Antimony glasses with large nonlinear refractive indices, small two-photon absorption coefficients and ultrafast response at telecom wavelengths

Nonlinear (NL) optical properties of antimony oxide based glasses (AG) were characterized for excitation wavelengths from 800 to 1600 m. The NL refractive indices, n2, and the two-photon absorption (TPA) coefficient, β, have been evaluated using the Z-scan technique. Values of n2≈ 10-15 - 10-14 cm2/W of electronic origin were measured and negligible TPA coefficients (β < 0.003 cm/GW) were determined. The response time of the nonlinearity is faster than 100 fs as determined using the Kerr shutter technique. The figure-of-merit usually considered for all-optical switching, T = 2βλ/n2 , indicates that AG are very good materials for ultrafast switches at telecom wavelengths. © 2007 IEEE.

Nonlinear effects generation in suspended core chalcogenide fibre

In this work we report our achievements in the elaboration and optical characterizations of low-losses suspended core optical fibers elaborated from As2S3 glass. For preforms elaboration, alternatively to other processes like the stack and draw or extrusion, we use a process based on mechanical drilling. The drawing of these drilled performs into fibers allows reaching a suspended core geometry, in which a 2 μm diameter core is linked to the fiber clad region by three supporting struts. The different fibers that have been drawn show losses close to 0.9 dB/m at 1.55 μm. The suspended core waveguide geometry has also an efficient influence on the chromatic dispersion and allows its management. Indeed, the zero dispersion wavelength, which is around 5 μm in the bulk glass, is calculated to be shifted towards around 2μm in our suspended core fibers. In order to qualify their nonlinearity we have pumped them at 1.995 μm with the help of a fibered ns source. We have observed a strong non linear response with evidence of spontaneous Raman scattering and strong spectral broadening. © 2011 SPIE.

Nonlinear optical properties of Bi2O3-GeO2 glass at 800 and 532 nm

The nonlinear (NL) optical properties of glassy xBi2O 3-(1-x) GeO2 with x = 0.72 and 0.82 were investigated. The experiments were performed with lasers at 800 nm (pulses of 150 fs) and 532 nm (pulses of 80 ps and 250 ns). Using the Kerr gate technique, we observed that the NL response of the samples at 800 nm is faster than 150 fs. NL refraction indices, | n 2 | ≈ 5 × 10-16 cm2/W, and two-photon absorption coefficients, α 2, smaller than 0.03 cm/GW, were measured at 800 nm. At 532 nm, we measured the NL transmittance of the samples. From the results obtained, we determined α 2 ≈1 cm/GW and excited-state absorption cross-sections of ≈10-22 cm2 due to free-carriers. © 2013 AIP Publishing LLC.

Nonlinear formulation based on FEM, Mazars damage criterion and Fick's law applied to failure assessment of reinforced concrete structures subjected to chloride ingress and reinforcements corrosion

Structural durability is an important design criterion, which must be assessed for every type of structure. In this regard, especial attention must be addressed to the durability of reinforced concrete (RC) structures. When RC structures are located in aggressive environments, its durability is strongly reduced by physical/chemical/mechanical processes that trigger the corrosion of reinforcements. Among these processes, the diffusion of chlorides is recognized as one of major responsible of corrosion phenomenon start. To accurate modelling the corrosion of reinforcements and to assess the durability of RC structures, a mechanical model that accounts realistically for both concrete and steel mechanical behaviour must be considered. In this context, this study presents a numerical nonlinear formulation based on the finite element method applied to structural analysis of RC structures subjected to chloride penetration and reinforcements corrosion. The physical nonlinearity of concrete is described by Mazars damage model whereas for reinforcements elastoplastic criteria are adopted. The steel loss along time due to corrosion is modelled using an empirical approach presented in literature and the chloride concentration growth along structural cover is represented by Fick's law. The proposed model is applied to analysis of bended structures. The results obtained by the proposed numerical approach are compared to responses available in literature in order to illustrate the evolution of structural resistant load after corrosion start. (C) 2014 Elsevier Ltd. All rights reserved.

Control of recycle processes using neural networks combined with PID controller

This thesis is focused on the control of a system with recycle. A new control strategy using neural network combined with PID controller was proposed. The combined controller was studied and tested on the pressure control of a vaporizer inside a para-xylene production process. The major problems are the negative effects of recycle and the delays on instability and performance. The neural network was designed to move the process close to the set points while the PID accomplishes the finer level of disturbance rejection and offset reductions. Our simulation results show that during control, the neural network was able to determine the nonlinear relationship between steady state and manipulated variables. The results also show the disturbance rejection was handled by PID controller effectively.

Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Seawater carbonate chemistry, growth rate and processes during experiments with Coccolithus pelagicus and Calcidiscus leptoporus, 2006

Uptake of half of the fossil fuel CO2 into the ocean causes gradual seawater acidification. This has been shown to slow down calcification of major calcifying groups, such as corals, foraminifera, and coccolithophores. Here we show that two of the most productive marine calcifying species, the coccolithophores Coccolithus pelagicus and Calcidiscus leptoporus, do not follow the CO2-related calcification response previously found. In batch culture experiments, particulate inorganic carbon (PIC) of C. leptoporus changes with increasing CO2 concentration in a nonlinear relationship. A PIC optimum curve is obtained, with a maximum value at present-day surface ocean pCO2 levels (?360 ppm CO2). With particulate organic carbon (POC) remaining constant over the range of CO2 concentrations, the PIC/POC ratio also shows an optimum curve. In the C. pelagicus cultures, neither PIC nor POC changes significantly over the CO2 range tested, yielding a stable PIC/POC ratio. Since growth rate in both species did not change with pCO2, POC and PIC production show the same pattern as POC and PIC. The two investigated species respond differently to changes in the seawater carbonate chemistry, highlighting the need to consider species-specific effects when evaluating whole ecosystem responses. Changes of calcification rate (PIC production) were highly correlated to changes in coccolith morphology. Since our experimental results suggest altered coccolith morphology (at least in the case of C. leptoporus) in the geological past, coccoliths originating from sedimentary records of periods with different CO2 levels were analyzed. Analysis of sediment samples was performed on six cores obtained from locations well above the lysocline and covering a range of latitudes throughout the Atlantic Ocean. Scanning electron micrograph analysis of coccolith morphologies did not reveal any evidence for significant numbers of incomplete or malformed coccoliths of C. pelagicus and C. leptoporus in last glacial maximum and Holocene sediments. The discrepancy between experimental and geological results might be explained by adaptation to changing carbonate chemistry.

Fault detection and isolation on a noisy nonlinear circuit

In this paper fault detection and isolation (FDI) schemes are applied in the context of the surveillance of emerging faults in an electrical circuit. The FDI problem is studied on a noisy nonlinear circuit, where both abrupt and incipient faults in the voltage source are considered. A rigorous analysis of fault detectability precedes the application of the fault detection (FD) scheme; then, the fault isolation (FI) phase is accomplished with two alternative FI approaches, proposed as new extensions of that FD approach. Numerical simulations illustrate the applicability of the mentioned schemes.

Control of time-dependent biological processes by temporally patterned input

Temporal patterning of biological variables, in the form of oscillations and rhythms on many time scales, is ubiquitous. Altering the temporal pattern of an input variable greatly affects the output of many biological processes. We develop here a conceptual framework for a quantitative understanding of such pattern dependence, focusing particularly on nonlinear, saturable, time-dependent processes that abound in biophysics, biochemistry, and physiology. We show theoretically that pattern dependence is governed by the nonlinearity of the input–output transformation as well as its time constant. As a result, only patterns on certain time scales permit the expression of pattern dependence, and processes with different time constants can respond preferentially to different patterns. This has implications for temporal coding and decoding, and allows differential control of processes through pattern. We show how pattern dependence can be quantitatively predicted using only information from steady, unpatterned input. To apply our ideas, we analyze, in an experimental example, how muscle contraction depends on the pattern of motorneuron firing.

Nonlinear-dynamical arrhythmia control in humans

Nonlinear-dynamical control techniques, also known as chaos control, have been used with great success to control a wide range of physical systems. Such techniques have been used to control the behavior of in vitro excitable biological tissue, suggesting their potential for clinical utility. However, the feasibility of using such techniques to control physiological processes has not been demonstrated in humans. Here we show that nonlinear-dynamical control can modulate human cardiac electrophysiological dynamics by rapidly stabilizing an unstable target rhythm. Specifically, in 52/54 control attempts in five patients, we successfully terminated pacing-induced period-2 atrioventricular-nodal conduction alternans by stabilizing the underlying unstable steady-state conduction. This proof-of-concept demonstration shows that nonlinear-dynamical control techniques are clinically feasible and provides a foundation for developing such techniques for more complex forms of clinical arrhythmia.

Multiphoton fluorescence excitation: new spectral windows for biological nonlinear microscopy.

Intrinsic, three-dimensionally resolved, microscopic imaging of dynamical structures and biochemical processes in living preparations has been realized by nonlinear laser scanning fluorescence microscopy. The search for useful two-photon and three-photon excitation spectra, motivated by the emergence of nonlinear microscopy as a powerful biophysical instrument, has now discovered a virtual artist's palette of chemical indicators, fluorescent markers, and native biological fluorophores, including NADH, flavins, and green fluorescent proteins, that are applicable to living biological preparations. More than 25 two-photon excitation spectra of ultraviolet and visible absorbing molecules reveal useful cross sections, some conveniently blue-shifted, for near-infrared absorption. Measurements of three-photon fluorophore excitation spectra now define alternative windows at relatively benign wavelengths to excite deeper ultraviolet fluorophores. The inherent optical sectioning capability of nonlinear excitation provides three-dimensional resolution for imaging and avoids out-of-focus background and photodamage. Here, the measured nonlinear excitation spectra and their photophysical characteristics that empower nonlinear laser microscopy for biological imaging are described.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Variational inference for diffusion processes

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multimodal. We propose a variational treatment of diffusion processes, which allows us to compute type II maximum likelihood estimates of the parameters by simple gradient techniques and which is computationally less demanding than most MCMC approaches. We also show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

Analysis of magnetoencephaoographic data as a nonlinear dynamical system

This thesis presents the results from an investigation into the merits of analysing Magnetoencephalographic (MEG) data in the context of dynamical systems theory. MEG is the study of both the methods for the measurement of minute magnetic flux variations at the scalp, resulting from neuro-electric activity in the neocortex, as well as the techniques required to process and extract useful information from these measurements. As a result of its unique mode of action - by directly measuring neuronal activity via the resulting magnetic field fluctuations - MEG possesses a number of useful qualities which could potentially make it a powerful addition to any brain researcher's arsenal. Unfortunately, MEG research has so far failed to fulfil its early promise, being hindered in its progress by a variety of factors. Conventionally, the analysis of MEG has been dominated by the search for activity in certain spectral bands - the so-called alpha, delta, beta, etc that are commonly referred to in both academic and lay publications. Other efforts have centred upon generating optimal fits of "equivalent current dipoles" that best explain the observed field distribution. Many of these approaches carry the implicit assumption that the dynamics which result in the observed time series are linear. This is despite a variety of reasons which suggest that nonlinearity might be present in MEG recordings. By using methods that allow for nonlinear dynamics, the research described in this thesis avoids these restrictive linearity assumptions. A crucial concept underpinning this project is the belief that MEG recordings are mere observations of the evolution of the true underlying state, which is unobservable and is assumed to reflect some abstract brain cognitive state. Further, we maintain that it is unreasonable to expect these processes to be adequately described in the traditional way: as a linear sum of a large number of frequency generators. One of the main objectives of this thesis will be to prove that much more effective and powerful analysis of MEG can be achieved if one were to assume the presence of both linear and nonlinear characteristics from the outset. Our position is that the combined action of a relatively small number of these generators, coupled with external and dynamic noise sources, is more than sufficient to account for the complexity observed in the MEG recordings. Another problem that has plagued MEG researchers is the extremely low signal to noise ratios that are obtained. As the magnetic flux variations resulting from actual cortical processes can be extremely minute, the measuring devices used in MEG are, necessarily, extremely sensitive. The unfortunate side-effect of this is that even commonplace phenomena such as the earth's geomagnetic field can easily swamp signals of interest. This problem is commonly addressed by averaging over a large number of recordings. However, this has a number of notable drawbacks. In particular, it is difficult to synchronise high frequency activity which might be of interest, and often these signals will be cancelled out by the averaging process. Other problems that have been encountered are high costs and low portability of state-of-the- art multichannel machines. The result of this is that the use of MEG has, hitherto, been restricted to large institutions which are able to afford the high costs associated with the procurement and maintenance of these machines. In this project, we seek to address these issues by working almost exclusively with single channel, unaveraged MEG data. We demonstrate the applicability of a variety of methods originating from the fields of signal processing, dynamical systems, information theory and neural networks, to the analysis of MEG data. It is noteworthy that while modern signal processing tools such as independent component analysis, topographic maps and latent variable modelling have enjoyed extensive success in a variety of research areas from financial time series modelling to the analysis of sun spot activity, their use in MEG analysis has thus far been extremely limited. It is hoped that this work will help to remedy this oversight.

Amplifier similariton fiber laser with nonlinear spectral compression

We propose a new concept of a fiber laser architecture supporting self-similar pulse evolution in the amplifier and nonlinear spectral pulse compression in the passive fiber. The latter process allows for transform-limited picosecond pulse generation, and improves the laser’s power efficiency by preventing strong spectral filtering from being highly dissipative. Aside from laser technology, the proposed scheme opens new possibilities for studying nonlinear dynamical processes. As an example, we demonstrate a clear period-doubling route to chaos in such a nonlinear laser system.