Negligible Error Research Articles

Interleaved signal multiplexing readout in depth encoding Prism-PET detectors.

Given the large number of readout pixels in clinical positron emission tomography (PET) scanners, signal multiplexing is an indispensable feature to reduce scanner complexity, power consumption, heat output, andcost. In this paper, we introduce interleaved multiplexing (iMux) scheme that utilizes the characteristic light-sharing pattern of depth-encoding Prism-PET detector modules with single-ended readout. In the iMux readout, four anodes from every other silicon photomultiplier (SiPM) pixels across rows and columns, which overlap with four distinct light guides, are connected to the same application-specific integrated circuit (ASIC) channel. The 4-to-1 coupled Prism-PET detector module was used which consisted of a 16× 16 array of 1.5 × 1.5 × 20 mm3 lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals coupled to an 8 × 8 array with 3× 3mm2 SiPM pixels. A deep learning-based demultiplexing model was investigated to recover the encoded energy signals. Two different experiments were performed with non-multiplexed and multiplexed readouts to evaluate the spatial, depth of interaction (DOI), and timing resolutions of our proposed iMuxscheme. The measured flood histograms, using the decoded energy signals from our deep learning-based demultiplexing architecture, achieved perfect crystal identification of events with negligible decoding error. The average energy, DOI, and timing resolutions were 9.6 ± 1.5%, 2.9 ± 0.9mm, and 266 ± 19ps for non-multiplexed readout and 10.3 ± 1.6%, 2.8 ± 0.8mm, and 311 ± 28ps for multiplexed readout,respectively. Our proposed iMux scheme improves on the already cost-effective and high-resolution Prism-PET detector module and provides 16-to-1 crystal-to-readout multiplexing without appreciable performance degradation. Also, only four SiPM pixels are shorted together in the 8× 8 array to achieve 4-to-1 pixel-to-readout multiplexing, resulting in lower capacitance per multiplexedchannel.

Smart Iterative Analysis Tool for the Size Distribution of Spherical Nanoparticles.

The size of nanoparticles is a critical parameter with regard to their performance. Therefore, precise measurement of the size distribution is often required. While electron microscopy (EM) is a useful tool to image large numbers of particles at once, manual analysis of individual particles in EM images is a time-consuming and labor-intensive task. Therefore, reliable automatic detection methods have long been desired. This paper introduces a novel automatic particle analysis software package based on the circular Hough transform (CHT). Our software package includes novel features to enhance precise particle analysis capabilities. We applied the CHT algorithm in an iterative workflow, which ensures optimal detection over wide radius intervals, to deal with overlapping particles. In addition, smart intensity criteria were implemented to resolve common difficult cases that lead to false particle detection. Implementing these criteria enabled an effective and precise analysis by minimizing detection of false particles. Overall, our approach showed reliable particle analysis results by resolving common types of particle overlaps and deformation with only negligible errors.

A Swarming Meyer Wavelet Computing Approach to Solve the Transport System of Goods

The motive of this work is to provide the numerical performances of the reactive transport model that carries trucks with goods on roads by exploiting the stochastic procedures based on the Meyer wavelet (MW) neural network. An objective function is constructed by using the differential model and its boundary conditions. The optimization of the objective function is performed through the hybridization of the global and local search procedures, i.e., swarming and interior point algorithms. Three different cases of the model have been obtained, and the exactness of the stochastic procedure is observed by using the comparison of the obtained and Adams solutions. The negligible absolute error enhances the exactness of the proposed MW neural networks along with the hybridization of the global and local search schemes. Moreover, statistical interpretations based on different operators, histograms, and boxplots are provided to validate the constancy of the designed stochastic structure.

Methods for Imputing Scores When All Responses Are Missing for One or More Polytomous Items: Accuracy and Impact on Psychometric Property

AbstractThough a substantial amount of research exists on imputing missing scores in educational assessments, there is little research on cases where responses or scores to an item are missing for all test takers. In this paper, we tackled the problem of imputing missing scores for tests for which the responses to an item are missing for all test takers. We considered three missing‐data imputation methods—the median method, the item response theory (IRT) method, and the two‐way method—for imputing scores. We compared the performance of these three imputation methods with respect to their accuracy in estimating scaled scores and test reliability for the aforementioned problem. Real data were used in the comparison. All three methods performed well in imputing scaled scores with negligible imputation error: The IRT method and the median method provided slightly more accurate scaled scores. The two‐way method provided the most accurate reliability estimates. Recommendations for practice are provided.

Calibrated transmission and reflection from a multi-qubit microwave package.

We report calibrated microwave transmission and reflection measurements of a qubit sample holder at millikelvin temperatures. The methodology we present extends our previous work on one-port cryogenic short-open-load (SOL) calibration to a two-port SOLT measurement by implementing an unknown thru (T) standard. We report the resulting calibrated transmission and reflection at millikelvin temperatures through a printed circuit board that is installed into the sample holder. Finally, we consider a cascade of components at the end of a qubit drive line that includes (1) a cryogenic attenuator, (2) a coaxial cable, and (3) a qubit sample holder. Using experimentally determined parameters for return losses for all three components, we calculate the negligible state-preparation error in the frequency band of 5-7GHz due to control pulse distortions arising from reflection at the coaxial launches. Taken together, our results highlight the utility of calibrated cryogenic scattering parameter measurements for the validation of qubit packaging and the wiring in its immediate vicinity.

Effects of Spatial Data Acquisition on Determination of a Gravel-Bed River Geomorphology

Bed irregularities of water bodies play a significant role in many hydraulic and river engineering experiments and models. Accurate measurement of river geomorphology requires great fieldwork effort. Optimizing the dataset size of measured points will reduce the time and costs involved. In this study, the geomorphology of a gravel bed river reach was measured using different spatial acquisition methods. Digital elevation models were created for each measurement method and the volumes of under/overestimation were calculated. The results show that the sampling methods had more effect on the accuracy of the interpolated geomorphology than the density of the measured points. By choosing an optimized sampling method, the measurement efforts decreased to less than 50%, with negligible errors of around 15 m3 and 10 m3 over and underestimation, respectively, in a water body area of around 2200 m2. These findings help to provide more accurate geomorphological data with less effort as inputs for experimental and numerical models to derive better results.

Approximations for spherical scattering coefficients with negligible integral errors in parametric space.

The solution of forward or inverse light scattering problems in astrophysical, biological, and atmospheric sensing applications is typically cost prohibitive for real-time applications. For example, given a probability density for the dimensions, refractive index, and wavelength, evaluating the expected scattering involves an integral over such parameters, and the number of scattering problems solved increases dramatically. In the case of dielectric and weakly absorbing spherical particles (both homogeneous and layered), we begin by highlighting a circular law that restricts scattering coefficients to a circle in the complex plane. Later, the Fraunhofer approximation of Riccati-Bessel functions is used to reduce the scattering coefficients into simpler nested trigonometric approximations. This results in relatively small errors of oscillatory signs that cancel out without a loss of accuracy in the integrals over scattering problems. Thus, the cost of evaluating the two spherical scattering coefficients for any mode is reduced by large factors ≈50, with a larger increase in the speed of the overall computation, as the approximations can be reused for multiple modes. We analyze the errors of the proposed approximation and present numerical results for a set of forward problems as a demonstration.

Analytical solution of volatile active ingredient release and subsequent dispersion in wind tunnel

This paper proposes a new mathematical model describing the diffusion-controlled release of an active ingredient (AI) from a cylindrical product, followed by convection-dispersion transport in a wind tunnel through which air flows. The model aims to support the development of products with targeted AI release profiles. It was motivated by the particular case of a new product formulation releasing phytochemicals with insecticide activity. The well-known analytical solution for the AI transient release from the product is coupled with the analytical solution for the convection-dispersion transport along the tunnel through a simple approximation that introduces a negligible error. An overall analytical solution is thus obtained, having obvious advantages against numerical approximations, particularly for intensive computational tasks such as model fitting to experimental data, uncertainty analysis and product composition optimization.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Frequency Reversal Alamouti Code-Based FBMC With Resilience to Inter-Antenna Frequency Offsets

Transmit diversity schemes for filter bank multicarrier (FBMC) are known to be challenging. No existing schemes have considered the presence of inter-antenna frequency offset (IAFO), which will result in performance degradation. In this letter, a new transmit scheme based on the frequency reversal Alamouti code (FRAC)-based structure to address the issue of IAFO is proposed and is proven to inherently cancel the inter-antenna inter-carrier interference (ICI) while preserving spatial diversity. Moreover, the proposed FRAC structure is applicable in frequency-selective channels. Numerical results show that the proposed scheme undergoes negligible bit error rate (BER) degradation even with considerable IAFOs.

Prediction and validation of the profiles of the top and mixed loading surfaces during discharge from multiple-outlet silos

One of the problems for the efficient operation of large industrial silos is that it is difficult to check the volume of the materials that remain inside the silo at any given time. This problem is more pronounced when materials from two or more sources are stored in a multiple-outlet silo, so that the profile of the mixed loading surface, i.e., the interface between materials from different sources, is not visible. To date, no research addressing this problem has been conducted. Therefore, this study proposes a model to predict the profiles of the top and mixed loading surfaces using the simple kinematic theory and considering the effect of the angle of repose on the shape of the top surface. The accuracy of the proposed prediction model was verified by comparing actual profiles of the top and mixed loading surfaces with those predicted by the model when materials were discharged from a multiple-outlet hopper with discharge sequences varying from the left, central, and right outlets. The results showed that the proposed model adequately predicted the profiles with relatively negligible errors.

Single Miller frequency compensation: Three stage CMOS

A simple and efficient frequency compensation network is exploited for a three-stage amplifier. Single Miller capacitor at the output of a differential stage forms compensation network. This configuration benefits from less dependency to compensation capacitor since only one and small value capacitor is used. This stems from the fact that the differential block intensifies Miller effect virtually by multiplying its gain value to capacitor value. The structure is modeled symbolically with exclusive formulation for involved dynamics. Also, circuit implementation is performed via 0.18μm CMOS technology. Both theoretical and simulation results are compatible with negligible error. According to the simulation results, proposed three stage amplifiers in this work show 135 dB, 7.5 MHz and 85º as DC gain, GBW and PM respectively.

All-Optical Phase Memory Circuit Based on Two Coupled Lasers and External Optical Injection

We propose a volatile static all-optical memory capable of storing phase information of a slowly-varying electric field. The scheme and its realization (a memory circuit) are based on two mutually coupled lasers subject to external optical injection. The proposed circuit has a single optical input for write and hold operations and two opposite-sign outputs for reading the memory. The proposed circuit operates with a single wavelength of light, a single direction of propagation, and without a need to switch the state of polarization. We prove mathematically that the proposed arrangement has equilibrium points that may discreetly quantify and store the phase in a bistable manner. The circuit is studied numerically for solid-state and semiconductor lasers with zero and non-zero linewidth enhancement factors, respectively. Simulations based on a rate equation system confirm the essential findings. Using typical parameters of a semiconductor laser and optimizing for a possibly wide range of operation, the write-read operations were simulated using PRBS-9 at the rate of 1 Gb/s with negligible errors. The proposed circuit will enable integrated memory implementations for future all-optical signal processing and computing systems.

Low Complexity CPM Detection Algorithm With the g-th Power Based on the LD-RD

Based on the Laurent decomposition (LD) and the Rimoldi decomposition (RD) with the reduced-search technique, we propose a low-complexity continuous phase modulation (CPM) detection algorithm through the operation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> -th power. In this way, the denominator of the modulation index is effectively equivalent to a smaller value, achieving the usage of much fewer trellis states in the Viterbi algorithm (VA). Finally, simulation results show that, compared to the optimal detection method, the proposed algorithms significantly reduce the complexity with negligible bit error rate (BER) loss.

Prediction of Three-Dimensional Consolidation Settlement: Observational Method and Its Applicability

This paper presents a closed form of the simplified three-dimensional (3D) (vertical and radial) consolidation solution, which has negligible errors for practical cases. Based on the simplified solution, two observational methods (namely, 3D modified curve fit and linear rule methods) that could predict the actual 3D consolidation settlement behaviors by reflecting the effect of sampling range were newly developed. The appropriateness of the proposed methods was verified by using 3D and two-dimensional (2D) theoretical data. Then, the proposed methods were applied to three case records. Back-analyzed results (i.e., the end of consolidation settlement S100 and consolidation coefficient ch(n)) were slightly affected by the differently approached methods and the inputted value of vertical consolidation coefficient (cv). S100 and ch(n) values obtained from the 3D modified methods were close to those secured from the 2D modified methods due to the insignificant effect of vertical drainage for the cases. Owing to incomplete measurement (93%–98% of S100, i.e., no consideration of sampling range effect), an existing 3D method and the original 2D Asaoka and exponential methods predicted a lower S100 and higher ch(n) than those predicted by the 3D or 2D modified curve fit methods. Therefore, the proposed 3D methods can be recommended for practical applications.

Robust Assessment of Macromolecular Fraction (MMF) in Muscle with Differing Fat Fraction Using Ultrashort Echo Time (UTE) Magnetization Transfer Modeling with Measured T1.

Magnetic resonance imaging (MRI) is widely regarded as the most comprehensive imaging modality to assess skeletal muscle quality and quantity. Magnetization transfer (MT) imaging can be used to estimate the fraction of water and macromolecular proton pools, with the latter including the myofibrillar proteins and collagen, which are related to the muscle quality and its ability to generate force. MT modeling combined with ultrashort echo time (UTE-MT modeling) may improve the evaluation of the myotendinous junction and regions with fibrotic tissues in the skeletal muscles, which possess short T2 values and higher bound-water concentration. The fat present in muscle has always been a source of concern in macromolecular fraction (MMF) calculation. This study aimed to investigate the impact of fat fraction (FF) on the estimated MMF in bovine skeletal muscle phantoms embedded in pure fat. MMF was calculated for several regions of interest (ROIs) with differing FFs using UTE-MT modeling with and without T1 measurement and B1 correction. Calculated MMF using measured T1 showed a robust trend, particularly with a negligible error (<3%) for FF < 20%. Around 5% MMF reduction occurred for FF > 30%. However, MMF estimation using a constant T1 was robust only for regions with FF < 10%. The MTR and T1 values were also robust for only FF < 10%. This study highlights the potential of the UTE-MT modeling with accurate T1 measurement for robust muscle assessment while remaining insensitive to fat infiltration up to moderate levels.

Intelligent solution predictive control strategy for nonlinear hepatitis B epidemic model with delay

This study exploits the artificial neural networks (ANNs) with backpropagation of the Levenberg-Marquardt method (LMM), i.e. ANNs-LMM, to explore the dynamics of a nonlinear hepatitis B epidemic model with the effect of delay (HBEMD). A system of three nonlinear ordinary differential equations having delay describes the susceptible liver cells, infected liver cells, and density of CTLs in the epidemic model. The referenced dataset is generated using the strength of the implicit Runge–Kutta numerical method in the variation of the incidence rate of disease, CTL immunological response rate of removing the disease-infected cells, virus specific CTL cells reproduction rate due to interaction with virally infected cells, time delay, white noise densities, and morality rate of individuals, infected cells death rate and virus specific CTL cells removing rate. The generated dataset is arbitrarily utilized for training, testing, and validation operations for each recurrent update in the Levenberg-Marquardt backpropagation to determine the numerical solutions for the dynamics of HBEMD. The effectiveness and reliability of the intelligent solution predictive control strategy of ANNs-LMM are endorsed with negligible absolute errors, mean square errors and near optimal regression measures.

A Radial Basis Scale Conjugate Gradient Deep Neural Network for the Monkeypox Transmission System

The motive of this study is to provide the numerical performances of the monkeypox transmission system (MTS) by applying the novel stochastic procedure based on the radial basis scale conjugate gradient deep neural network (RB-SCGDNN). Twelve and twenty numbers of neurons were taken in the deep neural network process in first and second hidden layers. The MTS dynamics were divided into rodent and human, the human was further categorized into susceptible, infectious, exposed, clinically ill, and recovered, whereas the rodent was classified into susceptible, infected, and exposed. The construction of dataset was provided through the Adams method that was refined further by using the training, validation, and testing process with the statics of 0.15, 0.13 and 0.72. The exactness of the RB-SCGDNN is presented by using the comparison of proposed and reference results, which was further updated through the negligible absolute error and different statistical performances to solve the nonlinear MTS.

Stability analysis of chaotic systems from data

The prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal perturbations is the subject of stability analysis. In stability analysis, we linearize the equations of the dynamical system around a reference point and compute the properties of the tangent space (i.e. the Jacobian). The main goal of this paper is to propose a method that infers the Jacobian, thus, the stability properties, from observables (data). First, we propose the echo state network (ESN) with the Recycle validation as a tool to accurately infer the chaotic dynamics from data. Second, we mathematically derive the Jacobian of the echo state network, which provides the evolution of infinitesimal perturbations. Third, we analyse the stability properties of the Jacobian inferred from the ESN and compare them with the benchmark results obtained by linearizing the equations. The ESN correctly infers the nonlinear solution and its tangent space with negligible numerical errors. In detail, we compute from data only (i) the long-term statistics of the chaotic state; (ii) the covariant Lyapunov vectors; (iii) the Lyapunov spectrum; (iv) the finite-time Lyapunov exponents; (v) and the angles between the stable, neutral, and unstable splittings of the tangent space (the degree of hyperbolicity of the attractor). This work opens up new opportunities for the computation of stability properties of nonlinear systems from data, instead of equations.

A Soft Computing Scaled Conjugate Gradient Procedure for the Fractional Order Majnun and Layla Romantic Story

The current study shows the numerical performances of the fractional order mathematical model based on the Majnun and Layla (FO-MML) romantic story. The stochastic computing numerical scheme based on the scaled conjugate gradient neural networks (SCGNNs) is presented to solve the FO-MML. The purpose of providing the solutions of the fractional derivatives is to achieve more accurate and realistic performances of the FO-MML romantic story model. The mathematical model is divided into four dynamics, while the exactness is authenticated through the comparison of obtained and reference Adam results. Moreover, the negligible absolute error enhances the accuracy of the stochastic scheme. Fourteen numbers of neurons have been taken and the information statics are divided into authorization, training, and testing, which are divided into 12%, 77% and 11%, respectively. The reliability, capability, and accuracy of the stochastic SCGNNs is performed through the stochastic procedures using the regression, error histograms, correlation, and state transitions for solving the mathematical model.