AM(1-x)Al(x)O(3-x) (A=Na or K; M=Nb or Ta): New anion-deficient Perovskite oxides exhibiting oxide ion conduction

Anion-deficient perovskite oxides of the formula AM(1-x)Al(x)O(3-x) (A = Na or K; M = Nb or Ta) have been prepared for 0 < x less than or equal to 0.5. Diffraction experiments reveal that while the potassium compounds adopt orthorhombic/cubic perovskite structures similar to the parent KNbO3/KTaO3, the sodium compound, NaNb0.5Al0.5O2.5, possesses a brownmillerite/LaSr-CuAlO5-like superstructure. Al-27 NMR spectra show an exclusive tetrahedral oxygen coordination for AI(III) in Na-Nb0.5Al0.5O2.5 (I) and both tetrahedral and octahedral coordination for Al(III) in KNb0.5Al0.5O2.5 (II). The results suggest a long-range and short-range ordering of oxide ion vacancies in I and II respectively. Electrical conductivity measurements show a significant oxide ion conduction for KNb1-xAlxO3-x, with the conductivity increasing with x up to x = 0.5. The differences in the Arrhenius plots of the ionic conductivity of I and II have been rationalized in terms of the long-range and short-range ordering of oxide ion vacancies in the anion-deficient perovskite oxides.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Neural network modeling of associative memory: Beyond the Hopfield model

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

H-1 MAS NMR study of protonic conduction in layered HNbWO6 center dot xH(2)O (x=1.5, 0.5)

H-1 Magic Angle Spinning (MAS) NMR of layered HNbWO6 . xH(2)O (x = 1.5, 0.5) is carried out at room temperature and at various spinning speeds (1-12 kHz). Results on the fully hydrated sample (x = 1.5) are consistent with the model of diffusion of H3O+ ions within the layers. In the partially dehydrated sample (x = 0.5) an exchange between the distinctly present cage protons and H3O+ protons leads to protonic conduction.

Mechanism of protonic conduction in defect pyrochlore HNbWO6.xH(2)O using MAS NMR

We have carried out H-1 Magic Angle Spinning (MAS) NMR measurements at various spinning speeds (1-12 kHz) on HNbWO(6)xH(2)O (x = 0 and 1) defect pyrochlore systems. The variation of the line width with the spinning speed in the two systems points towards the presence of motions with different time scales. We conclude that the mechanism of conduction in both the compounds are similar except that the proton hopping in hydrated form is assisted by the water of hydration.

Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithm to determine the flap time history for vibration minimization. Using the aeroelastic analysis, it is found that the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power ale obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

Electronic conduction in LaNiO3-delta: the dependence on the oxygen stoichiometry delta

We report a systematic study of the electronic transport properties of the metallic perovskite oxide LaNiO3-delta as a function of the oxygen stoichiometry delta (delta less than or equal to 0.14). The electrical resistivity, magnetoresistance, susceptibility, Hall effect and thermopower have been studied, All of the transport coefficients are dependent on the value of delta. The resistivity increases almost exponentially as delta increases. We relate this increase in rho to the creation of Ni2+ with square-planar coordination. We find that there is a distinct T-1.5-contribution to the resistivity over the whole temperature range. The thermopower is negative, as expected for systems with electrons as the carrier, but the Hall coefficient is positive. We have given a qualitative and quantitative explanation for the different quantities observed and their systematic variation with the stoichiometry delta.

Mathematical modelling of neurons and neural networks

The basic concepts and techniques involved in the development and analysis of mathematical models for individual neurons and networks of neurons are reviewed. Some of the interesting results obtained from recent work in this field are described. The current status of research in this field in India is discussed

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Monte Carlo simulation of ion conduction in silver based glassy electrolytes

A simplified structural model to study the ionic transport in silver based glasses has been formulated. The diffusion of silver ion under the influence of coulombic interactions of mobile cation and anions has been studied. Monte Carlo simulations of silver ion hopping in glass have suggested two different kinds of population of silver ions. We discuss the results of variation in diffusion constant with dopant (AgI) concentration using the diffusion path model. (C) 1997 Elsevier-Science S.A.

On the problem of spurious patterns in neural associative memory models

The problem of spurious patterns in neural associative memory models is discussed, Some suggestions to solve this problem from the literature are reviewed and their inadequacies are pointed out, A solution based on the notion of neural self-interaction with a suitably chosen magnitude is presented for the Hebb learning rule. For an optimal learning rule based on linear programming, asymmetric dilution of synaptic connections is presented as another solution to the problem of spurious patterns, With varying percentages of asymmetric dilution it is demonstrated numerically that this optimal learning rule leads to near total suppression of spurious patterns. For practical usage of neural associative memory networks a combination of the two solutions with the optimal learning rule is recommended to be the best proposition.

Nonlinear electrical conduction and broad band noise in the charge-ordered rare earth manganate Nd0.5Ca0.5MnO3

Measurements of the dc transport properties and the low-frequency conductivity noise in films of charge-ordered Nd0.5Ca0.5MnO3 grown on Si substrate reveal the existence of a threshold field in the charge-ordered regime beyond which strong nonlinear conduction sets in along with a large broad band conductivity noise. Threshold-dependent conduction disappears as T --> T-CO, the charge-ordering temperature. This observation suggests that the charge-ordered state gets depinned at the onset of the nonlinear conduction. (C) 1999 American Institute of Physics. [S0003-6951(99)05247-X].

The molecular dynamics study of lithium ion conduction in phosphate glasses and the role of non-bridging oxygen

Molecular dynamics (MD) simulation of lithium phosphate (Li2O-P2O5) glasses with varying Li2O content has been carried out. Two different P-O distances corresponding to phosphorus coordination with bridging oxygen (BO) and non-bridging oxygen (NBO) were identified in the simulated glasses. NBO-BO interconversion or bond switching was noted, which results in a dynamic equilibration of the tetrahedral phosphate units (P-n, n = 1,3 indicates the number of bridging oxygen atoms in the coordination of phosphorus). The NBO-BO bond switching is mildly activated with an effective activation barrier of 0.03-0.05 eV. Lithium ion jumps do not appear to be strongly coupled to bond switching. But the number of Li+ ions coordinated to an optimum number of NBOs and the number of Li+ ions jumping out of their sites appear to be correlated. Detailed analysis was made of the dynamics of P-n species and new insights have been obtained regarding ion migration in network-modified phosphate glasses.

Transitional intermittency detection by neural network

A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Alternating current conduction and impedance spectroscopy analysis on pulsed excimer laser ablated (Pb, La)TiO3 thin films

Lanthanum doped lead titanate (PLT) thin films were identified as the most potential candidates for the pyroelectric and memory applications. PLT thin films were deposited on Pt coated Si by excimer laser ablation technique. The polarization behavior of PLT thin films has been studied over a temperature range of 300 K to 550 K. A universal power law relation was brought into picture to explain the frequency dependence of ac conductivity. At higher frequency region ac conductivity of PLT thin films become temperature independent. The temperature dependence of ac conductivity and the relaxation time is analyzed in detail. The activation energy obtained from the ac conductivity was attributed to the shallow trap controlled space charge conduction in the bulk of the sample. The impedance analysis for PLT thin films were also performed to get insight of the microscopic parameters, like grain, grain boundary, and film-electrode interface etc. The imaginary component of impedance Z" exhibited different peak maxima at different temperatures. Different types of mechanisms were analyzed in detail to explain the dielectric relaxation behavior in the PLT thin films.