ELECTROCHEMISTRY AND IN-SITU IR MICROSPECTROELECTROCHEMISTRY IN A VERSATILE THIN-LAYER REFLECTION FOURIER-TRANSFORM IR MICROSPECTROELECTROCHEMICAL CELL

A simple, convenient and versatile thin layer reflection Fourier transform IR microspectroelectrochemical (FTIRMSEC) cell has been described and characterized. Electrochemistry and in situ FTIR microspectroscopy were studied by using the hexacyanoferrate redox couple in aqueous sulphate solution, indicating that this type of cell is characteristic of both micro- or ultramicroelectrode and thin layer spectroelectrochemistry. Furthermore, the application of this FTIRMSEC cell to IR for characterization of the products of electrochemical reactions was carried out for the oxidation of (mesotetraphenylporphinato)manganese(III) perchlorate in dichloromethane + tetrabutylammonium perchlorate solution. Finally, the advantages and problems of this type of cell compared with a conventional optically transparent thin layer FTIR spectroelectrochemical cell were discussed.

On the nonlinear integral transform of an ocean wave spectrum into an along-track interferometric synthetic aperture radar image spectrum

We present a new nonlinear integral transform relating the ocean wave spectrum to the along-track interferometric synthetic aperture radar (AT-INSAR) image spectrum. The AT-INSAR, which is a synthetic aperture radar (SAR) employing two antennas displaced along the platform's flight direction, is considered to be a better instrument for imaging ocean waves than the SAR. This is because the AT-INSAR yields the phase spectrum and not only the amplitude spectrum as with the conventional SAR. While the SAR and AT-INSAR amplitude spectra depend strongly on the modulation of the normalized radar cross section (NRCS) by the long ocean waves, which is poorly known, the phase spectrum depends only weakly on this modulation. By measuring the phase difference between the signals received by both antennas, AT-INSAR measures the radial component of the orbital velocity associated with the ocean waves, which is related to the ocean wave height field by a well-known transfer function. The nonlinear integral transform derived in this paper differs from the one previously derived by Bao et al. [1999] by an additional term containing the derivative of the radial component of the orbital velocity associated with the long ocean waves. By carrying out numerical simulations, we show that, in general, this additional term cannot be neglected. Furthermore, we present two new quasi-linear approximations to the nonlinear integral transform relating the ocean wave spectrum to the AT-INSAR phase spectrum.

自治式导航实验载体视觉系统（基于边缘图象的线性特征抽取及HougH变换方法研究）

移动式机器人系统的一个重要课题是视觉导航系统的研制。为了通过环境正确导航，移动式机器人必须感知和理解其环境结构，这样就必须提取与景物相关的模型特征。本论文一方面对视觉系统中的关键技术－－线性特征抽取进行描述与实施，并将其成果应用在道路边界的检测上；另一方面对视觉系统中的另一重要技术－－Hough变换，从理论到实验进行了详细的推导和验证。论文包括两大部分：1、线性特征抽取和道路界检测（包括模拟系统和实时系统）2、Hough变换的发展及其方法研究（包括快速Hough变换的硬件实现方案和用Systolic阵列机Hough变换的模拟实验及其结果）目前，线性特征抽取和道路边界检测的实时系统，己装在自治式导航实验载体上，用于视觉辅助导航。

Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning

Ellis, D. I., Broadhurst, D., Kell, D. B., Rowland, J. J., Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning. ? Applied and Environmental Microbiology, 68, (6), 2822-2828 Sponsorship: BBSRC

Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection

Elliott, G. N., Worgan, H., Broadhurst, D. I., Draper, J. H., Scullion, J. (2007). Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection. Soil Biology & Biochemistry, 39 (11), 2888-2896. Sponsorship: BBSRC / NERC RAE2008

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

© 2005-2012 IEEE.Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies.

A regularized iterative algorithm for limited-angle inverse Radon transform

The tomography problem is investigated when the available projections are restricted to a limited angular domain. It is shown that a previous algorithm proposed for extrapolating the data to the missing cone in Fourier space is unstable in the presence of noise because of the ill-posedness of the problem. A regularized algorithm is proposed, which converges to stable solutions. The efficiency of both algorithms is tested by means of numerical simulations. © 1983 Taylor and Francis Group, LLC.

New high-resolution analysis of the ν7 and ν9 fundamental bands of trans-formic acid by IR Fourier transform and millimeterwave spectroscopies

info:eu-repo/semantics/published

Laboratory Fourier transform spectroscopy of the water absorption continuum from 2500 to 22500 cm-1

info:eu-repo/semantics/published