and is supported by a large Q (formation constant) value.

Informal communication networks and the diffusion of innovations in UK construction projects: A research design

The UK industry has been criticised for being slow to adopt construction process innovations. Research shows that the idiosyncrasies of participants, their roles in the system and the contextual differences between sections of the industry make this a highly complex problem. There is considerable evidence that informal social networks play a key role in diffusion of innovations. The aim is to identify informal communication networks of project participants and the role these play in the diffusion of construction innovations. The characteristics of this network will be analysed in order to understand how they can be used to accelerate innovation diffusion within and between projects. Social Network Analysis is used to determine informal communication routes. Control and experiment case study projects are used within two different organizations. This allows informal communication routes concerning innovations to be mapped, whilst testing if the informal routes can facilitate diffusion. Analysis will focus upon understanding the combination of informal strong and weak ties, and how these impede or facilitate the diffusion of the innovation. Initial work suggests the presence of an informal communication network. Actors within this informal network, and the organization's management are unaware of its' existence and their informal roles within it. Thus, the network remains an untapped medium regarding innovation diffusion. It is proposed that successful innovation diffusion is dependent upon understanding informal strong and weak ties, at project, organization and industry level.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme. (C) 2007 Elsevier B.V. All rights reserved.

Wiener-System subspace identification for mobile wireless mm-wave networks

We discuss the feasibility of wireless terahertz communications links deployed in a metropolitan area and model the large-scale fading of such channels. The model takes into account reception through direct line of sight, ground and wall reflection, as well as diffraction around a corner. The movement of the receiver is modeled by an autonomous dynamic linear system in state space, whereas the geometric relations involved in the attenuation and multipath propagation of the electric field are described by a static nonlinear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a single-output Wiener model from time-domain measurements of the field intensity when the receiver motion is simulated using a constant angular speed and an exponentially decaying radius. The identification procedure is validated by using the model to perform q-step ahead predictions. The sensitivity of the algorithm to small-scale fading, detector noise, and atmospheric changes are discussed. The performance of the algorithm is tested in the diffraction zone assuming a range of emitter frequencies (2, 38, 60, 100, 140, and 400 GHz). Extensions of the simulation results to situations where a more complicated trajectory describes the motion of the receiver are also implemented, providing information on the performance of the algorithm under a worst case scenario. Finally, a sensitivity analysis to model parameters for the identified Wiener system is proposed.

Expert system aid for design of overload line distribution networks

The use of expert system techniques in power distribution system design is examined. The selection and siting of equipment on overhead line networks is chosen for investigation as the use of equipment such as auto-reclosers, etc., represents a substantial investment and has a significant effect on the reliability of the system. Through past experience with both equipment and network operations, most decisions in selection and siting of this equipment are made intuitively, following certain general guidelines or rules of thumb. This heuristic nature of the problem lends itself to solution using an expert system approach. A prototype has been developed and is currently under evaluation in the industry. Results so far have demonstrated both the feasibility and benefits of the expert system as a design aid.