Ternary vanadium sulfides

Ternary vanadium sulfides, MxVS2 (M = Fe, Co, Ni), with Image , were prepared and studied. The Image and Image series are isostructural with V5S8 and V3S4, respectively, while compounds with Image appear to have the hexagonal Cr2S3 structure. Structures of NiV2S4 and NiV4S8 were refined from powder X-ray diffraction intensities. Magnetic and electrical properties reveal that M ions in these sulfides exist in the divalent state having localized moments, while the vanadium 3 d electrons are itinerant.

Taylor series expansion based multidimensional image reconstruction for confocal and 4pi microscopy

We propose and experimentally demonstrate a three-dimensional (3D) image reconstruction methodology based on Taylor series approximation (TSA) in a Bayesian image reconstruction formulation. TSA incorporates the requirement of analyticity in the image domain, and acts as a finite impulse response filter. This technique is validated on images obtained from widefield, confocal laser scanning fluorescence microscopy and two-photon excited 4pi (2PE-4pi) fluorescence microscopy. Studies on simulated 3D objects, mitochondria-tagged yeast cells (labeled with Mitotracker Orange) and mitochondrial networks (tagged with Green fluorescent protein) show a signal-to-background improvement of 40% and resolution enhancement from 360 to 240 nm. This technique can easily be extended to other imaging modalities (single plane illumination microscopy (SPIM), individual molecule localization SPIM, stimulated emission depletion microscopy and its variants).

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Efficient use of correlation entropy for analysing time series data

The correlation dimension D 2 and correlation entropy K 2 are both important quantifiers in nonlinear time series analysis. However, use of D 2 has been more common compared to K 2 as a discriminating measure. One reason for this is that D 2 is a static measure and can be easily evaluated from a time series. However, in many cases, especially those involving coloured noise, K 2 is regarded as a more useful measure. Here we present an efficient algorithmic scheme to compute K 2 directly from a time series data and show that K 2 can be used as a more effective measure compared to D 2 for analysing practical time series involving coloured noise.

Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image

Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.

Turbulent mixed convection in a shallow enclosure with a series of heat generating components

Turbulent mixed convection flow and heat transfer in a shallow enclosure with and without partitions and with a series of block-like heat generating components is studied numerically for a range of Reynolds and Grashof numbers with a time-dependent formulation. The flow and temperature distributions are taken to be two-dimensional. Regions with the same velocity and temperature distributions can be identified assuming repeated placement of the blocks and fluid entry and exit openings at regular distances, neglecting the end wall effects. One half of such module is chosen as the computational domain taking into account the symmetry about the vertical centreline. The mixed convection inlet velocity is treated as the sum of forced and natural convection components, with the individual components delineated based on pressure drop across the enclosure. The Reynolds number is based on forced convection velocity. Turbulence computations are performed using the standard k– model and the Launder–Sharma low-Reynolds number k– model. The results show that higher Reynolds numbers tend to create a recirculation region of increasing strength in the core region and that the effect of buoyancy becomes insignificant beyond a Reynolds number of typically 5×105. The Euler number in turbulent flows is higher by about 30 per cent than that in the laminar regime. The dimensionless inlet velocity in pure natural convection varies as Gr1/3. Results are also presented for a number of quantities of interest such as the flow and temperature distributions, Nusselt number, pressure drop and the maximum dimensionless temperature in the block, along with correlations.

Solution conformations of penta and heptapeptides containing repetitive α-aminoisobutyryl-Image -alanyl and α -aminoisobutyryl-Image -valyl sequences

The presence of folded solution conformations in the peptides Boc-Ala-(Aib-Ala)2-OMe, Boc-Val-(Aib-Val) 2-OMe, Boc-Ala-(Aib-Ala)3-OMe and Boc-Val-(Aib-Val)3-OMe has been established by 270MHz 1H NMR. Intramolecularly H-bonded NH groups have been identified using temperature and solvent dependence of NH chemical shifts and paramagnetic radical induced broadening of NH resonances. Both pentapeptides adopt 310 helical conformations possessing 3 intramolecular H-bonds in CDCl3 and (CD3)2SO. The heptapeptides favour helical structures with 5 H-bonds in CDCl3. In (CD3)2SO only 4 H-bonds are readily detected.

Preparation, structure, and magnetic properties of isostructural La3MAlS7 and La3MFeS7 (M = Mg, Mn, Fe, Co, Ni, or Zn)

A series of quaternary metal sulfides of the general formula La3MM′S7 (M = Mn, Fe, Co; M′ = Al and M = Mg, Mn, Fe, Co, Ni; M′ = Fe) consisting of linear chains of face shared MS6 octahedra and isolated M′S4 tetrahedra has been prepared and studied. The aluminium compounds La3MAlS7 (M = Mn, Fe, Co) exhibit linear chain antiferromagnetism. Magnetic behavior of other La3MFeS7 sulfides has been examined in detail. The magnetic susceptibility of La3MgFeS7 shows that tetrahedral site Fe3+ undergoes a transition from Image to S = 2 spin state around 150 K.

Extrapolation and series acceleration procedures: Hard spheres and discs as illustrations

Three new procedures - in the context of estimation of virial coefficients and summation of the partial virial series for hard discs and hard spheres - are proposed. They are based on the parametrised Euler transformation, a novel resummation, identity and the ε-convergence methods respectively. A comparison with other estimates (molecular dynamics, graph theory and empirical methods) reveals satisfactory agreement.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.

Bismuth tungsten-oxide bronzes - a study of intergrowth phases and related aspects

Reaction of bismuth metal with WO$_3$ in the absence of oxygen yields interesting bronze-like phases. From analytical electron microscopy and X-ray photoelectron spectroscopy, the product phases are found to have the general composition Bi$_x$ WO$_3$ with bismuth in the 3+ state. Structural investigations made with high resolution electron micrscopy and cognate techniques reveal that when x < 0.02, a perovskite bronze is formed. When x $\geqslant$ 0.02, however, intergrowth tungsten bronzes (i.t.b.) containing varying widths of the WO$_3$ slab are formed, the lattice periodicity being in the range 2.3-5.1 nm in a direction perpendicular to the WO$_3$ slabs. Image-matching studies indicate that the bismuth atoms are in the tunnels of the hexagonal tungsten bronze (h.t.b.) strips and the h.t.b. strips always remain one-tunnel wide. Annealed samples show a satellite structure around the superlattice spots in the electron diffraction patterns, possibly owing to ordering of the bismuth atoms in the tunnels. The i.t.b. phases show recurrent intergrowths extending up to 100 nm in several crystals. The periodicity varies considerably within the same crystal wherever there is disordered intergrowth, but unit cell dimensions can be assigned from X-ray and electron diffraction patterns. The maximum value of x in the i.t.b. phases is ca. 0.07 and there is no evidence for the i.t.b. phase progressively giving way to the h.t.b. phase with increase in x. Hexagonal tungsten bronzes that contain bismuth with x up to 0.02 can be formed by starting from hexagonal WO$_3$, but the h.t.b. phase seems to be metastable. Optical, magnetic and electron transport properties of the i.t.b. phases have been measured and it appears that the electrons become itinerant when x > 0.05.