Dynamic two-axis curvature measurement using multicore fiber Bragg gratings interrogated by arrayed waveguide gratings

We describe the use of arrayed waveguide gratings (AWGs) in the interrogation of fiber Bragg gratings (FBGs) for dynamic strain measurement. The ratiometric AWG output was calibrated in a static deflection experiment over a ±200 με range. Dynamic strain measurement was demonstrated with a FBG in a conventional single-mode fiber mounted on the surface of a vibrating cantilever and on a piezoelectric actuator, giving a resolution of 0.5 με at 2.4 kHz. We present results of this technique extended to measure the dynamic differential strain between two FBG pairs within a multicore fiber. An arbitrary cantilever oscillation of the multicore fiber was determined from curvature measurements in two orthogonal axes at 1125 Hz with a resolution of 0.05 m-1. © 2006 Optical Society of America.

Non-Gaussian error probability in optical soliton transmission

We find the probability distribution of the fluctuating parameters of a soliton propagating through a medium with additive noise. Our method is a modification of the instanton formalism (method of optimal fluctuation) based on a saddle-point approximation in the path integral. We first solve consistently a fundamental problem of soliton propagation within the framework of noisy nonlinear Schrödinger equation. We then consider model modifications due to in-line (filtering, amplitude and phase modulation) control. It is examined how control elements change the error probability in optical soliton transmission. Even though a weak noise is considered, we are interested here in probabilities of error-causing large fluctuations which are beyond perturbation theory. We describe in detail a new phenomenon of soliton collapse that occurs under the combined action of noise, filtering and amplitude modulation. © 2004 Elsevier B.V. All rights reserved.

Non-Gaussian statistics of an optical soliton in the presence of amplified spontaneous emission

A study was performed on non-Gaussian statistics of an optical soliton in the presence of amplified spontaneous emission. An approach based on the Fokker-Planck equation was applied to study the optical soliton parameters in the presence of additive noise. The rigorous method not only allowed to reproduce and justify the classical Gordon-Haus formula but also led to new exact results.

Microwave photonic filtering using Gaussian-profiled superstructured fibre Bragg grating and dispersive fibre

Microwave photonic filtering is realised using a superstructured fibre Bragg grating. The time delay of the optical taps is precisely controlled by the grating characteristics and fibre dispersion. A bandpass response with a rejection level of >45 dB is achieved.

Simultaneous measurement of strain and curvature using superstructure fibre Bragg gratings

We report the first demonstration of the simultaneous measurement of strain and curvature, with temperature compensation, using a single superstructure fibre Bragg grating (SFBG). The SFBG exhibits the properties of both the fibre Bragg grating (FBG) and the long period fibre grating (LPG) such that its spectral response facilitates strain measurement from the wavelength shift of the FBG-like characteristic, and independent measurement of curvature from the LPG-like mode-splitting characteristic. The dependence of the LPG mode-splitting on the mode order has also been investigated and utilised for the measurement of very small curvatures.

Calculation of non-Gaussian statistics and bit error rate degradation due to pulse-to-pulse interaction

We develop a theoretical method to calculate jitter statistics of interacting solitons. Applying this approach, we have derived the non-Gaussian probability density function and calculated the bit-error rate as a function of noise level, initial separation and phase difference between solitons.

The effect of Gaussian noise on the threshold, dynamic range, and loudness of analogue cochlear implant stimuli

The deliberate addition of Gaussian noise to cochlear implant signals has previously been proposed to enhance the time coding of signals by the cochlear nerve. Potentially, the addition of an inaudible level of noise could also have secondary benefits: it could lower the threshold to the information-bearing signal, and by desynchronization of nerve discharges, it could increase the level at which the information-bearing signal becomes uncomfortable. Both these effects would lead to an increased dynamic range, which might be expected to enhance speech comprehension and make the choice of cochlear implant compression parameters less critical (as with a wider dynamic range, small changes in the parameters would have less effect on loudness). The hypothesized secondary effects were investigated with eight users of the Clarion cochlear implant; the stimulation was analogue and monopolar. For presentations in noise, noise at 95% of the threshold level was applied simultaneously and independently to all the electrodes. The noise was found in two-alternative forced-choice (2AFC) experiments to decrease the threshold to sinusoidal stimuli (100 Hz, 1 kHz, 5 kHz) by about 2.0 dB and increase the dynamic range by 0.7 dB. Furthermore, in 2AFC loudness balance experiments, noise was found to decrease the loudness of moderate to intense stimuli. This suggests that loudness is partially coded by the degree of phase-locking of cochlear nerve fibers. The overall gain in dynamic range was modest, and more complex noise strategies, for example, using inhibition between the noise sources, may be required to get a clinically useful benefit. © 2006 Association for Research in Otolaryngology.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Biconical-taper-assisted fiber interferometer with modes coupling enhancement for high-sensitive curvature measurement

A modal interferometer based on multimode-singlemode-multimode fiber structure built with a biconical taper for fiber curvature measurement is proposed and experimentally demonstrated. Due to the tapered singlemode fiber acting as a high-efficient mode power converter to enhance the modes coupling, curvature sensor with improved sensitivity is achieved by monitoring the defined fringe visibility of the interference spectrum. The measuring range can be tuned by changing the waist diameter of the fiber taper. Meanwhile, the sensor shows an intrinsic ability to overcome the influence of temperature cross-sensitivity and the power fluctuation of light source. The advantages of easy fabrication, high-quality spectrum with improved sensitivity, and small hysteresis will provide great potential for practical applications of the sensor. © 2013 Springer-Verlag Berlin Heidelberg.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Laparoscopic greater curvature plication in morbidly obese women with type 2 diabetes:effects on glucose homeostasis, postprandial triglyceridemia and selected gut hormones

Background: Laparoscopic greater curvature plication (LGCP) is an emerging bariatric procedure that reduces the gastric volume without implantable devices or gastrectomy. The aim of this study was to explore changes in glucose homeostasis, postprandial triglyceridemia, and meal-stimulated secretion of selected gut hormones [glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide-1 (GLP-1), ghrelin, and obestatin] in patients with type 2 diabetes mellitus (T2DM) at 1 and 6 months after the procedure. Methods: Thirteen morbidly obese T2DM women (mean age, 53.2 ± 8.76 years; body mass index, 40.1 ± 4.59 kg/m2) were prospectively investigated before the LGCP and at 1- and 6-month follow-up. At these time points, all study patients underwent a standardized liquid mixed-meal test, and blood was sampled for assessment of plasma levels of glucose, insulin, C-peptide, triglycerides, GIP, GLP-1, ghrelin, and obestatin. Results: All patients had significant weight loss both at 1 and 6 months after the LGCP (p≤0.002), with mean percent excess weight loss (%EWL) reaching 29.7 ;plusmn2.9 % at the 6-month follow-up. Fasting hyperglycemia and hyperinsulinemia improved significantly at 6 months after the LGCP (p<0.05), with parallel improvement in insulin sensitivity and HbA1c levels (p<0.0001). Meal-induced glucose plasma levels were significantly lower at 6 months after the LGCP (p<0.0001), and postprandial triglyceridemia was also ameliorated at the 6-month follow-up (p<0.001). Postprandial GIP plasma levels were significantly increased both at 1 and 6 months after the LGCP (p<0.0001), whereas the overall meal-induced GLP-1 response was not significantly changed after the procedure (p ;gt0.05). Postprandial ghrelin plasma levels decreased at 1 and 6 months after the LGCP (p<0.0001) with no significant changes in circulating obestatin levels. Conclusion: During the initial 6-month postoperative period, LGCP induces significant weight loss and improves the metabolic profile of morbidly obese T2DM patients, while it also decreases circulating postprandial ghrelin levels and increases the meal-induced GIP response. © 2013 Springer Science+Business Media New York.

Super-Gaussian conical refraction beam

We demonstrate the transformation of Gaussian input beams into super-Gaussian beams with a quasi flat-top transverse profile by means of the conical refraction phenomenon by adjusting the ratio between the ring radius and the waist radius of the input beam to 0.445. We discuss the beam propagation of the super-Gaussian beam and show that it has a confocal parameter three times larger than the one that would be obtained from a Gaussian beam. The experiments performed with a KGd(WO4)2 biaxial crystal are in good agreement with the theoretical predictions. © 2014 Optical Society of America.

Short-term wind power forecasting using Gaussian processes

Since wind has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safety and economics of wind energy utilization. In this paper, we investigate a combination of numeric and probabilistic models: one-day-ahead wind power forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical Weather Prediction (NWP) model. Firstly the wind speed data from NWP was corrected by a GP. Then, as there is always a defined limit on power generated in a wind turbine due the turbine controlling strategy, a Censored GP was used to model the relationship between the corrected wind speed and power output. To validate the proposed approach, two real world datasets were used for model construction and testing. The simulation results were compared with the persistence method and Artificial Neural Networks (ANNs); the proposed model achieves about 11% improvement in forecasting accuracy (Mean Absolute Error) compared to the ANN model on one dataset, and nearly 5% improvement on another.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.