Feature extraction and classification models for high‐dimensional profile data

As manufacturing transitions to real‐time sensing, it becomes more important to handle multiple, high‐dimensional (non‐stationary) time series that generate thousands of measurements for each batch. Predictive models are often challenged by such high‐dimensional data and it is important to reduce the dimensionality for better performance. With thousands of measurements, even wavelet coefficients do not reduce the dimensionality sufficiently. We propose a two‐stage method that uses energy statistics from a discrete wavelet transform to identify process variables and appropriate resolutions of wavelet coefficients in an initial (screening) model. Variable importance scores from a modern random forest classifier are exploited in this stage. Coefficients that correspond to the identified variables and resolutions are then selected for a second‐stage predictive model. The approach is shown to provide good performance, along with interpretable results, in an example where multiple time series are used to indicate the need for preventive maintenance. In general, the two‐stage approach can handle high dimensionality and still provide interpretable features linked to the relevant process variables and wavelet resolutions that can be used for further analysis. Copyright © 2011 John Wiley & Sons, Ltd.     

Carbon and sulfur cycling by microbial communities in a gypsum-treated oil sands tailings pond

Oil sands tailings ponds receive and store the solid and liquid waste from bitumen extraction and are managed to promote solids densification and water recycling. The ponds are highly stratified due to increasing solids content as a function of depth but can be impacted by tailings addition and removal and by convection due to microbial gas production. We characterized the microbial communities in relation to microbial activities as a function of depth in an active tailings pond routinely treated with gypsum (CaSO(4)·2H(2)O) to accelerate densification. Pyrosequencing of 16S rDNA gene sequences indicated that the aerobic surface layer, where the highest level of sulfate (6 mM) but no sulfide was detected, had a very different community profile than the rest of the pond. Deeper anaerobic layers were dominated by syntrophs (Pelotomaculum, Syntrophus, and Smithella spp.), sulfate- and sulfur-reducing bacteria (SRB, Desulfocapsa and Desulfurivibrio spp.), acetate- and H(2)-using methanogens, and a variety of other anaerobes that have been implicated in hydrocarbon utilization or iron and sulfur cycling. The SRB were most abundant from 10 to 14 mbs, bracketing the zone where the sulfate reduction rate was highest. Similarly, the most abundant methanogens and syntrophs identified as a function of depth closely mirrored the fluctuating methanogenesis rates. Methanogenesis was inhibited in laboratory incubations by nearly 50% when sulfate was supplied at pond-level concentrations suggesting that in situ sulfate reduction can substantially minimize methane emissions. Based on our data, we hypothesize that the emission of sulfide due to SRB activity in the gypsum treated pond is also limited due to its high solubility and oxidation in surface waters.     

A novel nanostructured iron oxide-gold bioelectrode for hydrogen peroxide sensing

Fe(3)O(4) nanoparticles covalently linked to a gold electrode have been used for immobilizing catalase (CAT) enzyme to sense the presence of various concentrations of H(2)O(2). These nanoparticles ranging from 20 to 30 nm were synthesized by thermal co-precipitation of ferric and ferrous chlorides. SEM and XRD have been used for morphological and structural characterization of Fe(3)O(4) nanoparticles. CAT enzyme was linked covalently to the surface of iron oxide using carbodiimide in phosphate buffer (pH 7.4) at 4?°C. The enzyme-iron oxide link was confirmed by FT-IR spectroscopy. Sensing studies carried out using cyclic voltammetry showed a linear response of the CAT/nano Fe(3)O(4)/Au bioelectrode towards H(2)O(2) between 1.5 and 13.5 μM with a very sharp response time of 2 s.     

Estimation of wind power availability in Tamil Nadu

Eleven years' daily wind speed data at 21 locations in the state of Tamil Nadu, India were analysed to assess the available wind power potential using Weibull distribution under two different methods. The mean wind speed varied from 1.0 to 5.0 m/s dividing the state into four regions. Judged by mean and standard deviation of available wind power, six locations have been identified as possible sites for a wind energy system.     

As(V) removal using carbonized yeast cells containing silver nanoparticles

The present study involves the development of adsorbent containing silver nanoparticles for arsenate removal using silver reducing property of a novel yeast strain Saccharomyces cerevisiae BU-MBT-CY1 isolated from coconut cell sap. Biological reduction of silver by the isolate was deduced at various time intervals. The yeast cells after biological silver reduction were harvested and subjected to carbonization at 400 °C for 1 h and its properties were analyzed using Fourier Transform Infra-Red spectroscopy, X-ray diffraction, scanning electron microscope attached with energy dispersive spectroscopy and transmission electron microscope. The average size of the silver nanoparticles present on the surface of the carbonized silver containing yeast cells (CSY) was 19 ± 9 nm. The carbonized control yeast cells (CCY) did not contain any particles on its surface. As(V) adsorption efficiency of CCY and CSY was deduced in batch mode by varying parameters like contact time, initial concentration, and pH. Desorption studies were also carried out by varying the pH. The experimental data were fitted onto Langmuir and D-R Isotherms and Lagergren and pseudo second order kinetic models. The CSY was more efficient in arsenate removal when compared to CCY.     

Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

Accurate and rapid assessment of the as-built status on any construction site provides the opportunity to understand the current performance of a project easily and quickly. Rapid project assessment further identifies discrepancies between the as-built and as-planned progress, and facilitates decision making on the necessary remedial actions. Currently, manual visual observations and surveying are the most dominant data capturing techniques but they are time-consuming, error-prone, and infrequent, making quick and reliable decision-making difficult. Therefore, research on new approaches that allow automatic recognition of as-built performance and visualization of construction progress is essential. This paper presents and compares two methods for obtaining point cloud models for detection and visualization of as-built status for construction projects: (1) A new method of automated image-based reconstruction and modeling of the as-built project status using unordered daily construction photo collections through analysis of Structure from Motion (SfM); (2) 3D laser scanning and analysis of the as-built dense point cloud models. These approaches provide robust means for recognition of progress, productivity, and quality on a construction site. In this paper, an overview of the newly developed automated image-based reconstruction approach and exclusive features which distinct it from other image-based or conventional photogrammetric techniques is presented. Subsequently the terrestrial laser scanning approach carried out for reconstruction and comparison of as-built scenes is presented. Finally the accuracy and usability of both of these techniques for metric reconstruction, automated production of point cloud models, 3D CAD shape modeling, and as-built visualizations is evaluated and compared on eight different case studies. It is shown that for precise defect detection or alignment tasks, image-based point cloud models may not be as accurate and dense as laser scanners' point cloud models. Nonetheless image-based point cloud models provide an opportunity to extract as-built semantic information (i.e., progress, productivity, quality and safety) through the content of the images, are easy to use, and do not need add burden on the project management teams by requiring expertise for data collection or analysis. Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.     

Clique Number vs. Chromatic Number in Wireless Interference Graphs: Simulation Results

Interference due to transmissions by adjacent nodes in a multi-hop wireless network can be modeled using a unit disc graph (UDG). We investigate the reliability associated with using the clique number instead of the chromatic number of the UDG while computing the interference. In our extensive simulations with UDGs of random networks, we observed that the clique number and the chromatic number values were typically very close to one another and the maximum deviation was much less than the theoretical bounds. This implies very high reliability in the proposed approximation.     

Design of Cryogenic SiGe Low-Noise Amplifiers

This paper describes a method for designing cryogenic silicon-germanium (SiGe) transistor low-noise amplifiers and reports record microwave noise temperature, i.e., 2 K, measured at the module connector interface with a 50-Omega generator. A theory for the relevant noise sources in the transistor is derived from first principles to give the minimum possible noise temperature and optimum generator impedance in terms of dc measured current gain and transconductance. These measured dc quantities are then reported for an IBM SiGe BiCMOS-8HP transistor at temperatures from 295 to 15 K. The measured and modeled noise and gain for both a single-and two-transistor cascode amplifier in the 0.2-3-GHz range are then presented. The noise model is then combined with the transistor equivalent-circuit elements in a circuit simulator and the noise in the frequency range up to 20 GHz is compared with that of a typical InP HEMT.     

Node-Level Energy Management for Sensor Networks in the Presence of Multiple Applications

Wireless Networks - Energy related research in wireless ad hoc sensor networks (WASNs) is focusing on energy saving techniques in the application-, protocol-, service-, or hardware-level. Little...     

A novel magnetic random access memory design using square ring elements for the hard layer

The behavior of narrow permalloy square rings under the influence of a magnetic field was studied using magnetic force microscopy (MFM). Two stable states of opposite polarity at remanence and simple switching were observed. We propose a design for the hard layer of magnetic random access memory (MRAM) that uses these states in square rings for data storage.