109 resultados para Sparse sensing


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A novel, pyrene-functionalised copolymer has been synthesised in a single step via imidisation of poly(maleic anhydride-alt-1-octadecene) with 1-pyrenemethylamine, and its potential for the detection of volatile nitro aromatic compounds (NACs) evaluated. The new copolymer forms complexes in solution with NACs such as 2,5-dinitrobenzonitrile, as shown by H-1 NMR, UV-vis and fluorescence spectroscopy. Moreover, thin films of this copolymer, cast from THF solution, undergo almost instantaneous fluorescence quenching when exposed to the vapour of 2,5-dinitrobenzonitrile (a model for TNT) at ambient temperatures and pressures.

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A tetraazamacrocycle containing ferrocene moieties has been synthesized and characterized. The tetraprotonated form of this compound was evaluated as a receptor (R) for anion recognition of several substrates (S), Cl-, PF6-, HSO4-, H2PO4- and carboxylates, such as p-nitrobenzoate (p-nbz(-)), phthalate (ph(2-)), isophthalate (iph(2-)) and dipicolinate (dipic(2-)). H-1 NMR titrations in CD3OD indicated that this receptor is not suitable for recognizing HSO4- and H2PO4-, but weakly binds p-nbz(-), and strongly interacts with ph(2-), dipic(2-), and iph(2-) anions forming 1 : 2 assembled species. The largest beta(2) binding constant was determined for ph(2-), followed by dipic(2-) and finally iph(2-). The effect of the anionic substrates on the electron-transfer process of the ferrocene units of R was evaluated using cyclic voltammetry (CV) and square wave voltammetry (SWV) in methanol solution and 0.1 mol dm(-3) (CH3)(4)NCl as the supporting electrolyte. Titrations of the receptor were undertaken by addition of anion solutions in their tetrabutylammonium or tetramethylammonium forms. The protonated ligand exhibits a reversible voltammogram, which shifts cathodically in the presence of the substrates. The data revealed kinetic constraints in the formation of the receptor/substrate entity for dipic(2-), ph(2-) and iph(2-) anions, but not for p-nbz(-). In spite of the slow kinetics of assembled species formation with the ph(2-) substrate, this anion provides the largest redox-response when the supramolecular entity is formed, followed by dipic(2-), iph(2-) and finally p-nbz(-) anions. This trend is in agreement with the H-1 NMR results and the values of the binding constants. Single crystal X-ray structures of the receptor with PF6-, ph(2-), iph(2-) and p-nbz(-) were carried out and showed that supermolecules with a RS2 stoichiometry are formed with the first three anions, but RS4 with p-nbz(-). In all cases the binding occurs outside the macrocyclic cavity via N-H center dot center dot center dot O=C hydrogen bonds for carboxylate anions and N - H center dot center dot center dot F hydrogen bonds for the PF6- anion, which is in agreement with the solution results. The macrocyclic framework adopts different conformations in order to interact with each substrate having Fe center dot center dot center dot Fe intramolecular distances ranging from 10.125(14) to 12.783(15) angstrom.

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Fabrication of a thin praseodymium oxide film is of great technological interest in sensor, semiconducting, and ceramic industries. It is shown for the first time that an ultrathin layer of praseodymium oxide can be deposited on tin-doped indium oxide surface (ITO) by applying a negative sweeping voltage (cathodic electrodeposition) to the aqueous solution containing Pr(NO3)(3) and H2O2 using cyclic voltammetry, followed by annealing the film at 500 S C for 1 h. X-ray diffraction suggested that the predominant phase of the film is Pr6O11 and atomic force microscopy and scanning electron microscopy characterizations indicated that this film is assembled with a monolayer coverage of spherical praseodymium oxide nanoparticles packed closely on the ITO surface. AC impedance measurements of the thin Pr6O11 film on ITO also revealed that the composite material displays a much higher electrical conductivity compared to the pure ITO. As a result, the material could suitably be used as a new chemical sensor. (c) 2006 The Electrochemical Society.

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Thermal non-destructive testing (NDT) is commonly used for assessing aircraft structures. This research work evaluates the potential of pulsed -- transient thermography for locating fixtures beneath aircraft skins in order to facilitate accurate automated assembly operations. Representative aluminium and carbon fibre aircraft skin-fixture assemblies were modelled using thermal modelling software. The assemblies were also experimentally investigated with an integrated pulsed thermographic evaluation system, as well as using a custom built system incorporating a miniature un-cooled camera. Modelling showed that the presence of an air gap between skin and fixture significantly reduced the thermal contrast developed, especially in aluminium. Experimental results show that fixtures can be located to accuracies of 0.5 mm.

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The importance of biological materials has long been recognized from the molecular level to higher levels of organization. Whereas, in traditional engineering, hardness and stiffness are considered desirable properties in a material, biology makes considerable and advantageous use of softer, more pliable resources. The development, structure and mechanics of these materials are well documented and will not be covered here. The purpose of this paper is, however, to demonstrate the importance of such materials and, in particular, the functional structures they form. Using only a few simple building blocks, nature is able to develop a plethora of diverse materials, each with a very different set of mechanical properties and from which a seemingly impossibly large number of assorted structures are formed. There is little doubt that this is made possible by the fact that the majority of biological ‘materials’ or ‘structures’ are based on fibres and that these fibres provide opportunities for functional hierarchies. We show how these structures have inspired a new generation of innovative technologies in the science and engineering community. Particular attention is given to the use of insects as models for biomimetically inspired innovations.

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Physiological parameters measured by an embedded body sensor system were demonstrated to respond to changes of the air temperature in an office environment. The thermal parameters were monitored with the use of a wireless sensor system that made possible to turn any existing room into a field laboratory. Two human subjects were monitored over daily activities and at various steady-state thermal conditions when the air temperature of the room was altered from 22-23°C to 25-28°C. The subjects indicated their thermal feeling on questionnaires. The measured skin temperature was distributed close to the calculated mean skin temperature corresponding to the given activity level. The variation of Galvanic Skin Response (GSR) reflected the evaporative heat loss through the body surfaces and indicated whether sweating occurred on the subjects. Further investigations are needed to fully evaluate the influence of thermal and other factors on the output given by the investigated body sensor system.

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Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

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A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

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A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

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We discuss a novel approach to the development of an ultrasonic optical force-feedback measurement microphone suitable for observing biophotonic related photoacoustic and photothermal phenomena at high modulation frequencies and spatial resolution.

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In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.