Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Spectral Values

This series of drawings takes a diagrammatically creative approach to understanding the economic theories and personalities at the centre of the Global Financial Crisis. Mimicking the form of US currency, the work removes labels from common economic diagrams and portrays financial titans in repose as a way to express a personal and ambivalent experience of contemporary capitalism.

Hydrolysis and dispersion properties of aqueous Y2Si 2O7 suspensions

The dispersion of aqueous γ-Y2Si2O7 suspensions, which contain only one component but have a complex ion environment, was studied by the introduction of two different polymer dispersants, polyethylenimine (PEI) and polyacrylic acid (PAA). The suspension without any dispersant remains stable in the pH range of 9-11.5 because of electrostatic repulsion, while it is flocculated upon stirring due to the readsorption of hydrolyzed ions on the colloid surface. However, suspensions with 1 dwb% PEI exhibit greater stability in the pH range of 4-11.5. The addition of PEI shifts the isoelectric point (IEP) of the suspensions from pH 5.8 to 10.8. Near the IEP (pHIEP=10.8), the stability of the suspensions with PEI is dominated by the steric effect. When the pH is decreased to acid direction, the stabilization mechanism is changed from steric hindrance to an electrosteric effect little by little. PAA also has the effect of reducing the hydrolysis speed via a "buffer effect" in the basic pH range, but the lack of adsorption between the highly ionized anionic polymer molecules and the negative colloid particle surfaces shows no positive effect on hydrolysis of colloids and on the stabilization of Y2Si 2O7 suspensions.

Surface chemistry, dispersion behavior, and slip casting of Ti 3AlC2 suspensions

The surface chemistry and dispersion properties of aqueous Ti 3AlC2 suspension were studied in terms of hydrolysis, adsorption, electrokinetic, and rheological measurements. The Ti 3AlC2 particle had complex surface hydroxyl groups, such as ≡Ti-OH,=Al-OH, and -OTi-(OH)2, etc. The surface charging of the Ti3AlC2 particle and the ion environment of suspensions were governed by these surface groups, which thus strongly influenced the stability of Ti3AlC2 suspensions. PAA dispersant was added into the Ti3AlC2 suspension to depress the hydrolysis of the surface groups by the adsorption protection mechanism and to increase the stability of the suspension by the steric effect. Ti3AlC2 suspensions with 2.0 dwb% PAA had an excellent stability at pH=∼5 and presented the characteristics of Newtonian fluid. Based on the well-dispersed suspension, dense Ti3AlC2 materials were obtained by slip casting and after pressureless sintering. This work provides a feasible forming method for the engineering applications of MAX-phase ceramics, wherein complex shapes, large dimensions, or controlled microstructures are needed.

Eddy diffusivity: A single dispersion analysis of high resolution drifters in a tidal shallow estuary

In an estuary, mixing and dispersion resulting from turbulence and small scale fluctuation has strong spatio-temporal variability which cannot be resolved in conventional hydrodynamic models while some models employs parameterizations large water bodies. This paper presents small scale diffusivity estimates from high resolution drifters sampled at 10 Hz for periods of about 4 hours to resolve turbulence and shear diffusivity within a tidal shallow estuary (depth < 3 m). Taylor's diffusion theorem forms the basis of a first order estimate for the diffusivity scale. Diffusivity varied between 0.001 – 0.02 m2/s during the flood tide experiment. The diffusivity showed strong dependence (R2 > 0.9) on the horizontal mean velocity within the channel. Enhanced diffusivity caused by shear dispersion resulting from the interaction of large scale flow with the boundary geometries was observed. Turbulence within the shallow channel showed some similarities with the boundary layer flow which include consistency with slope of 5/3 predicted by Kolmogorov's similarity hypothesis within the inertial subrange. The diffusivities scale locally by 4/3 power law following Okubo's scaling and the length scale scales as 3/2 power law of the time scale. The diffusivity scaling herein suggests that the modelling of small scale mixing within tidal shallow estuaries can be approached from classical turbulence scaling upon identifying pertinent parameters.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.