Evidence for the adsorption of molecules at special sites located at copper/zinc oxide interfaces. Part 3. -- Fourier-transform infrared study of methyl formate adsorption on reduced and oxidised Cu/ZnO/SiO2 catalysts

FTIR spectra are reported of methyl formate adsorbed at 295 K on ZnO/SiO2, reduced Cu/ZnO/SiO2 and on Cu/ZnO/SiO2 which had been preoxidised by exposure to nitrous oxide. Methyl formate on ZnO/SiO2 gave adsorbed zinc formate species and strongly physisorbed molecular methanol on silica. The comparable reaction of methyl formate with reduced Cu/ZnO/SiO2 catalyst produced bridging formate species on copper and a diminished quantity of zinc formate relative to that formed on ZnO/SiO2 catalyst. This effect is explained in terms of site blockage on the ZnO surface by small copper clusters. Addition of methyl formate to a reoxidised Cu/ZnO/SiO2 catalyst produced a considerably greater amount of formate species on zinc oxide and methoxy groups on copper were detected. The increase in concentration of zinc formate species was rationalised in terms of rearrangement of unidentate copper formate species to become bonded to copper and zinc oxide sites located at the interface between these two components.

A fourier-transform infrared study of CO2 and CO2/H2 interactions with caesium-doped copper catalysts

FTIR spectra are reported of CO2 and COi/Hi on a silica-supported caesium-doped copper catalyst. Adsorption of COj on a "caesium"/silica surface resulted in the formation of COj and complexed CO species. Exposure of CO2 to' a caesium-doped reduced copper catalyst produced not only these species but also two forms of adsorbed carboxylate giving bands at 1550, 1510, 1365 and 1345 cm"1. Reaction of carboxylate species with hydrogen at 388 K gave formate species on copper and caesium oxide in addition to methoxy groups associated with caesium oxide. Methoxy species were not detected on undoped copper catalyst suggesting that caesium may be a promoter for the methanol synthesis reaction. Methanol decomposition on a caesium-doped copper catalyst produced a small number of formate species on copper and caesium oxide. Methoxy groups on caesium oxide decomposed to CO and U.2, and subsequent reaction between CO and adsorbed oxygen resulted in carboxylate formation. Methoxy species located at interfacial sites appeared to exhibit unusual adsorption properties.

The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery

Diagnostics of rotating machinery has developed significantly in the last decades, and industrial applications are spreading in different sectors. Most applications are characterized by varying velocities of the shaft and in many cases transients are the most critical to monitor. In these variable speed conditions, fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In the past, this issue was often solved by synchronously sampling data by means of phase locked circuits governing the acquisition; however, thanks to the spread of cheap and powerful microprocessors, this procedure is nowadays rarer; sampling is usually performed at constant time intervals, and the conversion to the order domain is made by means of digital signal processing techniques. In the last decades different algorithms have been proposed for the extraction of an order spectrum from a signal sampled asynchronously with respect to the shaft rotational velocity; many of them (the so called computed order tracking family) use interpolation techniques to resample the signal at constant angular increments, followed by a common discrete Fourier transform to shift from the angular domain to the order domain. A less exploited family of techniques shifts directly from the time domain to the order spectrum, by means of modified Fourier transforms. This paper proposes a new transform, named velocity synchronous discrete Fourier transform, which takes advantage of the instantaneous velocity to improve the quality of its result, reaching performances that can challenge the computed order tracking.

Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm

Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.

Structural characterization of hydrogen peroxide‐oxidized anthracites by X-ray diffraction, Fourier transform infrared spectroscopy, and Raman spectra

The structural characteristics of raw coal and hydrogen peroxide (H2O2)-oxidized coals were investigated using scanning electron microscopy, X-ray diffraction (XRD), Raman spectra, and Fourier transform infrared (FT-IR) spectroscopy. The results indicate that the derivative coals oxidized by H2O2 are improved noticeably in aromaticity and show an increase first and then a decrease up to the highest aromaticity at 24 h. The stacking layer number of crystalline carbon decreases and the aspect ratio (width versus stacking height) increases with an increase in oxidation time. The content of crystalline carbon shows the same change tendency as the aromaticity measured by XRD. The hydroxyl bands of oxidized coals become much stronger due to an increase in soluble fatty acids and alcohols as a result of the oxidation of the aromatic and aliphatic C‐H bonds. In addition, the derivative coals display a decrease first and then an increase in the intensity of aliphatic C‐H bond and present a diametrically opposite tendency in the aromatic C‐H bonds with an increase in oxidation time. There is good agreement with the changes of aromaticity and crystalline carbon content as measured by XRD and Raman spectra. The particle size of oxidized coals (<200 nm in width) shows a significant decrease compared with that of raw coal (1 μm). This study reveals that the optimal oxidation time is ∼24 h for improving the aromaticity and crystalline carbon content of H2O2-oxidized coals. This process can help us obtain superfine crystalline carbon materials similar to graphite in structure.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Chemical functionalities of high and low sulfur Australian coals : a case study using micro attenuated total reflectance–fourier transform infrared (ATR–FTIR) spectrometry

The macerals in bituminous coals with varying organic sulfur content from the Early Permian Greta Coal Measures at three locations (Southland Colliery, Drayton Colliery and the Cranky Corner Basin), in and around the Sydney Basin (Australia), have been studied using light-element electron microprobe (EMP) analysis and micro-ATR–FTIR. Electron microprobe analysis of individual macerals reveals that the vitrinite in both the Cranky Corner Basin and Drayton Colliery (Puxtrees seam) samples have similar carbon contents (ca. 78% C in telocollinite), suggesting that they are of equivalent rank. However, the Cranky Corner coals have anomalously low vitrinite reflectance (down to 0.45%) vs. the Drayton materials (ca. 0.7%). They also have very high organic S content (3–6.5%) and lower O content (ca. 10%) than the equivalent macerals in the Drayton sample (0.7% S and 15.6% O). A study was carried out to investigate the impacts of the high organic S on the functional groups of the macerals in these two otherwise iso-rank, stratigraphically-equivalent seams. An iso-rank low-S coal from the overlying Wittingham Coal Measures near Muswellbrook and coals of slightly higher rank from the Greta Coal Measures at Southland Colliery near Cessnock were also evaluated using the same techniques to extend the data set. Although the telocollinite in the Drayton and Cranky Corner coals have very similar carbon content (ca.78% C), the ATR–FTIR spectra of the vitrinite and inertinite macerals in these respectively low S and high S coals show some distinct differences in IR absorbance from various aliphatic and aromatic functional groups. The differences in absorbance of the aliphatic stretching bands (2800–3000 cm−1) and the aromatic carbon (CC) peak at 1606 cm−1 are very obvious. Compared to that of the Drayton sample (0.7% S and 15% O), the telocollinite of the Cranky Corner coal (6% S and 10% O) clearly shows: (i) less absorbance from OH groups, represented by a broad region around 3553 cm−1, (ii) much stronger aliphatic C–H absorbance (stretching modes around 3000–2800 cm−1 and bending modes around 1442 cm−1) and (iii) less absorbance from aromatic carbon functional groups (peaking at 1606 cm−1). Evaluation of the iso-rank Drayton and Cranky Corner coals shows that: (i) the aliphatic C–H absorbances decrease with increasing oxygen content but increase with increasing organic S content and (ii) the aromatic H to aliphatic H ratio (Har/Hali) for the telocollinite increases with (organic) O%, but decreases progressively with increasing organic S. The high organic S content in the maceral appears to be accompanied by a greater proportion of aliphatic functional groups, possibly as a result of some of the O within maceral ring structures in the high S coal samples being replaced.