This PDF file contains the front matter associated with SPIE Proceedings Volume 6417, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

This paper deals with a novel strategy for the detection of weak static electric fields. The approach proposed here is based on the exploitation of the nonlinear behaviors shown by a circuit made up by the ring connection of an odd number of elements containing a ferroelectric capacitor. The presence of a weak external dc perturbation interacting with the system state can be detected and quantified via its effect on the oscillation frequency and on the asymmetry of the system output signals. The dynamic behavior of the ferroelectric ring can be described by using the equations of the "quartic double well" potential that model the ferroelectric capacitors where the target electric field is considered as a perturbation in the polarization status of each ferroelectric element. Simulation results have been obtained where it can be observed, for a coupling factor greater than the critical one, as related to the external field amplitude, the change in the harmonic content of the permanent oscillation that the coupled system generates. A detailed spice model of the ferroelectric capacitor and of the ring circuit will be described in this paper together with some results regarding the experimental characterization and modeling of ferroelectric capacitors to be included in the actual circuit. Several experimental confirmation have been already obtained. Work is currently in progress toward the realization of a novel ring circuit that will include the ferroelectric capacitors presented here.

We have proposed and modified the dynamical model of drying process of polymer solution coated on a flat substrate for flat polymer film fabrication and have presented the fruits through some meetings and so on. Though basic equations of the dynamical model have characteristic nonlinearity, character of the nonlinearity has not been studied enough yet. In this paper, at first, we derive nonlinear equations from the dynamical model of drying process of polymer solution. Then we introduce results of numerical simulations of the nonlinear equations and consider roles of various parameters. Some of them are indirectly concerned in strength of non-equilibriumity. Through this study, we approach essential qualities of nonlinearity in non-equilibrium process of drying process.

Pooling networks of noisy threshold devices are good models for natural networks (e.g. neural networks in some parts of sensory pathways in vertebrates, networks of mossy fibers in the hippothalamus, . . . ) as well as for artificial networks (e.g. digital beamformers for sonar arrays, flash analog-to-digital converters, rate-constrained distributed sensor networks, . . . ). Such pooling networks exhibit the curious effect of suprathreshold stochastic resonance, which means that an optimal stochastic control of the network exists. Recently, some progress has been made in understanding pooling networks of identical, but independently noisy, threshold devices. One aspect concerns the behavior of information processing in the asymptotic limit of large networks, which is a limit of high relevance for neuroscience applications. The mutual information between the input and the output of the network has been evaluated, and its extremization has been performed. The aim of the present work is to extend these asymptotic results to study the more general case when the threshold values are no longer identical. In this situation, the values of thresholds can be described by a density, rather than by exact locations. We present a derivation of Shannon's mutual information between the input and output of these networks. The result is an approximation that relies a weak version of the law of large numbers, and a version of the central limit theorem. Optimization of the mutual information is then discussed.

The relaxation in complex systems is studied. It is shown that charge relaxation in complex systems has non-exponential non-Maxwell character. The physical mechanisms of non-Maxwell relaxation are established. The new generalized relaxation equations of fractional order are deduced for non-exponential relaxation.

We study a Brownian overdamped motion driven by the sequence of non-Gaussian correlated random impulses. A main characteristic of this external noise is that a following impulse has strictly opposed sign relative to the previous one. It is generated by a time derivative of stationary random jump function that may be equal or similar to a random telegraphic signal. Therefore, the noise is "green" by definition [Phys. Lett. A240 (1998) 43]. In order to find the mean drift velocity of a Brownian particle we employ two approaches: a Krylov-Bogolubov averaging method and a numerical simulation. The first method is used for the case of the jump function to be the random telegraphic signal. Then the probability dis-tribution density that describes statistics of time interval between the delta-function impulses of external noise is an ex-ponential function. The numerical calculation is performed by means of using the narrow rectangular impulse instead of the delta-function. We consider two models of such noise. In the first case the distribution density of time interval be-tween the rectangular impulses is again described by the exponential function. In other case the interval is uniformly distributed. We show that a locking effect (or a synchronization) exists even if a mean frequency of impulses is small. This effect exists with a high accuracy even if noise is strong. According to the theory an effective locking band is equal to the cosine of the amplitude of the original jump function. In particular, if the amplitude is π, the band is zero, how-ever, if it is equal to π, the band is unity as well as in the ideal case of zero noise. It is interesting that this property holds true even if the averaging method becomes inapplicable. We show also that the theory good coincide with the numerical simulation.

Real-world complex networks such the Internet, social networks and biological networks have increasingly attracted the interest of researchers from many areas. Accurate modelling of the statistical regularities of these large-scale networks is critical to understand their global evolving structures and local dynamical patterns. Two main families of models have been widely studied: those based on the Erdos and Renyi random graph model and those on the Barabasi-Albert scale-free model. In this paper we develop a new model: the Hybrid model, which incorporates two stages of growth. The aim of this model is to simulate the transition process between a static randomly connected network and a growing scale-free network through a tuning parameter. We measure the Hybrid model by extensive numerical simulations, focusing on the critical transition point from Poisson to Power-law degree distribution.

The theory of fractal homeomorphisms is applied to transform a Sierpinski triangle into what we call a Kigami triangle. The latter is such that the corresponding harmonic functions and the corresponding Laplacian Δ take a relatively simple form. This provides an alternative approach to recent results of Teplyaev. Using a second fractal homeomorphism we prove that the outer boundary of the Kigami triangle possesses a continuous first derivative at every point. This paper shows that IFS theory and the chaos game algorithm provide important tools for analysis on fractals.

Marital infidelity is usually examined solely in terms of strategies of men and women, with an emphasis on the enhanced payoff for male infidelity (provided he can get away with it). What are not clear are the strategies used, in terms of how often to engage in extra-marital affairs. It has been proposed that female strategies are governed by a "decision" to maximize the genetic diversity of her offspring, in order to better guarantee that at least some will survive against a common pathogen. This strategy would then impact on the strategies and diversity of pathogens. I make a number of predictions about both strategies and the genetic diversity of humans and pathogens, couched in game-theoretic terms. These predictions are then compared with the existing evidence on the strategies used by women and also in terms of the genetic diversity of human populations.

The electrical activity of the brain has been observed for over a century and is widely used to probe brain function and disorders, chiefly through the electroencephalogram (EEG) recorded by electrodes on the scalp. However, the connections between physiology and EEGs have been chiefly qualitative until recently, and most uses of the EEG have been based on phenomenological correlations. A quantitative mean-field model of brain electrical activity is described that spans the range of physiological and anatomical scales from microscopic synapses to the whole brain. Its parameters measure quantities such as synaptic strengths, signal delays, cellular time constants, and neural ranges, and are all constrained by independent physiological measurements. Application of standard techniques from wave physics allows successful predictions to be made of a wide range of EEG phenomena, including time series and spectra, evoked responses to stimuli, dependence on arousal state, seizure dynamics, and relationships to functional magnetic resonance imaging (fMRI). Fitting to experimental data also enables physiological parameters to be infered, giving a new noninvasive window into brain function, especially when referenced to a standardized database of subjects. Modifications of the core model to treat mm-scale patchy interconnections in the visual cortex are also described, and it is shown that resulting waves obey the Schroedinger equation. This opens the possibility of classical cortical analogs of quantum phenomena.

We study minimal multistable systems of coupled model neurons with combined excitatory and inhibitory connections. With slow potassium currents, multistability of several firing regimes with distinctively different firing rates is observed. In the presence of noise, there is noise-driven switching between these states of which transient dynamics have 1/f-type power spectra. The selection between higher- and lower-frequency oscillations depends on external inputs, which results in coherence between the periodic input and the system's firing rate. Without slow potassium currents, there are multistable solutions in which two inhibitory neurons fire synchronously or anti-synchronously. Addition of a small amount of noise results in increased synchronizability of the two neurons depending on the level of external inputs. These results suggest adaptable dynamics of multistable neural attractors to external inputs enhanced by additional noise.