Acoustic emission source location and damage detection in a metallic structure using a graph-theory-based geodesic pproach

A geodesic-based approach using Lamb waves is proposed to locate the acoustic emission (AE) source and damage in an isotropic metallic structure. In the case of the AE (passive) technique, the elastic waves take the shortest path from the source to the sensor array distributed in the structure. The geodesics are computed on the meshed surface of the structure using graph theory based on Dijkstra's algorithm. By propagating the waves in reverse virtually from these sensors along the geodesic path and by locating the first intersection point of these waves, one can get the AE source location. The same approach is extended for detection of damage in a structure. The wave response matrix of the given sensor configuration for the healthy and the damaged structure is obtained experimentally. The healthy and damage response matrix is compared and their difference gives the information about the reflection of waves from the damage. These waves are backpropagated from the sensors and the above method is used to locate the damage by finding the point where intersection of geodesics occurs. In this work, the geodesic approach is shown to be suitable to obtain a practicable source location solution in a more general set-up on any arbitrary surface containing finite discontinuities. Experiments were conducted on aluminum specimens of simple and complex geometry to validate this new method.

Multiple excitation nano-spot generation and confocal detection for far-field microscopy

An imaging technique is developed for the controlled generation of multiple excitation nano-spots for far-field microscopy. The system point spread function (PSF) is obtained by interfering two counter-propagating extended depth-of-focus PSF (DoF-PSF), resulting in highly localized multiple excitation spots along the optical axis. The technique permits (1) simultaneous excitation of multiple planes in the specimen; (2) control of the number of spots by confocal detection; and (3) overcoming the point-by-point based excitation. Fluorescence detection from the excitation spots can be efficiently achieved by Z-scanning the detector/pinhole assembly. The technique complements most of the bioimaging techniques and may find potential application in high resolution fluorescence microscopy and nanoscale imaging.

Coordination-Driven Self-Assembly of M3L2 Trigonal Cages from Preorganized Metalloligands Incorporating Octahedral Metal Centers and Fluorescent Detection of Nitroaromatics

The design and preparation of novel M3L2 trigonal cages via the coordination-driven self-assembly of preorganized metalloligands containing octahedral aluminum(III), gallium(III), or ruthenium(II) centers is described. When tritopic or dinuclear linear metalloligands and appropriate complementary subunits are employed, M3L2 trigonal-bipyramidal and trigonal-prismatic cages are self-assembled under mild conditions. These three-dimensional cages were characterized with multinuclear NMR spectroscopy (H-1 and P-31) and high-resolution electrospray ionization mass spectrometry. The structure of one such trigonal-prismatic cage, self-assembled from an arene ruthenium metalloligand, was confirmed via single-crystal X-ray crystallography. The fluorescent nature of these prisms, due to the presence of their electron-rich ethynyl functionalities, prompted photophysical studies, which revealed that electron-deficient nitroaromatics are effective quenchers of the cages' emission. Excited-state charge transfer from the prisms to the nitroaromatic substrates can be used as the basis for the development of selective and discriminatory turn-off fluorescent sensors for nitroaromatics.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

Ultrasonic Guided Wave Characterization and Damage Detection in Foam-core Sandwich Panel using PWAS and LDV

Lamb wave type guided wave propagation in foam core sandwich structures and detectability of damages using spectral analysis method are reported in this paper. An experimental study supported by theoretical evaluation of the guided wave characteristics is presented here that shows the applicability of Lamb wave type guided ultrasonic wave for detection of damage in foam core sandwich structures. Sandwich beam specimens were fabricated with 10 mm thick foam core and 0.3 mm thick aluminum face sheets. Thin piezoelectric patch actuators and sensors are used to excite and sense guided wave. Group velocity dispersion curves and frequency response of sensed signal are obtained experimentally. The nature of damping present in the sandwich panel is monitored by measuring the sensor signal amplitude at various different distances measured from the center of the linear phased array. Delaminations of increasing width are created and detected experimentally by pitch-catch interrogation with guided waves and wavelet transform of the sensed signal. Signal amplitudes are analyzed for various different sizes of damages to differentiate the damage size/severity. A sandwich panel is also fabricated with a planer dimension of 600 mm x 400 mm. Release film delamination is introduced during fabrication. Non-contact Laser Doppler Vibrometer (LDV) is used to scan the panel while exciting with a surface bonded piezoelectric actuator. Presence of damage is confirmed by the reflected wave fringe pattern obtained from the LDV scan. With this approach it is possible to locate and monitor the damages by tracking the wave packets scattered from the damages.

Selective and Efficient Detection of Nitro-Aromatic Explosives in Multiple Media including Water, Micelles, Organogel, and Solid Support

Selective detection of nitro-aromatic compounds (NACs) at nanomolar concentration is achieved for the first time in multiple media including water, micelles or in organogels as well as using test strips. Mechanism of interaction of NACs with highly fluorescent p-phenylenevinylene-based molecules has been described as the electron transfer phenomenon from the electron-rich chromophoric probe to the electron deficient NACs. The selectivity in sensing is guided by the pK(a) of the probes as well as the NACs under consideration. TNP-induced selective gel-to-sol transition in THF medium is also observed through the reorganization of molecular self-assembly and the portable test trips are made successfully for rapid on-site detection purpose.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Highly sensitive and rapid detection of acetylcholine using an ITO plate modified with platinum-graphene nanoparticles

Determining the concentrations of acetylcholine (ACh) and choline (Ch) is clinically important. ACh is a neurotransmitter that acts as a key link in the communication between neurons in the spinal cord and in nerve skeletal junctions in vertebrates, and plays an important role in transmitting signals in the brain. A bienzymatic sensor for the detection of ACh was prepared by co-immobilizing choline oxidase (ChO) and acetylcholinesterase (AChE) on graphene matrix/platinum nanoparticles, and then electrodepositing them on an ITO-coated glass plate. Graphene nanoparticles were decorated with platinum nanoparticles and were electrodeposited on a modified ITO-coated glass plate to form a modified electrode. The modified electrode was characterized by scanning electron microscopy (SEM), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) studies. The optimum response of the enzyme electrode was obtained at pH 7.0 and 35 degrees C. The response time of this ACh-sensing system was shown to be 4 s. The linear range of responses to ACh was 0.005-700 mu M. This biosensor exhibits excellent anti-interferential abilities and good stability, retaining 50% of its original current even after 4 months. It has been applied for the detection of ACh levels in human serum samples.

Polyfunctional Lewis Acids: Intriguing Solid-State Structure and Selective Detection and Discrimination of Nitroaromatic Explosives

Synthesis and crystal structures of three porphyrin-based polyfunctional Lewis acids 1-3 are reported. Intermolecular HgClHgCl (linear and -type) interactions in the solid state of the peripherally ArHgCl-decorated compound 3 lead to a fascinating 3D supramolecular architecture. Compound3 shows a selective fluorescence quenching response to picric acid and discriminates other nitroaromatic-based explosives. For the first time, an electron-deficient polyfunctional Lewis acid is shown to be useful for the selective detection and discrimination of nitroaromatic explosives. The Stern-Volmer quenching constant and detection limits of compound3 for picric acid are the best among the reported small-molecular receptors for nitroaromatic explosives. The electronic structure, Lewis acidity, and selective sensing characteristics of 3 are well corroborated by DFT calculations.

Boron and Nitrogen Co-doped Carbon Nanoparticles as Photoluminescent Probes for Selective and Sensitive Detection of Picric Acid

Here, we report the synthesis of boron and nitrogen Co-doped carbon nanoparticles (BN-CNPs) by a hydrothermal method using sucrose, boric acid, and urea as the precursors. The BN-CNPs show excellent photoluminescence with a quantum yield of similar to 14.2% in aqueous solution and can be used as photoluminescent probes for selective and sensitive detection of picric acid (PA). PA quenches the photoluminescence signal remarkably, while other explosives cause a little quenching confirming the high selectivity of BN-CNPs. The sensitivity toward PA sensing is high at pH 7 and increases with temperature. The detection limit as well as the sensitivity are shown to improve by adding NaCl to the PA. The low detection limit can be as low as 10 nM at room temperature and pH 7, which indicates the BN-CNPs are superior as compared to other luminescent probes reported in the literature.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Improving fault detection and reliability in FDDI networks

FDDI (Fibre Distributed Data Interface) is a 100 Mbit/s token ring network with two counter rotating optical rings. In this paper various possible faults (like lost token, link failures, etc.) are considered, and fault detection and the ring recovery process in case of a failure and the reliability mechanisms provided are studied. We suggest a new method to improve the fault detection and ring recovery process. The performance improvement in terms of station queue length and the average delay is compared with the performance of the existing fault detection and ring recovery process through simulation. We also suggest a modification for the physical configuration of the FDDI networks within the guidelines set by the standard to make the network more reliable. It is shown that, unlike the existing FDDI network, full connectivity is maintained among the stations even when multiple single link failures occur. A distributed algorithm is proposed for link reconfiguration of the modified FDDI network when many successive as well as simultaneous link failures occur. The performance of the modified FDDI network under link failures is studied through simulation and compared with that of the existing FDDI network.

Multilayer assemblies of polyelectrolyte-gold nanoparticles for the electrocatalytic oxidation and detection of arsenic(III)

Multilayers of poly(diallyldimethylammonium chloride) (PDDA) and citrate capped Au nanoparticles (AuNPs) anchored on sodium 3-mercapto-1-propanesulfonate modified gold electrode by electrostatic layer-by-layer assembly (LbL) technique are shown to be an excellent architecture for the direct electrochemical oxidation of As(III) species. The growth of successive layers in the proposed LbL architecture is followed by atomic force microscopy, UV-vis spectroscopy, quartz crystal microbalance with energy dissipation, and electrochemistry. The first bilayer is found to show rather different physico-chemical characteristics as compared to the subsequent bilayers, and this is attributed to the difference in the adsorption environments. The analytical utility of the architecture with five bilayers is exploited for arsenic sensing via the direct electrocatalytic oxidation of As(III), and the detection limit is found to be well below the WHO guidelines of 10 ppb. When the non-redox active PDDA is replaced by the redoxactive Os(2,2'-bipyridine)(2)Cl-poly(4-vinylpyridine) polyelectrolyte (PVPOs) in the LbL assembly, the performance is found to be inferior, demonstrating that the redox activity of the polyelectrolyte is futile as far as the direct electro-oxidation of As(III) is concerned. (C) 2012 Elsevier Inc. All rights reserved.

Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages. (C) 2014 Acoustical Society of America.

Cloning and development of pathotype-specific SCAR marker associated with Sclerospora graminicola isolates from pearl millet

Downy mildew pathogen of pearl millet in India is associated with the spread of the highly virulent Sclerospora graminicola pathotype-1. Twenty-seven S. graminicola isolates were screened using 20 intersimple sequence repeats (ISSR). Dinucleotide repeat primer [17898A-(CA)(6) AC] amplified a similar to 600 bp fragment specific to five isolates of pathotype-1 (Sg 048, Sg 153, Sg 212, DM-11 and DM-90). The ISSR fragment linked with pathotype-1 was cloned successfully and sequenced. To convert ISSR fragments into pathotype-specific sequence characterised amplified region (SCAR) markers, PCR primers were designed using a sequence of the cloned DNA fragment. PCR amplification using SCAR primer pair (UOM3-Sg-Path1-F/R) amplified a single 284 bp band only in isolates of S. graminicola pathotype-1. This SCAR primer pair did not amplify the 284 bp product from the other five S. graminicola pathotypes or a negative control, which demonstrates primer specificity for pathotype-1. The SCAR primer pair (UOM3-Sg-Path1-F/R) obtained in this study will provide a valuable tool for rapid identification and specific detection of S. graminicola pathotype-1.