On the Consistency of the Normalized Eight-Point Algorithm

A recently proposed argument to explain the improved performance of the eight-point algorithm that results from using normalized data (Chojnacki, W., et al. in IEEE Trans. Pattern Anal. Mach. Intell. 25(9):1172–1177, 2003) relies upon adoption of a certain model for statistical data distribution. Under this model, the cost function that underlies the algorithm operating on the normalized data is statistically more advantageous than the cost function that underpins the algorithm using unnormalized data. Here we extend this explanation by introducing a more refined, structured model for data distribution. Under the extended model, the normalized eight-point algorithm turns out to be approximately consistent in a statistical sense. The proposed extension provides a link between the existing statistical rationalization of the normalized eight-point algorithm and the approach of Mühlich and Mester for enhancing total least squares estimation methods via equilibration. The paper forms part of a wider effort to rationalize and interrelate foundational methods in vision parameter estimation.     

Terrain classification and identification of tree stems using ground‐based LiDAR

To operate autonomously in forested terrain, unmanned ground vehicles must be able to identify the load‐bearing surface of the terrain (i.e., the ground) and obstacles in the environment. To travel long distances, they must be able to track their position even when the forest canopy obstructs GPS signals, e.g., by tracking progress relative to tree stems. This paper presents a novel, robust approach for modeling the ground plane and tree stems in forests from a single viewpoint using a lightweight LiDAR scanner. Ground plane identification is implemented using a two‐stage approach. The first stage, a local height‐based filter, discards most nonground points. The second stage, based on a support vector machine classifier, identifies which of the remaining points belong to the ground. Main tree stems are modeled as cylinders or cones to estimate the diameter 130 cm above the ground plane. To fit these models, candidate main stem data are selected by finding points approximately 130 cm above the ground. These points are clustered into separate point clouds for each stem. Cylinders and cones are fit to each point cloud, and heuristic filters identify which fits correspond to tree stems. Experimental results from five forested environments demonstrate the effectiveness of this approach. For ground plane estimation, the overall classification accuracy was 86.28% with a mean error for the ground height of approximately 4.7 cm. For stem estimation, up to 50% of the main stems were accurately modeled using cones, with a root mean square diameter error of 13.2 cm.© 2012 Wiley Periodicals, Inc.     

Increased conversion efficiency in cadmium telluride photovoltaics by luminescent downshifting with quantum dot/poly(methyl methacrylate) films

Commercially available quantum dots have been encapsulated in a poly(methyl methacrylate) film and used as a luminescent downshifting layer on cadmium sulfide/cadmium telluride photovoltaic devices. Application of these films has resulted in a relative improvement to the short‐circuit current of over 4% by I–V measurement, with a significant increase in the contribution of short‐wavelength light resulting in 25% of the current available in this part of the spectrum being captured. The films have been shown to be highly scattering and the associated difficulties this provides to external quantum efficiency measurements have been discussed. A range of optical characterisation techniques, particularly laser beam induced current, have been used to probe the effect the films have on a cadmium sulfide/cadmium telluride device. An alternate methodology for performing external quantum efficiency measurements with the quantum dot films has been proposed. Copyright © 2013 John Wiley & Sons, Ltd.     

Conversion of waste polystyrene through catalytic degradation into valuable products

Waste expanded polystyrene (EPS) represents a source of valuable chemical products like styrene and other aromatics. The catalytic degradation was carried out in a batch reactor with a mixture of polystyrene (PS) and catalyst at 450 °C for 30 min in case of Mg and at 400 °C for 2 h both for MgO and MgCO₃ catalysts. At optimum degradation conditions, EPS was degraded into 82.20±3.80 wt%, 91.60±0.20 wt% and 81.80±0.53 wt% liquid with Mg, MgO and MgCO₃ catalysts, respectively. The liquid products obtained were separated into different fractions by fractional distillation. The liquid fractions obtained with three catalysts were compared, and characterized using GC-MS. Maximum conversion of EPS into styrene monomer (66.6 wt%) was achieved with Mg catalyst, and an increase in selectivity of compounds was also observed. The major fraction at 145 °C showed the properties of styrene monomer. The results showed that among the catalysts used, Mg was found to be the most effective catalyst for selective conversion into styrene monomer as value added product.     

Biscuit-Making Quality Prediction Using Heritability Estimates and Correlations

The aim of this study was to predict biscuit-making quality by using correlations between and, heritabilities of, the most important soft wheat quality characteristics. A full diallele cross was made between six spring wheats. The parents and 30 F ₁ hybrids were used as experimental material. Percentage vitreous kernels, breakflour yield, farinograph absorption, farinograph development time and alkaline water retention capacity were highly heritable and were also significantly correlated with sugar-snap biscuit diameter. Selection for these characteristics should lead to improvement of biscuit-making quality. In general, softer kernels and a weak dough with a low water absorption were desirable for good biscuit-making quality.     

Determination of Glyphosate and Its Metabolite AMPA (Aminomethylphosphonic Acid) in Cereals After Derivatization by Isotope Dilution and UPLC-MS/MS”

A straightforward analytical method has been developed for the determination of glyphosate and aminomethylphosphonic acid (AMPA), its major metabolite in cereals. This method entails a rapid extraction and derivatization with 9-fluorenylmethyl chloroformate followed by separation with a conventional reversed-phase rapid chromatography used in daily routine analysis and detection by electrospray ionization tandem mass spectrometry. To overcome matrix effects and compensate for any analyte losses during sample treatment, an isotopic dilution approach was used. Since 2010, the monitoring of cereals for the widely used herbicide glyphosate is obligatory to all European Union (EU)-member states, laid down in Commission Regulation (EC) No. 1213/2008. Hence, there is definitively a need for a reliable and easy-to-handle analytical method for monitoring of this compound. The proposed method can be run without having to make time-consuming changes on the equipment used for daily routine analysis. The analytical performance of the method was evaluated according to SANCO/10684/2009 criteria and demonstrated that this method is rugged and cost-effective, therefore suitable for monitoring purposes as well as legislative enforcements within the EU. The apparent recoveries of both analytes were between 97% and 113% with relative standard deviations less than 20%. The limits of quantification of glyphosate and AMPA were 0.02 mg/kg in cereal matrices. Finally, the accuracy of the method was assessed by analyzing a proficiency test material which was available from a previous round (EUPT-C4).     

Spatial ecology of reintroduced walleye (Sander vitreus) in Hamilton Harbour of Lake Ontario

Many coastal embayments in the Laurentian Great Lakes have been subjected to extensive human physical modification and pollution that has led to the loss of freshwater biodiversity. For example, Hamilton Harbour is a large coastal embayment situated at the western end of Lake Ontario, with a long history of industrial and urban development that has resulted in the loss and degradation of aquatic habitat and the extirpation of several fish species. To restore the fish community in Hamilton Harbour, several attempts have been made to increase apex predator biodiversity by reintroducing native walleye (Sander vitreus). To assess how reintroduced (i.e., stocked) walleye use Hamilton Harbour, we used acoustic telemetry to characterize the residency of individuals within the boundaries of the harbour as well as their seasonal space use, with a focused interest on the spring spawning period. During the 1?yr tracking period tagged walleye spent an average of 357?days (range 135–365?days) within the harbour. Most individuals (12/15) remained within the harbour during the entire spring spawning period, and over half of the tagged fish departed (n?=?7) at the end of summer and beginning of fall. Core use areas appeared to gradually shift more easterly as the seasons progressed from winter to summer. Results from this study indicate that stocked fish are resident within Hamilton Harbour for most of the year, including the reproductive period, which suggests that stocking efforts to re-establish walleye populations may be an effective restoration strategy if recruitment is successful.     

General heat balance for oxygen steelmaking

Energy balances are a general fundamental approach for analyzing the heat requirements for metallurgical processes.The formulation of heat balance equations was...     

Student success prediction in MOOCs

Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model. We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012. This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models. Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.