Naveed R. Butt

Robust Detection of Stochastic Nuclear Quadrupole Resonance Signals

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique, allowing the detection of compounds containing quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. The practical use of NQR is restricted by the inherently low signal-to-noise ratio (SNR) of the observed signals, a problem that is further exacerbated by the presence of strong RF interference (RFI). The current literature focuses on the use of conventional, multiple-pulsed NQR (cNQR) to obtain signals. Here, we investigate an alternative method called stochastic NQR (sNQR), having many advantages over cNQR, one of which is the availability of signal-of-interest free samples. In this paper, we exploit these samples forming a matched subspace-type detector and a detector employing a prewhitening approach, both of which are able to efficiently reduce the influence of RFI. Further, many of the ideas already developed for cNQR, including providing robustness to uncertainties in the assumed complex amplitudes and exploiting the temperature dependencies of the NQR spectral components, are recast for sNQR. The presented detectors are evaluated on both simulated and measured trinitro-toluene (TNT) data.

Coherence Spectrum Estimation From Nonuniformly Sampled Sequences

Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto- and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly sampled sequences. To achieve higher statistical accuracy, the estimation problem is formulated so as to allow for overlapped segmentation of the available data. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator.

Countering Radio Frequency Interference in Single-Sensor Quadrupole Resonance

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique that allows for the detection of many narcotics and highly explosive substances. Unfortunately, the practical use of NQR is often restricted by the presence of strong RF interference (RFI). In this letter, extending our recent work on stochastic NQR (sNQR), we propose acquiring signal-of-interest free samples, containing only corrupting signals, and exploiting them to reduce the effects of RFI on conventional NQR (cNQR) measurements. Similar to the sNQR case, the presented detectors are able to substantially outperform previous cNQR detectors when RFI is present.

Classification of Raman Spectra to Detect Hidden Explosives

Raman spectroscopy is a laser-based vibrational technique that can provide spectral signatures unique to a multitude of compounds. The technique is gaining widespread interest as a method for detecting hidden explosives due to its sensitivity and ease of use. In this letter, we present a computationally efficient classification scheme for accurate standoff identification of several common explosives using visible-range Raman spectroscopy. Using real measurements, we evaluate and modify a recent correlation-based approach to classify Raman spectra from various harmful and commonplace substances. The results show that the proposed approach can, at a distance of 30 m, or more, successfully classify measured Raman spectra from several explosive substances, including nitromethane, trinitrotoluene, dinitrotoluene, hydrogen peroxide, triacetone triperoxide, and ammonium nitrate.

Frequency-selective robust detection and estimation of polymorphic QR signals

Nuclear quadrupole resonance (NQR) is a non-invasive, solid state, radio frequency (RF) technique, able to distinguish between polymorphic forms of certain compounds. Exploiting the signals from multiple polymorphs is important in explosives detection, whilst quantifying these polymorphs is important in pharmaceutical applications. Recently proposed hybrid algorithms, able to process the signals from multiple polymorphs, assume that the amplitudes associated with each polymorph are known to be within a scaling. Any error in this a priori information will lead to performance degradation in these algorithms. In this paper, we develop a robust hybrid algorithm allowing for uncertainties in the assumed amplitudes, extending a recently proposed robust algorithm, formulated for single polymorphs, to process signals from multiple polymorphs. In the proposed robust algorithm, the amplitudes are allowed to vary within an uncertainty hyper-sphere whose radius is evaluated using analytical expressions derived herein. Extensive numerical investigations indicate that the proposed algorithm provides significant performance gains as compared to both the existing hybrid algorithms, when uncertainties in the amplitudes exist, and the existing robust algorithm, when there are multiple polymorphs. Finally, the Cramer-Rao lower bound is derived for the uncertain data case as a reference for the quantification problem.

Robust fundamental frequency estimation in the presence of inharmonicities

We develop a general robust fundamental frequency estimator that allows for non-parametric inharmonicities in the observed signal. To this end, we incorporate the recently developed multi-dimensional covariance fitting approach by allowing the Fourier vector corresponding to each perturbed harmonic to lie within a small uncertainty hypersphere centered around its strictly harmonic counterpart. Within these hyperspheres, we find the best perturbed vectors fitting the covariance of the observed data. The proposed approach provides the estimate of the fundamental frequency in two steps, and, unlike other recentmethods, involves only a single 1-D search over a range of candidate fundamental frequencies. The proposed algorithm is numerically shown to outperform the current competitors under a variety of practical conditions, including various degrees of inharmonicity and different levels of noise.

Robust Multichannel Detection of Mixtures Using Nuclear Quadrupole Resonance

Nuclear quadrupole resonance (NQR) is a radio-frequency (RF) spectroscopic technique, able to detect the presence of many high explosives and narcotics. In practice, the weak NQR signal is often corrupted by strong RF interference (RFI); therefore, various multichannel detection algorithms have recently been proposed for NQR. However, these algorithms allow for only a single compound/polymorph, whose associated amplitudes are assumed to be known to within a scaling. Regrettably, these amplitudes are typically prone to some level of uncertainty; and, in several cases of interest, signals from a mixture of NQR compounds/polymorphs may be present, leading to performance degradation in the aforementioned algorithms. In this paper, we develop a robust multisensor hybrid algorithm able to process NQR signals from mixtures while also allowing for errors in the assumed amplitudes. Numerical investigations indicate that the proposed algorithm provides significant performance gains as compared to existing algorithms in the case when there are multiple polymorphs present and/or when uncertainties in the amplitudes exist.

Real-time adaptive tracking of DC motor speed using U-model based IMC

A novel technique, involving U-model based IMC (Internal Model Control), is proposed for the adaptive control of nonlinear dynamic plants such as the DC-Motor. The proposed scheme combines the robustness of the IMC and the ability of Neural Networks to identify arbitrary nonlinear functions, with the control-oriented nature of the U-model to achieve adaptive tracking of stable nonlinear plants. The proposed structure has a more general appeal than many other schemes involving polynomial NARMAX (Nonlinear Autoregressive Moving Average with Exogenous inputs) model and the Hammerstein model, etc. Additionally, the control law is shown to be more simplistic in nature. The effectiveness of the proposed scheme is demonstrated with the help of simulations and real-time application to the speed control of DC motor system.

Robust coherence spectrum estimation

In this paper, we introduce robust versions of the recently introduced Capon- and APES-based Magnitude Square Coherence (MSC) spectral estimators. The estimators exploit the related recent development on robust beamforming to allow for imperfect knowledge of the estimated sample correlation matrix. The resulting estimators are found to yield improved estimates as compared to the earlier estimators.

Detection of Stochastic Nuclear Quadrupole Resonance Signals

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique, allowing the detection of compounds containing quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. The practical use of NQR is restricted by the inherently low signal- to-noise ratio of the observed signals, indicating a need for more effective techniques. The current literature focuses on the use of conventional, multiple-pulsed NQR (cNQR) to obtain signals. Here, we investigate an alternative method called stochastic NQR, having many advantages over cNQR, also proposing several detection strategies for the method. The presented detectors are evaluated on both simulated and measured trinitrotoluene (TNT) data.