Robert Barat

THz imaging and sensing for security applications—explosives, weapons and drugs

Over the past 5 years, there has been a significant interest in employing terahertz (THz) technology, spectroscopy and imaging for security applications. There are three prime motivations for this interest: (a) THz radiation can detect concealed weapons since many non-metallic, non-polar materials are transparent to THz radiation; (b) target compounds such as explosives and illicit drugs have characteristic THz spectra that can be used to identify these compounds and (c) THz radiation poses no health risk for scanning of people. In this paper, stand-off interferometric imaging and sensing for the detection of explosives, weapons and drugs is emphasized. Future prospects of THz technology are discussed.

Terahertz imaging using an interferometric array

Most methods of imaging in the terahertz (THz) spectral region utilize either pulsed-laser sources or require the THz generation and detection sources to be phase coherent. The application of interferometric imaging to the THz range is described. Interferometric imaging offers considerable advantages in this regard due to its ability to image with only a handful of detector elements, image many sources of THz radiation at once, image incoherent as well as coherent sources, and provide spectral information as well as spatial imaging information. The THz interferometric imaging method is potentially useful for remote detection of explosives.

Terahertz near-field interferometric and synthetic aperture imaging

The imaging properties of planar, spherical, and circular interferometric imaging arrays are examined in the near-field region limit. In this region, spherical and circular array architectures can compensate for near-field distortions and increase the field of view and depth of focus. The application of near-field interferometric imaging to the Terahertz frequency range for detection of concealed objects is emphasized.

Process modeling and on-line monitoring of benzene and other species during the two-stage combustion of ethylene in air

Abstract The on-line measurement of combustion effluents is important in the study of reaction mechanisms, process control, and environmental monitoring and impact. Microtrap gas chromatography for real-time analysis of volatile organics preconcentrates the sample, and uses rapid desorption to yield low detection limits with a relatively rapid analysis. This technique is used here for the on-line monitoring of low levels of benzene, a known precursor to polyaromatics and dioxins, produced during the combustion of ethylene in air. In addition, several other species are monitored by more conventional on-line methods. Process modeling with detailed reaction mechanisms of the combustion runs provided reasonably good simulations of the observed concentrations.

Process control of a laboratory combustor using artificial neural networks

Abstract Active process control of nitric oxide (NO) emissions from a two-stage combustor burning ethylene (doped with ammonia) in air is demonstrated using two clusters of feed-forward multi-layer-perceptron neural networks. Steady-state experimental data are used for static back-propagation network training. The first cluster consists of two neural networks. The first network identifies the amount of ammonia in the feed. Based on that value and the NO set point, the second network adjusts the first-stage fuel equivalence ratio φ 1 . The second cluster also consists of two neural networks. It is the process emulator and serves as a Smith time-delay compensator. A visual basic interface control program accepts incoming concentration and flow rate data signals, accesses the neural networks, and outputs feedback control signals to selected electronic valves. Closed-loop results are compared to the open-loop results. The neural network-based controller successfully brought NO emissions into control after a step disturbance in the feed composition stream (ammonia dopant). The neural network-based controller shows a superior performance over the conventional proportional–integral-derivative controller.

Neural network analysis of terahertz spectra of explosives and bio-agents

A proposed, non-invasive, means to detect and characterize concealed biological and explosive agents in near real-time with a wide field-of-view uses spatial imaging of their characteristic transmission or reflectivity wavelength spectrum in the Terahertz (THz) electro-magnetic range (0.1-3 THz). Neural network analyses of the THz spectra and images will provide the specificity of agent detection and reduce the frequency of false alarms. Artificial neural networks are mathematical devices for modeling complex, non-linear functionalities. The key to a successful neural network is adequate training with known input-output data. Important challenges in the research include identification of the preferred network structure (e.g. multi-layer perceptron), number of hidden nodes, training algorithm (e.g. back propagation), and determination of what type of THz spectral image pre-processing is needed prior to application of the network. Detector array images containing both spectral and spatial information are analyzed with the aid of the Neurosolutions(TM) commercial neural network software package.

Study of Morphological Effects on Terahertz Spectra Using Ammonium Nitrate

The effect of morphology on Terahertz spectra in the region from 0.2 to 1.2 THz was studied using Ammonium Nitrate of different grain sizes. The results agree with Mie scattering theory for small grain sizes.

Optical properties around resonance peaks by THz-TDS

Terahertz Time domain spectroscopy (THz -TDS ) can provide the optical response of a medium in both amplitude and phase. We show that such capability can enable a detail analysis of optical properties around a resonance regime. Such study is important for standoff detection of explosive material where numerous absorption peaks exist. We proposed a model where one can synthesize the optical properties with THz-TDS around the resonance regimes.

PROCESS CONTROL OF A LABORATORY COMBUSTOR USING NEURAL NETWORKS

PROCESS CONTROL OF A LABORATORY COMBUSTOR USING NEURAL NETWORKS by Thana Slanvetpan Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory twostage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem — the variation of outlet oxygen level with overall equivalence ratio (00). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem — the variation of outlet nitric oxide level with first-stage equivalence ratio (4,) — was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral-derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields. PROCESS CONTROL OF A LABORATORY COMBUSTOR USING NEURAL NETWORKS

Detection of the agent inside or behind a barrier material

Terahertz Time domain spectroscopy (THz -TDS ) can provide the optical response of a medium in both amplitude and phase. We show that such capability can enable a detail analysis of optical properties of RDX sample. Such study is important for standoff detection of presence of RDX sample, where a detail analysis is difficult if not possible due to a complicated system involved and multiple effects involved. We proposed a match filter method for detection of RDX inside or behind a barrier.