Loss comparison between an SPWM and harmonic elimination excited small, (<1kW) induction motor drive using Pspice simulation and calorimetry

Adopting square wave excitation to drive induction motors (IMs) can substantially reduce inverter switching losses. However, the low-order time harmonics inherent in the output voltage generates parasitic torques that degrade motor performance and reduce efficiency. In this paper, a novel harmonic elimination modulation technique with full voltage control is studied as an interesting and alternative means of operating small (<1kW) IM drives efficiently. A fully verified harmonic elimination scheme, which removes the 5th, 7th, 11th, 13th and 17 th time harmonics, was implemented and applied to an IGBT driven IM. The power losses incurred in the inverter and the IM as a result of the switching scheme have been determined. © 2008 Crown copyright.

Use of wavefront encoding in optical interconnects and fiber switches for cross talk mitigation

A technique of cross talk mitigation developed for liquid crystal on silicon spatial light modulator based optical interconnects and fiber switches is demonstrated. By purposefully introducing an appropriate aberration into the system, it is possible to reduce the worst-case cross talk by over 10 dB compared to conventional Fourier-transform-based designs. Tests at a wavelength of 674nm validate this approach, and show that there is no noticeable reduction in diffraction efficiency. A 27% spot increase in beam diameter is observed, which is predicted to reduce at longer datacom and telecom wavelengths. © 2012 Optical Society of America.

Atmospheric turbulence mitigation using complex wavelet-based fusion

Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full-and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. © 1992-2012 IEEE.

Designing climate change mitigation plans that add up.

Mitigation plans to combat climate change depend on the combined implementation of many abatement options, but the options interact. Published anthropogenic emissions inventories are disaggregated by gas, sector, country, or final energy form. This allows the assessment of novel energy supply options, but is insufficient for understanding how options for efficiency and demand reduction interact. A consistent framework for understanding the drivers of emissions is therefore developed, with a set of seven complete inventories reflecting all technical options for mitigation connected through lossless allocation matrices. The required data set is compiled and calculated from a wide range of industry, government, and academic reports. The framework is used to create a global Sankey diagram to relate human demand for services to anthropogenic emissions. The application of this framework is demonstrated through a prediction of per-capita emissions based on service demand in different countries, and through an example showing how the "technical potentials" of a set of separate mitigation options should be combined.

Model-based compressive Harmonic-Aware Matching Pursuit: An evaluation

This paper addresses devising a reliable model-based Harmonic-Aware Matching Pursuit (HAMP) for reconstructing sparse harmonic signals from their compressed samples. The performance guarantees of HAMP are provided; they illustrate that the introduced HAMP requires less data measurements and has lower computational cost compared with other greedy techniques. The complexity of formulating a structured sparse approximation algorithm is highlighted and the inapplicability of the conventional thresholding operator to the harmonic signal model is demonstrated. The harmonic sequential deletion algorithm is subsequently proposed and other sparse approximation methods are evaluated. The superior performance of HAMP is depicted in the presented experiments. © 2013 IEEE.