SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The different kinds of nuclear track recognition algorithms are represented. Several complicated samples of use them in physical experiments are considered. The some processing methods of complicated images are described.
Purpose: Small field-of-view CdZnTe (CZT) gamma cameras are increasingly studied for breast lesion detection to complement mammography or ultrasonographic findings. However, in classical collimation configurations, they remain limited by the trade-off between spatial resolution and sensitivity. The HiSens architecture was proposed to overcome these limitations. Using an accurate 3D localization of the interactions inside the detector, this architecture leads to a gain in sensitivity without loss in spatial resolution. In this article, the relevance of the HiSens architecture for planar scintimammography is studied. Methods: A detective quantum efficiency (DQE) computation method is developed and used to optimize the dimensioning of a parallel hole collimator dedicated to scintimammography. Based on the DQE curves, the impact of the collimator-to-detector distance is studied. Two algorithms are proposed to combine data acquired with different collimator-to-detector distances. Results: It is shown that CZT detector virtual pixelization increases system sensitivity by 3.3 while preserving a standard LEHR spatial resolution. The introduction of a gap between the CZT detector and the collimator is useful to modulate the DQE curve shape. The combination of data acquired using different gaps in the image formation process leads to enhanced restoration of the frequency content of the images, resulting in image contrast and spatial resolution improvements. Conclusions: Acquisition duration or injected activity could be markedly reduced if the HiSens architecture with an appropriate collimator-detector gap were used.
The detection of a single nitrogen-vacancy center in diamond has attracted much interest, since it is expected to lead to innovative applications in various domains of quantum information, including quantum metrology, information processing and communications, as well as in various nanotechnologies, such as biological and subdiffraction limit imaging, and tests of entanglement in quantum mechanics. We propose a novel scheme of a single NV center coupled with a multi-mode superconducting microwave cavity driven by coherent fields in squeezed vacuum. We numerically investigate the spectra in-phase quadrature and out-of-phase quadrature for different driving regimes with or without detunings. It shows that the maximum squeezing can be obtained for optimal Rabi fields. Moreover, with the same parameters, the maximum squeezing is greatly increased when the detunings are nonzero compared to the resonance case. - Highlights: • The detection of a single nitrogen-vacancy center in diamond is expected to lead to innovative applications in various domains. • A single NV center coupled with a multi-mode superconducting microwave cavity in squeezed vacuum is proposed. • Numerically investigation of the spectra in-phase quadrature and out-of-phase quadrature are considered. • The maximum squeezing can be obtained for optimal Rabi fields.
We report the possibility of controlling the distribution of quantum bits within an inhomogeneously broadened line of a resonant transition in recording and transforming information in optical echo-processors. We consider the efficiency of realisation of the elementary logic XOR gate based on a two-pulse excitation of a resonant medium with phase memory. The encoded information is incorporated into the temporal shape of laser pulses in the form of amplitude modulation of an 'echelon' of present ('1') and absent ('0') pulse-codes for obtaining more efficient logic elements that reduce the noise in a quantum communication channel. (optical information processing)
Some well known mathematical models in the form of partial differential equations representing real world systems are introduced along with fundamental concepts of Image Processing. Notions such as seismic texture, seismic attributes, core data, well logging, seismic tomography and reservoirs simulation are discussed.
The IR camera is a relatively novel device for remote sensing of atmospheric thermal processes from the Planetary Boundary Layer (PBL) based on measurements of the infrared radiation. Infrared radiation is energy radiated by the motion of atoms and molecules on the surface of aerosols, when their temperature is more than absolute zero. The IR camera measures directly the intensity of radiation emitted by aerosols which is converted by an imaging sensor into an electric signal, resulting a thermal image. Every image pixel that corresponds to a specific radiance is pre-processed to identify the brightness temperature. The thermal infrared imaging radiometer used in this study, NicAir, is a precision radiometer developed by Prata et al. The device was calibrated for the temperature range of 270–320 K and using a calibration table along with image processing software, important information about variations in temperature can be extracted from acquired IR images. The PBL is the lowest layer of the troposphere where the atmosphere interacts with the ground surfaces. The importance of PBL lies in the fact that it provides a finite but varying volume in which pollutants can disperse. The aim of this paper is to analyze the PBL altitude and thickness variations over Iasi region using the IR imaging camera as well as its behavior from day to night and thermal processes occurring in PBL.
The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics.
Purpose: To present the design and procedure of an experimental method for acquiring densely sampled organ dose map for CT applications, based on optically stimulated luminescence (OSL) dosimeters “nanoDots” and standard ATOM anthropomorphic phantoms; and to provide the results of applying the method—a dose data set with good statistics for the comparison with Monte Carlo simulation result in the future.Methods: A standard ATOM phantom has densely located holes (in 3 × 3 cm or 1.5 × 1.5 cm grids), which are too small (5 mm in diameter) to host many types of dosimeters, including the nanoDots. The authors modified the conventional way in which nanoDots are used, by removing the OSL disks from the holders before inserting them inside a standard ATOM phantom for dose measurements. The authors solved three technical difficulties introduced by this modification: (1) energy dependent dose calibration for raw OSL readings; (2) influence of the brief background exposure of OSL disks to dimmed room light; (3) correct pairing between the dose readings and measurement locations. The authors acquired 100 dose measurements at various positions in the phantom, which was scanned using a clinical chest protocol with both angular and z-axis tube current modulations.Results: Dose calibration was performed according to the beam qualities inside the phantom as determined from an established Monte Carlo model of the scanner. The influence of the brief exposure to dimmed room light was evaluated and deemed negligible. Pairing between the OSL readings and measurement locations was ensured by the experimental design. The organ doses measured for a routine adult chest scan protocol ranged from 9.4 to 18.8 mGy, depending on the composition, location, and surrounding anatomy of the organs. The dose distribution across different slices of the phantom strongly depended on the z-axis mA modulation. In the same slice, doses to the soft tissues other than the spinal cord demonstrated relatively small variations, with the maximum COV around 11.4%. This might be attributed to the angular mA modulation, the placement of the dosimeters, the chest cavity of the scanned region, and the size of the phantom. Doses to the spinal cord were consistently lower than those to other soft tissues.Conclusions: The method is suited for acquiring densely sampled organ dose maps, and can be used for studying dose distributions relevant to subject size, organ location, and clinical CT protocols.
Highlights: Black-Right-Pointing-Pointer Development of a quantitative high throughput in situ expression profiling method. Black-Right-Pointing-Pointer Application to a tissue microarray of 242 AML bone marrow samples. Black-Right-Pointing-Pointer Identification of HOXA4, HOXA9, Meis1 and DNMT3A as prognostic markers in AML. -- Abstract: Measurement and validation of microarray gene signatures in routine clinical samples is problematic and a rate limiting step in translational research. In order to facilitate measurement of microarray identified gene signatures in routine clinical tissue a novel method combining quantum dot based oligonucleotide in situ hybridisation (QD-ISH) and post-hybridisation spectral image analysis was used for multiplex in-situ transcript detection in archival bone marrow trephine samples from patients with acute myeloid leukaemia (AML). Tissue-microarrays were prepared into which white cell pellets were spiked as a standard. Tissue microarrays were made using routinely processed bone marrow trephines from 242 patients with AML. QD-ISH was performed for six candidate prognostic genes using triplex QD-ISH for DNMT1, DNMT3A, DNMT3B, and for HOXA4, HOXA9, Meis1. Scrambled oligonucleotides were used to correct for background staining followed by normalisation of expression against the expression values for the white cell pellet standard. Survival analysis demonstrated that low expression of HOXA4 was associated with poorer overall survival (p = 0.009), whilst high expression of HOXA9 (p < 0.0001), Meis1 (p = 0.005) and DNMT3A (p = 0.04) were associated with early treatment failure. These results demonstrate application of a standardised, quantitative multiplex QD-ISH method for identification of prognostic markers in formalin-fixed paraffin-embedded clinical samples, facilitating measurement of gene expression signatures in routine clinical samples.
Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.
Purpose: The purpose of this work is to assess the magnitude of speed of sound (SOS) aberrations in three-dimensional ultrasound (US) imaging systems in image guided radiotherapy. The discrepancy between the fixed SOS value of 1540 m/s assumed by US systems in human soft tissues and its actual nonhomogeneous distribution in patients produces small but systematic errors of up to a few millimeters in the positions of scanned structures. Methods: A correction, provided by a previously published density-based algorithm, was applied to a set of five prostate, five liver, and five breast cancer patients. The shifts of the centroids of target structures and the change in shape were evaluated. Results: After the correction the prostate cases showed shifts up to 3.6 mm toward the US probe, which may explain largely the reported positioning discrepancies in the literature on US systems versus other imaging modalities. Liver cases showed the largest changes in volume of the organ, up to almost 9%, and shifts of the centroids up to more than 6 mm either away or toward the US probe. Breast images showed systematic small shifts of the centroids toward the US probe with a maximum magnitude of 1.3 mm. Conclusions: The applied correction in prostate and liver cancer patients shows positioning errors of several mm due to SOS aberration; the errors are smaller in breast cancer cases, but possibly becoming more important when breast tissue thickness increases.
Not Available
We investigate a framework for binary image denoising via restricted Boltzmann machines (RBMs) that introduces a denoising objective in quadratic unconstrained binary optimization (QUBO) form and is well-suited for quantum annealing. The denoising objective is attained by balancing the distribution learned by a trained RBM with a penalty term for derivations from the noisy image. We derive the statistically optimal choice of the penalty parameter assuming the target distribution has been well-approximated, and further suggest an empirically supported modification to make the method robust to that idealistic assumption. We also show under additional assumptions that the denoised images attained by our method are, in expectation, strictly closer to the noise-free images than the noisy images are. While we frame the model as an image denoising model, it can be applied to any binary data. As the QUBO formulation is well-suited for implementation on quantum annealers, we test the model on a D-Wave Advantage machine, and also test on data too large for current quantum annealers by approximating QUBO solutions through classical heuristics.
Purpose: Current clinical image quality assessment techniques mainly analyze image quality for the imaging system in terms of factors such as the capture system modulation transfer function, noise power spectrum, detective quantum efficiency, and the exposure technique. While these elements form the basic underlying components of image quality, when assessing a clinical image, radiologists seldom refer to these factors, but rather examine several specific regions of the displayed patient images, further impacted by a particular image processing method applied, to see whether the image is suitable for diagnosis. In this paper, the authors developed a novel strategy to simulate radiologists' perceptual evaluation process on actual clinical chest images. Methods: Ten regional based perceptual attributes of chest radiographs were determined through an observer study. Those included lung grey level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Each attribute was characterized in terms of a physical quantity measured from the image algorithmically using an automated process. A pilot observer study was performed on 333 digital chest radiographs, which included 179 PA images with 10:1 ratio grids (set 1) and 154 AP images without grids (set 2), to ascertain the correlation between image perceptual attributes and physical quantitative measurements. To determine the acceptable range of each perceptual attribute, a preliminary quality consistency range was defined based on the preferred 80% of images in set 1. Mean value difference ({mu}{sub 1}-{mu}{sub 2}) and variance ratio ({sigma}{sub 1}{sup 2}/{sigma}{sub 2}{sup 2}) were investigated to further quantify the differences between the selected two image sets. Results: The pilot observer study demonstrated that our regional based physical quantity metrics of chest radiographs correlated very well with their corresponding perceptual attributes. The distribution comparisons, mean value difference estimations, and variance ratio estimations of each physical quantity between sets of images from two different techniques matched our expectation that the image quality of set 1 should be better than that of set 2. Conclusions: The measured physical quantities provide a robust reflection of perceptual image quality in clinical images. The methodology can be readily applied for automated evaluation of perceptual image quality in clinical chest radiographs.
An improved type of starting materials for the fabrication of silicon-based imaging integrated circuits that include back-illuminated photodetectors has been conceived, and a process for making these starting materials is undergoing development. These materials are intended to enable reductions in dark currents and increases in quantum efficiencies, relative to those of comparable imagers made from prior silicon-on-insulator (SOI) starting materials. Some background information is prerequisite to a meaningful description of the improved starting materials and process. A prior SOI starting material, depicted in the upper part the figure, includes: a) A device layer on the front side, typically between 2 and 20 m thick, made of p-doped silicon (that is, silicon lightly doped with an electron acceptor, which is typically boron); b) A buried oxide (BOX) layer (that is, a buried layer of oxidized silicon) between 0.2 and 0.5 m thick; and c) A silicon handle layer (also known as a handle wafer) on the back side, between about 600 and 650 m thick. After fabrication of the imager circuitry in and on the device layer, the handle wafer is etched away, the BOX layer acting as an etch stop. In subsequent operation of the imager, light enters from the back, through the BOX layer. The advantages of back illumination over front illumination have been discussed in prior NASA Tech Briefs articles.
Extensive studies have been performed on MQW junctions and structures because of their potential applications as avalanche photodetectors in optical communications and imaging systems. The role of the avalanche photodiode is to provide for the conversion of an optical signal into charge. Knowledge of junction physics, and the various carrier generation/recombination mechanisms, is crucial for effectively optimizing the conversion process and increasing the structure's quantum efficiency. In addition, the recent interest in the use of APDs in imaging systems has necessitated the development of semiconductor junctions with low dark currents and high gains for low light applications. Because of the high frame rate and high pixel density requirements in new imaging applications, it is necessary to provide some front-end gain in the imager to allow operation under reasonable light conditions. Understanding the electron/hole impact ionization process, as well as diffusion and surface leakage effects, is needed to help maintain low dark currents and high gains for such applications. In addition, the APD must be capable of operating with low power, and low noise. Knowledge of the effects of various doping configurations and electric field profiles, as well as the excess noise resulting from the avalanche process, are needed to help maintain low operating bias and minimize the noise output.
Interferometric method, which utilizes the interference of coherent light beams, is used to determine the temperature distribution in the vicinity of a vertical heater plate. The optical components are arranged so as to obtain wedge fringe and infinite fringe patterns and isotherms obtained in each case were compared. In wedge fringe setting, image processing techniques has been used for obtaining isotherms by digital subtraction of initial parallel fringe pattern from deformed fringe pattern. The experimental results obtained are compared with theoretical correlations. The merits and demerits of the fringe analysis techniques are discussed on the basis of the experimental results.
The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024