Hyperspectral imagery is a new class of image data which is mainly used in remote sensing. It is characterized by a wealth of spatial and spectral information that can be used to improve detection and estimation accuracy in chemical and biological standoff detection applications. Finding spectral endmembers is a very important task in hyperspectral data exploitation. Over the last decade, several algorithms have been proposed to find spectral endmembers in hyperspectral data. Existing algorithms may be categorized into two different classes: 1) endmember extraction algorithms (EEAs), designed to find pure (or purest available) pixels, and 2) endmember generation algorithms (EGAs), designed to find pure spectral signatures. Such a distinction between an EEA and an EGA has never been made before in the literature. In this paper, we explore the concept of endmember generation as opposed to that of endmember extraction by describing our experience with two EGAs: the optical real-time adaptative spectral identification system (ORASIS), which generates endmembers based on spectral criteria, and the automated morphological endmember extraction (AMEE), which generates endmembers based on spatial/spectral criteria. The performance of these two algoriths is compared to that achieved by two standard algorithms which can perform both as EEAs and EGAs, i.e., the pixel purity index (PPI) and the iterative error analysis (IEA). Both the PPI and IEA may also be used to generate new signatures from existing pixel vectors in the input data, as opposed to the ORASIS method, which generates new spectra using an minimum volume transform. A standard algorithm which behaves as an EEA, i.e., the N-FINDR, is also used in the comparison for demonstration purposes. Experimental results provide several intriguing findings that may help hyperspectral data analysts in selection of algorithms for specific applications.

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,n)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral imaging sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced by either sunlight or the thermal emission of the earth after passage through a region containing the CWA cloud. The purpose of this paper is threefold. First, to discuss a simple phenomenological model for the radiance measured by the sensor in the case of optically thin clouds. This model provides the mathematical framework for the development of optimum algorithms and their analytical evaluation. Second, we identify the fundamental aspects of the data exploitation problem and we develop detection algorithms that can be used by different sensors as long as they can provide the required measurements. Finally, we discuss performance metrics for detection, identification, and quantification and we investigate their dependance on CWA spectral signatures, sensor noise, and background spectral variability.

We consider the problem of remotely identifying gaseous materials using passive sensing of long-wave infrared (LWIR) spectral features at hyperspectral resolution. Gaseous materials are distinguishable in the LWIR because of their unique spectral fingerprints. A sensor degraded in capability by noise or limited spectral resolution, however, may be unable to positively identify contaminants, especially if they are present in low concentrations or if the spectral library used for comparisons includes materials with similar spectral signatures. This paper will quantify the relative importance of these parameters and express the relationships between them in a functional form which can be used as a rule of thumb in sensor design or in assessing sensor capability for a specific task. This paper describes the simulation of remote sensing datacontaining a gas cloud.In each simulation, the spectra are degraded in spectral resolution and through the addition of noise to simulate spectra collected by sensors of varying design and capability. We form a trade space by systematically varying the number of sensor spectral channels and signal-to-noise ratio over a range of values. For each scenario, we evaluate the capability of the sensor for gas identification by computing the ratio of the F-statistic for the truth gas tothe same statistic computed over the rest of the library.The effect of the scope of the library is investigated as well, by computing statistics on the variability of the identification capability as the library composition is varied randomly.

Target detection is one of the most useful applications of hyperspectral remote sensing. In supervised spectral-analysis based target detection, it is assumed that the spectral signature d of a target to be detected is known a prior. In practice, the signature of a material is varied due to the weather, atmospheric, and background conditions. So it may not exactly match the signature d in a spectral library. In addition, most of pixels in a remote sensing image are mixed pixels. How a target detector handles mixed pixels and detects the target component at the subpixel level is another issue. In this paper, we will investigate the performance of five frequently used target detectors when the prior target spectral information is not precise and targets are embedded at the subpixel level. Detailed computer simulation is performed, based on which preliminary conclusions are drawn. This study is instructive to algorithm selection in practical implementation.

Hyperspectral remotely sensed imagery is rapidly developed recently. It collects radiance from the ground with hundreds of channels which results in hundreds of co-registered images. How to process this huge amount of data is a great challenge. Feature extraction methods are designed to remove redundant and remain useful information in the hyperspectral images. Many feature extraction approaches have been developed in the past, including the well known Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (LDA). The PCA is designed to search for directions with maximum variances. It compress most of the signal in the first a few principal components, but the experimental result shows that the extracted features by PCA does not perform well for target classification. On the other hand, Fisher's LDA is designed target classification, which maximize the between class distance while minimize the within class distance, but it can only find number of features which equal to the number of classes minus one. This will become a problem for subpixel target classification. Under this circumstance, this paper presents a modified Fisher's LDA which can extract features more than number of classes. The experiments are conducted to compare the classification results of PCA, Fisher's LDA and proposed method.

Anomaly detection for remote sensing has drawn a lot of attention lately. An anomaly has distinct spectral features from its neighborhood, whose spectral signature is not known a priori, and it usually has small size with only a few pixels. It is difficult to detect anomalies, and it is more challenge to detect anomalies without any information of the background environment in hyperspectral data with hundreds of co-registered image bands. Several methods are devoted to this problem, such as the well-known RX algorithm which takes advantage of the second-order statistics. The RX algorithm assumes Gaussian noise and uses sample covariance matrix for data whitening. However, when the anomalies pixel number exceeds certain percentage or the data is ill distributed, the sample covariance matrix can not represent the background distribution. In this case, the RX algorithm will not perform well. In order to solve this problem, in this paper we propose a weighted covariance matrix for anomaly detection. It gives weight to the each pixel in the covariance matrix by its distance to the data center, and then followed by the anomaly detection approach based on second-order statistics. We will compare the experimental results with the original RX methods.

Hyperspectral imagery is a class of image data which is used in many scientific areas, most notably, medical imaging and remote sensing. It is characterized by a wealth of spatial and spectral information. Over the last years, many algorithms have been developed with the purpose of finding "spectral endmembers," which are assumed to be pure signatures in remotely sensed hyperspectral data sets. Such pure signatures can then be used to estimate the abundance or concentration of materials in mixed pixels, thus allowing sub-pixel analysis which is crucial in many remote sensing applications due to current sensor optics and configuration. One of the most popular endmember extraction algorithms has been the pixel purity index (PPI), available from Kodak's Research Systems ENVI software package. This algorithm is very time consuming, a fact that has generally prevented its exploitation in valid response times in a wide range of applications, including environmental monitoring, military applications or hazard and threat assessment/tracking (including wildland fire detection, oil spill mapping and chemical and biological standoff detection). Field programmable gate arrays (FPGAs) are hardware components with millions of gates. Their reprogrammability and high computational power makes them particularly attractive in remote sensing applications which require a response in near real-time. In this paper, we present an FPGA design for implementation of PPI algorithm which takes advantage of a recently developed fast PPI (FPPI) algorithm that relies on software-based optimization. The proposed FPGA design represents our first step toward the development of a new reconfigurable system for fast, onboard analysis of remotely sensed hyperspectral imagery.

During the last years, several terrestrial ecosystems have suffered from large spill oil events threatening coastal habitats and species. Some recent examples include the 2002 Prestige tanker oil spill in Galicia, Northern Spain, as well as repeated oil spill leaks evidenced in the Santa Barbara coastline in California, and the Patuxent river (Chesapeake watershed) in Maryland. Both spaceborne and airborne hyperspectral sensors allow detailed identification of materials, and very accurate (sub-pixel) estimates of their fractional abundance covers. In the event of an oil spill, the information produced by remotely sensed hyperspectral instruments can be used to design an effective environmental oil spill protection and response plan, which could help to reduce the environmental consequences of the spill and cleanup efforts, as well as to protect human life. In this paper, we discuss a novel automated hyperspectral target detection technique for determining the level of oil contamination of polluted areas in the shoreline. The method is based on the simultaneous use of spatial and spectral information by extended mathematical morphology operations. Both simulated and real hyperspectral data, collected over polluted areas, are used in this work to illustrate the effectiveness of the proposed approach.

Under the U.S. Army sponsored Joint Service Agent Water Monitor (JSAWM) program, developing hand-held assays using tickets for chemical/biological agent detection has been of major interest. One of keys to success is to develop detection algorithms that not only can effectively detect the presence of various agents, but also can quantify the detected agents. This paper presents a recent development of detection software that can perform 3-dimensional (3D) receiver operating characteristics (ROC) analysis which is based on quantified agent concentration. The ROC curves have been widely used in communications, signal processing and medical communities to evaluate the effectiveness of a detection technique. It generally formulates a signal detection problem as a binary composite hypothesis testing problem with the null hypothesis and the alternative hypothesis represents the case of no signal and the case of signal presence respectively. The ROC curve is then plotted based on the detection probability (power) P_D versus the false alarm probability, P_F. Unfortunately, such a two-dimensional (2D) (P_D,P_F)-based ROC curve does not factor in the concentration detected in an agent signal which is a crucial parameter in chemical/biological agent detection. The proposed 3D ROC analysis is developed from such a need. It includes an additional parameter, referred to as threshold t, which is used to threshold the detected agent signal concentration. Consequently, a different value of t results in a different 2D ROC curve. In order to take into account the thresholding factor t, a 3D ROC curve is derived and plotted based on three parameters, (P_D,P_F,t). As a result of the 3D ROC curve, three 2D ROC curves can be also derived. One is the conventional 2D (P_D,P_F)-ROC curve. Another is a 2D (P_D,t)-ROC curve which describes the relationship between P_D and the threshold value t. A third one is a 2D (P_F,t)-ROC curve which shows the effect of the threshold value t on P_F. The utility of the proposed 3D ROC analysis will be demonstrated by the detection software developed by the UMBC for the tickets used in HHA for water monitoring.

The US Army Joint Service Agent Water Monitor (JSAWM) program is currently interested in an approach that can implement a hardware- designed device in ticket-based hand-held assay (currently being developed) used for chemical/biological agent detection. This paper presents a preliminary investigation of the proof of concept. Three components are envisioned to accomplish the task. One is the ticket development which has been undertaken by the ANP, Inc. Another component is the software development which has been carried out by the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County (UMBC). A third component is an embedded system development which can be used to drive the UMBC-developed software to analyze the ANP-developed HHA tickets on a small pocket-size device like a PDA. The main focus of this paper is to investigate the third component that is viable and is yet to be explored. In order to facilitate to prove the concept, a flatbed scanner is used to replace a ticket reader to serve as an input device. The Stargate processor board is used as the embedded System with Embedded Linux installed. It is connected to an input device such as scanner as well as output devices such as LCD display or laptop etc. It executes the C-Coded processing program developed for this embedded system and outputs its findings on a display device. The embedded system to be developed and investigated in this paper is the core of a future hardware device. Several issues arising in such an embedded system will be addressed. Finally, the proof-of-concept pilot embedded system will be demonstrated.

Algorithm development for detecting and discriminating atmospheric aerosols using range-resolved lidar is a straightforward, if non-trivial, application of well-established techniques of statistical signal processing assuming the aerosol backscatter coefficients are known as a function of wavelength. Unfortunately, in contrast to the analogous case of vapors, in most aerosol applications those coefficients are rarely known accurately. This is due to a combination of factors: (1) unknown refractive index dependence on wavelength, particularly for bioaerosols; (2) unknown particle size distribution; and (3) lack of particle sphericity making M e calculations unreliable. Uncertainties in any of these factors can distort the backscatter cross-section spectral dependence to the extent that aerosol identification becomes impossible. This paper presents a sequential algorithm for estimating both the aerosol concentration dependence on range and time and backscatter coefficient spectral signatures for a set of materials using M wavelengths with data available prior to the aerosol release for estimating the ambient lidar return. The rangedependence of the aerosol is modeled as an expansion of the concentration in an orthonormal basis set whose coefficients carry the time dependence. The basic idea is to run two estimators in parallel: a Kalman filter for the expansion coefficients, and a maximum likelihood estimator for the set of aerosol backscatter coefficients. These algorithms exchange information continuously over the data processing stream. The approach is illustrated on atmospheric backscatter lidar data collected by the U.S. Army multi-wavelength lidar from aerosol releases at the recent JBSDS trials at Dugway Proving Ground, UT.

The biological threat has emerged as one of today's primary security challenges due to the increased accessibility to biological warfare technology and the limited efficiency of detection and protection measures against such menace. Defence Research and Development Canada (DRDC) has investigated various methods, including the improvement of atmospheric bioaerosol monitoring, to increase the readiness against such threat. By the end of the 90s, DRDC developed a standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This work has showed an important potential of detecting and discriminating in real-time several bioaerosols. The LIDAR system that monitors atmosphere cells from a standoff position induces specific spectrally wide fluorescence signals originating from inelastic interactions with complex molecules forming the building blocks of the bioaerosols. This LIF signal is spectrally collected by a combination of a dispersive element and a range-gated ICCD that records the spectral information within a range-selected atmospheric volume. To assess further the potential of discrimination of such technique, this innovative sensor was used to obtain spectral data of various natural bioaerosols. In order to evaluate the discrimination of biological agent simulants from naturally occurring background fluorescing materials, the obtained results were compared with the ones of bioaerosol simulants (Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH)) acquired in 2001. The robustness of the spectral data with time was also investigated. From our results, most of the studied natural materials showed a spectral shift of various degrees, and up to 10 nm, to the longer wavelength one year later.

Standoff LIDAR detection of BW agents depends on accurate knowledge of the infrared and ultraviolet optical elastic scatter (ES) and ultraviolet fluorescence (UVF) signatures of bio-agents and interferents. MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) for measuring ES cross sections from BW simulants and interferents at all angles including 180º (direct backscatter). Measurements of interest include the dependence of the ES and UVF signatures on several spore production parameters including growth medium, sporulation protocol, washing protocol, fluidizing additives, and degree of aggregation. Using SAAST, we have made measurements of the ES signature of Bacillus globigii (atropheaus, Bg) spores grown under different growth methods. We have also investigated one common interferent (Arizona Test Dust). Future samples will include pollen and diesel exhaust. This paper presents the details of the SAAST apparatus along with the results of recent measurements.

The passive remote monitoring of multi-gas vapour mixtures by FTIR spectroscopy is investigated experimentally. The spectral radiance data were collected with the CATSI interferometer for a variety of multi-gas plumes at a distance of 60 m. Two basic sets of mixtures were studied. The first set corresponds to mixtures formed of three gases with no overlapping spectral bands (C₂H₂, C₂H₄ and R14). The second set corresponds to mixtures formed of three gases having overlapping spectral bands (C₂H₄, R114 and R134a). For each mixture the flow rates of individual constituents were adjusted to yield specific constituent CL ratios. These ratios are compared to the CL ratios retrieved from infrared radiance spectra. Results of this study indicate that for both sets of multi-gas mixtures the CL ratios retrieved by the passive remote monitoring technique agree well with those derived from the release flow rates. This good level of agreement was achieved by introducing a simple correction scheme to compensate for the limited accuracy of the fast radiance model implemented in the GASEM monitoring algorithm.

Standoff detection, identification and quantification of chemicals in the gaseous state are fundamental needs in several fields of applications. Sensor requirements derived from these applications include high sensitivity, low false alarms and real-time operation, all in a compact and robust package suitable for field use. The thermal infrared portion of the electromagnetic spectrum has been utilized to implement such chemical sensors, either with spectrometers (with no or moderate imaging capability) or with imagers (with moderate spectral capability). Only with the recent emergence of high-speed, large format infrared imaging arrays has it been possible to design chemical sensors offering uncompromising performance in the spectral, spatial, as well as the temporal domain. It is clear from analytical studies that the combined spatial and spectral information holds enormous promises on improving the current performance of passive detection, identification and quantification of chemical agents. This paper presents detection, identification and quantification algorithms developed for hyperspectral imagers operating in the thermal infrared. The effectiveness of these algorithms is illustrated using gaseous releases datacubes acquired using the Telops FIRST imaging spectrometer in the field.

A simple method is presented for quantitatively measuring the absorbance of chemical warfare (CW) agents and their simulants in the vapour phase. The technique is based on a standard lab-bench FTIR spectrometer, 10-cm gas cell, a high accuracy Baratron pressure manometer, vacuum pump and simple stainless-steel hardware components. The results of this measurement technique are demonstrated for sarin (GB) and soman (GD). A second technique is also introduced for the passive IR detection of CW agents in an open- air path located in a fumehood. Using a modified open-cell with a pathlength of 45 cm, open-air passive infrared measurements have been obtained for simulants and several classical CW agents. Detection, identification and quantification results based on passive infrared measurements are presented for GB and the CW agent simulant, DMMP, using the CATSI sensor which has been developed by DRDC Valcartier. The open-cell technique represents a relatively simple and feasible method for examining the detection capability of passive sensors, such as CATSI, for CW agents.

Backscatter gas imaging uses laser absorption spectroscopy to detect the presence of a gas by illuminating a region with light from an infrared laser and imaging the returned light. Contrast can be enhanced by comparing the back-scattered intensity on and off the absorption feature. Wavelength modulation spectroscopy can provide just such a capability, but the detector signal must be processed with a lock-in amplifier, which is incompatible or prohibitively expensive with most array detectors. Images can be recorded using a single photodiode by spatially modulating the laser or the detected image. This paper describes initial experiments to demonstrate the feasibility of a combined wavelength- and spatially- modulated gas imager. It is based on a single near-infrared laser, a single detector, lock-in detection, and a commercial micromirror array. The gases imaged include water vapor, mono-deuterated water vapor, acetylene and hydrogen cyanide. Doppler imaging is demonstrated using heterodyne detection and spatial image modulation.

The German ministry of the interior, represented by the civil defense agency BBK, is currently establishing analytical task forces for the analysis of released chemicals in the case of fires or chemical accidents. One part of the equipment of these emergency response forces will be a remote sensing system that allows the identification of hazardous clouds from long distances. Therefore, a new scanning infrared gas imaging system, SIGIS 2, is currently being developed at TUHH. The system is based on an interferometer with a single detector element (Bruker OPAG 33) in combination with a telescope and a synchronized scanning mirror. The new scanning system allows 360° surveillance. For simple interpretation of the results, the system is equipped with a video camera and the results of the analyses of the spectra are displayed by an overlay of a false color image on the video image. This allows a simple evaluation of the position and the size of a cloud. In order to allow simultaneous display of false color representations of measurement results and of the video image in real-time, a new scanner module has been developed. In the standard measurement mode, 16 two-sided interferograms per second are measured, analyzed, and the results are displayed. The spectral resolution is 4 cm^-1. The new interferometer, the new scanning system, the data analysis method, and first results of measurements are presented.

A novel dual-band hyperspectral imager has been developed to collect 128-band hyperspectral image cubes simultaneously in both 4-5.25 μm (mid wave IR, MWIR) and 8-10.5 μm (long wave IR, LWIR) bands for both target detection and standoff detection of chemical and biological agents. The imager uses a specially designed diffractive optics Ge lens with a dual-band 320×240 HgCdTe infrared (IR) focal plane array (FPA) cooled with a closed cycle Sterling-cooler. The diffractive optics lens acts both as a focusing as well as a dispersive device. The imager simultaneously collects a single-color full scene image with a narrow band in the LWIR region (e.g., at 8 μm) using the first order diffraction and corresponding single-color image in the MWIR region (e.g., at 4 μm) using the second order diffraction. Images at different wavelengths are obtained by moving the lens along its optical axis to focus the corresponding wavelengths. Contributions of out of focus wavelengths are removed in post processing. In this paper we will briefly discuss the imager and present data and results from a recent field test.

We present the design, fabrication, and impedance measurements of plasma wave detectors fabricated from GaAs/AlGaAs heterostructures. The design principles will allow broadband (dc to 7 GHz) measurements of the device power coupling and responsivity as a detector, which is a "scale model" of a THz plasma wave detector. We demonstrate clear resonance behavior in the impedance spectrum.

The development of efficient biological agent detection techniques requires in-depth understanding of THz absorption spectral features of different cell components. Chromosomal DNA, RNAs, proteins, bacterial cell wall, proteinaceous coat might be essential for bacterial cells and spores THz signature. As a first step, the DNA's contribution into entire cell THz spectra was analyzed. The experimental study of cells and DNAs of E. coli and cells/spores and DNA of Bacillus subtilis was conducted. Samples were prepared in the form of water solutions (suspension) with the concentrations in the range 0.01-1 mg/ml. The measurable difference in the THz transmission spectra of E. coli and Bacillus subtilis DNAs was observed. The correlation between chromosomal DNA signature and a corresponding entire spore/cell signature was observed. This correlation was especially pronounced for spores of Bacillus subtilis and their DNA. These experimental results justify our approach to develop a model for THz signatures of biological simulants and agents. In parallel with the experimental study, for the first time, the computer modeling and simulation of chromosome DNAs of E. coli and Bacillus subtilis was performed and their THz signatures were calculated. The DNA structures were optimized using the Amber software package. Also, we developed the initial model of the DNA fragment poly(dAT)-poly(dTA) solvated in water to be used in the simulations of genetic material (DNA and RNA) of spores and cells. Molecular dynamical simulations were conducted using explicit solvent (3-point TIP3P water) and implicit solvent (generalized Born) models. The calculated THz signatures of E. coli and Bacillus subtilis DNAs and poly(dAT)-poly(dTA) reproduce many features of our measured spectra. The results of this study demonstrate that THz Fourier transform infrared spectroscopy is a promising tool in generating spectral data for complex biological objects such as bacterial cells and spores.

The terahertz dielectric response of partially thermally denatured, hen egg white lysozyme (HEWL) films is measured as a function of frequency and hydration using terahertz time domain spectroscopy (THz-TDS). Results are compared to similar measurements on native state samples. The frequency and hydration dependence of the absorbance for the two sample types are highly similar except for a notable suppression at ~ 0.4 THz (13 cm^-1) in the partially denatured sample. In contrast to the native state sample which has a nearly frequency independent index of refraction, the index of the partially denatured sample decreases as a function of frequency. A transition is observed in both the absorbance and the index at a hydration level of ~ 0.25h (grams H₂O per gram lysozyme). Below the transition, the response slowly increases while above 0.25h, the slope of both the absorbance and index sharply increases. Interestingly, we observed similar behavior in the native sample. The Cole-Cole plots exhibit a hydration dependence that is distinct from the native sample and indicative of neither pure resonance nor dielectric relaxation. We consider the implications of these results on THz biomolecular sensors.

The 2DEG adjacent to a diluted magnetic semiconductor heterobarrier is altered in the presence of a magnetic field. The alteration is dependent upon at least three factors: the Zeeman energy, the self consistent potential energy and the equilibrium distribution. Concentrating on the first two features we present results for the alteration of the 2DEG for a spin heterodiode configuration and for a single barrier DMS structure, demonstrating that the 2DEG can be modified by a magnetic field, thereby permitting the magnetic field to function as a gate in two terminal structures with spin dependent contacts.

We proposed two scenarios for signal encoding and transmission in molecular circuits that can be used for standoff detection of biological and chemical agents: one is based on the characteristic vibrational behavior of molecules and clusters and the other is based on their molecular electrostatic potentials. It is proposed that these two scenarios can be used for molecular signal processing and transfer in molecular sensors; theoretical demonstrations using state of the art and precise computational techniques are presented for these two paradigms. The molecular electrostatic potential in the neighborhood of a molecule has very well defined zones of positive and negative potential that can be manipulated to encode information. On the other hand, vibrational modes of long molecules can be used to transfer signals between distances not accessible by standard fabrication techniques. In additions, the development of molecular amplifiers allows us to transfer signals through the nano- micro interface needed to pass the information to the macroscopic world. These scenarios allow extremely lower energies, higher speeds, and higher integration densities than in any other technology. Thus, the use of these two low-power consumption and extremely large bandwidth approaches allow us to operate at the THz range, the natural operation frequency of biological and chemical species. A review of our search for other scenarios for coding, processing and transport of information for sensing detection are provided.

The double-barrier AlGaSb/InAs/AlGaSb heterostructure with staggered bandgap alignment can admit significant interband tunneling current in addition to the conduction band electron transport. The resulting positive hole-charge accumulation in the right valence-band (VB) well will electrostatically modify the spatial potential profile across the device structure, thereby effectively altering the conduction of conduction-band electron transport. A sequentially triggered optical discharging process can be used to annihilate, or substantially reduce, the trapped holes that are generated from the interband tunneling process. Hence, an artificially induced electro-optic interaction can be used to return the device to its initial state and to produce a two-cycle oscillation process - i.e., one with a interband-induced charging transient followed by a optically-induced discharging transient to the initial state. These charging-discharging cycles obtained from this hybrid type of interband resonant-tunneling-diode (I-RTD) device constitute steady-state oscillatory behavior at very high frequency and produce alternating-current (ac) power as long as very short (i.e., sub-picosecond) and intense far-infrared laser pulses are presented to the diode. Initial studies of non-optimized structures and designs predict impressive figures of merit for oscillation frequencies (e.g., ~ 300-600 GHz) and substantial output powers (e.g., ~ 10 mW) for very modest device areas (i.e., 100 μm²). This paper will present physics-based I-RTD diode simulation results to precisely describe transport dynamics and transient electric current for both charging (initiated by Zener tunneling) and discharging (artificially induced by photons flux) processes. A basic electro-optical design concept and modeling approach for the analysis and synthesis of non-linear hybrid I-RTD circuits will also be presented. The main objectives of this paper are: (1) to perform a detailed assessment of the ac output power and efficiency of an optically-triggered (OT) I-RTD hybrid oscillator in the frequency range approximately 300 to 600 GHz, and (2) to prescribe the general requirements for realizing a diode-laser pair upon a single solid-state platform in the future. Therefore, guidelines for a practical engineering implementation and performance estimates for an OT-I-RTD hybrid oscillator design will be presented.

The U.S. Army Redstone Technical Test Center (RTTC) has been supporting captive flight testing of missile sensors and seekers since the 1980's. Successful integration and test of sensors in an airborne environment requires attention to a broad range of disciplines. Data collection requirements drive instrumentation and flight profile configurations, which along with cost and airframe performance factors influence the choice of test aircraft. Installation methods used for instrumentation must take into consideration environmental and airworthiness factors. In addition, integration of test equipment into the aircraft will require an airworthiness release; procedures vary between the government for military aircraft, and the Federal Aviation Administration (FAA) for the use of private, commercial, or experimental aircraft. Sensor mounting methods will depend on the type of sensor being used, both for sensor performance and crew safety concerns. Pilots will require navigation input to permit the execution of accurate and repeatable flight profiles. Some tests may require profiles that are not supported by standard navigation displays, requiring the use of custom hardware/software. Test locations must also be considered in their effect on successful data collection. Restricted airspace may also be required, depending on sensor emissions and flight profiles.

Captive flight testing (CFT) of sensors and seekers requires accurate data collection and display for sensor performance evaluation. The U.S. Army Redstone Technical Test Center (RTTC), in support of the U.S. Army Edgewood Chemical Biological Center (ECBC), has developed a data collection suite to facilitate airborne test of hyperspectral chemical/biological sensors. The data collection suite combines global positioning system (GPS) tracking, inertial measurement unit (IMU) data, accurate timing streams, and other test scenario information. This data collection suite also contains an advanced real-time display of aircraft and sensor field-of-view information. The latest evolution of this system has been used in support of the Adaptive InfraRed Imaging Spectroradiometer (AIRIS), currently under development by Physical Sciences Incorporated for ECBC. For this test, images from the AIRIS sensor were overlaid on a digitized background of the test area, with latencies of 1 second or less. Detects of surrogate chemicals were displayed and geo-referenced. Video overlay was accurate and reliable. This software suite offers great versatility in the display of imaging sensor data; support of future tests with the AIRIS sensor are planned as the system evolves.

In order to meet current and emerging needs for remote passive standoff detection of chemical agent threats, MIT Lincoln Laboratory has developed a Wide Area Chemical Sensor (WACS) testbed. A design study helped define the initial concept, guided by current standoff sensor mission requirements. Several variants of this initial design have since been proposed to target other applications within the defense community. The design relies on several enabling technologies required for successful implementation. The primary spectral component is a Wedged Interferometric Spectrometer (WIS) capable of imaging in the LWIR with spectral resolutions as narrow as 4 cm^-1. A novel scanning optic will enhance the ability of this sensor to scan over large areas of concern with a compact, rugged design. In this paper, we shall discuss our design, development, and calibration process for this system as well as recent testbed measurements that validate the sensor concept.

A systems analysis framework for assessing performance of long wave infra-red (LWIR) hyperspectral chemical imaging sensors (HCIS) is presented. The trade space study includes assessment of HCIS detection sensitivity and deployment impact on meeting specified mission requirements.

This paper will demonstrate a particular one-channel optical-fiber- based CH₄ gas real-time monitoring system in the mining complexes and residential area. A long-distance silica fiber link with self-design gas sensor heads has been employed in conjunction with a wavelength-tunable InGaAsP DFB laser diode at 1.64μm (around R (6) absorption peak of methane) to realize highly sensitive remote interrogation of CH₄. By wavelength modulation with the DFB laser diode and a self-design processing circuit, sensitivities of less than 0.1% (volume) have been achieved.

This paper examines the use of bi-static lidar to remotely detect the release of aerosolized biological agent. The detection scheme exploits bio-aerosol induced changes in the Stokes parameters of scattered radiation in comparison to scattered radiation from ambient background aerosols alone. A polarization distance metric is introduced to discriminate between changes caused by the two types of aerosols. Scattering code computations are the information source. Three application scenarios are considered: outdoor arena, indoor auditorium, and building heating-ventilation-air-conditioning (HVAC) system. Numerical simulations are employed to determine sensitivity of detection to laser wavelength and to particle physical properties. Results of the study are described and details are given for the specific example of a 1.50 μm lidar system operating outdoors over a 1000-m range.

In the case of chemical accidents, terrorist attacks, or war, hazardous compounds may be released into the atmosphere. Remote sensing by Fourier-transform infrared spectrometry allows identification and quantification of these hazardous clouds. The output of current standoff detection systems is a yes/no decision by an automatic identification algorithm that analyses the measured spectrum. The interpretation of the measured spectrum by the operator is complicated and thus this task requires an expert. Even if a scanning system is used for surveillance of a large area the operator is dependent on the decision of the algorithm. In contrast to that, imaging systems allow automatic identification but also simple interpretation of the result, the image of the cloud. Therefore, an imaging spectrometer, the scanning infrared gas imaging system (SIGIS) has been developed. The system is based on an interferometer with a single detector element (Bruker OPAG 22) in combination with a telescope and a synchronised scanning mirror. The results of the analyses of the spectra are displayed by an overlay of a false colour image, the "chemical cloud image", on a video image. In this work, the first application of the system as chemical warfare agent identification and imaging system is described. The system, the data analysis method, and results of measurements of chemical warfare agents are presented.