To improve the resolution and field of view of high-energy Compton-scattered x-ray and gamma-ray imaging systems,
we have developed and tested apodized imaging optics based on apertures with depth-dependent cross sections
fabricated in an x-ray-absorbing material. Through ray-tracing modeling, we determined the optimum aperture shapes
(apodizations) that maximize the field of view and/or resolution of the system. Such apodized apertures can be used
either in single-aperture optics, or in coded-aperture arrays. Potential applications of this technology include
nondestructive evaluation (NDE) of materials and structures, in particular Compton imaging tomography (CIT), x-ray
and gamma-ray astronomy, and medical imaging.
In this paper we describe a new nondestructive evaluation (NDE) technique called Compton Imaging Tomography (CIT)
for reconstructing the complete three-dimensional internal structure of an object, based on the registration of multiple
two-dimensional Compton-scattered x-ray images of the object. CIT provides high resolution and sensitivity with
virtually any material, including lightweight structures and organics, which normally pose problems in conventional
x-ray computed tomography because of low contrast. The CIT technique requires only one-sided access to the object,
has no limitation on the object's size, and can be applied to high-resolution real-time in situ NDE of large
aircraft/spacecraft structures and components. Theoretical and experimental results will be presented.
Physical Optics Corporation (POC) presents a novel Mobile ELISA-based Pathogen Detection system that is based on a
disposable microfluidic chip for multiple-threat detection and a highly sensitive portable microfluidic fluorescence
measurement unit that also controls the flow of samples and reagents through the microfluidic channels of the chip. The
fluorescence detection subsystem is composed of a commercial 635-nm diode laser, an avalanche photodiode (APD) that
measures fluorescence, and three filtering mirrors that provide more than 100 dB of excitation line suppression in the
signal detection channel. Special techniques to suppress the fluorescence and scattering background allow optimizing the
dynamic range for a compact package. Concentrations below 100 ng/mL can be reliably identified. The entire instrument
is powered using a USB port of a notebook PC and operates as a plug-and-play human-interface device, resulting in a
truly peripheral biosensor. The operation of the system is fully automated, with minimal user intervention through the
detection process. The resolved challenges of the design and implementation are presented in detail in this publication.
In this paper, Bayesian inference is applied to performance metrics definition of the important class of recent Homeland
Security and defense systems called binary sensors, including both (internal) system performance and (external)
CONOPS. The medical analogy is used to define the PPV (Positive Predictive Value), the basic Bayesian metrics
parameter of the binary sensors. Also, Small System Integration (SSI) is discussed in the context of recent Homeland
Security and defense applications, emphasizing a highly multi-technological approach, within the broad range of clusters
("nexus") of electronics, optics, X-ray physics, γ-ray physics, and other disciplines.
Binary sensor systems are various types of analog sensors (optical, MEMS, X-ray, gamma-ray, acoustic, electronic, etc.),
based on the binary decision process. Typical examples of such "binary sensors" are X-ray luggage inspection systems,
product quality control systems, automatic target recognition systems, numerous medical diagnostic systems, and many
others. In all these systems, the binary decision process provides only two mutually exclusive responses: "signal" and
"noise." There are also two types of key parameters that characterize either system (such as false positive and false
negative), or a priori external-to-system conditions (such as absolute probabilities). In this paper, by using a strong
medical analogy, we analyze a third type of key parameter that combines both system-like and a priori information, in
the form of so called Bayesian Figures of Merit, and we show that the latter parameter, in the best way, characterizes a
binary sensor system.
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