Sol–gel derived coating applied to long-period gratings for enhanced refractive index sensing properties

Long-period fibre gratings (LPGs) have previously been used to detect quantities such as temperature, strain, and refractive index (RI). We report here, to the best of our knowledge, the first investigation on refractive index sensing properties of LPGs with sol–gel derived titanium and silicon oxide coatings. It is revealed that the RI sensitivity of an LPG is affected by both the thickness and the index value of the coating; a coating with higher index and thickness will enhance the LPG RI sensitivity significantly. The surrounding refractive index induced LPG resonance shift has been evaluated over the LPGs' most sensitive RI region from 1.42 to 1.44. We have identified that, in this region, the uncoated LPG has an RI sensitivity of (-673.0 ± 0.4) nm/uri (unit of refractive index) while the LPG coated with titanium oxide exhibits a sensitivity as high as (-1067.15 ± 0.04) nm/uri. The experimental results also reveal that, even in the RI insensitive region around 1.33, there still is a marked enhancement in RI sensitivity of the sol–gel coated LPG compared to the uncoated one. This is potentially significant as coated LPGs may be extended to low RI gas and semi-liquidized based sensing applications.

Microchannelled chirped fibre Bragg grating based refractive index sensor formed by femto-second laser processing and HF-etching

The microchannelled chirped fibre Bragg grating (MCFBG) was fabricated using femtosecond laser processing and HF-etching. Intrinsical refractive-index sensitivity induced by the microchannel makes MCFBGs ideal for biochemical sensing.

800 nm WDM interrogation system for strain, temperature, and refractive index sensing based on tilted fiber Bragg grating

A low-cost high-resolution wavelength-division-multiplexing (WDM) interrogation system operating around 800 nm region with operational bandwidth up to 60 nm and resolution of 12.7 pm utilizing a tilted fiber Bragg grating (TFBG) and a CCDarray detector has been implemented. The system has been evaluated for interrogating fiber Bragg grating based strain, temperature sensors, giving sensitivities of 0.59 pm/µe and 5.6 pm/°C, which are in good agreement with previously reported values. Furthermore, the system has been utilized to detect the refractive index change of sample liquids, demonstrating a capability of measuring index change as small as 10¯5. In addition, the vectorial expression of phase match condition and fabrication of TFBG have been discussed.

Microchanneled chirped fibre Bragg gratings for simultaneous refractive index and temperature measurements

We report here the fabrication, charaterisation and refractive index sensing of two microchanneled chirped fiber Bragg gratings (MCFBGs) with different channel sizes (~550µm and ~1000µm). The chirped grating structures were UV-inscribed in optical fibre and the microchannels were created in the middle of the CFBGs by femtosecond (fs) laser assisted chemical etching method. The creation of microchannels in the CFBG structures gives an access to the external index liquid, thus inducing refractive index (RI) sensitivity to the structure. In comparison with previously reported FBG based RI sensors, for which the cladding layers usually were removed, the MCFBGs represent a more ideal solution for robust devices as the microchannel will not degrade the structure strength. The two MCFBGs were spectrally charaterised for their RI and temperature responses and both gratings exhibited unique thermal and RI sensitivities, which may be utilised for implementation of bio-chemical sensors with capability to eliminate temperature crosssensitivity.

A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Large monitoring networks are becoming increasingly common and can generate large datasets from thousands to millions of observations in size, often with high temporal resolution. Processing large datasets using traditional geostatistical methods is prohibitively slow and in real world applications different types of sensor can be found across a monitoring network. Heterogeneities in the error characteristics of different sensors, both in terms of distribution and magnitude, presents problems for generating coherent maps. An assumption in traditional geostatistics is that observations are made directly of the underlying process being studied and that the observations are contaminated with Gaussian errors. Under this assumption, sub–optimal predictions will be obtained if the error characteristics of the sensor are effectively non–Gaussian. One method, model based geostatistics, assumes that a Gaussian process prior is imposed over the (latent) process being studied and that the sensor model forms part of the likelihood term. One problem with this type of approach is that the corresponding posterior distribution will be non–Gaussian and computationally demanding as Monte Carlo methods have to be used. An extension of a sequential, approximate Bayesian inference method enables observations with arbitrary likelihoods to be treated, in a projected process kriging framework which is less computationally intensive. The approach is illustrated using a simulated dataset with a range of sensor models and error characteristics.

Bayesian error bars for regression

Regression problems are concerned with predicting the values of one or more continuous quantities, given the values of a number of input variables. For virtually every application of regression, however, it is also important to have an indication of the uncertainty in the predictions. Such uncertainties are expressed in terms of the error bars, which specify the standard deviation of the distribution of predictions about the mean. Accurate estimate of error bars is of practical importance especially when safety and reliability is an issue. The Bayesian view of regression leads naturally to two contributions to the error bars. The first arises from the intrinsic noise on the target data, while the second comes from the uncertainty in the values of the model parameters which manifests itself in the finite width of the posterior distribution over the space of these parameters. The Hessian matrix which involves the second derivatives of the error function with respect to the weights is needed for implementing the Bayesian formalism in general and estimating the error bars in particular. A study of different methods for evaluating this matrix is given with special emphasis on the outer product approximation method. The contribution of the uncertainty in model parameters to the error bars is a finite data size effect, which becomes negligible as the number of data points in the training set increases. A study of this contribution is given in relation to the distribution of data in input space. It is shown that the addition of data points to the training set can only reduce the local magnitude of the error bars or leave it unchanged. Using the asymptotic limit of an infinite data set, it is shown that the error bars have an approximate relation to the density of data in input space.

Statistical physics of error-correcting codes

In this thesis we use statistical physics techniques to study the typical performance of four families of error-correcting codes based on very sparse linear transformations: Sourlas codes, Gallager codes, MacKay-Neal codes and Kanter-Saad codes. We map the decoding problem onto an Ising spin system with many-spins interactions. We then employ the replica method to calculate averages over the quenched disorder represented by the code constructions, the arbitrary messages and the random noise vectors. We find, as the noise level increases, a phase transition between successful decoding and failure phases. This phase transition coincides with upper bounds derived in the information theory literature in most of the cases. We connect the practical decoding algorithm known as probability propagation with the task of finding local minima of the related Bethe free-energy. We show that the practical decoding thresholds correspond to noise levels where suboptimal minima of the free-energy emerge. Simulations of practical decoding scenarios using probability propagation agree with theoretical predictions of the replica symmetric theory. The typical performance predicted by the thermodynamic phase transitions is shown to be attainable in computation times that grow exponentially with the system size. We use the insights obtained to design a method to calculate the performance and optimise parameters of the high performance codes proposed by Kanter and Saad.

Global non-dynamic refinement of radial orbit error for altimetric earth satellites

The accuracy of altimetrically derived oceanographic and geophysical information is limited by the precision of the radial component of the satellite ephemeris. A non-dynamic technique is proposed as a method of reducing the global radial orbit error of altimetric satellites. This involves the recovery of each coefficient of an analytically derived radial error correction through a refinement of crossover difference residuals. The crossover data is supplemented by absolute height measurements to permit the retrieval of otherwise unobservable geographically correlated and linearly combined parameters. The feasibility of the radial reduction procedure is established upon application to the three day repeat orbit of SEASAT. The concept of arc aggregates is devised as a means of extending the method to incorporate longer durations, such as the 35 day repeat period of ERS-1. A continuous orbit is effectively created by including the radial misclosure between consecutive long arcs as an infallible observation. The arc aggregate procedure is validated using a combination of three successive SEASAT ephemerides. A complete simulation of the 501 revolution per 35 day repeat orbit of ERS-1 is derived and the recovery of the global radial orbit error over the full repeat period is successfully accomplished. The radial reduction is dependent upon the geographical locations of the supplementary direct height data. Investigations into the respective influences of various sites proposed for the tracking of ERS-1 by ground-based transponders are carried out. The potential effectiveness on the radial orbital accuracy of locating future tracking sites in regions of high latitudinal magnitude is demonstrated.

Intelligent execution monitoring and error analysis in planning involving processes

An intelligent agent, operating in an external world which cannot be fully described in its internal world model, must be able to monitor the success of a previously generated plan and to respond to any errors which may have occurred. The process of error analysis requires the ability to reason in an expert fashion about time and about processes occurring in the world. Reasoning about time is needed to deal with causality. Reasoning about processes is needed since the direct effects of a plan action can be completely specified when the plan is generated, but the indirect effects cannot. For example, the action `open tap' leads with certainty to `tap open', whereas whether there will be a fluid flow and how long it might last is more difficult to predict. The majority of existing planning systems cannot handle these kinds of reasoning, thus limiting their usefulness. This thesis argues that both kinds of reasoning require a complex internal representation of the world. The use of Qualitative Process Theory and an interval-based representation of time are proposed as a representation scheme for such a world model. The planning system which was constructed has been tested on a set of realistic planning scenarios. It is shown that even simple planning problems, such as making a cup of coffee, require extensive reasoning if they are to be carried out successfully. The final Chapter concludes that the planning system described does allow the correct solution of planning problems involving complex side effects, which planners up to now have been unable to solve.

An econometric analysis of international tourism demand in Hong Kong : a cointegration and error correction approach

The purpose of this study is to develop econometric models to better understand the economic factors affecting inbound tourist flows from each of six origin countries that contribute to Hong Kong’s international tourism demand. To this end, we test alternative cointegration and error correction approaches to examine the economic determinants of tourist flows to Hong Kong, and to produce accurate econometric forecasts of inbound tourism demand. Our empirical findings show that permanent income is the most significant determinant of tourism demand in all models. The variables of own price, weighted substitute prices, trade volume, the share price index (as an indicator of changes in wealth in origin countries), and a dummy variable representing the Beijing incident (1989) are also found to be important determinants for some origin countries. The average long-run income and own price elasticity was measured at 2.66 and – 1.02, respectively. It was hypothesised that permanent income is a better explanatory variable of long-haul tourism demand than current income. A novel approach (grid search process) has been used to empirically derive the weights to be attached to the lagged income variable for estimating permanent income. The results indicate that permanent income, estimated with empirically determined relatively small weighting factors, was capable of producing better results than the current income variable in explaining long-haul tourism demand. This finding suggests that the use of current income in previous empirical tourism demand studies may have produced inaccurate results. The share price index, as a measure of wealth, was also found to be significant in two models. Studies of tourism demand rarely include wealth as an explanatory forecasting long-haul tourism demand. However, finding a satisfactory proxy for wealth common to different countries is problematic. This study indicates with the ECM (Error Correction Models) based on the Engle-Granger (1987) approach produce more accurate forecasts than ECM based on Pesaran and Shin (1998) and Johansen (1988, 1991, 1995) approaches for all of the long-haul markets and Japan. Overall, ECM produce better forecasts than the OLS, ARIMA and NAÏVE models, indicating the superiority of the application of a cointegration approach for tourism demand forecasting. The results show that permanent income is the most important explanatory variable for tourism demand from all countries but there are substantial variations between countries with the long-run elasticity ranging between 1.1 for the U.S. and 5.3 for U.K. Price is the next most important variable with the long-run elasticities ranging between -0.8 for Japan and -1.3 for Germany and short-run elasticities ranging between – 0.14 for Germany and -0.7 for Taiwan. The fastest growing market is Mainland China. The findings have implications for policies and strategies on investment, marketing promotion and pricing.

Generation of infrared surface plasmon resonances with high refractive index sensitivity utilizing tilted fiber Bragg gratings

We demonstrate the use of tilted fiber gratings to assist the generation of localized infrared surface plasmons with short propagation lengths and a sensitivity of d lambda/dn = 3365 nm in the aqueous index regime. It was also found that the resonances could be spectrally tuned over 1000 nm at the same spatial region with high coupling efficiency (in excess of 25 dB) by altering the polarization of the light illuminating the device.

Application of long-period-grating sensors to respiratory plethysmography

A series of in-line curvature sensors on a garment are used to monitor the thoracic and abdominal movements of a human during respiration. These results are used to obtain volumetric tidal changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The curvature sensors are based on long-period gratings (LPGs) written in a progressive three-layered fiber to render the LPGs insensitive to the refractive index external to the fiber. A curvature sensor consists of the fiber long-period grating laid on a carbon fiber ribbon, which is then encapsulated in a low-temperature curing silicone rubber. The sensors have a spectral sensitivity to curvature, d lambda/dR from similar to 7-nm m to similar to 9-nm m. The interrogation technique is borrowed from derivative spectroscopy and monitors the changes in the transmission spectral profile of the LPG's attenuation band due to curvature. The multiplexing of the sensors is achieved by spectrally matching a series of distributed feedback (DFB) lasers to the LPGs. The versatility of this sensing garment is confirmed by it being used on six other human subjects covering a wide range of body mass indices. Just six fully functional sensors are required to obtain a volumetric error of around 6%. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Dual-peak long-period fiber gratings with enhanced refractive index sensitivity by finely tailored mode dispersion that uses the light cladding etching technique

We have experimentally investigated the mode dispersion property and refractive index sensitivity of dual-peak long-period fiber gratings (LPGs) that were sensitized by hydrofluoric acid (HF) etching. The nature of the coupled cladding modes close to the dispersion turning point makes the dual-peak LPGs ultrasensitive to cladding property, permitting a fine tailoring of the mode dispersion and index sensitivity by the light cladding etching method using HF acid of only 1% concentration. As an implementation of an optical biosensor, the etched device was used to detect the concentration of hemoglobin protein in a sugar solution, showing a sensitivity as high as 20 nm/1%.

Simultaneous measurement of temperature and external refractive index by use of a hybrid grating in D fiber with enhanced sensitivity by HF etching

We propose a dual-parameter optical sensor device achieved by UV inscription of a hybrid long-period grating-fiber Bragg grating structure in D fiber. The hybrid configuration permits the detection of the temperature from the latter's response and measurement of the external refractive index from the former's response. In addition, the host D fiber permits effective modification of the device's sensitivity by cladding etching. The grating sensor has been used to measure the concentrations of aqueous sugar solutions, demonstrating its potential capability to detect concentration changes as small as 0.01%.