Three dimensional number density mapping in the plume of a low temperature laser ablated magnesium plasma

Simultaneous optical absorption and laser-induced fluorescence measurements have been used to map the three-dimensional number densities of ground-state ions and neutrals within a low-temperature KrF laser-produced magnesium plasma expanding into vacuum. Data is reported for the symmetry plane of the plasma, which includes the laser interaction point at a delay of 1 μs after the ∼30 ns KrF laser ablation pulse and for a laser fluence of 2 J cm−2 on target. The number density distributions of ion and neutral species within this plane indicate that two distinct regions exist within the plume; one is a fast component containing ions and neutrals at maximum densities of ∼3×1013 cm−3 and ∼4×1012 cm−3, respectively and the second is a high-density region containing slow neutral species, at densities up to ∼1×1015 cm−3.

Quantitative mapping of aqueous microfluidic temperature with sub-degree resolution using fluorescence lifetime imaging microscopy

The use of a water-soluble, thermo-responsive polymer as a highly sensitive fluorescence-lifetime probe of microfluidic temperature is demonstrated. The fluorescence lifetime of poly(N-isopropylacrylamide) labelled with a benzofurazan fluorophore is shown to have a steep dependence on temperature around the polymer phase transition and the photophysical origin of this response is established. The use of this unusual fluorescent probe in conjunction with fluorescence lifetime imaging microscopy (FLIM) enables the spatial variation of temperature in a microfluidic device to be mapped, on the micron scale, with a resolution of less than 0.1 degrees C. This represents an increase in temperature resolution of an order of magnitude over that achieved previously by FLIM of temperature-sensitive dyes

Mapping NGO Competency to Reduce Human Vulnerability in Post-disaster Communities: Comparing Strategies in Sri Lanka and Bangladesh

Natural hazards trigger disasters, the scale of which is largely determined by vulnerability. Developing countries suffer the most from disasters due to various conditions of vulnerability which exist and there is an opportunity after disasters to take mitigative action. NGOs implementing post-disaster rehabilitation projects must be able to address the issues causing communities to live at risk of disaster and therefore must build dynamic capacity, capabilities and competencies, enabling them to operate in unstable environments. This research is built upon a theoretical framework of dynamic competency established by combining elements of disaster management, strategic management and project management theory. A number of NGOs which have implemented reconstruction and rehabilitation projects both in Sri Lanka following the Asian Tsunami and Bangladesh following Cyclone Sidr are being investigated in great depth using a causal mapping procedure. ‘Event’ specific maps have been developed for each organization in each disaster. This data will be analysed with a view to discovering the strategies which lead to vulnerability reduction in post-disaster communities and the competencies that NGOs must possess in order to achieve favourable outcomes. It is hypothesized that by building organizational capacity, capabilities and competencies to be dynamic in nature, while focusing on a more emergent strategic approach, with emphasis on adaptive capability and innovation, NGOs will be better equipped to contribute to sustainable community development through reconstruction. We believe that through this study it will be possible to glean a new understanding of social processes that emerge within community rehabilitation projects.

Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.

Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) and Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these features are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area. One should carefully consider this fact when selecting the appropriate palm region for the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area by ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances as evidenced by an Equal Error Rate (EER) of 0.03%.