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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Information-theoretic discrepancy based iterative reconstructions (IDIR) for polychromatic x-ray tomography

Purpose: X-ray photons generated from a typical x-ray source for clinical applications exhibit a broad range of wavelengths, and the interactions between individual particles and biological substances depend on particles' energy levels. Most existing reconstruction methods for transmission tomography, however, neglect this polychromatic nature of measurements and rely on the monochromatic approximation. In this study, we developed a new family of iterative methods that incorporates the exact polychromatic model into tomographic image recovery, which improves the accuracy and quality of reconstruction.Methods: The generalized information-theoretic discrepancy (GID) was employed as a new metric for quantifying the distance between the measured and synthetic data. By using special features of the GID, the objective function for polychromatic reconstruction which contains a double integral over the wavelength and the trajectory of incident x-rays was simplified to a paraboloidal form without using the monochromatic approximation. More specifically, the original GID was replaced with a surrogate function with two auxiliary, energy-dependent variables. Subsequently, the alternating minimization technique was applied to solve the double minimization problem. Based on the optimization transfer principle, the objective function was further simplified to the paraboloidal equation, which leads to a closed-form update formula. Numerical experiments on the beam-hardening correction and material-selective reconstruction were conducted to compare and assess the performance of conventional methods and the proposed algorithms.Results: The authors found that the GID determines the distance between its two arguments in a flexible manner. In this study, three groups of GIDs with distinct data representations were considered. The authors demonstrated that one type of GIDs that comprises “raw” data can be viewed as an extension of existing statistical reconstructions; under a particular condition, the GID is equivalent to the Poisson log-likelihood function. The newly proposed GIDs of the other two categories consist of log-transformed measurements, which have the advantage of imposing linearized penalties over multiple discrepancies. For all proposed variants of the GID, the aforementioned strategy was used to obtain a closed-form update equation. Even though it is based on the exact polychromatic model, the derived algorithm bears a structural resemblance to conventional methods based on the monochromatic approximation. The authors named the proposed approach as information-theoretic discrepancy based iterative reconstructions (IDIR). In numerical experiments, IDIR with raw data converged faster than previously known statistical reconstruction methods. IDIR with log-transformed data exhibited superior reconstruction quality and faster convergence speed compared with conventional methods and their variants.Conclusions: The authors' new framework for tomographic reconstruction allows iterative inversion of the polychromatic data model. The primary departure from the traditional iterative reconstruction was the employment of the GID as a new metric for quantifying the inconsistency between the measured and synthetic data. The proposed methods outperformed not only conventional methods based on the monochromatic approximation but also those based on the polychromatic model. The authors have observed that the GID is a very flexible means to design an objective function for iterative reconstructions. Hence, the authors expect that the proposed IDIR framework will also be applicable to other challenging tasks.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Toward quantum plasmonic networks

Here, we demonstrate the transduction of macroscopic quantum entanglement by independent, distant plasmonic structures embedded in separate thin silver films. In particular, we show that the plasmon-mediated transmission through each film conserves spatially dependent, entangled quantum images, opening the door for the implementation of parallel quantum protocols, super-resolution imaging, and quantum plasmonic sensing geometries at the nanoscale level. The conservation of quantum information by the transduction process shows that continuous variable multi-mode entanglement is momentarily transferred from entangled beams of light to the space-like separated, completely independent plasmonic structures, thus providing a first important step toward establishing a multichannel quantum network across separate solid-state substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Stability and time-domain analysis of the dispersive tristability in microresonators under modal coupling

Coupled nonlinear resonators have potential applications for the integration of multistable photonic devices. The dynamic properties of two coupled-mode nonlinear microcavities made of Kerr material are studied by linear stability analysis. Using a suitable combination of the modal coupling rate and the frequency detuning, it is possible to obtain configurations where a hysteresis loop is included inside other bistable cycles. We show that a single resonator with two modes both linearly and nonlinearly coupled via the cross-Kerr effect can have a multistable behavior. This could be implemented in semiconductor nonlinear whispering-gallery-mode microresonators under modal coupling for all optical signal processing or ternary optical logic applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Integrated multi-wavelength microscope combining TIRFM and IRM modalities for imaging cellulases and other processive enzymes

We describe a multimodal microscope for visualizing processive enzymes moving on immobilized substrates. The instrument combines interference reflection microscopy (IRM) with multi-wavelength total internal reflectance fluorescence microscopy (TIRFM). The microscope can localize quantum dots with a precision of 2.8 nm at 100 frames/s, and was used to image the dynamics of the cellulase, Cel7a interacting with surface-immobilized cellulose. The instrument, which was built with off-the-shelf components and is controlled by custom software, is suitable for tracking other degradative enzymes such as collagenases, as well as motor proteins moving along immobilized tracks.

09 BIOMASS FUELS↗
DOE OSTI2015

The correlation study of parallel feature extractor and noise reduction approaches

This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation, image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Process monitoring of additive manufacturing by using optical tomography

Parts fabricated by means of additive manufacturing are usually of complex shape and owing to the fabrication procedure by using selective laser melting (SLM), potential defects and inaccuracies are often very small in lateral size. Therefore, an adequate quality inspection of such parts is rather challenging, while non-destructive-techniques (NDT) are difficult to realize, but considerable efforts are necessary in order to ensure the quality of SLM-parts especially used for aerospace components. Thus, MTU Aero Engines is currently focusing on the development of an Online Process Control system which monitors and documents the complete welding process during the SLM fabrication procedure. A high-resolution camera system is used to obtain images, from which tomographic data for a 3dim analysis of SLM-parts are processed. From the analysis, structural irregularities and structural disorder resulting from any possible erroneous melting process become visible and may be allocated anywhere within the 3dim structure. Results of our optical tomography (OT) method as obtained on real defects are presented.

42 ENGINEERING↗
NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗
NASA NTRS1989

HgCdTe 256x256 NWIR FPA

Researchers developed a HgCdTe 256x256 focal plane array (FPA) which operates in the 1 to 5 micron band. This is presently the largest demonstrated HgCdTe FPA. The detector material is HgCdTe on sapphire (PACE-1 technology) which has a low thermal expansion mismatch with silicon. The multiplexer is a CMOS FET-switch device processed through a commercial silicon foundry. The multiplexer input is direct injection and the charge capacity is about 2 times 10 to the 7th power electrons. The kTC limited read noise is 400 electrons. Researchers demonstrated high background imaging using the device. The broadband quantum efficiency is measured to be 59 percent. Dark currents less than 0.1 pA were measured at 77 K for detectors processed on PACE-1 material with 4.9 microns cutoff. The dark currents decrease as the temperature is lowered, and researchers are presently studying the T less than 77 K characteristics. The interconnect yield is greater than 95 percent. The devices are available for astronomical applications.

Vural, Kadri↗
DOE OSTI2017

Hot-Carrier-Mediated Photon Upconversion in Metal-Decorated Quantum Wells

Manipulating the frequency of electromagnetic waves forms the core of many modern technologies, ranging from imaging and spectroscopy to radio and optical communication. The process of converting photons from higher to lower energy is easily accomplished and technologically widespread. However, upconversion, which is the process of converting lower-energy photons into higher-energy photons, is still a growing field of study with nascent applications and burgeoning interest. Here, we experimentally demonstrate a new photon upconversion technique mediated by hot carriers in plasmonic nanostructures. Hot holes and hot electrons generated via plasmon decay in illuminated metal nanoparticles are injected into an adjacent semiconductor quantum well where they radiatively recombine to emit higher-energy photons. Using GaN/InGaN quantum wells decorated with gold and silver nanoparticles, we show photon upconversion from 2.4 to 2.8 eV. The process scales linearly with illumination power and enables both geometry- and polarization-based tunability. The conversion of plasmonic losses into upconverted optical emission has the potential to impact bioimaging, on-chip wavelength conversion, and high-efficiency photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2016

A concept for non-invasive temperature measurement during injection moulding processes

Current models of the injection moulding process insufficiently consider the thermal interactions between melt, solidified material and the mould. A detailed description requires a deep understanding of the underlying processes and a precise observation of the temperature. Because todays measurement concepts do not allow a non-invasive analysis it is necessary to find new measurement techniques for temperature measurements during the manufacturing process. In this work we present the idea of a set up for a tomographic ultrasound measurement of the temperature field inside a plastics melt. The goal is to identify a concept that can be installed on a specialized mould for the injection moulding process. The challenges are discussed and the design of a prototype is shown. Special attention is given to the spatial arrangement of the sensors. Besides the design of a measurement set up a reconstruction strategy for the ultrasound signals is required. We present an approach in which an image processing algorithm can be used to calculate a temperature distribution from the ultrasound scans. We discuss a reconstruction strategy in which the ultrasound signals are converted into a spartial temperature distribution by using pvT curves that are obtained by dilatometer measurements.

42 ENGINEERING↗
NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗
DOE OSTI2015

High speed automated microtomography of nuclear emulsions and recent application

The development of high-speed automatic scanning systems was the key-factor for massive and successful emulsions application for big neutrino experiments like OPERA. The emulsion detector simplicity, the unprecedented sub-micron spatial resolution and the unique ability to provide intrinsically 3-dimensional spatial information make it a perfect device for short-living particles study, where the event topology should be precisely reconstructed in a 10-100 um scale vertex region. Recently the exceptional technological progress in image processing and automation together with intensive R&D done by Italian and Japanese microscopy groups permit to increase the scanning speed to unbelievable few years ago m{sup 2}/day scale and so greatly extend the range of the possible applications for emulsion-based detectors to other fields like: medical imaging, directional dark matter search, nuclear physics, geological and industrial applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Strain-Layer-Superlattice Light Modulator

Conceptual device combines resonant reflection and photovoltaic action to enable one light beam to impose spatial and temporal modulation on another light beam. Such spatial light modulator, with high speed and multiplicity of parallel signal channels, used in image processing or similar computation requiring high data-throughput rates. Microstructures of GaAs and InAs with multiple quantum wells and compositional superlattices grown by molecular-beam epitaxy. Enhanced electro-optical properties of arrangement of alternating layers enables writing light beam to modulate reading light beam.

Maserjian, Joseph↗
DOE OSTI2012

Characterization of imaging performance in differential phase contrast CT compared with the conventional CT: Spectrum of noise equivalent quanta NEQ(k)

Purpose: Differential phase contrast CT (DPC-CT) is emerging as a new technology to improve the contrast sensitivity of conventional attenuation-based CT. The noise equivalent quanta as a function over spatial frequency, i.e., the spectrum of noise equivalent quanta NEQ(k), is a decisive indicator of the signal and noise transfer properties of an imaging system. In this work, we derive the functional form of NEQ(k) in DPC-CT. Via system modeling, analysis, and computer simulation, we evaluate and verify the derived NEQ(k) and compare it with that of the conventional attenuation-based CT. Methods: The DPC-CT is implemented with x-ray tube and gratings. The x-ray propagation and data acquisition are modeled and simulated through Fresnel and Fourier analysis. A monochromatic x-ray source (30 keV) is assumed to exclude any system imperfection and interference caused by scatter and beam hardening, while a 360 Degree-Sign full scan is carried out in data acquisition to avoid any weighting scheme that may disrupt noise randomness. Adequate upsampling is implemented to simulate the x-ray beam's propagation through the gratings G{sub 1} and G{sub 2} with periods 8 and 4 {mu}m, respectively, while the intergrating distance is 193.6 mm (1/16 of the Talbot distance). The dimensions of the detector cell for data acquisition are 32 Multiplication-Sign 32, 64 Multiplication-Sign 64, 96 Multiplication-Sign 96, and 128 Multiplication-Sign 128 {mu}m{sup 2}, respectively, corresponding to a 40.96 Multiplication-Sign 40.96 mm{sup 2} field of view in data acquisition. An air phantom is employed to obtain the noise power spectrum NPS(k), spectrum of noise equivalent quanta NEQ(k), and detective quantum efficiency DQE(k). A cylindrical water phantom at 5.1 mm diameter and complex refraction coefficient n= 1 -{delta}+i{beta}= 1 -2.5604 Multiplication-Sign 10{sup -7}+i1.2353 Multiplication-Sign 10{sup -10} is placed in air to measure the edge transfer function, line spread function and then modulation transfer function MTF(k), of both DPC-CT and the conventional attenuation-based CT. The x-ray flux is set at 5 Multiplication-Sign 10{sup 6} photon/cm{sup 2} per projection and observes the Poisson distribution, which is consistent with that of a micro-CT for preclinical applications. Approximately 360 regions, each at 128 Multiplication-Sign 128 matrix, are used to calculate the NPS(k) via 2D Fourier transform, in which adequate zero padding is carried out to avoid aliasing in noise. Results: The preliminary data show that the DPC-CT possesses a signal transfer property [MTF(k)] comparable to that of the conventional attenuation-based CT. Meanwhile, though there exists a radical difference in their noise power spectrum NPS(k) (trait 1/|k| in DPC-CT but |k| in the conventional attenuation-based CT) the NEQ(k) and DQE(k) of DPC-CT and the conventional attenuation-based CT are in principle identical. Conclusions: Under the framework of ideal observer study, the joint signal and noise transfer property NEQ(k) and detective quantum efficiency DQE(k) of DPC-CT are essentially the same as those of the conventional attenuation-based CT. The findings reported in this paper may provide insightful guidelines on the research, development, and performance optimization of DPC-CT for extensive preclinical and clinical applications in the future.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Impact of bowtie filter and object position on the two-dimensional noise power spectrum of a clinical MDCT system

Purpose: Noise characteristics of clinical multidetector CT (MDCT) systems can be quantified by the noise power spectrum (NPS). Although the NPS of CT has been extensively studied in the past few decades, the joint impact of the bowtie filter and object position on the NPS has not been systematically investigated. This work studies the interplay of these two factors on the two dimensional (2D) local NPS of a clinical CT system that uses the filtered backprojection algorithm for image reconstruction. Methods: A generalized NPS model was developed to account for the impact of the bowtie filter and image object location in the scan field-of-view (SFOV). For a given bowtie filter, image object, and its location in the SFOV, the shape and rotational symmetries of the 2D local NPS were directly computed from the NPS model without going through the image reconstruction process. The obtained NPS was then compared with the measured NPSs from the reconstructed noise-only CT images in both numerical phantom simulation studies and experimental phantom studies using a clinical MDCT scanner. The shape and the associated symmetry of the 2D NPS were classified by borrowing the well-known atomic spectral symbols s, p, and d, which correspond to circular, dumbbell, and cloverleaf symmetries, respectively, of the wave function of electrons in an atom. Finally, simulated bar patterns were embedded into experimentally acquired noise backgrounds to demonstrate the impact of different NPS symmetries on the visual perception of the object. Results: (1) For a central region in a centered cylindrical object, an s-wave symmetry was always present in the NPS, no matter whether the bowtie filter was present or not. In contrast, for a peripheral region in a centered object, the symmetry of its NPS was highly dependent on the bowtie filter, and both p-wave symmetry and d-wave symmetry were observed in the NPS. (2) For a centered region-ofinterest (ROI) in an off-centered object, the symmetry of its NPS was found to be different from that of a peripheral ROI in the centered object, even when the physical positions of the two ROIs relative to the isocenter were the same. (3) The potential clinical impact of the highly anisotropic NPS, caused by the interplay of the bowtie filter and position of the image object, was highlighted in images of specific bar patterns oriented at different angles. The visual perception of the bar patterns was found to be strongly dependent on their orientation. Conclusions: The NPS of CT depends strongly on the bowtie filter and object position. Even if the location of the ROI with respect to the isocenter is fixed, there can be different symmetries in the NPS, which depend on the object position and the size of the bowtie filter. For an isolated off-centered object, the NPS of its CT images cannot be represented by the NPS measured from a centered object.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Optical properties of graphene-based materials in transparent polymer matrices

Different aspects of graphene-based materials (GBMs) and GBM-nanocomposites have been investigated due to their intriguing features; one of these features is their transparency. Transparency of GBMs has been of an interest to scientists and engineers mainly with regard to electronic devices. In this study, optical transmittance of structural, purpose-made nanocomposites reinforced with GBMs was analyzed to lay a foundation for optical microstructural characterization of nanocomposites in future studies. Two main types of GBM reinforcements were studied, graphene oxide (GO) and graphite nanoplates (GNPs). The nanocomposites investigated are GO/poly(vinyl alcohol), GO/sodium alginate, and GNP/epoxy with different volume fractions of GBMs. Together with UV-visible spectrophotometry, image-processing-assisted micro and macro photography were used to assess the transparency of GBMs embedded in the matrices. The micro and macro photography methods developed were proven to be an alternative way of measuring light transmittance of semi-transparent materials. It was found that there existed a linear relationship between light absorbance and a volume fraction of GBMs embedded in the same type of polymer matrices, provided that the nanocomposites of interest had the same thicknesses. This suggests that the GBM dispersion characteristics in the same type of polymer are similar and any possible change in crystal structure of polymer due to different volumetric contents of GBM does not have an effect on light transmittance of the matrices. The study also showed that the same types of GBMs could display different optical properties in different matrix materials. The results of this study will help to develop practical microstructural characterization techniques for GBM-based nanocomposites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Simulations for x-ray imaging of wave-packet dynamics

Previous work on imaging wave-packet dynamics with x-ray scattering revealed that the scattering patterns deviate substantially from the notion of instantaneous momentum density of the wave packet. Here we show that scattering patterns can provide clear insights into the electron wave-packet dynamics if the final state of the scattered electron and the scattered photon momentum are determined simultaneously. The scattering probability is shown to be proportional to the modulus square of the Fourier transform of the instantaneous electronic spatial wave function weighted by the final state of the electron. Several cases for the choice of final state of the electron are explored. First, the case where the final state can be measured up to a given principal quantum number n and orbital angular momentum l are presented. Next, the case where the final states can only be determined up to a given energy is discussed. Lastly, the case of an initial wave packet consisting of a large amount of a known stationary state and a small amount of an unknown stationary state is examined. The scattering profile is used to determine the properties of the unknown state in the wave packet.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1977

X-ray sensitive oblique imaging device

Instrument employs light reflecting surface (evaporated aluminum coating or minor substrate) behind phosphor screen to improve effective quantum efficiency in dual process: fraction of incoming X-rays are converted to photoelectrons at photocathode; and X-rays that pass through photocathode and thin X-ray transparent membrane enter phosphor screen.

Hallam, K. L.↗