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Power Spectrum Analysis (PSA) for Detecting Counterfeit Electronics.
Abstract not provided.
Power Spectrum Analysis (PSA) for Counterfeit and Aging Detection.
Abstract not provided.
Linearized spectrum correlation analysis for line emission measurements
A new spectral analysis method, Linearized Spectrum Correlation Analysis (LSCA), for charge exchange and passive ion Doppler spectroscopy is introduced to provide a means of measuring fast spectral line shape changes associated with ion-scale micro-instabilities. This analysis method is designed to resolve the fluctuations in the emission line shape from a stationary ion-scale wave. The method linearizes the fluctuations around a time-averaged line shape (e.g., Gaussian) and subdivides the spectral output channels into two sets to reduce contributions from uncorrelated fluctuations without averaging over the fast time dynamics. In principle, small fluctuations in the parameters used for a line shape model can be measured by evaluating the cross spectrum between different channel groupings to isolate a particular fluctuating quantity. High-frequency ion velocity measurements (100–200 kHz) were made by using this method. We also conducted simulations to compare LSCA with a moment analysis technique under a low photon count condition. Both experimental and synthetic measurements demonstrate the effectiveness of LSCA.
System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra
A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.
Cyclic Prefix Direct Sequence Spread Spectrum Capacity Analysis
Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a novel waveform that has the potential to solve 5G objectives such as ultra reliable low latency communication (URLLC) and massive machine Type Communication (mMTC). This paper analyzes the capacity of the CP-DSSS waveform in comparison with Orthogonal Frequency Domain Multiplexing OFDM). CP-DSSS can be optimized to achieve the same capacity as OFDM when optimized by the water-filling algorithm. A significant advantage for CP-DSSS is that this capacity can be achieved with all symbols being transmitted with the same effective rate. As a result, stronger forward error correction codes can be used in a CP-DSSS implementation compared to an OFDM implementation with resource block constraints. In addition, the applicability of CP-DSSS as a waveform for a secondary network operating in the same frequency band as the primary network is discussed.
SU-E-T-242: Monte Carlo Simulations Used to Test the Perturbation of a Reference Ion Chamber Prototype Used for Small Fields
Purpose: To study the perturbation due to the use of a novel Reference Ion Chamber designed to measure small field dosimetry (KermaX Plus C by IBA). Methods: Using the Phase-space files for TrueBeam photon beams available by Varian in IAEA-compliant format for 6 and 15 MV. Monte Carlo simulations were performed using BEAMnrc and DOSXYZnrc to investigate the perturbation introduced by a reference chamber into the PDDs and profiles measured in water tank. Field sizes ranging from 1×1, 2×2,3×3, 5×5 cm2 were simulated for both energies with and without a 0.5 mm foil of Aluminum which is equivalent to the attenuation equivalent of the reference chamber specifications in a water phantom of 30×30×30 cm3 and a pixel resolution of 2 mm. The PDDs, profiles, and gamma analysis of the simulations were performed as well as a energy spectrum analysis of the phase-space files generated during the simulation. Results: Examination of the energy spectrum analysis performed shown a very small increment of the energy spectrum at the build-up region but no difference is appreciated after dmax. The PDD, profiles and gamma analysis had shown a very good agreement among the simulations with and without the Al foil, with a gamma analysis with a criterion of 2% and 2mm resulting in 99.9% of the points passing this criterion. Conclusion: This work indicates the potential benefits of using the KermaX Plus C as reference chamber in the measurement of PDD and Profiles for small fields since the perturbation due to in the presence of the chamber the perturbation is minimal and the chamber can be considered transparent to the photon beam.
Cathodoluminescence spectrum imaging analysis of CdTe thin-film bevels
We conducted T = 6 K cathodoluminescence (CL) spectrum imaging with a nanoscale electron beam on beveled surfaces of CdTe thin films at the critical stages of standard CdTe solar cell fabrication. We find that the through-thickness CL total intensity profiles are consistent with a reduction in grain-boundary recombination due to the CdCl 2 treatment. The color-coded CL maps of the near-band-edge transitions indicate significant variations in the defect recombination activity at the micron and sub-micron scales within grains, from grain to grain, throughout the film depth, and between films with different processing histories. We estimated the grain-interior sulfur-alloying fraction in the interdiffused CdTe/CdS region of the CdCl 2 -treated films from a sample of 35 grains and found that it is not strongly correlated with CL intensity. A kinetic rate-equation model was used to simulate grain-boundary (GB) and grain-interior CL spectra. Simulations indicate that the large reduction in the exciton band intensity and relatively small decrease in the lower-energy band intensity at CdTe GBs or dislocations can be explained by an enhanced electron-hole non-radiative recombination rate at the deep GB or dislocation defects. Additionally, simulations also show that higher GB concentrations of donors and/or acceptors can increase the lower-energy band intensity, while slightly decreasing the exciton band intensity.
Atomic Layer Deposition of Nanolayered Carbon Films
In this paper, carbon thin films were grown using the plasma-enhanced atomic layer deposition (PE-ALD). Methane (CH4) was used as the carbon precursor to grow the carbon thin film. The grown film was analyzed by the high-resolution transmission electron micrograph (TEM), X-ray photoelectron spectroscopy (XPS) analysis, and Raman spectrum analysis. The analyses show that the PE-ALD-grown carbon film has an amorphous structure. It was found that the existence of defective sites (nanoscale holes or cracks) on the substrate of copper foil could facilitate the formation of nanolayered carbon films. The mechanism for the formation of nanolayered carbon film in the nanoscale holes was discussed. This finding could be used for the controlled growth of nanolayered carbon films or other two-dimensional nanomaterials while combining with modern nanopatterning techniques.
In Vitro Fabrication and Biocompatibility Assay of a Biomimetic Osteoblastic Niche
A novel HAp-CS/Gel biomimetic osteoblastic niche was fabricated by freeze-drying, and its mechanical strength and biocompatibility were characterized. HAp-CS/Gel scaffolds in various ratios of 100:5, 100:10, and 100:20 (CS/Gel to HAp) were prepared by freeze-drying prior to chemical cross-linking followed by infrared spectrum analysis, EDS, FITR, SEM, fluorescence microscopy, MTT, and ALP experiments. Results from the infrared spectrum analysis showed that HAp doping remained the surface morphology and the architecture of scaffold with interconnected pores in the size range of 135 to 150 μm. The HAp doping ratio of 100:20 was found to be optimal based on its high porosity of 90%, better water uptake folds of 19.1. In addition, EDS and FITR analyses demonstrated that HAps were uniformly distributed on the surface of a scaffold with aggregates and particles, which has sufficient roughness for cell attachment and proliferation of osteoblasts. Under SEM and fluorescent microscopy, osteoblasts seeded onto the scaffold showed evenly distributed viable cells, which is believed to form a biomimetic niche. In the present study, we further demonstrate that osteoblasts can maintain their function and grow well on the scaffold through MTT and ALP tests. Thus, the scaffold has favorable physical properties and biocompatibility to support the proliferation and differentiation of osteoblasts and further to support the constructs of biomimetic osteoblastic niche.
Improved neutron-gamma discrimination for a {sup 6}Li-glass neutron detector using digital signal analysis methods
A {sup 6}Li-glass scintillator (GS20) based neutron Anger camera was developed for time-of-flight single-crystal diffraction instruments at Spallation Neutron Source. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio (defined as NGD ratio) on the order of 10{sup 4}. The NGD ratios of Anger cameras need to be improved for broader applications including neutron reflectometers. For this purpose, six digital signal analysis methods of individual waveforms acquired from photomultiplier tubes were proposed using (i) charge integration, (ii) pulse-amplitude histograms, (iii) power spectrum analysis combined with the maximum pulse-amplitude, (iv) two event parameters (a{sub 1}, b{sub 0}) obtained from a Wiener filter, (v) an effective amplitude (m) obtained from an adaptive least-mean-square filter, and (vi) a cross-correlation coefficient between individual and reference waveforms. The NGD ratios are about 70 times those from the traditional PHA method. Our results indicate the NGD capabilities of neutron Anger cameras based on GS20 scintillators can be significantly improved with digital signal analysis methods.
A new code for spectrometric analysis for environmental radiological surveillance on monitors focused on gamma radioactivity on aerosols
pGamma is a code developed by the NERG group of the Technical University of Catalonia - Barcelona Tech for the analysis of gamma spectra generated by the Equipment for the Continuous Measurement and Identification of Gamma Radioactivity on Aerosols with Paper Filter developed for our group and Raditel Servies company. Nowadays the code is in the process of adaptation for the monitors of the Environmental Radiological Surveillance Network of the Local Government of Catalonia (Generalitat of Catalonia), Spain. The code is a Spectrum Analysis System, it identifies the gamma emitters on the spectrum, determines its Concentration of Activity, generates alarms depending on the Activity of the emitters and generates a report. The Spectrum Analysis System includes a library with emitters of interest, NORM and artificial. The code is being used on the three stations with the aerosol monitor of the Network (Asco and Vandellos, near both Nuclear Power Plants and Barcelona). (authors)
Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis
Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.
Affine Transformations to Correlate Experimental and Simulated EDS Spectra for Multi-Element Systems
Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.
Observation of small-scale anisotropy in the arrival direction distribution of TeV cosmic rays with HAWC
The High-Altitude Water Cherenkov (HAWC) Observatory is sensitive to gamma rays and charged cosmic rays at TeV energies. The detector is still under construction, but data acquisition with the partially deployed detector started in 2013. An analysis of the cosmic-ray arrival direction distribution based on 4.9 × 10{sup 10} events recorded between 2013 June and 2014 February shows anisotropy at the 10{sup –4} level on angular scales of about 10°. The HAWC cosmic-ray sky map exhibits three regions of significantly enhanced cosmic-ray flux; two of these regions were first reported by the Milagro experiment. A third region coincides with an excess recently reported by the ARGO-YBJ experiment. An angular power spectrum analysis of the sky shows that all terms up to ℓ = 15 contribute significantly to the excesses.
Decadal Prediction and Stochastic Simulation of Hydroclimate Over Monsoonal Asia
We developed further our advanced methods of time series analysis and empirical model reduction (EMR) and applied them to climatic time series relevant to hydroclimate over Monsoonal Asia. The EMR methodology was both generalized further and laid on a rigorous mathematical basis via multilayered stochastic models (MSMs). We identified easily testable conditions that imply the existence of a global random attractor for MSMs and allow for non-polynomial predictors. This existence, in turn, guarantees the numerical stability of the MSMs so obtained. We showed that, in the presence of low-frequency variability (LFV), EMR prediction can be improved further by including information from selected times in the system’s past. This prediction method, dubbed Past-Noise Forecasting (PNF), was successfully applied to the Madden-Julian Oscillation (MJO). Our time series analysis and forecasting methods, based on singular-spectrum analysis (SSA) and its enhancements, were applied to several multi-centennial proxy records provided by the Lamont team. These included the Palmer Drought Severity Index (PDSI) for 1300–2005 from the Monsoonal Asia Drought Atlas (MADA), and a 300-member ensemble of pseudo-reconstructions of Indus River discharge for 1702–2005. The latter was shown to exhibit a robust 27-yr low-frequency mode, which helped multi-decadal retroactive forecasts with no look-ahead over this 300-year interval.