A separate least squares algorithm for efficient arithmetic coding in lossless image compression

The overall performance of discrete wavelet transforms for losssless image compression may be further improved by properly designing efficient entropy coders. In this paper a novel technique is proposed for the implementation of context-based adaptive arithmetic entropy coding. It is based on the prediction of the value of the current transform coefficient. The proposed algorithm employs a weighted least squares method applied separately for the HH, HL and LH bands of each level of the multiresolution structure, in order to achieve appropriate context selection for arithmetic coding. Experimental results illustrate and evaluate the performance of the proposed technique for lossless image compression

Fretting Fatigue Performance of Unidirectional, Laminated Carbon Fibre Reinforced Polymer Straps at Elevated Service Temperature

The fretting fatigue performance of laminated, unidirectional (UD), pin-loaded, carbon fibre-reinforced polymer (CFRP) straps that can be used as bridge hanger cables was investigated at a sustained service temperature of 60 °C. The aim of this paper is to elucidate the influence of the slightly elevated service temperature on the tensile fatigue performance of CFRP straps. First, steady state thermal tests at ambient temperature and at 60 °C are presented, in order to establish the behaviour of the straps at these temperatures. These results indicated that the static tensile performance of the straps is not affected by the increase in temperature. Subsequently, nine upper stress levels (USLs) between 650 and 1400 MPa were chosen in order to establish the S–N curve at 60 °C (frequency 10 Hz; R = 0.1) and a comparison with an existing S–N curve at ambient temperature was made. In general, the straps fatigue limit was slightly decreased by temperature, up to 750 MPa USL, while, for the higher USLs, the straps performed slightly better as compared with the S–N curve at ambient temperature

Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries

Full battery models are highly complex, which limits their application to tasks such as optimization and uncertainty quantification. To lower the computational burden, sensitivity analysis (SA) can be used as a precursor to identify the most important parameters in the model, but SA itself relies on a high number of full model evaluations, which has motivated the use of emulators. For high-dimensional output problems, emulators are challenging to construct. In this paper we develop a probabilistic framework for SA of high-dimensional output models using a Gaussian process emulator based on dimensionality reduction. This allows us to perform SA under uncertainty for multi-ouput problems, providing error bounds for the emulator predictions of sensitivity measures. We show how this can be achieved using Monte Carlo sampling or possibly by using semi-analytical expressions with highly efficient sampling. Moreover, we can perform SA for multivariate outputs by ranking the sensitivity measures related to (uncorrelated) coefficients in a basis for the output space

Catalytic Fast Pyrolysis of Kraft Lignin With Conventional, Mesoporous and Nanosized ZSM-5 Zeolite for the Production of Alkyl-Phenols and Aromatics

The valorization of lignin that derives as by product in various biomass conversion processes has become a major research and technological objective. The potential of the production of valuable mono-aromatics (BTX and others) and (alkyl)phenols by catalytic fast pyrolysis of lignin is investigated in this work by the use of ZSM-5 zeolites with different acidic and porosity characteristics. More specifically, conventional microporous ZSM-5 (Si/Al = 11.5, 25, 40), nano-sized (≤20 nm, by direct synthesis) and mesoporous (9 nm, by mild alkaline treatment) ZSM-5 zeolites were tested in the fast pyrolysis of a softwood kraft lignin at 400–600°C on a Py/GC-MS system and a fixed-bed reactor unit. The composition of lignin (FT-IR, 2D HSQC NMR) was correlated with the composition of the thermal (non-catalytic) pyrolysis oil, while the effect of pyrolysis temperature and catalyst-to-lignin (C/L) ratio, as well as of the Si/Al ratio, acidity, micro/mesoporosity and nano-size of ZSM-5, on bio-oil composition was thoroughly investigated. It was shown that the conventional microporous ZSM-5 zeolites are more selective toward mono-aromatics while the nano-sized and mesoporous ZSM-5 exhibited also high selectivity for (alkyl)phenols. However, the nano-sized ZSM-5 zeolite exhibited the lowest yield of organic bio-oil and highest production of water, coke and non-condensable gases compared to the conventional microporous and mesoporous ZSM-5 zeolites

On the self-pinning character of synchro-Shockley dislocations in a Laves phase during strain rate cyclical compressions

Strain rate cyclical tests in compression, between 1350 and 1500 degrees C, have been employed to study the self-pinning character of thermally activated synchro-Shockley dislocations in the C15 Cr2Nb Laves phase. An average minimum effective (pinning) stress was calculated to be necessary for their propagation. The dislocation velocity cannot respond instantly to the strain rate changes and requires variations in the mobile dislocation density because the synchro-Shockleys can be pinned if the cooperating motion of their two Shockley components is hindered. (c) 2008 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved

Manifold learning for the emulation of spatial fields from computational models

Repeated evaluations of expensive computer models in applications such as design optimization and uncertainty quantification can be computationally infeasible. For partial differential equation (PDE) models, the outputs of interest are often spatial fields leading to high-dimensional output spaces. Although emulators can be used to find faithful and computationally inexpensive approximations of computer models, there are few methods for handling high-dimensional output spaces. For Gaussian process (GP) emulation, approximations of the correlation structure and/or dimensionality reduction are necessary. Linear dimensionality reduction will fail when the output space is not well approximated by a linear subspace of the ambient space in which it lies. Manifold learning can overcome the limitations of linear methods if an accurate inverse map is available. In this paper, we use kernel PCA and diffusion maps to construct GP emulators for very high-dimensional output spaces arising from PDE model simulations. For diffusion maps we develop a new inverse map approximation. Several examples are presented to demonstrate the accuracy of our approach