Acoustic and optical phonon dynamics from femtosecond time-resolved optical spectroscopy of the superconducting iron pnictide Ca(Fe0.944Co0.056)(2)As-2

We report the temperature evolution of coherently excited acoustic and optical phonon dynamics in the superconducting iron pnictide single crystal Ca(Fe0.944Co0.056)(2)As-2 across the spin density wave transition at T-SDW similar to 85 K and the superconducting transition at T-SC similar to 20 K. The strain pulse propagation model applied to the generation of the acoustic phonons yields the temperature dependence of the optical constants, and longitudinal and transverse sound velocities in the temperature range from 3.1 K to 300 K. The frequency and dephasing times of the phonons show anomalous temperature dependence below T-SC indicating a coupling of these low-energy excitations with the Cooper-pair quasiparticles. A maximum in the amplitude of the acoustic modes at T similar to 170 is seen, attributed to spin fluctuations and strong spin-lattice coupling before T-SDW. Copyright (c) EPLA, 2012

Nanoparticle agglomeration in an evaporating levitated droplet for different acoustic amplitudes

Radiatively heated levitated functional droplets with nanosilica suspensions exhibit three distinct stages namely pure evaporation, agglomeration, and finally structure formation. The temporal history of the droplet surface temperature shows two inflection points. One inflection point corresponds to a local maximum and demarcates the end of transient heating of the droplet and domination of vaporization. The second inflection point is a local minimum and indicates slowing down of the evaporation rate due to surface accumulation of nanoparticles. Morphology and final precipitation structures of levitated droplets are due to competing mechanisms of particle agglomeration, evaporation, and shape deformation. In this work, we provide a detailed analysis for each process and propose two important timescales for evaporation and agglomeration that determine the final diameter of the structure formed. It is seen that both agglomeration and evaporation timescales are similar functions of acoustic amplitude (sound pressure level), droplet size, viscosity, and density. However, we show that while the agglomeration timescale decreases with initial particle concentration, the evaporation timescale shows the opposite trend. The final normalized diameter can be shown to be dependent solely on the ratio of agglomeration to evaporation timescales for all concentrations and acoustic amplitudes. The structures also exhibit various aspect ratios (bowls, rings, spheroids) which depend on the ratio of the deformation timescale (t(def)) and the agglomeration timescale (t(g)). For t(def) < t(g), a sharp peak in aspect ratio is seen at low concentrations of nanosilica which separates high aspect ratio structures like rings from the low aspect ratio structures like bowls and spheroids. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4775791]

Advanced multifunctional inorganic nanostructured oxides for controlled release and sensing

ADVANCED MULTIFUNCTIONAL INORGANIC NANOSTRUCTURED OXIDES FOR CONTROLLED RELEASE AND SENSING. We demonstrate here certain examples of multifunctional nanostructured oxidematerials for biotechnological and environmental applications.Various in-house synthesized homogeneous nanostructured viz.mesoporous and nanotubes silica and titania have been employed for controlled drug delivery and electrochemical biosensing applications. Confinement of macromolecules such as proteins studied via electrochemical, thermal and spectroscopic methods showed no detrimental effect on native protein structure and function, thus suggesting effective utility of oxide nanostructures as bio-encapsulators. Multi-functionalitywas demonstrated via employing similar nanostructures for sensing organic water pollutants e.g. textile dyes.

Novel low-temperature growth of SnO2 nanowires and their gas-sensing properties

A simple thermal evaporation method is presented for the growth of crystalline SnO2 nanowires at a low substrate temperature of 450 degrees C via an gold-assisted vapor-liquid-solid mechanism. The as-grown nanowires were characterized by scanning electron microscopy, transmission electron microscopy and X-ray diffraction, and were also tested for methanol vapor sensing. Transmission electron microscopy studies revealed the single-crystalline nature of the each nanowire. The fabricated sensor shows good response to methanol vapor at an operating temperature of 450 degrees C. (C) 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Coordination self-assembly of tetranuclear Pt(II) macrocycles with an organometallic backbone for sensing of acyclic dicarboxylic acids

Coordination self-assembly of a series of tetranuclear Pt(II) macrocycles containing an organometallic backbone incorporating ethynyl functionality is presented. The 1 : 1 combination of a linear acceptor 1,4-bistrans-Pt(PEt3)(2)(NO3)(ethynyl)]benzene (1) with three different dipyridyl donor `clips' (L-a-L-c) afforded three 2 + 2] self-assembled Pt-4(II) macrocycles (2a-2c) in quantitative yields, respectively L-a = 1,3-bis-(3-pyridyl)isothalamide; L-b = 1,3-bis(3-pyridyl)ethynylbenzene; L-c = 1,8-bis(4-pyridyl)ethynylanthracene]. These macrocycles were characterized by multinuclear NMR (H-1 and P-31); ESI-MS spectroscopy and the molecular structures of 2a and 2b were established by single crystal X-ray diffraction analysis. These macrocycles (2a-2c) are fluorescent in nature. The amide functionalized macrocycle 2a is used as a receptor to check the binding affinity of aliphatic acyclic dicarboxylic acids. Such binding affinity is examined using fluorescence and UV-Vis spectroscopic methods. A solution state fluorescence study showed that macrocycle 2a selectively binds (K-SV = 1.4 x 10(4) M-1) maleic acid by subsequent enhancement in emission intensity. Other aliphatic dicarboxylic acids such as fumaric, succinic, adipic, mesaconic and itaconic acids caused no change in the emission spectra; thereby demonstrating its potential use as a macrocyclic receptor in distinction of maleic acid from other aliphatic dicarboxylic acids.

Error exponent analysis of energy-based bayesian spectrum sensing under fading channels

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

A novel algorithm for cooperative distributed sequential spectrum sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

SpecNet: Spectrum Sensing Sans Frontieres

While the under-utilization of licensed spectrum based on measurement studies conducted in a few developed countries has spurred lots of interest in opportunistic spectrum access, there exists no infrastructure today for measuring real-time spectrum occupancy across vast geographical regions. In this paper, we present the design and implementation of SpecNet, a first-of-its-kind platform that allows spectrum analyzers around the world to be networked and efficiently used in a coordinated manner for spectrum measurement as well as implementa- tion and evaluation of distributed sensing applications. We demonstrate the value of SpecNet through three applications: 1) remote spectrum measurement, 2) primary transmitter coverage estimation and 3) Spectrum-Cop that quickly identifies and localizes transmitters in a frequency range and geographic region of interest.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

Frequency detection from multiplexed compressed sensing of noisy signals

Signal acquisition under a compressed sensing scheme offers the possibility of acquisition and reconstruction of signals sparse on some basis incoherent with measurement kernel with sub-Nyquist number of measurements. In particular when the sole objective of the acquisition is the detection of the frequency of a signal rather than exact reconstruction, then an undersampling framework like CS is able to perform the task. In this paper we explore the possibility of acquisition and detection of frequency of multiple analog signals, heavily corrupted with additive white Gaussian noise. We improvise upon the MOSAICS architecture proposed by us in our previous work to include a wider class of signals having non-integral frequency components. This makes it possible to perform multiplexed compressed sensing for general frequency sparse signals.

Three-dimensional acoustic analysis of an elliptical chamber muffler with a side inlet and a side outlet

The acoustical behaviour of an elliptical chamber muffler having a side inlet and side outlet port is analyzed in this paper, wherein a uniform velocity piston source is assumed to model the 3-D acoustic field in the elliptical chamber cavity. Towards this end, we consider the modal expansion of the acoustic pressure field in the elliptical cavity in terms of the angular and radial Mathieu func-tions, subjected to the rigid wall condition. Then, the Green's function due to the point source lo-cated on the side (curved) surface of the elliptical chamber is obtained. On integrating this function over the elliptical piston area on the curved surface of the elliptical chamber and subsequent divi-sion by the area of the elliptic piston, one obtains the acoustic pressure field due to the piston driven source which is equivalent to considering plane wave propagation in the side ports. Thus, one can obtain the acoustic pressure response functions, i.e., the impedance matrix (Z) parameters due to the sources (ports) located on the side surface, from which one may also obtain a progressive wave rep-resentation in terms of the scattering matrix (S). Finally, the acoustic performance of the muffler is evaluated in terms of the Transmission loss (TL) which is computed in terms of the scattering pa-rameters. The effect of the axial length of the muffler and the angular location of the ports on the TL characteristics is studied in detail. The acoustically long chambers show dominant axial plane wave propagation while the TL spectrum of short chambers indicates the dominance of the trans-versal modes. The 3-D analytical results are compared with the 3-D FEM simulations carried on a commercial software and are shown to be in an excellent agreement, thereby validating the analyti-cal procedure suggested in this work.

Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.