Atmospheric corrosion of reference metals in Antarctic sites

This paper presents the results obtained at three Antarctic test sites participating in the “Ibero-American Map of Atmospheric Corrosiveness” (MICAT), a project on atmospheric corrosion carried out during the period 1988–1994 at some 70 sites distributed across 12 countries of the Latin-American region, Spain and Portugal. The three Antarctic sites are located near the coastline.The singular climatic characteristics of Antarctic regions are related with the purity of the air, the absence of rainfall and the formation of ice on the metallic surface during an important part of the exposure time. However, electrochemical activity is possible below ice layers. This situation affects the structure and morphology of corrosion product films and the resulting corrosion rates of metallic surfaces.     

Modeling and Analysis of a Long Thin Conducting Stripline

A long thin conducting stripline embedded in a dielectric and centered between two large conducting plates, i.e., the stripline environment, is considered. The stripline is modeled as infinitely long, infinitely thin, and perfectly conducting by first considering a stripline of finite length, thickness, and conductivity in a dielectric layer. Starting from Maxwell's equations and assuming that the current on the stripline is a propagating wave in length direction, asymptotic expressions for the fields inside and in the neighbourhood of the stripline are deduced. These expressions are used to model the stripline in the stripline environment, which leads to a boundary-value problem for the electric potential. This problem is solved by two different approaches, leading to integral equations for the current and for an auxiliary function describing the electric potential. A relation between the current and the auxiliary function is deduced, which is used to obtain asymptotic expressions for current and impedance. Results are compared with a numerical solution of the integral equation for the current and with results in literature.     

Strategies to improve the performance of very low bit rate speech coders and application to a variable rate 1.2 kb/s codec

This paper presents several strategies to improve the performance of very low bit rate speech coders and describes a speech codec that incorporates these strategies and operates at an average bit rate of 1.2 kb/s. The encoding algorithm is based on several improvements in a mixed multiband excitation (MMBE) linear predictive coding (LPC) structure. A switched-predictive vector quantiser technique that outperforms previously reported schemes is adopted to encode the LSF parameters. Spectral and sound specific low rate models are used in order to achieve high quality speech at low rates. An MMBE approach with three sub-bands is employed to encode voiced frames, while fricatives and stops modelling and synthesis techniques are used for unvoiced frames. This strategy is shown to provide good quality synthesised speech, at a bit rate of only 0.4 kb/s for unvoiced frames. To reduce coding noise and improve decoded speech, spectral envelope restoration combined with noise reduction (SERNR) postfilter is used. The contributions of the techniques described in this paper are separately assessed and then combined in the design of a low bit rate codec that is evaluated against the North American Mixed Excitation Linear Prediction (MELP) coder. The performance assessment is carried out in terms of the spectral distortion of LSF quantisation, mean opinion score (MOS), A/B comparison tests and the ITU-T P.862 perceptual evaluation of speech quality (PESQ) standard. Assessment results show that the improved methods for LSF quantisation, sound specific modelling and synthesis and the new postfiltering approach can significantly outperform previously reported techniques. Further results also indicate that a system combining the proposed improvements and operating at 1.2 kb/s, is comparable (slightly outperforming) a MELP coder operating at 2.4 kb/s. For tandem connection situations, the proposed system is clearly superior to the MELP coder.     

Integration of prior knowledge of measurement noise in kernel density classification

Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.     

Synthesis and characterization of functional gradient copolymers of glycidyl methacrylate and butyl acrylate

Epoxy-functional spontaneous gradient copolymers of glycidyl methacrylate (G) and n-butyl acrylate (B) were synthesized via atom transfer radical polymerization (ATRP). The copolymerization reactions were carried out in toluene solution at 70 °C, using methyl 2-bromopropionate (MBrP) as initiator and copper chloride with N,N,N′,N′′,N′′-pentamethyldiethylenetriamine (PMDETA) as the catalyst system. The kinetic behaviour of the statistical copolymerizations was studied in a wide composition interval with molar fractions of G ranging from 0.10 to 0.75. The synthesized copolymers were characterized by size exclusion chromatography (SEC) and nuclear magnetic resonance (NMR) spectroscopy. ¹H NMR was employed to determine the copolymer composition, demonstrating the gradient character of the copolymers along the main chain in the whole monomer conversion interval. Apart from this, the sequence distribution and stereoregularity were analyzed. These microstructural experimental data agreed well with those calculated from Mayo-Lewis terminal model (MLTM) and a Bernoullian statistic with an isotacticity parameter of σ_G = 0.28 and a coisotacticity parameter of σ = 0.30.     

Grouped Approach for the Design of H.264/AVC Motion Estimation Architectures

This letter presents a novel approach for organizing computational resources into groups within H.264/AVC motion estimation architectures, leading to reductions of up to 75% in the equivalent gate count with respect to state‐of‐the‐art designs.     

Use of recycled copper slag for blended cements

Copper slag is a by‐product generated during smelting to extract copper metal from the ore. The copper slag obtained may exhibit pozzolanic activity and may therefore be used in the manufacture of addition‐containing cements. In this paper the effect of the incorporation of the copper slag in cement is measured. Blends of copper slag with Portland cement generally possess properties equivalent to Portland cement containing fly ash, but very different to the silica fume incorporation. Copper slag and fly ash reduce the heat of hydration more effectively than silica fume in mortars. The replacement of 30% cement by copper slag reduces the flexural and compressive strength in a similar way to fly ash; however, after 28 days, the reduction is less than the percentage of substitution. Hydrated calcium aluminate phases were analysed using scanning electron microscopy (SEM) and X‐ray diffraction (XRD) techniques. The pozzolanic activity of copper slag is similar to that of fly ash and higher than silica fume. In the presence of low water/cement ratios, certain pozzolanic materials produce a very compact cement paste that limits the space available for hydration products, a determining factor in the formation of hydrated calcium aluminates. SEM was found to be a useful analytical technique when aluminates are formed and can be clearly detected by XRD. Copyright © 2008 Society of Chemical Industry     

Application of autoregressive spectral analysis to missing data problems

Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood algorithm is presented that uses only previous observations falling in a finite interval to compute the likelihood, instead of all previous observations. The resulting nonlinear estimation algorithm requires no user-provided initial solution, is suited for order selection, and can give very accurate spectra even if less than 10% of the data remains.     

Finite sample properties of ARMA order selection

The cost of order selection is defined as the loss in model quality due to selection. It is the difference between the quality of the best of all available candidate models that have been estimated from a finite sample of N observations and the quality of the model that is actually selected. The order selection criterion itself has an influence on the cost because of the penalty factor for each additionally selected parameter. Also, the number of competitive candidate models for the selection is important. The number of candidates is, of itself, small for the nested and hierarchical autoregressive/moving average (ARMA) models. However, intentionally reducing the number of selection candidates can be beneficial in combined ARMA(p,q) models, where two separate model orders are involved: the AR order p and the MA order q. The selection cost can be diminished by creating a nested sequence of ARMA(r,r-1) models. Moreover, not evaluating every combination (p,q) of the orders considerably reduces the required computation time. The disadvantage may be that the true ARMA(p,q) model is no longer among the nested candidate models. However, in finite samples, this disadvantage is largely compensated for by the reduction in the cost of order selection by considering fewer candidates. Thus, the quality of the selected model remains acceptable with only hierarchically nested ARMA(r,r-1) models as candidates.