Unknown Spectrum Research Articles

An Analytical Method for High-Frequency Electromagnetic Scattering of Arbitrary Undulating Rough Surfaces

This article presents a new approach to analytically compute high-frequency (HF) electromagnetic (EM) scattering from an arbitrary undulating rough surface according to the statistical characteristics of its geometric fluctuation. The integral expression of the scattering coefficient is derived from Kirchhoff approximation (KA) and extended to arbitrary spectra. Two methods are presented to calculate the integral: 1) the discrete Fourier transforming method based on the autocorrelation function (ACF) (KAC) and 2) the convolution method based on the power spectrum density function (KAW). For some analytically expressed spectra, the scattering coefficients can be calculated directly by the KAW and KAC methods according to its power spectrum density (PSD). The analytical expressions of classical KA for Gaussian and exponential spectrum are used to validate the proposed methods. For the case of unknown undulation height spectra of rough surfaces, the ACF is statistically calculated first, and then the KAC and KAW methods are used to calculate the average scattering coefficients numerically. Moreover, the numerical results show that the KAW method takes up more memory to retain enough energy of the ACF for accurate scattering coefficients. The two new methods are effective in the range of $\theta _{i} $ from 0° to 45° and have broad application prospects for real scenarios.

Chronic wasting disease (CWD) prion strains evolve via adaptive diversification of conformers in hosts expressing prion protein polymorphisms

Chronic wasting disease (CWD) is caused by an unknown spectrum of prions and has become enzootic in populations of cervid species that express cellular prion protein (PrPC) molecules varying in amino acid composition. These PrPC polymorphisms can affect prion transmission, disease progression, neuropathology, and emergence of new prion strains, but the mechanistic steps in prion evolution are not understood. Here, using conformation-dependent immunoassay, conformation stability assay, and protein-misfolding cyclic amplification, we monitored the conformational and phenotypic characteristics of CWD prions passaged through deer and transgenic mice expressing different cervid PrPC polymorphisms. We observed that transmission through hosts with distinct PrPC sequences diversifies the PrPCWD conformations and causes a shift toward oligomers with defined structural organization, replication rate, and host range. When passaged in host environments that restrict prion replication, distinct co-existing PrPCWD conformers underwent competitive selection, stabilizing a new prion strain. Nonadaptive conformers exhibited unstable replication and accumulated only to low levels. These results suggest a continuously evolving diversity of CWD conformers and imply a critical interplay between CWD prion plasticity and PrPC polymorphisms during prion strain evolution.

Characterizing metabolic stress-induced phenotypes of Synechocystis PCC6803 with Raman spectroscopy.

BackgroundDuring their long evolution, Synechocystis sp. PCC6803 developed a remarkable capacity to acclimate to diverse environmental conditions. In this study, Raman spectroscopy and Raman chemometrics tools (RametrixTM) were employed to investigate the phenotypic changes in response to external stressors and correlate specific Raman bands with their corresponding biomolecules determined with widely used analytical methods.MethodsSynechocystis cells were grown in the presence of (i) acetate (7.5–30 mM), (ii) NaCl (50–150 mM) and (iii) limiting levels of MgSO4 (0–62.5 mM) in BG-11 media. Principal component analysis (PCA) and discriminant analysis of PCs (DAPC) were performed with the RametrixTM LITE Toolbox for MATLABⓇ. Next, validation of these models was realized via RametrixTM PRO Toolbox where prediction of accuracy, sensitivity, and specificity for an unknown Raman spectrum was calculated. These analyses were coupled with statistical tests (ANOVA and pairwise comparison) to determine statistically significant changes in the phenotypic responses. Finally, amino acid and fatty acid levels were measured with well-established analytical methods. The obtained data were correlated with previously established Raman bands assigned to these biomolecules.ResultsDistinguishable clusters representative of phenotypic responses were observed based on the external stimuli (i.e., acetate, NaCl, MgSO4, and controls grown on BG-11 medium) or its concentration when analyzing separately. For all these cases, RametrixTM PRO was able to predict efficiently the corresponding concentration in the culture media for an unknown Raman spectra with accuracy, sensitivity and specificity exceeding random chance. Finally, correlations (R > 0.7) were observed for all amino acids and fatty acids between well-established analytical methods and Raman bands.

Surface-Enhanced Hyper Raman Spectra of Aromatic Thiols on Gold and Silver Nanoparticles.

We report the two-photon excited nonresonant surface-enhanced hyper Raman scattering (SEHRS) spectra of six aromatic thiol molecules during their interaction with gold and silver nanostructures. SEHRS spectra were obtained from thiophenol, benzyl mercaptan, and phenylethyl mercaptan and from the three isomers 2-aminothiophenol (2-ATP), 3-aminothiophenol (3-ATP), and 4-aminothiophenol (4-ATP). All SEHRS spectra were excited off-resonance at a wavelength of 1064 nm and compared to surface-enhanced Raman scattering (SERS) spectra excited at 785 nm or at 633 nm. The SEHRS spectra show a different interaction of thiophenol, benzyl mercaptan, and phenylethyl mercaptan with silver and gold nanostructures. Density functional theory calculations were used to support band assignments, in particular, for the unknown SERS spectrum of 3-ATP, and identify a band of phenylethyl mercaptan as a vibrational mode unique to the SEHRS spectrum and very weak in the Raman and infrared spectra. 2-ATP, 3-ATP, and 4-ATP show a different interaction with gold nanostructures that was found to depend on pH. Bands in the SEHRS spectrum of 2-ATP could be assigned to 2,2′-dimercaptoazobenzene, suggested to be obtained in a plasmon-assisted reaction that occurred during the SEHRS experiment. The results provide the basis for a better characterization of organic thiols at surfaces in a variety of fields, including surface functionalization and plasmonic catalysis.

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.

The Rametrix™ PRO Toolbox v1.0 for MATLAB®.

BackgroundExisting tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix™ LITE Toolbox v1.0 for MATLAB® is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix™ PRO Toolbox v1.0 is introduced to provide this capability.MethodsThe Rametrix™ PRO Toolbox v1.0 was constructed for MATLAB® and works with the Rametrix™ LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix™PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix™ PRO was used to validate Rametrix™ LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD.ResultsThe number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix™ PRO in evaluating chemometric models constructed with Rametrix™ LITE.

Plant species determination by coding leaf reflectance spectrum and its derivatives

ABSTRACT The identification of plant species using spectrometry is utilized in various applications. Some researchers have reported the success of using the reflectance spectrum along with its derivatives and their coding to identify different Phenomena, including plants. In this study, based on these parameters, we developed a new method to improve the accuracy of identifying plant species. Two experimental datasets (LOPEX and ANGERS) containing the leaf reflectance spectrum of different plant species from different families were used. The proposed method includes two main stages. In the first stage, the unique regions of wavelengths were found for each plant family. By comparing an unknown species with each family only in the specified regions and coding results with the innovative coding method named Coding Based on Selected Threshold (CBSeT) and used similarity criteria, the family of unknown specie is determined. In the second step, similarly unique regions were found to separate the species within a family from each other using species standard deviation analysis, and the type of unknown species was similarly identified. The obtained results for the identification of plant species of unknown spectra show the accuracies of 91.66% and 90% for the proposed method with Similarity Coding Index (SCI) and Spectral Angle Mapper (SAM) criteria respectively. These numbers are more acceptable results compared to the other reflectance coding methods such as Spectral analysis manager (SPAM), Spectral feature-based binary coding (SFBC), Spectral derivative feature coding (SDFC), Spectral feature probabilistic coding (SFPC) with 38%, 37%, 29%, and 20% of accuracies improvement respectively.

Extremely Fast and Accurate Open Modification Spectral Library Searching of High-Resolution Mass Spectra Using Feature Hashing and Graphics Processing Units.

Open modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides.We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. Based on these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome.ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo.

DESIGNING AND CONSTRUCTION OF THE SPECTRAL LIBRARY FOR TYPICAL PIGMENTS USED IN CHINESE PAINTINGS

Abstract. Suffered from the environmental changes and human’s influence, Chinese paintings are facing a worrying situation in documentation, which leads urgent into establishing digital archives to realize the permanent preservation of its surface information. Non-destructive identification of pigment types using hyperspectral techniques is the key to ensure the colour restoration scientifically. At present, one commonly used method for spectral identification is to calculate the similarities between unknown spectra and standard spectra in spectral library. The purpose of this study is to establish a spectral library of typical pigments specifically for the information preservation and pigment types identification on surface of Chinese paintings. The main works of the research are: (1) collecting the standard spectra in laboratory and the pigment spectra on surface of real cultural relics as the data source of the spectral library; (2) adding the expert knowledge of traditional pigments and normative descriptions of spectra measurement as part of the spectral library; and (3) constructing the spectral library website with the Browser/Server structure, and by using MySQL database for storage, SSM framework for background building and JSP page for displaying. The significance of the design and construction of the Typical Pigments of Chinese Paintings (TPCP) spectral library is not only to establish a standard spectral library for pigment types identification, but also a digital archive of pigment information on the surface of real cultural relics, to achieve permanent preservation, and provide spectral data sources and pigment information references for conservation workers and researchers in related fields for further research.

Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components

This research was initiated as part of the fight against public health problems of rising counterfeit, substandard and poor quality medicines and herbal products. An effective screening strategy using a two-step combination approach of an incremental near infrared spectral database (step 1) followed by principal component analysis (step 2) was developed to overcome the limitations of current procedures for the identification of medicines by near infrared spectroscopy which rely on the direct comparison of the unknown spectra to spectra of reference samples or products. The near infrared spectral database consisted of almost 4000 spectra from different types of medicines acquired and stored in the database throughout the study. The spectra of the test samples (pharmaceutical and herbal formulations) were initially compared to the reference spectra of common medicines from the database using a correlation algorithm. Complementary similarity assessment of the spectra was conducted based on the observation of the principal component analysis score plot. The validation of the approach was achieved by the analysis of known counterfeit Viagra samples, as the spectra did not fully match with the spectra of samples from reliable sources and did not cluster together in the principal component analysis score plot. Pre-screening analysis of an herbal formulation (Pronoton) showed similarity with a product containing sildenafil citrate in the database. This finding supported by principal component analysis has indicated that the product was adulterated. The identification of a sildenafil analogue, hydroxythiohomosildenafil, was achieved by mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses. This approach proved to be a suitable technique for quick, simple and cost-effective pre-screening of products for guiding the analysis of pharmaceutical and herbal formulations in the quest for the identification of potential adulterants.

Scanning Spectrometer-on-a-Chip Using Thermo-Optical Spike-Filters or Vernier-Comb Filters

This theoretical modeling and simulation paper presents designs and projected performance of an on-chip scanning spectrometer using thermo-optical (TO) spike- or Vernier-comb filters operating around 3600 nm for the germanium-on-nitride technological platform. Raman spectra and absorption spectra are targeted. The spike filter consists of a thermo-optically tunable waveguide Bragg grating resonator (WBGR). The length of the WBGR is chosen to realize a Lorentzian resonant spectral profile with a bandwidth changing between 0.1 and 1 nm. The Vernier-comb filter is obtained using the cascade of two multi-Sagnac loop Mach–Zehnder interferometers, in which through the spectrum of the second MZI is shifted along the wavelength axis by means of a low-power TO heater stripe atop each loop-connector. The sagnac loop reflectors length is designed according to the wavelength-channel spacing requirements in order to induce the Type-1 Vernier effect. Time-scanned discrete sampling of the unknown input spectrum is obtained by driving the TO heater stripe with a periodic ramp current. We examined the device performance in terms of signal reconstruction in the mid-infrared region. The investigation demonstrated that high quality spectrum reconstruction is obtained for arbitrary input signals with an observation wavelength range from 23.4 to 77.4 nm. We also show that spectrum reconstruction of arbitrary signals is feasible in the germanium on nitride chips even in presence of additive noise.

Passive broadband source localization based on a Riemannian distance with a short vertical array in the deep ocean.

A passive broadband source localization approach in the deep ocean is proposed based on the coherent matched-field processing combined with a Riemannian distance. The spatial coherence between hydrophones over frequency bandwidth is utilized for eliminating the unknown source spectrum. Three Riemannian distances are introduced for the measurement of the similarity between the data and replica matrices. The experimental results indicate that the proposed processors with a short array can effectively suppress the sidelobe and improve the localization performance. The source localization influenced by the factors such as the aperture, number of hydrophones, and desired bandwidth is also demonstrated.

Airborne hyperspectral data for mineral mapping in Southeastern Rajasthan, India

Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms.

Radiations from line source buried beneath a non-integer dimensional planar dielectric slab

Radiation due to an electric line source placed under a planar dielectric slab of non-integer dimensional space is determined. It has been assumed that the non-integer dimensional dielectric slab is sandwiched between two integer dimensional dielectric mediums. Radiated fields are written in terms of unknown spectrums of propagating waves. The unknown spectrum functions are determined using the boundary conditions. Special cases have also been discussed to verify the derived results. Radiation pattern is given for different parameters related to the geometry.

Neutron spectroscopy with soft computing: development of a computational code based on Support Vector Machine (SVM) for reconstruction of neutron energy spectrum

This paper presents a developed computational code based on Support Vector Machine (SVM) for reconstruction of energy spectrum of neutron source. To reconstruct unknown energy spectrum using known neutron pulse height distribution, the developed machine is trained by known neutron pulse height distribution of detector and corresponding energy spectrum of neutron source. Validation and testing are the next steps to verify the validity of the calculations done with the developed computational code. The calculated neutron pulse height distributions due to randomly generated energy spectrum using MCNPX-ESUT (MCNPX-Energy engineering of Sharif University of Technology) computational code are used as input data of the developed computational code for reconstruction of energy spectrum of neutron source. The considered energy spectrums of neutron source are output data. In the present study, the simulation of neutron pulse height distribution of 241Am-9Be neutron source in the NE-213 liquid organic scintillator is performed using MCNPX-ESUT computational code. In the constructed matrix form of Fredholm integral equation, the effective response matrix is usually close to singular or badly scaled and its condition number is so high. The solution of inverse problem in which the matrix has high condition number does not give accurate results. The motivation of the present study is the development of computational coded based on soft computing algorithm for reconstruction of energy spectrum of the neutron source with high accuracy. To this end, the computational code based on Support Vector Machine (SVM) is developed, where there is no need to solve the inverse problems. The unfolded energy spectra of 241Am-9Be neutron sources using the developed computational code have an excellent agreement with the reference spectrum provided by ISO. A comparison between the results of present study and results of developed computational code based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is performed.

Constraining neutrino lifetimes and magnetic moments via solar neutrinos in the large xenon detectors

The multi-ton-scale liquid xenon detectors, with an excellent energy resolution of a few keV, will be constructed to probe the dark-matter particles. In this paper, we show that precision measurements of the low-energy solar neutrinos via the elastic neutrino-electron scattering in this kind of detectors are able to improve the present limits on neutrino lifetimes and neutrino magnetic moments by about one order of magnitude. We carefully study the impact of the unknown neutrino mass spectrum on the ultimate limits in the case of non-radiative visible neutrino decays. In the case of invisible neutrino decays, the lower bounds τ1/m1 ≳ 3 × 10−2 s/eV and τ2/m2 ≳ 8× 10−3 s/eV at the 2σ level can be obtained for a total exposure of 70 ton⋅ year. Furthermore, a restrictive constraint on the effective magnetic moment of neutrinos μeff ≲ 2.6× 10−12 μB, with μB being the Bohr magneton, can be achieved. This is among the best results that will be available in the laboratory experiments in the near future.

The hMSSM approach for Higgs self-couplings revisited

We compare the decay of the heavy Higgs boson into two SM-like Higgs bosons, Hrightarrow hh, calculated in a Feynman-diagrammatic approach at the one-loop level based on the one hand on the full effective potential involving the top quark and stops in the Minimal Supersymmetric Standard Model (MSSM) accompanied by the matched Two-Higgs-Doublet Model (2HDM) as its low-energy limit and on the other hand on the hMSSM approximation. We identify missing contributions due to the top quark in the Higgs self-couplings of the hMSSM, that – when taken into account – lead to a good agreement between the hMSSM and a full MSSM calculation, at least in the limit of the Higgsino mass parameter mu being small compared to the stop spectrum. We also thoroughly analyze momentum-dependent and kinetic corrections intrinsic to the Feynman-diagrammatic approach and the matching to the effective Lagrangian, respectively, for both our calculation in the MSSM and the hMSSM and for the latter suggest to include additional corrections from the top quark, which are independent of the unknown supersymmetric spectrum.

Knowledge-Aided Target Detection for Multistatic Passive Radar

This paper studies the detection problem for multistatic passive radar. We consider scenarios where prior knowledge about the spectrum and peak-to-average ratio (PAR) of the non-cooperative illuminators of opportunity (IOs) is available. We develop several knowledge-aided (KA) detectors within the framework of the generalized likelihood ratio test (GLRT) to exploit such prior knowledge. Particularly, the knowledge about the bandwidth of the transmitted signal is employed to suppress the out-of-band noise, and the knowledge about the PAR constraint is exploited to eliminate the remaining high-power noise. The challenge of unknown spectrum condition is also addressed, where block sparse Bayesian learning (BSBL) is exploited to derive the maximum-likelihood estimates (MLEs) of the unknown, temporally correlated signal. The numerical results indicate that the proposed KA detectors offer significant performance improvements compared with the traditional detectors, which do not exploit such prior information.

Improvement of the MVC-NMF Problem Using Particle Swarm Optimization for Mineralogical Unmixing of Noisy Hyperspectral Data

The Hyperion data are broadly available for different parts of the world. Considering the spatial resolution of Hyperion (30 × 30 m2), it is rarely possible to find a pixel consisting of only one mineral. Spectral unmixing is therefore an important procedure in which dataset pixels are demixed into various constituents. Endmember determination is the key stage in spectral unmixing. The algorithms which are not depended on the existence of pure pixels in images are more efficient particularly when the spatial resolution is low (e.g., Hyperion data). On the other hand, the lower signal-to-noise ratio of Hyperion data is a disadvantage. Minimum volume-constrained nonnegative matrix factorization (MVC-NMF) is an appropriate non-pure pixel-based algorithm in low SNR conditions. Still, MVC-NMF is based on a gradient technique and is therefore problematic in the case of large amount of data. Particle swarm optimization (PSO) is a metaheuristic algorithm and computational simplicity is its main advantage. The minimum volume-constrained version of PSO (MVC-PSO) was then investigated on Western Ardabil Hyperion data and the results were compared with MVC-NMF. To validate the accuracy of the results, 20 surface samples were collected and analyzed by spectrometry and X-ray diffraction (XRD). Measured spectra by analytical spectral devices Inc. FieldSpec were used to create a native spectral library. Native spectra as well as United States Geological Survey mineral spectral library were applied for identification of unknown endmembers spectra. The XRD results were implemented for quantitative validation of abundances maps of endmembers using Average Abundance Ratio.

FPGA-Based Interface of Digital DAQ System for Double-Scattering Compton Camera.

The double-scattering Compton camera (DSCC) is a radiation imaging system that can provide both unknown source energy spectra and 3D spatial source distributions. The energies and detection locations measured in coincidence with three CdZnTe (CZT) detectors contribute to reconstructing emission energies and a spatial image based on conical surface integrals. In this study, we developed a digital data acquisition (DAQ) board to support our research into coincidence detection in the DSCC. The main components of the digital DAQ board were 12 ADCs and one field programmable gate array (FPGA). The ADCs digitized the analog 96-channel CZT signals at a sampling rate of 50MHz and transferred the serialized ADC samples and the bit and frame clocks to the FPGA. In order to correctly capture the ADC sample bits in the FPGA, we conducted individual sync calibrations for all the ADC channels to align the bit and frame clocks to the right positions of the ADC sample bits. The FPGA logic design was composed of IDELAY and IDDR components, six shift registers, and bit slip buffer resources. Using a Deskew test pattern, the delay value of the IDELAY component was determined to align the bit clock to the center of each sample bit. We determined the bit slip in the 12-bit ADC sample using an MSB test pattern by checking where the MSB value of one is located in the captured parallel data. After sync calibration, we tested the interface between the ADCs and the FPGA with a synthetic analog Gaussian signal. The 96 ADC channels yielded a mean R 2 goodness-of-fit value of 0.95 between the Gaussian curve and the captured 12-bit parallel data.