Self-Assembly of an Octanuclear Platinum(II) Tetragonal Prism from a New Pt-4(II) Organometallic Star-Shaped Acceptor and Its Nitroaromatic Sensing Study

2 + 4] self-assembly of a pyrene-functionalized Pt-8(II) tetragonal prism (2) is achieved using a newly designed star-shaped organometallic acceptor (1) in combination with an amide-based ``clip'' donor (L). The propensity of this prism (2) as a selective sensor for nitroaromatics (2,4-dinitrotoluene, 1,3,5-trinitrotoluene, and picric acid), which are the chemical constituents of many commercial explosives, has been examined.

Optimal Linear Codes with a Local-Error-Correction Property

Motivated by applications to distributed storage, Gopalan et al recently introduced the interesting notion of information-symbol locality in a linear code. By this it is meant that each message symbol appears in a parity-check equation associated with small Hamming weight, thereby enabling recovery of the message symbol by examining a small number of other code symbols. This notion is expanded to the case when all code symbols, not just the message symbols, are covered by such ``local'' parity. In this paper, we extend the results of Gopalan et. al. so as to permit recovery of an erased code symbol even in the presence of errors in local parity symbols. We present tight bounds on the minimum distance of such codes and exhibit codes that are optimal with respect to the local error-correction property. As a corollary, we obtain an upper bound on the minimum distance of a concatenated code.

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.

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.

Multiple transmitter localization and communication footprint identification using sparse reconstruction techniques

This paper considers the problem of identifying the footprints of communication of multiple transmitters in a given geographical area. To do this, a number of sensors are deployed at arbitrary but known locations in the area, and their individual decisions regarding the presence or absence of the transmitters' signal are combined at a fusion center to reconstruct the spatial spectral usage map. One straightforward scheme to construct this map is to query each of the sensors and cluster the sensors that detect the primary's signal. However, using the fact that a typical transmitter footprint map is a sparse image, two novel compressive sensing based schemes are proposed, which require significantly fewer number of transmissions compared to the querying scheme. A key feature of the proposed schemes is that the measurement matrix is constructed from a pseudo-random binary phase shift applied to the decision of each sensor prior to transmission. The measurement matrix is thus a binary ensemble which satisfies the restricted isometry property. The number of measurements needed for accurate footprint reconstruction is determined using compressive sampling theory. The three schemes are compared through simulations in terms of a performance measure that quantifies the accuracy of the reconstructed spatial spectral usage map. It is found that the proposed sparse reconstruction technique-based schemes significantly outperform the round-robin scheme.

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.

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.

Discrimination of alpha-Amino Acids Using Green Tea Flavonoid (-)-Epigallocatechin Gallate as a Chiral Solvating Agent

We report a special, hitherto-unexplored property of (-)-epigallocatechin gallate (EGCG) as a chiral solvating agent for enantiodiscrimination of alpha-amino acids in the polar solvent DMSO. This phenomenon has been investigated by H-1 NMR spectroscopy. The mechanism of the interaction property of EGCG with alpha-amino acids has been understood as arising out of hydrogen-bonded noncovalent interactions, where the -OH groups of two phenyl rings of EGCG play dominant roles. The conversion of the enantiomeric mixture into diastereomers yielded well-resolved peaks for D and L amino acids permitting the precise measurement of enantiomeric composition. Often one encounters complex situations when the spectra are severely overlapped or partially resolved hampering the testing of enantiopurity and the precise measurement of enantiomeric excess (ee). Though higher concentration of EGCG yielded better discrimination, the use of lower concentration being economical, we have exploited an appropriate 2D NMR experiment in overcoming such problems. Thus, in the present study we have successfully demonstrated the utility of the bioflavonoid (-)-EGCG, a natural product as a chiral solvating agent for the discrimination of large number of alpha-amino acids in a polar solvent DMSO. Another significant advantage of this new chiral sensing agent is that it is a natural product and does not require tedious multistep synthesis unlike many other chiral auxiliaries.