Modelling and classification of respiratory volume wavefourms

A technique is proposed for classifying respiratory volume waveforms(RVW) into normal and abnormal categories of respiratory pathways. The proposed method transforms the temporal sequence into frequency domain by using an orthogonal transform, namely discrete cosine transform (DCT) and the transformed signal is pole-zero modelled. A Bayes classifier using model pole angles as the feature vector performed satisfactorily when a limited number of RVWs recorded under deep and rapid (DR) manoeuvre are classified.

Classification of road damage due to earthquakes

Earthquakes cause massive road damage which in turn causes adverse effects on the society. Previous studies have quantified the damage caused to residential and commercial buildings; however, not many studies have been conducted to quantify road damage caused by earthquakes. In this study, an attempt has been made to propose a new scale to classify and quantify the road damage due to earthquakes based on the data collected from major earthquakes in the past. The proposed classification for road damage due to earthquake is called as road damage scale (RDS). Earthquake details such as magnitude, distance of road damage from the epicenter, focal depth, and photographs of damaged roads have been collected from various sources with reported modified Mercalli intensity (MMI). The widely used MMI scale is found to be inadequate to clearly define the road damage. The proposed RDS is applied to various reported road damage and reclassified as per RDS. The correlation between RDS and earthquake parameters of magnitude, epicenter distance, hypocenter distance, and combination of magnitude with epicenter and hypocenter distance has been studied using available data. It is observed that the proposed RDS correlates well with the available earthquake data when compared with the MMI scale. Among several correlations, correlation between RDS and combination of magnitude and epicenter distance is appropriate. Summary of these correlations, their limitations, and the applicability of the proposed scale to forecast road damages and to carry out vulnerability analysis in urban areas is presented in the paper.

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Minimal unsatisfiable sets: Classification and bounds

Proving the unsatisfiability of propositional Boolean formulas has applications in a wide range of fields. Minimal Unsatisfiable Sets (MUS) are signatures of the property of unsatisfiability in formulas and our understanding of these signatures can be very helpful in answering various algorithmic and structural questions relating to unsatisfiability. In this paper, we explore some combinatorial properties of MUS and use them to devise a classification scheme for MUS. We also derive bounds on the sizes of MUS in Horn, 2-SAT and 3-SAT formulas.

Time Series Classification for Online Tamil Handwritten Character Recognition – A Kernel Based Approach

In this paper, we consider the problem of time series classification. Using piecewise linear interpolation various novel kernels are obtained which can be used with Support vector machines for designing classifiers capable of deciding the class of a given time series. The approach is general and is applicable in many scenarios. We apply the method to the task of Online Tamil handwritten character recognition with promising results.

Growth of rutile TiO2 nanorods on TiO2 seed layer deposited by electron beam evaporation

Dense rutile TiO2 nanorods were grown on anatase TiO2 seed layer coated glass substrate by solution technique. The crystalline nature of nanorods has confirmed by transmission electron microscopy. The band gap of the TiO2 seed layer and nanorods were calculated using the UV-vis absorption spectrum and the band gap value of the anatase seed layer and rutile nanorods were 3.39 eV and 3.09 eV respectively. Water contact angle measurements were also made and showed that the contact angle of rutile nanorods was (134 degrees) larger than the seed layer contact angle (93 degrees). The RMS surface roughness of the TiO2 seed layer (0.384 nm) and nanorods film (18.5 nm) were measured by an atomic force microscope and correlated with their contact angle values. (C) 2011 Elsevier B.V. All rights reserved.

Seed dispersal in changing landscapes

A growing understanding of the ecology of seed dispersal has so far had little influence on conservation practice, while the needs of conservation practice have had little influence on seed dispersal research. Yet seed dispersal interacts decisively with the major drivers of biodiversity change in the 21st century: habitat fragmentation, overharvesting, biological invasions, and climate change. We synthesize current knowledge of the effects these drivers have on seed dispersal to identify research gaps and to show how this information can be used to improve conservation management. The drivers, either individually, or in combination, have changed the quantity, species composition, and spatial pattern of dispersed seeds in the majority of ecosystems worldwide, with inevitable consequences for species survival in a rapidly changing world. The natural history of seed dispersal is now well-understood in a range of landscapes worldwide. Only a few generalizations that have emerged are directly applicable to conservation management, however, because they are frequently confounded by site-specific and species-specific variation. Potentially synergistic interactions between disturbances are likely to exacerbate the negative impacts, but these are rarely investigated. We recommend that the conservation status of functionally unique dispersers be revised and that the conservation target for key seed dispersers should be a population size that maintains their ecological function, rather than merely the minimum viable population. Based on our analysis of conservation needs, seed dispersal research should be carried out at larger spatial scales in heterogenous landscapes, examining the simultaneous impacts of multiple drivers on community-wide seed dispersal networks. (C) 2011 Elsevier Ltd. All rights reserved.

Rodent seed predation: effects on seed survival, recruitment, abundance, and dispersion of bird-dispersed tropical trees

Tropical tree species vary widely in their pattern of spatial dispersion. We focus on how seed predation may modify seed deposition patterns and affect the abundance and dispersion of adult trees in a tropical forest in India. Using plots across a range of seed densities, we examined whether seed predation levels by terrestrial rodents varied across six large-seeded, bird-dispersed tree species. Since inter-specific variation in density-dependent seed mortality may have downstream effects on recruitment and adult tree stages, we determined recruitment patterns close to and away from parent trees, along with adult tree abundance and dispersion patterns. Four species (Canarium resiniferum, Dysoxylum binectariferum, Horsfieldia kingii, and Prunus ceylanica) showed high predation levels (78.5-98.7%) and increased mortality with increasing seed density, while two species, Chisocheton cumingianus and Polyalthia simiarum, showed significantly lower seed predation levels and weak density-dependent mortality. The latter two species also had the highest recruitment near parent trees, with most abundant and aggregated adults. The four species that had high seed mortality had low recruitment under parent trees, were rare, and had more spaced adult tree dispersion. Biotic dispersal may be vital for species that suffer density-dependent mortality factors under parent trees. In tropical forests where large vertebrate seed dispersers but not seed predators are hunted, differences in seed vulnerability to rodent seed predation and density-dependent mortality can affect forest structure and composition.