Amber L. Dagel

Four-channel optically pumped atomic magnetometer for magnetoencephalography.

We have developed a four-channel optically pumped atomic magnetometer for magnetoencephalography (MEG) that incorporates a passive diffractive optical element (DOE). The DOE allows us to achieve a long, 18-mm gradiometer baseline in a compact footprint on the head. Using gradiometry, the sensitivities of the channels are < 5 fT/Hz1/2, and the 3-dB bandwidths are approximately 90 Hz, which are both sufficient to perform MEG. Additionally, the channels are highly uniform, which offers the possibility of employing standard MEG post-processing techniques. This module will serve as a building block of an array for magnetic source localization.

Optical systems for task-specific compressive classification

Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.

The state of grating-based x-ray phase contrast imaging (Conference Presentation)

X-ray phase contrast imaging (XPCI) reveals structure and detail of low density materials with a sensitivity not accessible to conventional absorption based x-ray imaging or other non-destructive inspection techniques. The wide use of low density materials in defense and security applications has driven development outside the medical domain. In the laboratory environment (instantiations that do not employ a synchrotron), XPCI has moved beyond nascent demonstrations. Advances have been made in grating fabrication, source development, and specialized detectors. As the application space grows, new algorithms for acquisition, reconstruction, and corrections are being developed. I will review the state of the art in laboratory grating-based XPCI with emphasis on the growing interest in materials science applications. Hurdles remain for XPCI to move beyond laboratory demonstrations and become a widely used non-destructive inspection technique. The most common three-grating system has limitations defined by grating fabrication limits, which determine attainable energy levels, and relevant samples. The system geometry, signal levels, and speed of acquisition must be realistic for real world applications. This talk will provide a perspective on the global state of XPCI and development trends that seek to expand the operational space.

Defect detection in foams and encapsulants using grating-based x-ray phase contrast imaging (Rising Researcher Presentation) (Conference Presentation)

Foams and encapsulants serve important roles in the protection of the components they surround. These low density materials may be used to provide shock protection, to protect against high voltage breakdown, or to minimize thermal fluctuations. Voids and gaps in the material, delaminations from a mating material, or non-uniformities in the encapsulating materials can lead to critical failures in the encapsulated component. Despite the important role these low density materials serve, traditional non-destructive inspection tools are limited in their ability to study this material set, especially in the presence of high density materials such as wires. The default approach has been destructive post-mortums where components are deconstructed after a failure and cause and effect are difficult to distinguish. X-ray phase contrast imaging has a longer history at synchrotrons, but this is not a realistic solution for non-destructive inspection. We have demonstrated grating-based x-ray phase contrast 3-D tomography in a laboratory environment with a conventional x-ray tube. Our large format grating fabrication capability enables imaging with large fields of view (10 cm2) at 28 keV for the successful non-destructive inspection of these low-density materials. We demonstrate that the complementary image modalities available with XPCI provide unique information and higher contrast for the inspection of defects in low density materials than conventional x-ray alone.

Computational optical physical unclonable functions

Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict. Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions. We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs. This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element. We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing. The sensitivity of the COPUF system is also explored. We explore non-traditional PUF configurations enabled by the COPUF architecture. The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate. This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle. Our results show that it is possible to construct inexpensive optical PUFs using computational imaging. This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.

Lensless computational imaging using 3D printed transparent elements

Lensless imaging systems have the potential to provide new capabilities for lower size and weight configuration than traditional imaging systems. Lensless imagers frequently utilize computational imaging techniques, which moves the complexity of the system away from optical subcomponents and into a calibration process whereby the measurement matrix is estimated. We report on the design, simulation, and prototyping of a lensless imaging system that utilizes a 3D printed optically transparent random scattering element. Development of end-to-end system simulations, which includes simulations of the calibration process, as well as the data processing algorithm used to generate an image from the raw data are presented. These simulations utilize GPU-based raytracing software, and parallelized minimization algorithms to bring complete system simulation times down to the order of seconds. Hardware prototype results are presented, and practical lessons such as the effect of sensor noise on reconstructed image quality are discussed. System performance metrics are proposed and evaluated to discuss image quality in a manner that is relatable to traditional image quality metrics. Various hardware instantiations are discussed.

Big-data x-ray phase contrast imaging simulation challenges

This position paper describes a potential implementation of a large-scale grating-based X-ray Phase Contrast Imaging System (XPCI) simulation tool along with the associated challenges in its implementation. This work proposes an implementation based off of an implementation by Peterzol et. al. where each grating is treated as an object imaged in the field-of-view. Two main challenges exist; the first, is the required sampling and information management in object space due to the micron-scale periods of each grating propagating over significant distances. The second is maintaining algorithmic numerical stability for imaging systems relevant to industrial applications. We present preliminary results for a numerical stability study using a simplified algorithm that performs Talbot imaging in a big-data context

3D Imaging with Structured Illumination for Advanced Security Applications

Three-dimensional (3D) information in a physical security system is a highly useful dis- criminator. The two-dimensional data from an imaging systems fails to provide target dis- tance and three-dimensional motion vector, which can be used to reduce nuisance alarm rates and increase system effectiveness. However, 3D imaging devices designed primarily for use in physical security systems are uncommon. This report discusses an architecture favorable to physical security systems; an inexpensive snapshot 3D imaging system utilizing a simple illumination system. The method of acquiring 3D data, tests to understand illumination de- sign, and software modifications possible to maximize information gathering capability are discussed.