Optical characterisation of polymeric nanocomposites using tomographic, spectroscopic and Fraunhofer wavefront assessment

Polymers are often embedded with specific nanofillers such that the functional characteristics and properties of the resulting polymeric nanocomposite (PNC) are enhanced. The degree to which these enhancements can be achieved depends not only on the level of particle loading of nanofillers, but most importantly on the resulting dispersion profile achieved within the matrix. Agglomeration (often referred to as clustering) is a result of the mixing process and very much depends on the chemistry between the polymer and nanofiller. Depending on the PNC type, different mixing processes can be applied but the general consensus is that such processes are not repeatable themselves. Not only it is quite difficult to achieve the desired level of dispersion, but in addition there is a limited number of characterization tools that can be employed to routinely check the homogeneity achieved within a produced sample. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) techniques are usually employed, but they are very time consuming, expensive, require special sample preparation and treatment, often produce results that are difficult to interpret and can only analyse very small areas of sample. This work reports on the adaptation and development and three optical techniques that are non-destructive, can accurately characterize the dispersion achieved as a result of the mixing process and can analyse larger material areas. The techniques reported are based on static and dynamic visible and infra-red light scattering

Clustering measure-valued data with Wasserstein barycenters

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.) or the data nature is more complex and the traditional learning algorithms are not applicable or effective to treat them (e.g. network data, interval data, high frequency records, matrix data, etc.). Under this perspective, each observation is identified by an appropriate probability measure and the proposed statistical learning schemes rely on discrimination criteria that utilize the geometric structure of the space of probability measures through core techniques from the optimal transport theory. The discussed approaches are implemented in two real world applications: (a) clustering eurozone countries according to their observed government bond yield curves and (b) classifying the areas of a satellite image to certain land uses categories which is a standard task in remote sensing. In both case studies the results are particularly interesting and meaningful while the accuracy obtained is high.Comment: 18 pages, 3 figure

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

Multi-temporal land-cover classification and change analysis with conditional probability networks: The case of Lesvos Island (Greece)

This study uses a series of Landsat images to map the main land-cover types on the Mediterranean island of Lesvos, Greece. We compare a single-year maximum likelihood classification (MLC) with a multi-temporal maximum likelihood classification (MTMLC) approach, with time-series class labels modelled using a first-order hidden Markov model comprising continuous and discrete variables. A rigorous validation scheme shows statistically significant higher accuracy figures for the multi-temporal approach. Land-cover change accuracies were also greatly improved by the proposed methodology: from 46% to 70%. The results show that when only two dates are used, the mapping of land use/cover is unreliable and a large number of the changes identified are due to the individual-year commission and omission errors

Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands

Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the ‘from–to’ change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985–1993 and 1993–2005 image pairs, respectively

Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar.

Hydraulic resistance of riparian forests is an unknown but important term in flood conveyance modeling. Lidar has proven to be a very important new data source to physically characterize floodplain vegetation. This research outlines a recent campaign that aims to retrieve vegetation fluid resistance terms from airborne laser scanning to parameterize trunk roughness. Information on crown characteristics and vegetation spacing can be extracted for individual trees to aid in the determining of trunk stem morphology. Airborne lidar data were used to explore the potential to characterize some of the prominent tree morphometric properties from natural and planted riparian poplar zones such as tree position, tree height, trunk location, and tree spacing. Allometric equations of tree characteristics extrapolated from ground measurements were used to infer below-canopy morphometric variables. Results are presented from six riparian-forested zones on the Garonne and Allier rivers in southern and central France. The tree detection and crown segmentation (TDCS) method identified individual trees with 85% accuracy, and the TreeVaW method detected trees with 83% accuracy. Tree heights were overall estimated at both river locations with an RMSE error of around 19% for both methods, but crown diameter at the six sites produced large deviations from ground-measured values of above 40% for both methods. Total height-derived trunk diameters using the TDCS method produced the closest roughness coefficient values to the ground-derived roughness coefficients. The stem roughness values produced from this method fell within guideline values

Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis

Spatial relationships between tree species and gap characteristics in broad-leaved deciduous woodland.

Questions: 1. What are the spatial patterns of all trees, individual tree species, trees within particular height classes, all gaps and gaps with specific properties across the study site in broad-leaved deciduous forest at a range of scales? 2. Are patterns of the above features spatially associated? 3. Are these patterns indicative of gap creation mechanisms and phases of regeneration? Location: Frame Wood, New Forest, UK. Methods: Ripley�s K-function analysis was applied to spatial information derived from airborne remotely sensed imagery to characterize the patterns of trees and gaps and to test for spatial interactions between these patterns. The patterns of trees and gaps with specific physical and spatial properties were analysed. Results: The pattern of all tree species combined was random for most scales; Quercus robur followed the same random pattern, while Fagus sylvatica and Betula pendula were clustered over most spatial scales. Large gaps (> 250 m2) and larger trees (> 17.5 m) were randomly distributed, while smaller gaps and smaller trees were clustered. Significant spatial relationships were demonstrated between the patterns of different tree species and between trees within different size classes, as well as between the patterns of trees and gaps with specific properties. Conclusions: Small gap patterns and field evidence indicated that progressive gap enlargement is the most likely creation mechanism for large gaps (> 250 m2). Clustered patterns of younger individuals were indicative of patches of past regeneration. As a complement to field-based data, data derived from remotely sensed imagery provides spatially comprehensive information with which to further investigate woodland stand/community processes and gap dynamics

Analysing the spatial structure of semi-natural deciduous woodlands through high-resolution airborne imagery and Geographic Information Systems

Available from British Library Document Supply Centre-DSC:DX217891 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo