Modeling Ultrasonic NDE and Guided Wave based Structural Health Monitoring

Structural Health Monitoring (SHM) systems require integration of non-destructive technologies into structural design and operational processes. Modeling and simulation of complex NDE inspection processes are important aspects in the development and deployment of SHM technologies. Ray tracing techniques are vital simulation tools to visualize the wave path inside a material. These techniques also help in optimizing the location of transducers and their orientation with respect to the zone of interrogation. It helps in increasing the chances of detection and identification of a flaw in that zone. While current state-of-the-art techniques such as ray tracing based on geometric principle help in such visualization, other information such as signal losses due to spherical or cylindrical shape of wave front are rarely taken into consideration. The problem becomes a little more complicated in the case of dispersive guided wave propagation and near-field defect scattering. We review the existing models and tools to perform ultrasonic NDE simulation in structural components. As an initial step, we develop a ray-tracing approach, where phase and spectral information are preserved. This enables one to study wave scattering beyond simple time of flight calculation of rays. Challenges in terms of theory and modelling of defects of various kinds are discussed. Various additional considerations such as signal decay and physics of scattering are reviewed and challenges involved in realistic computational implementation are discussed. Potential application of this approach to SHM system design is highlighted and by applying this to complex structural components such as airframe structures, SHM is demonstrated to provide additional value in terms of lighter weight and/or longevity enhancement resulting from an extension of the damage tolerance design principle not compromising safety and reliability.

Event-triggered sampling using signal extrema for instantaneous amplitude and instantaneous frequency estimation

Event-triggered sampling (ETS) is a new approach towards efficient signal analysis. The goal of ETS need not be only signal reconstruction, but also direct estimation of desired information in the signal by skillful design of event. We show a promise of ETS approach towards better analysis of oscillatory non-stationary signals modeled by a time-varying sinusoid, when compared to existing uniform Nyquist-rate sampling based signal processing. We examine samples drawn using ETS, with events as zero-crossing (ZC), level-crossing (LC), and extrema, for additive in-band noise and jitter in detection instant. We find that extrema samples are robust, and also facilitate instantaneous amplitude (IA), and instantaneous frequency (IF) estimation in a time-varying sinusoid. The estimation is proposed solely using extrema samples, and a local polynomial regression based least-squares fitting approach. The proposed approach shows improvement, for noisy signals, over widely used analytic signal, energy separation, and ZC based approaches (which are based on uniform Nyquist-rate sampling based data-acquisition and processing). Further, extrema based ETS in general gives a sub-sampled representation (relative to Nyquistrate) of a time-varying sinusoid. For the same data-set size captured with extrema based ETS, and uniform sampling, the former gives much better IA and IF estimation. (C) 2015 Elsevier B.V. All rights reserved.

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.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos

In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

Near-Optimal Detection Thresholds for Bayesian Spectrum Sensing Under Fading

This paper considers the problem of energy-based, Bayesian spectrum sensing in cognitive radios under various fading environments. Under the well-known central limit theorem based model for energy detection, we derive analytically tractable expressions for near-optimal detection thresholds that minimize the probability of error under lognormal, Nakagami-m, and Weibull fading. For the Suzuki fading case, a generalized gamma approximation is provided, which saves on the computation of an integral. In each case, the accuracy of the theoretical expressions as compared to the optimal thresholds are illustrated through simulations.

Pricing and Power allocation in Femtocells using Stackelberg Game Theory

We consider a system with multiple Femtocells operating in a Macrocell. The transmissions in one Femtocell interfere with its neighboring Femtocells as well as with the Macrocell Base Station. We model Femtocells as selfish nodes and the Macrocell Base Station protects itself by pricing subchannels for each usage. We use Stackelberg game model to study this scenario and obtain equilibrium policies that satisfy certain quality of service.

Specific detection of the cleavage activity of mycobacterial enzymes using a quantum dot based DNA nanosensor

We present a quantum dot based DNA nanosensor specifically targeting the cleavage step in the reaction cycle of the essential DNA-modifying enzyme, mycobacterial topoisomerase I. The design takes advantages of the unique photophysical properties of quantum dots to generate visible fluorescence recovery upon specific cleavage by mycobacterial topoisomerase I. This report, for the first time, demonstrates the possibility to quantify the cleavage activity of the mycobacterial enzyme without the pre-processing sample purification or post-processing signal amplification. The cleavage induced signal response has also proven reliable in biological matrices, such as whole cell extracts prepared from Escherichia coli and human Caco-2 cells. It is expected that the assay may contribute to the clinical diagnostics of bacterial diseases, as well as the evaluation of treatment outcomes.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

Sparse representation-based human detection: a scale-embedded dictionary approach

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.