[n]peristylanes and [n]oxa[n]peristylanes (n = 3-6): A theoretical study

Theoretical studies at the HF and Becke3LYP levels using 6-31G* basis sets were carried out on a series of [n]peristylanes and [n]oxa[n]peristylanes (n = 3-6) to understand their structure and energetics. The structures of the [3]- and [4]peristylanes (1, 2) and their era-derivatives (5, 6) were calculated to have the anticipated high symmetry, C-nv. In contrast, a C-s structure (9) at HF/6-31G* and another (25) at the Becke3LYP/6-31G* level were calculated for the [5]oxa[5]peristylane. The energy difference between them is extremely small even though there are major differences in the structures indicating every soft potential energy surface: On the other hand, the potential energy surface of [6]oxa[6]peristylane is not as soft. Similar structures were also calculated for the top rings. Calculations on the seco-compounds 11-14 and 15-19 (Table 4) indicate that there is no unusual strain involved in the formation of 27 from 19. The Li+ interaction energies of the [n]oxa[n]peristylanes are 61.7 (n = 3), 72.8 (n = 4), 84.2 (n = 5) and 91.7 (n = 6) kcal mol(-1) at the Becke3LYP/6-3IG* level. Dramatic differences between the C-C bond lengths obtained from the solid state X-ray diffraction studies and those from the calculations for the [n]oxa[n]peristylanes were also observed.

Structural characterisation of a uracil containing hairpin DNA by NMR and molecular dynamics

Three-dimensional (3D) structure of a hairpin DNA d-CTAGAGGATCCTTTUGGATCCT (22mer; abbreviated as U4-hairpin), which has a uracil nucleotide unit at the fourth position from the 5' end of the tetra-loop has been solved by NMR spectroscopy. The H-1 resonances of this hairpin have been assigned almost completely. NMR restrained molecular dynamics and energy minimisation procedures have been used to describe the 3D structure of the U4 hairpin. This study establishes that the stem of the hairpin adopts a right handed B-DNA conformation while the T-12 and U-15 nucleotide stack upon 3' and 5' ends of the stem, respectively. Further, T-14 stacks upon both T-12 and U-15 while T-13 partially stacks upon T-14. Very weak stacking interaction is observed between T-13 and T-12. All the individual nucleotide bases adopt 'anti' conformation with respect to their sugar moiety. The turning phosphate in the loop is located between T-13 and T-14. The stereochemistry of U-15 mimics the situation where uracil would stack in a B-DNA conformation. This could be the reason as to why the U4-hairpin is found to be the best substrate for its interaction with uracil DNA glycosylase (UDG) compared to the other substrates in which the uracil is at the first, second and third positions of the tetra-loop from its 5' end, as reported previously.

Characterization of alkyl chain conformation in an intercalated cationic lipid bilayer by IR spectroscopy

A structural analysis of alkyl chain conformation of an intercalated cationic lipid bilayer is described. Dialkyl dimethylammonium ions (di-C(n)DA) were ion exchanged into the galleries of layered cadmium thiophosphate to give Cd0.83PS3(di-C(n)DA)(0.34). The grafting density and interlayer expansions were identical to those for the intercalated single chain alkyl trimethylammonium (C(n)TA) bilayers. The increased methylene chain density in the galleries, however, forces the intercalated lipid to adopt a more trans ordered structure. Progression bands arising from the coupling of vibrational modes of trans methylene units are used to establish the extent of trans registry. Two types of ordered structures of the intercalated cationic lipid may be distinguished. One in which both alkyl chains adopt an all-trans geometry, and one in which the methylene bond adjacent to the headgroup on one of the alkyl chains is gauche. The latter structure is typically found in the crystalline state of these cationic lipids. The concentrations of the two structures were determined from the ratio of the intensities of the progression bands and were found to remain unchanged with temperature.

Conformation and orientation of alkyl chains in the layered organic-inorganic hybrids: (CnH2n+1NH3)(2)PbI4 (n=12,16,18)

Layered organic inorganic hybrids based on perovskite-derived alkylammonium lead halides have been demonstrated as important new materials in the construction of molecular electronic devices. Typical of this class of materials are the single-perovskite slab lead iodides of the general formula (CnH2n+1NH3)(2)PbI4. While for small n, these compounds are amenable to single-crystal structure determination, the increasing degree of disorder in the long chain (n = 12,14...) compounds makes such an analysis difficult. In this study, we use powder X-ray diffraction, and vibrational and C-13 NMR spectroscopies to establish the conformation, orientation and organization of hydrocarbon chains in the series of layered alkylammonium lead iodides (CnH2n+1NH3)(2)PbI4 (n = 12,16,18). We find that the alkyl chains adopt a tilted bilayer arrangement, while the structure of the inorganic layer remains invariant with respect to the value of n. Conformation-sensitive methylene stretching modes in the infrared and Raman spectra, as well as the C-13 NMR spectra indicate that bonds in the methylene chain are in trans configuration. The skeletal modes of the alkyl chain in the Raman spectra establish that there is a high degree of all-trans conformational registry for the values of n studied here. From the orientation dependence of the infrared spectra of crystals of (CnH2n+1NH3)(2)PbI4 ( n = 12,16), we find that the molecular axis of the all-trans alkyl chains are tilted away from the interlayer normal by an angle of 55degrees. This value of this tilt angle is consistent with the dependence of the c lattice expansion as a function of n, as determined from powder X-ray diffraction.

Phase transitions in the anchored organic bilayers of long-chain alkylammonium lead iodides (CnH2n+1NH3)(2)PbI4; n=12, 16, 18

The single perovskite slab alkylammonium lead iodides (CnH2n+1NH3)(2)PbI4, n = 12, 16, 18, display two phase transitions, just above room temperature, associated with changes in the alkylammonium chains. We have followed these two phase transitions using scanning calorimetry, X-ray powder diffraction, and IR and Raman spectroscopies. We find the first phase transition to be associated with symmetry changes arising from a dynamic rotational disordering of the ammonium headgroup of the chain whereas the second transition, the melting of the chains in two dimensions, is characterized by an increased conformational disorder of the methylene units of the alkyl chains. We examine these phase transitions in light of the interesting optical properties of these materials, as well as the relevance of these systems as models for phase transitions in lipid bilayers.

GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Landslide susceptibility mapping in the downstream region of Sharavathi river basin, Central Western Ghats

Landslides are hazards encountered during monsoon in undulating terrains of Western Ghats causing geomorphic make over of earth surface resulting in significant damages to life and property. An attempt is made in this paper to identify landslides susceptibility regions in the Sharavathi river basin downstream using frequency ratio method based on the field investigations during July- November 2007. In this regard, base layers of spatial data such as topography, land cover, geology and soil were considered. This is supplemented with the field investigations of landslides. Factors that influence landslide were extracted from the spatial database. The probabilistic model -frequency ratio is computed based on these factors. Landslide susceptibility indices were computed and grouped into five classes. Validation of LHS, showed an accuracy of 89% as 25 of the 28 regions tallied with the field condition of highly vulnerable landslide regions. The landslide susceptible map generated for the downstream would be useful for the district officials to implement appropriate mitigation measures to reduce hazards.

Landslide Susceptible Locations in Western Ghats: Prediction through openModeller

Many shallow landslides are triggered by heavy rainfall on hill slopes resulting in enormous casualties and huge economic losses in mountainous regions. Hill slope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed due to a number of reasons such as increased soil moisture content, change in land use causing slope instability, etc. Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration and information related to land surface susceptibility. Terrain analysis applications using spatial data such as aspect, slope, flow direction, compound topographic index, etc. along with information derived from remotely sensed data such as land cover / land use maps permit us to quantify and characterise the physical processes governing the landslide occurrence phenomenon. In this work, the probable landslide prone areas are predicted using two different algorithms – GARP (Genetic Algorithm for Rule-set Prediction) and Support Vector Machine (SVM) in a free and open source software package - openModeller. Several environmental layers such as aspect, digital elevation data, flow accumulation, flow direction, slope, land cover, compound topographic index, and precipitation data were used in modelling. A comparison of the simulated outputs, validated by overlaying the actual landslide occurrence points showed 92% accuracy with GARP and 96% accuracy with SVM in predicting landslide prone areas considering precipitation in the wettest month whereas 91% and 94% accuracy were obtained from GARP and SVM considering precipitation in the wettest quarter of the year.

Eye-View: A retinal image capture system

This paper presents a low cost but high resolution retinal image acquisition system of the human eye. The images acquired by a CMOS image sensor are communicated through the Universal Serial Bus (USB) interface to a personal computer for viewing and further processing. The image acquisition time was estimated to be 2.5 seconds. This system can also be used in telemedicine applications.

Voltage and Temperature-Aware SSTA Using Neural Network Delay Model

With the emergence of voltage scaling as one of the most powerful power reduction techniques, it has been important to support voltage scalable statistical static timing analysis (SSTA) in deep submicrometer process nodes. In this paper, we propose a single delay model of logic gate using neural network which comprehensively captures process, voltage, and temperature variation along with input slew and output load. The number of simulation programs with integrated circuit emphasis (SPICE) required to create this model over a large voltage and temperature range is found to be modest and 4x less than that required for a conventional table-based approach with comparable accuracy. We show how the model can be used to derive sensitivities required for linear SSTA for an arbitrary voltage and temperature. Our experimentation on ISCAS 85 benchmarks across a voltage range of 0.9-1.1V shows that the average error in mean delay is less than 1.08% and average error in standard deviation is less than 2.85%. The errors in predicting the 99% and 1% probability point are 1.31% and 1%, respectively, with respect to SPICE. The two potential applications of voltage-aware SSTA have been presented, i.e., one for improving the accuracy of timing analysis by considering instance-specific voltage drops in power grids and the other for determining optimum supply voltage for target yield for dynamic voltage scaling applications.

Filling Characteristics for an Activated Carbon Based Adsorbed Natural Gas Storage System

The storage capacity of an activated carbon bed is studied using a 2D transport model with constant inlet flow conditions. The predicted filling times and variation in bed pressure and temperature are in good agreement with experimental observations obtained using a 1.82 L prototype ANG storage cylinder. Storage efficiencies based on the maximum achievable V/V (volume of gas/volume of container) and filling times are used to quantify the performance of the charging process. For the high permeability beds used in the experiments, storage efficiencies are controlled by the rate of heat removal. Filling times, defined as the time at which the bed pressure reaches 3.5 MPa, range from 120 to 3.4 min for inlet flow rates of 1.0 L min(-1) and 30.0 L min(-1), respectively. The corresponding storage efficiencies, eta(s), vary from 90% to 76%, respectively. Simulations with L/D ratios ranging from 0.35 to 7.8 indicate that the storage efficiencies can be improved with an increase in the LID ratios and/or with water cooled convection. Thus for an inlet flow rate of 30.0 L min(-1), an eta(s) value of 90% can be obtained with water cooling for an L/D ratio of 7.8 and a filling time of a few minutes. In the absence of water cooling the eta(s) value reduces to 83% at the same L/D ratio. Our study suggests that with an appropriate choice of cylinder dimensions, solutions based on convective cooling during adsorptive storage are possible with some compromise in the storage capacity.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).