A dissimilarity function for geospatial polygons

Similarity plays an important role in many data mining tasks and information retrieval processes. Most of the supervised, semi-supervised, and unsupervised learning algorithms depend on using a dissimilarity function that measures the pair-wise similarity between the objects within the dataset. However, traditionally most of the similarity functions fail to adequately treat all the spatial attributes of the geospatial polygons due to the incomplete quantitative representation of structural and topological information contained within the polygonal datasets. In this paper, we propose a new dissimilarity function known as the polygonal dissimilarity function (PDF) that comprehensively integrates both the spatial and the non-spatial attributes of a polygon to specifically consider the density, distribution, and topological relationships that exist within the polygonal datasets. We represent a polygon as a set of intrinsic spatial attributes, extrinsic spatial attributes, and non-spatial attributes. Using this representation of the polygons, PDF is defined as a weighted function of the distance between two polygons in the different attribute spaces. In order to evaluate our dissimilarity function, we compare and contrast it with other distance functions proposed in the literature that work with both spatial and non-spatial attributes. In addition, we specifically investigate the effectiveness of our dissimilarity function in a clustering application using a partitional clustering technique (e.g. \(k\) -medoids) using two characteristically different sets of data: (a) Irregular geometric shapes determined by natural processes, i.e., watersheds and (b) semi-regular geometric shapes determined by human experts, i.e., counties.     

Topology-based kernels with application to inference problems in Alzheimer's disease

Alzheimer's disease (AD) research has recently witnessed a great deal of activity focused on developing new statistical learning tools for automated inference using imaging data. The workhorse for many of these techniques is the support vector machine (SVM) framework (or more generally kernel-based methods). Most of these require, as a first step, specification of a kernel matrix K between input examples (i.e., images). The inner product between images I(i) and I(j) in a feature space can generally be written in closed form and so it is convenient to treat K as "given." However, in certain neuroimaging applications such an assumption becomes problematic. As an example, it is rather challenging to provide a scalar measure of similarity between two instances of highly attributed data such as cortical thickness measures on cortical surfaces. Note that cortical thickness is known to be discriminative for neurological disorders, so leveraging such information in an inference framework, especially within a multi-modal method, is potentially advantageous. But despite being clinically meaningful, relatively few works have successfully exploited this measure for classification or regression. Motivated by these applications, our paper presents novel techniques to compute similarity matrices for such topologically-based attributed data. Our ideas leverage recent developments to characterize signals (e.g., cortical thickness) motivated by the persistence of their topological features, leading to a scheme for simple constructions of kernel matrices. As a proof of principle, on a dataset of 356 subjects from the Alzheimer's Disease Neuroimaging Initiative study, we report good performance on several statistical inference tasks without any feature selection, dimensionality reduction, or parameter tuning.     

Significance of Al on the morphological and optical properties of Ti1−xAlxN thin films

TiN and Ti_1−xAl_xN thin films with different aluminum concentrations (x = 0.35, 0.40, 0.55, 0.64 and 0.81) were synthesized by reactive magnetron co-sputtering technique. The structure, surface morphology and optical properties were examined using Grazing Incidence X-ray Diffraction (GIXRD), Atomic Force Microscopy (AFM), Raman spectroscopy and spectroscopic ellipsometry, respectively. The structure of the films were found to be of rocksalt type (NaCl) for x = 0.0–0.64 and X-ray amorphous for x = 0.81. AFM topographies show continuous mound like structure for the films of x between 0.0 and 0.64, whereas the film with x = 0.81 showed smooth surface with fine grains. Micro-Raman spectroscopic studies indicate structural phase separation of AlN from TiAlN matrix for x > 0.40. Ti_1−xAl_xN has the tendency for decomposition with the increase of Al concentration whereas c-TiN and hcp-AlN are stable mostly. The optical studies carried out by spectroscopic ellipsometry measurements showed a change from metallic to insulating behavior with the increase in x. These films are found to be an insulator beyond x = 0.81.     

Dyeing of woven polyester fabric with curcumin: effect of dye concentrations and surface pre‐activation using air atmospheric plasma and ultraviolet excimer treatment

Dyeing of polyester fabric with curcumin was studied at 90 and 130 °C without and with a prior surface activation of polyester fabric using two different ecotechnologies: air atmospheric plasma treatment and ultraviolet excimer lamp at 172 nm. Without surface activation, dyeing with curcumin followed classical disperse dye behaviour, with higher dye uptake at 130 °C than at 90 °C, and saturation was readily reached at 2% dye owf at 130 °C with a colour yield of 22. Surface‐sorbed curcumin molecules extracted with ethanol seemed to increase the colour yield values at 90 °C dyeing, while at 130 °C they decreased the colour yield values. When dyeing was carried out after a prior surface activation of the polyester fabrics, increased colour yield was observed at both dyeing temperatures for the ultraviolet excimer lamp only (with colour yield increasing from 2 to 10 at 90 °C and from 22 to 28 at 130 °C for a 2% dye owf). Indeed, both surface activation methods yielded hydrophilic species at the polyester fabric fibre surface, which were confirmed by water contact angle, X‐ray photoelectron spectroscopy measurements and atomic force microscopy. However, the surface of the polyester fabric activated using plasma lost all of its hydrophilic species, reaching the water contact angle of untreated polyester when subjected to the dyeing conditions. The excimer treatment yields hydrophilic species that are more resistant to high temperature and pressure dyeing.     

Preparation and application of silver nanoparticles on silk for imparting antimicrobial properties

Silver nanoparticles were produced by a chemical reduction method that reduced silver nitrate with reducing agents such as hydrazine and glucose. The silver nanoparticles were characterized with transmission electron microscope, scanning electron microscope, and optical microscope. The effects of process parameters such as the stirring speed, temperature, type of reducing agent, and dispersing agent on the particle size were studied. The particle size decreased with an increase in the stirring speed and a decrease in the process temperature. Smaller particles were formed when the silver nitrate was reduced by glucose versus those that were formed by reduction with hydrazine. Silver nanoparticles with average sizes of 10 and 35 nm, produced by reduction with hydrazine at 5 and 40°C, were applied to silk by an exhaust method. Silk fabrics treated with 40 ppm silver hydrosol produced at 5°C and 60 ppm silver hydrosol produced at 40°C showed 100% antimicrobial activity against the gram‐positive bacterium Staphylococcus aureus. The durability of the antimicrobial property of the treated silk fabric to washing was also examined and is presented. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Self-heating and Negative Differential Conductance Improvement by Substrate Bias Voltage in Tri-gate Junctionless Transistor

Silicon - Self-heating effects (SHE) in silicon-on-insulator (SOI) based tri-gate junctionless field effect transistor (TG-JLFET) due to low thermal conductivity of buried oxide (SiO2) is studied...     

Isolated word language identification system with hybrid features from a deep belief network

The representation of good audio features is the first and foremost requirement for improving the identification performance of any system. Most of the representation learning approaches are based on connectionist systems to learn and extract latent features from the speech data. This research work presents a hybrid feature extraction approach to integrate Mel-Frequency Cepstral Coefficients (MFCC) features with Shifted Delta Cepstral (SDC) coefficients features, which are further stacked to Deep Belief Network (DBN), for yielding new feature representations of the speech signals. DBN is utilized for unsupervised feature learning on the extracted MFCC-SDC acoustic features. A 3-layer Back Propagation Neural Network (BPNN) classifier is initialized in terms of the learning outcomes of hidden layers of DBN for identifying language from the uttered speech. The efficiency of the proposed approach is evaluated by simulating several experimental algorithms on the user-defined database of isolated words in four languages, namely, Tamil, Malayalam, Hindi, and English, in the working platform of MATLAB. The obtained results for the proposed hybrid approach MFCC-SDC-DBN are promising. The proposed approach is also compared with the baseline feature extraction approach MFCC-SDC by utilizing traditional acoustic features and BPNN classifier. The accuracy obtained with our proposed approach is 98.1% whereas that of the baseline approach is 82%, thereby providing an overall improvement of 16.1%.