Parameter Estimation Performance Research Articles

Computational Polarization Imaging In Vivo through Surgical Smoke Using Refined Polarization Difference.

In surgery, the surgical smoke generated during tissue dissection and hemostasis can degrade the image quality, affecting tissue visibility and interfering with the further image processing. Developing reliable and interpretable computational imaging methods for restoring smoke-affected surgical images is crucial, as typical image restoration methods relying on color-texture information are insufficient. Here a computational polarization imaging method through surgical smoke is demonstrated, including a refined polarization difference estimation based on the discrete electric field direction, and a corresponding prior-based estimation method, for better parameter estimation and image restoration performance. Results and analyses for ex vivo, the first in vivo animal experiments, and human oral cavity tests show that the proposed method achieves visibility restoration and color recovery of higher quality, and exhibits good generalization across diverse imaging scenarios with interpretability. The method is expected to enhance the precision, safety, and efficiency of advanced image-guided and robotic surgery.

Tikhonov regularization as a nonparametric method for uncertainty quantification in aggregate data problems

Dynamical systems are widely used in mathematical models in engineering and in the applied sciences. However, the parameters of these systems are usually unknown and they must be estimated from measured data. Performing parameter estimation and quantifying the uncertainty in these estimates becomes critical to realize the predictive power of dynamical systems. In some applications, only measurements about the states of an ensemble of systems are available, where each one evolves according to the same model but for different parameter values, leading to aggregate data problems. In this case, an approach proposed in the literature is to estimate a density that is consistent with some estimates of the expected value of some quantities of interest. To solve the problem in practice, the density is discretized and, to solve the resulting ill-posed problem, Tikhonov regularization is used. In this work, we propose a Bayesian model that shows this approach can be interpreted as a maximum a posteriori estimate for the density. We show that the infinite-dimensional problem defining the MAP can be reformulated as a finite-dimensional problem that does not require discretizing the density. In several cases of interest, this problem is convex, unconstrained, and the objective function is smooth. Thus, it can be solved using algorithms with optimal convergence rates. The trade-off is that the objective is defined by an integral. However, our results characterize the regularity of the integrand, allowing the use of tailored numerical schemes to approximate it. Furthermore, our theoretical results characterize the form of the optimal density, whereas our numerical results illustrate the performance of our method and confirm our theoretical findings.

Experimental Research on Shipborne SINS Rapid Mooring Alignment with Variance-Constraint Kalman Filter and GNSS Position Updates.

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states' variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment.

Markov modeling on dynamic state space for genetic disorders and infectious diseases with mutations: Probabilistic framework, parameter estimation, and applications

Abstract The emergence and dynamic prevalence of genetic disorders and infectious diseases with mutations pose significant challenges for public health interventions. This study investigated the parameter estimation approach and the application of the dynamic state-space Markov modeling of these conditions. Using extensive simulations, the model demonstrated robust parameter estimation performance, with biases and mean-squared errors decreasing as sample size increased. Applying the model to COVID-19 data revealed distinct temporal patterns for each variant, highlighting their unique emergence, peak dominance, and decline or persistence trajectories. Despite the absence of clear trends in the data, the model exhibited a remarkable accuracy in predicting future prevalence trends for most variants, showcasing its potential for real-time monitoring and analysis. While some discrepancies were observed for specific variants, these findings suggest the model’s promise as a valuable tool for informing public health strategies. Further validation with larger datasets and exploration of incorporating additional factors hold the potential for enhancing the model’s generalizability and applicability to other evolving diseases.

A nonparametric Bayesian item response modelling approach for clustering items and individuals simultaneously

Item response theory (IRT) is a popular modelling paradigm for measuring subject latent traits and item properties according to discrete responses in tests or questionnaires. There are very limited discussions on heterogeneity pattern detection for both items and individuals. In this paper, we introduce a nonparametric Bayesian approach for clustering items and individuals simultaneously under the Rasch model. Specifically, our proposed method is based on the mixture of finite mixtures (MFM) model, which obtains the number of clusters and the clustering configurations for both items and individuals simultaneously. The performance of parameter estimation and parameter clustering under the MFM Rasch model is evaluated by simulation studies, and two real examples are applied to further illustrate the MFM Rasch modelling.

AffineMortality: An R package for estimation, analysis, and projection of affine mortality models

Abstract This paper presents the AffineMortality R package which performs parameter estimation, goodness-of-fit analysis, simulation, and projection of future mortality rates for a set of affine mortality models for use in pricing and reserving. The computational routines build on the univariate Kalman Filtering approach of Koopman and Durbin ((2000). Journal of Time Series Analysis,21(3), 281–296.) along other numerical methods to enhance the robustness of the results. This paper provides a discussion of how the package works in order to effectively estimate and project survival curves, and describes the available functions. Illustration of the package for mortality analysis of the US male data set is provided.

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

Simulating the epoch of helium reionization in photon-conserving semi-numerical code SCRIPT

The reionization of the second electron of helium (HeII) leaves important imprints on the thermal and ionization state of the intergalactic medium (IGM). Observational evidence suggests that HeII reionization ended at z ≃ 3 due to ionizing photons emitted predominantly by quasars. We present efficient semi-numerical simulations of helium reionization in a 230 h-1 Mpc box, that takes into account the spatial patchiness of reionization coupled with photoheating of the IGM. Dark matter haloes are assigned quasars using empirical measurements of the quasar luminosity function, assuming a universal quasar lifetime consistent with duty cycle values inferred from measurements of the quasar clustering. The ionizing photon field from quasars is then included in the semi-numerical Code for ReionIzation with PhoTon conservation (SCRIPT), which was originally developed for modeling hydrogen reionization. In this work, we make appropriate modifications to SCRIPT for modeling inhomogenous HeII reionization and the corresponding thermal history of the IGM is modelled via a subgrid prescription. Our model has three main free parameters i.e. the global clumping factor 𝒞HeIII, the temperature increase due to photoheating T re He and the quasar spectral energy distribution (SED) index, α UV. Our fiducial model with 𝒞HeIII = 15.6 and T re He ∼ 6000 K gives reasonable values for the empirical measurements of the temperature density equation of state at these redshifts, assuming that quasars brighter than M1450 < -21 and having α UV = 1.7 contribute to HeII reionization. The efficiency of our code shows promising prospects for performing parameter estimation in future, for models of HeII reionization using observations of the Lyα forest.

Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model

We propose an ASHARV-MIDAS model that incorporates the asymmetric and long-memory characteristics of financial asset returns, while integrating current return information into the volatility equation to enhance prediction accuracy. Additionally, we derive the lag order expression and conditional variance of short-term volatility in the novel model to analyze its distinction from the classical GARCH-MIDAS model that does not consider current return information. Empirical and robustness tests demonstrate superior in-sample parameter estimation performance and more precise out-of-sample volatility prediction capabilities of our proposed model.

Gesture generation by the robotic hand for aiding speech and hard of hearing persons based on indian sign language

Speech and hearing impairments are among the most common problems in Indian societies. It can affect anyone, whether children, adults, or more. Many different treatments can help to overcome hearing problems. Different types of hearing aids and cochlear implants help amplify sounds for better hearing. The type of language known as sign language is very scientific and has its grammar and syntax. Still, due to a need for more awareness among hard-of-hearing persons, they need to be made familiar with the institutions where they can learn and equip themselves for communication. This paper describes an approach to aid speech and hard-of-hearing persons so that they are free to communicate with persons who do not have speech and hearing disabilities based on the Indian Sign Language System. To find an appropriate solution, there is a need to develop a system that can act as an interpreter for speech and hard-of-hearing persons. The interpreter system is designed with the help of the Robotic hand model and is programmed using Raspberry Pi 4. Based on the experimental results, it can be observed that the robotic hands generated different signs of the alphabet corresponding to the speech commands uttered by an individual. Several experimental trials were conducted by ten persons who do not have any hearing disabilities. The results of the five experimental trials are shown in this paper. The estimation of performance parameters and statistical analysis are also carried out to analyze better and interpret the experimental results. Based on the experimental results, the proposed robotic hand interpreter system model accurately generates gestures corresponding to different alphabets used in the Indian Sign Language system, yielding an overall accuracy of 94 percent.

Leveraging deep learning with progressive growing GAN and ensemble smoother with multiple data assimilation for inverse modeling

The incorporation of hard data in geostatistical modeling is crucial for enhancing the accuracy of interpolating or stochastically estimating subsurface spatial features. The hard data at specified points in the model domain serve as a guide in optimizing the unknown parameters to follow the patterns of the hard data. Recently, a novel approach to solving hydrogeologic/reservoir modeling problems has emerged by using deep generative models, specifically generative adversarial networks (GANs), to generate realistic and diverse images of channelized aquifers. This subsequently can be coupled with inverse models to solve parameter estimation problems. This study focused on using an improved GAN, called a progressive growing generative adversarial network (PGGAN), conditioned with hard data to perform parameter estimation of complex facies models by coupling an ensemble smoother with multiple data assimilation (ES-MDA). First, the PGGAN was trained to an image with 128 × 128 resolution. The trained PGGAN was used to generate hydraulic conductivity fields when fed an ensemble of latent variables and hard data. The ES-MDA then was used to update the latent variable with the help of hydraulic head data obtained from the groundwater model. The approach was tested on synthetic hydraulic conductivity data. Results show that this approach was able to perform efficient estimation of an unknown facies model domain. Additionally, the proposed method was applied to a different test case of a facies model exhibiting different statistical characteristics. The results were satisfactory, demonstrating that the method is not constrained to the particular hydraulic conductivity fields introduced in the generator’s training.

Performance evaluation and parameter estimation of the advanced primary filtration (APF) process in wastewater treatment plants

Conventional technologies employed in wastewater treatment plants (WWTPs) demand significant energy consumption. Concurrently, the wastewater infrastructure is experiencing a heavy strain as a result of the increased influent loads and pollutants. Advanced primary filtration (APF) is an emerging wastewater treatment process for early removal of suspended solids from raw wastewater. In this work, the APF process comprises of a two-stage filtration system, consisting of a proprietary rotating belt filter (RBF) (collectively known as microsieve) followed by a proprietary continuous backwash upflow media filter (CBUMF). The principal focus of this study is the assessment of the APF process performance, and the prediction of the total suspended solids (TSS) effluent concentrations of the APF process, using a parameter estimation method. According to pilot scale experimental results, the percentages of TSS (average TSS removal rate of 51 %) and biological oxygen demand (BOD5) (average BOD5 removal rate of 30 %), removed by the microsieve were notably higher compared to conventional primary treatment (sedimentation). The removal efficiency of the APF process during stable operation, varied from 69 to 86 % for TSS, as compared to 50–70 % typically achieved with primary sedimentation. The removal performance of BOD5 varied from 44 to 82 %, as compared to 25 to 40 % typically achieved with primary sedimentation. The chemical oxygen demand (COD) removal rates for the APF process ranged between 62 and 90%, as compared to 25–40 % typically achieved with primary sedimentation. The early removal of TSS prior to aeration tank has the potential to significantly enhance the capacity of the WWTP and decrease the energy requirements of the aeration tank. A high degree of concordance between model predictions and experimental data was observed. This study demonstrated the potential of using the APF process for the early removal of TSS from raw municipal wastewater just upstream of the aeration tank.

Integrated Doppler Positioning in a Narrowband Satellite System: Performance Bound, Parameter Estimation, and Receiver Architecture

Integrated Doppler Positioning in a Narrowband Satellite System: Performance Bound, Parameter Estimation, and Receiver Architecture

Model-based exploration of the rationality of off-label use of cetirizine in Chinese pediatric patients: a prospective cohort study.

Aims: Cetirizine is frequently administered at an increased dosage in clinical practice and recommended by several guidelines. Nonetheless, the pharmacokinetic (PK) profile and real-world safety data remain insufficient in the Chinese pediatric population. The objective of the current study is to develop a population pharmacokinetic (PPK) model for cetirizine in Chinese pediatric patients and to investigate the rationale behind its off-label usage. Methods: A prospective cohort study was conducted, enrolling children who had been diagnosed with allergic diseases and prescribed cetirizine. The outcomes were safety and pharmacokinetic (PK) parameters. Cetirizine concentrations were measured using a pre-established analytical method. Subsequently, a PK model was developed, followed by model evaluation and simulation. The developed PK model was employed to investigate the drug exposure differences across various age groups and to simulate scenarios of potential overdose. Results: Sixty-three children were enrolled, and 24 of them received a cetirizine dose exceeding the recommended dosage. A PPK model, based on published literature, served as the basis of our analysis, with adjustment made to estimate certain parameters. The final model evaluation and validation indicated accurate predictive performance and robust parameter estimation. Simulations conducted for the label-dose among age 1-12 indicated median maximum concentration at steady state (Cmax,ss) of 7year old children could be the highest. The model was also used to predict the off-label dose scenarios and overdose patient to support the clinical decision. There were no adverse drug reactions in either group. Conclusion: This study provides evidence-based and model-based exploration for optimizing cetirizine usage in Chinese pediatric patients. The cetirizine PPK model showed accurate predictive performance and could be utilized to simulate individual patient exposure in real-world clinical scenarios.

Bilby in space: Bayesian inference for transient gravitational-wave signals observed with LISA

ABSTRACT The Laser Interferometer Space Antenna (LISA) is scheduled to launch in the mid-2030s, and is expected to observe gravitational-wave candidates from massive black hole binary mergers, extreme mass ratio inspirals, and more. Accurately inferring the source properties from the observed gravitational-wave signals is crucial to maximize the scientific return of the LISA mission. bilby, the user-friendly Bayesian inference library, is regularly used for performing gravitational-wave inference on data from existing ground-based gravitational-wave detectors. Given that Bayesian inference with LISA includes additional subtitles and complexities beyond its ground-based counterpart, in this work we introduce bilby_lisa , a python package that extends bilby to perform parameter estimation with LISA. We show that full nested sampling can be performed to accurately infer the properties of LISA sources from transient gravitational-wave signals in (a) zero noise and (b) idealized instrumental noise. By focusing on massive black hole binary mergers, we demonstrate that higher order multipole waveform models can be used to analyse a year’s worth of simulated LISA data, and discuss the computational cost and performance of full nested sampling compared with techniques for optimizing likelihood calculations, such as the heterodyned likelihood.

An analytical study on the effect of different photovoltaic technologies on enviro-economic parameter and energy metrics of active solar desalting unit

The enviro-economic and energy metrics analyses of the solar system by incorporating different photovoltaic technologies are required to fulfill the sustainable development goal of the United Nations. However, the effect of different photovoltaic technologies on the solar distillation system has not been informed by any of the researchers to date. This research gap has been explored in this study. The present manuscript is an endeavour to explore the best photovoltaic technology aimed at a single-slope solar desalting unit comprised of N identical photovoltaic thermal flat-plate-collectors. Data regarding all four weather conditions for a year for New Delhi's climate have been procured from the Indian Meteorological Department situated in the western part of India. Fundamental equations along with input data have been provided to a computational code developed in MATLAB for the estimation of various performance parameters. Concludingly, the energy payback time is minimum (1.64 years) for the case of copper indium gallium selenide photovoltaic technology, however, maximum values of life cycle conversion efficiency and net CO2 mitigation are respectively 0.171 and 199.10 tons of CO2 for the case of crystalline silicon photovoltaic technology. The carbon credit is also found to be a maximum (15269.25 US$) for crystalline silicon photovoltaic technology.

Accurate parameter estimation of Au/GaN/GaAs schottky diode model using grey wolf optimization

The Au/GaN/GaAs Schottky diode is a fundamental electronic component with versatile applications. In this study we delve into the parameter estimation of Au/GaN/GaAs Schottky diodes using the Grey Wolf Optimizer (GWO) algorithm. Our research encompasses experimental procedures, mathematical modeling, and optimization techniques to extract critical electrical parameters, including the ideality factor (n), Schottky barrier height (φbn), and series resistance (RS). The primary aim is to enhance our comprehension of the behavior of Au/GaN/GaAs Schottky diodes and showcase the effectiveness of GWO in achieving precise parameter estimates. These diodes, featuring metal-semiconductor junctions, play pivotal roles in electronics, necessitating accurate parameter determination for optimized functionality. The effectiveness of the GWO algorithm was examined through a comparative analysis, employing analytical techniques pioneered by Cheung and Cheung. This study sought to assess the algorithm’s performance and accuracy in parameter estimation for Au/GaN/GaAs Schottky diodes, providing valuable insights into its practical applicability in electronic device characterization and optimization. Keywords: Schottky diodes, Grey Wolf Optimization, Electrical measurement. Parameter estimation, Gan, GaAs

Imputation of plasma lipid species to facilitate integration of lipidomic datasets

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

New and robust gravitational-waveform model for high-mass-ratio binary neutron star systems with dynamical tidal effects

For the analysis of gravitational-wave signals, fast and accurate gravitational-waveform models are required. These enable us to obtain information on the system properties from compact binary mergers. In this article, we introduce the model, which contains a closed-form expression that describes tidal effects, focusing on the description of binary neutron star systems. The model improves upon previous versions by employing a larger set of numerical-relativity data for its calibration, by including high-mass ratio systems covering also a wider range of equations of state. It also takes into account dynamical tidal effects and the known post-Newtonian mass-ratio dependence of individual calibration parameters. We implemented the model in the publicly available uite software library by augmenting different binary black hole waveform models (, , and ). We test the validity of by comparing it with numerical-relativity waveforms, as well as other tidal models. Finally, we perform parameter estimation for GW170817 and GW190425 with the new tidal approximant and find overall consistent results with respect to previous studies. Published by the American Physical Society 2024

Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.