Estimation Accuracy Research Articles

Sex estimation from mandibular morphometry using discriminant analysis–CT scan based retrospective study

It is well known that skeletal characteristics vary among different populations. Sex dimorphic characteristics of the mandible have been reported from studies using either mandible bone or computer tomography (CT) scan images of the mandible to test its efficacy in estimating sex. Mandibular studies for estimating sex in any particular population group may not hold well for different populations owing to inherent geographical variations. With this background, we assessed the validity of mandible measurements from CT scan images to determine sex in the South Indian population. As a part of our study, 11 metric parameters and 1 angular parameter were measured and analyzed. The mean values of all the parameters were significantly higher for males as compared to females. Bigonial breadth indicated the best discriminatory ability of the study. Mandibular angle had the least predictive accuracy among all the discriminant functions studied. We further observed that a combination of parameters gave the best overall classification rate (92.3 %). When the same functions were tested on an independent testing data set, a combination of parameters and bigonial breadth gave the best classification rate (82.1 %) which was consistent. We concluded that the mandible has a high accuracy for sex estimation. All the parameters studied demonstrated statistically significant differences between the two sexes except the mandibular angle.

The impact of diffusion tensor imaging tractography settings on muscle fascicle architecture and diffusion parameter estimates: Tract length, completion, and curvature are most sensitive to tractography settings.

Diffusion-tensor (DT)-MRI tractography provides information about properties relevant to muscle health and function, including estimates of architectural properties such as fascicle length, pennation angle, and curvature and diffusion properties such as mean diffusivity (MD) and fractional anisotropy (FA). Tractography settings, including integration algorithms, thresholds for early tract termination, and tract smoothing approaches, impact the accuracy of the muscle property estimates. However, muscle DT-MRI tractography is performed using a variety of these settings, complicating comparisons between different studies. The effects of different tractography settings on muscle architecture estimates have not been fully explored, and optimized settings for muscle tractography have not yet been determined. We examined the influence of integration algorithm and termination check settings combined with a range of step sizes, termination criteria, and smoothing polynomial orders on tract characteristics, completion/reason for termination, and goodness of fit between fiber tracts and smoothing polynomials using 3-T DT-MR images of the lower leg muscles of seven healthy adults. We found that tract length and completion were highly sensitive to strict FA and intersegment angle thresholds (25%-69% reduction in complete fiber tracts from lowest to highest minimum FA threshold and 11%-36% reduction from highest to lowest intersegment angle threshold). Higher order polynomials (third and fourth order vs. second order) better fit the muscle fiber trajectories, but curvature estimates were highly sensitive to smoothing polynomial order (3.9-6.6 m-1 increase for second- vs. fourth-order fitting polynomials). Step size impacted curvature estimates, albeit to a lesser degree. Integration algorithm had little impact, and mean pennation angle, and tract-based FA and MD, were relatively insensitive to all parameters. The results demonstrate which muscle diffusion measures and architectural estimates are most sensitive to varying tractography settings and support the need for consistent reporting of tractography details to aid interpretation and comparison of results between studies.

Depth Prior-Guided 3D Voxel Feature Fusion for 3D Semantic Estimation from Monocular Videos

Existing 3D semantic scene reconstruction methods utilize the same set of features extracted from deep learning networks for both 3D semantic estimation and geometry reconstruction, ignoring the differing requirements of semantic segmentation and geometry construction tasks. Additionally, current methods allocate 2D image features to all voxels along camera rays during the back-projection process, without accounting for empty or occluded voxels. To address these issues, we propose separating the features for 3D semantic estimation from those for 3D mesh reconstruction. We use a pretrained vision transformer network for image feature extraction and depth priors estimated by a pretrained multi-view stereo-network to guide the allocation of image features within 3D voxels during the back-projection process. The back-projected image features are aggregated within each 3D voxel via averaging, creating coherent voxel features. The resulting 3D feature volume, composed of unified voxel feature vectors, is fed into a 3D CNN with a semantic classification head to produce a 3D semantic volume. This volume can be combined with existing 3D mesh reconstruction networks to produce a 3D semantic mesh. Experimental results on real-world datasets demonstrate that the proposed method significantly increases 3D semantic estimation accuracy.

Accuracy of Healthcare Professionals' Estimations of Health Literacy and Numeracy of Patients Visiting Metabolic Bariatric Surgery Clinic.

To effectively support patients through their weight loss journey, it is vital that healthcare professionals (HCPs) understand the health literacy skills of their patients and communicate in a way that meets these needs. This is the first study looking at the accuracy of HCPs' estimations of their patients' health literacy and numeracy attending a metabolic bariatric surgery (MBS) clinic. A cross-sectional study was completed at a tertiary-level MBS clinic in London. Patients completed a demographic questionnaire and a validated measure of health literacy and numeracy, the Medical Term Recognition Test (METER) and General Health Numeracy Test-Short Form (GHNT-6), respectively. HCPs provided estimations of their patient's health literacy and numeracy based on each questionnaire's scoring categories. Data was collected for 31 patients. A 80.6% of patients had functional health literacy based on METER. HCPs estimated patients' health literacy correctly 61.1% of the time; inter-rater agreement was poor (ICC = 0.14; 95% CI = - 0.19, 0.443; p = 0.202). A total of 22.6% of patients scored 0 out of 6 on GHNT-6. HCPs estimated health numeracy correctly 13.9% of the time and were more likely to overestimate than underestimate health numeracy. Inter-rater agreement for health numeracy was poor (ICC = - 0.2; 95% CI = - 0.49, 0.14; p = 0.878). There is poor agreement between HCPs' perception of their patients' health literacy and numeracy and their assessed ability. HCPs' understanding of their patient's health literacy and numeracy skills is vital in ensuring HCPs can support patients through the challenging bariatric surgical pathway, consenting process and post-operative course.

Accuracy of Healthcare Professionals' Estimations of Health Literacy and Numeracy of Patients Visiting Metabolic Bariatric Surgery Clinic.

To effectively support patients through their weight loss journey, it is vital that healthcare professionals (HCPs) understand the health literacy skills of their patients and communicate in a way that meets these needs. This is the first study looking at the accuracy of HCPs' estimations of their patients' health literacy and numeracy attending a metabolic bariatric surgery (MBS) clinic. A cross-sectional study was completed at a tertiary-level MBS clinic in London. Patients completed a demographic questionnaire and a validated measure of health literacy and numeracy, the Medical Term Recognition Test (METER) and General Health Numeracy Test-Short Form (GHNT-6), respectively. HCPs provided estimations of their patient's health literacy and numeracy based on each questionnaire's scoring categories. Data was collected for 31 patients. A 80.6% of patients had functional health literacy based on METER. HCPs estimated patients' health literacy correctly 61.1% of the time; inter-rater agreement was poor (ICC = 0.14; 95% CI = - 0.19, 0.443; p = 0.202). A total of 22.6% of patients scored 0 out of 6 on GHNT-6. HCPs estimated health numeracy correctly 13.9% of the time and were more likely to overestimate than underestimate health numeracy. Inter-rater agreement for health numeracy was poor (ICC = - 0.2; 95% CI = - 0.49, 0.14; p = 0.878). There is poor agreement between HCPs' perception of their patients' health literacy and numeracy and their assessed ability. HCPs' understanding of their patient's health literacy and numeracy skills is vital in ensuring HCPs can support patients through the challenging bariatric surgical pathway, consenting process and post-operative course.

Influence of photoperiod on the developmental times of the forensically relevant blow fly species Calliphora vicina (Diptera: Calliphoridae)

Blow flies (Diptera: Calliphoridae) are frequently used in forensic investigations due to their rapid colonization of cadavers. As with other insects, environmental temperature strongly influences their developmental rates. While published research has typically explored not only the impact of the environmental temperature, but also of other factors like tissue type and drug presence on developmental rates, the influence of photoperiod on the developmental rates of forensically relevant blow fly species has remained largely underexplored. Understanding the relationship between photoperiod and developmental times is crucial, as neglecting this aspect could compromise the accuracy of minimum post-mortem interval (minPMI) estimations. The present study investigates the impact of three photoperiod conditions (0:24, 8:16, and 12:12 light:darkness) on the developmental rates of Calliphora vicina, focusing on the duration of the different immature stages and on the total developmental time. Our results revealed significant variation in the intra-puparial stage and total development time across different photoperiods. Notably, a 12:12 photoperiod led to a significantly prolonged intra-puparial stage and total development time compared to the 0:24 photoperiod, suggesting that Calliphora vicina develops faster in total darkness. These findings highlight the importance of considering photoperiod in both laboratory rearing protocols and forensic casework to improve the accuracy and reliability of minPMI estimations. In this regard, preliminary guidelines and recommendations are provided.

Machine learning approaches for estimation of the fraction of absorbed photosynthetically active radiation and net photosynthesis rate of maize using multi-spectral sensor

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R2 = 0.873 and RMSE = 0.045 during the kharif and MLR with R2 = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R2 = 0.741 and RMSE = 2.531 during the kharif and R2 = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks

Spacecraft entering Mars require precise navigation algorithms capable of accurately estimating the vehicle’s position and velocity in dynamic and uncertain atmospheric environments. Discrepancies between the true Martian atmospheric density and the onboard density model can significantly impair the navigation performance. This work introduces a new approach to online filtering for Martian entry using a neural network to estimate atmospheric density and employing a “consider” analysis to account for the uncertainty in the estimate. The network is trained on an exponential atmospheric density model, and its parameters are dynamically adapted in real time to account for any mismatch between the true and estimated densities. The adaptation of the network is formulated as a maximum likelihood problem by leveraging the measurement innovations of the filter to identify optimal network parameters. Within the context of the maximum likelihood approach, incorporating a neural network enables the use of stochastic optimizers known for their efficiency in the machine learning domain. Performance comparisons are conducted against two online adaptive approaches, covariance matching and state augmentation and correction, in various realistic Martian entry navigation scenarios. The results show superior estimation accuracy compared to other approaches and precise alignment of the estimated density with a broad selection of realistic Martian atmospheres sampled from perturbed Mars Global Reference Atmospheric Model data.

Estimation of multi-stress strength reliability under progressive first failure censoring using generalized inverted exponential distribution

We investigate the estimation of multi-stress strength reliability under progressively first failure censoring for the generalized inverted exponential distribution using classical and Bayesian approaches. Classical estimation employs maximum likelihood estimators with the Expectation-Maximization algorithm to handle incomplete data. Bayesian estimation utilizes Markov Chain Monte Carlo (MCMC) techniques and Tierney-Kadane's approximation to approximate the posterior distribution. Furthermore, A generalized entropy loss function is employed to measure estimation accuracy. Finally, Validation is provided through Monte Carlo simulations and real-world applications. Findings highlight the robustness and accuracy of Bayesian methods, particularly with informative priors, and underscore the strengths of MCMC techniques and Bayesian Credible Intervals in specific aspects of estimation.

Vision‐Based Online Key Point Estimation of Deformable Robots

The precise control of soft and continuum robots requires knowledge of their shape, which has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built‐in sensors, resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on expensive tracking systems with reflective markers placed on all components, which are infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, a regression approach is presented for three‐dimensional key point estimation using a convolutional neural network. The proposed approach uses data‐driven supervised learning and is capable of online markerless estimation during inference. Two images of a robotic system are captured simultaneously at 25 Hz from different perspectives and fed to the network, which returns for each pair the parameterized key point or piecewise constant curvature shape representations. The proposed approach outperforms markerless state‐of‐the‐art methods by a maximum of 4.5% in estimation accuracy while being more robust and requiring no prior knowledge of the shape. Online evaluations on two types of soft robotic arms and a soft robotic fish demonstrate the method's accuracy and versatility on highly deformable systems.

Application of the Heat Flow Meter Method and Extended Average Method to Improve the Accuracy of In Situ U-Value Estimations of Highly Insulated Building Walls

In the context of remodeling old buildings, enhancing insulation performance in the exterior skin necessitates an accurate assessment of a wall’s thermal performance. The conventional method for determining the thermal transmittance (U-value) of a wall is the heat flow meter (HFM) as outlined in the ISO 9869-1. However, this measurement is susceptible to errors influenced by indoor and outdoor environmental conditions and the wall’s material composition. This study evaluates the U-value of an internally insulated wall, specifically constructed for this purpose, utilizing both the average and dynamic methodologies of an HFM. Furthermore, it introduces a novel estimation method: the extended average method (EXAM). The effectiveness of this proposed method is ascertained by comparing the accuracy and convergence of the U-value estimations with those derived from existing methodologies. Additionally, the study explores the limitations of the HFM by analyzing the heat flow traversing the interior of a wall. The findings revealed that the EXAM method enhanced the precision of U-value estimation in all scenarios. Particularly, in walls with superior insulation, the HFM tended to underestimate the heat flow observed indoors, leading to negative errors. The EXAM method, incorporating considerations of both insulation and structural materials, offers an accurate in situ measurement of the U-value relative to the HFM.

Scaling effects on peak wind load estimation for low-rise buildings: An experimental and analytical study

Though scaling effects on estimation of peak wind loads are extensively documented in the literature, there is a lack of systematic comparison of such scaling effects across different model scales tested in wind tunnels. Moreover, the limited studies on large-scale physical models accepted the inadequacy of simulating the large turbulent eddies rather than addressing the missing low-frequency turbulence simulation inherent in such large-scale testing. This study provides an in-depth comparison of estimates of peak wind loads obtained using a range of experimental scaled models (1:100 and 1:6 model scales) of the Wind Engineering Research Field Laboratory (WERFL) building while applying a Partial Turbulence Simulation (PTS) methodology to incorporate the effects of turbulence. Large-scale wind tunnel testing of structures provides the advantage of achieving an improved Reynolds number (Re) similarity, more accurate geometric similarity, and enhanced resolution of flow details such as corner vortices, than is possible with the use of smaller model scales. However, as the model scale increases, achieving turbulence similarity becomes challenging due to limitations in simulating the low-frequency turbulent eddies in the wind tunnel. The PTS method has been used in the recent past to account for the low-frequency turbulence effects in the peak wind load estimation. The method is referred to as 1DPTS when considering longitudinal turbulence components only and 2DPTS when considering both longitudinal and lateral turbulence components in the analysis. The computationally intensive 2DPTS can be further simplified by using a weighted average method as described in this paper as “weighted average PTS.” The current study explores the effect of scaling on peak wind load estimation by using the 1DPTS, 2DPTS, and weighted average PTS methods. Results from the multi-scale experimental tests demonstrate that using larger model scales and compensating for the low-frequency turbulence deficit in the post-test analysis significantly improve the agreement with the prototype data owing to the simulation of a higher Reynolds number. Furthermore, comparing the results of the proposed weighted average method with full-scale data reveals its potential as a substitute for the original 2DPTS method, offering enhanced accuracy, particularly at the roof corner for oblique wind directions. While the effects of Re are well known, the findings of this study explore its impact in conjunction with the application of the PTS methods on peak Cp estimation across five different model scales with Re ranging from 3.65 × 104 to 2.43 × 106, the latter being closest to the full-scale (field) Re. The findings provide new insights into how large-scale physical model testing in high Re flows, supported by analytical PTS-based compensation, can enhance the accuracy of peak wind load estimations for critical scenarios including cornering winds that generate conical vortices.

A Practical Approach to Tracking Estimation Using Object Trajectory Linearization

In the field of image processing and machine vision, object tracking is a significant and rapidly developing subfield. The numerous potential applications of object tracking have garnered much attention in recent years. The effectiveness of tracking and detecting moving targets is directly related to the quality of motion detection algorithms. This paper presents a new method for estimating the tracking of objects by linearizing their trajectories. Estimating the movement paths of objects in dynamic and complex environments is one of the fundamental challenges in various fields, such as surveillance systems, autonomous navigation, and robotics. Existing methods, such as the Kalman filter and particle filter, each have their strengths and weaknesses. The Kalman filter is suitable for linear systems but less efficient in nonlinear systems, while the particle filter can better handle system nonlinearity but requires more computations. The main goal of this research is to improve the accuracy and efficiency of estimating the movement paths of objects by combining path linearization techniques with existing advanced methods. In this method, the nonlinear model of the object's path is first transformed into a simpler linear model using linearization techniques. The Kalman filter is then used to estimate the states of the linearized system. This approach simplifies the calculations while increasing the estimation accuracy. In the subsequent step, a particle filter-based method is employed to manage noise and sudden changes in the object's trajectory. This combination of two different methods allows leveraging the advantages of both, resulting in a more accurate and robust estimate. Experimental results show that the proposed method performs better than traditional methods, achieving higher accuracy in various conditions, including those with high noise and sudden changes in the movement path. Specifically, the proposed approach improves movement forecasting accuracy by about 12% compared to existing methods. In conclusion, this research demonstrates that object trajectory linearization can be an effective tool for improving object tracking estimation. Combining this technique with existing advanced methods can enhance the accuracy and efficiency of tracking systems. Consequently, the results of this research can be applied to the development of advanced surveillance systems, self-driving cars, and other applications.

Monitoring soil moisture content in the root zone of winter wheat with multi-angle multispectral imagery

ABSTRACT Exploring the impact of different monitoring angles from unmanned aerial vehicle (UAV) on the monitoring accuracy of soil moisture content (SMC) is crucial for precision irrigation. To this end, experiment was conducted to monitor the SMC of winter wheat at different growth stages under different irrigation treatments in Yangling, Shaanxi, China. At a solar zenith angle of 45°, multispectral remote sensing data from a UAV were collected at thirteen different monitoring zenith angles. Simultaneously, the SMC in the wheat root zone was measured. From the UAV multispectral images, spectral reflectance was extracted for the construction of vegetation indices. Then the correlation between the vegetation indices and the measured SMC was analyzed. With the vegetation indices as input variables, SMC monitoring models were constructed using Extreme Learning Machine (ELM), Random Forest (RF), and Back Propagation Neural Network (BPNN). The study also examined the effect of specific angles (hotspot and dark spot angles) on the estimation accuracy of the SMC at the nadir angle. The results indicated that different monitoring angles significantly impact the SMC estimation accuracy. Band reflectance and vegetation indices exhibited significant peak values and angular effects at the monitoring zenith angle of 45°. The models achieved the optimal inversion accuracy at the hotspot angle (at a monitoring zenith angle of 45° in the solar principal plane), and the accuracy was ranked as follows: BPNN (R2 = 0.71; RMSE = 1.69) > ELM (R2 = 0.52; RMSE = 1.94) > RF (R2 = 0.48; RMSE = 2.10). By eliminating shadows at the nadir angle through the threshold of dark spot, inversion accuracy similar to the hotspot direction is achieved. This study provides a basis for appropriate selection of UAV flight angles for the monitoring of SMC in the root zone of winter wheat.

The nonparametric GARCH model estimation using intraday high-frequency data

Most of nonparametric GARCH models typically employ daily frequency data to forecast the returns, correlations, and risk indicators of financial assets, without incorporating alternative frequency data. As a result, valuable financial market information may remain underutilized during the estimation process. To partially mitigate this issue, we introduce the intraday high-frequency data to enhance the estimation of the volatility function in a nonparametric GARCH model. To achieve this objective, we introduce a nonparametric proxy model for volatility. Under mild assumptions, we derive the asymptotic bias and variance of the estimator and further investigate the impact of various volatility proxies on estimation accuracy. Our findings from both simulations and empirical analysis indicate a considerable improvement in the estimation of the volatility function through the introduction of high-frequency data.

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale.

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

Abstract The Global Navigation Satellite System/Strapdown Inertial Navigation System (GNSS/SINS) integrated navigation system is an important technology for UAV measurement and vehicle movement measurement. But in the operational process of the GNSS/SINS integrated navigation system, the Global Navigation Satellite System (GNSS) signal is vulnerable to external interference, resulting in abnormal system measurement data, and system faults. These faults will reduce the navigation and positioning performance of the system and reduce the measurement accuracy of the system. Aiming at this problem, a GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width is proposed. The algorithm evaluates the system measurement data based on the innovation residual and incorporates the sliding average filter to design the fault detection function based on the smooth bounded width layer. Accurate detection of system faults using fault detection function. The fault detection function value is used to construct the robust cofactor matrix to correct the measurement error in real-time, to improve the accuracy and robustness of the state estimation. The experimental results show that: The proposed algorithm in the paper compares with two traditional robust adaptive algorithms based on smooth bounded layer fault detection and residual chi-square fault detection. The fault detection rates for small step faults show an increase of more than 44.26% and 9.54%, respectively. Similarly, for slowly varying faults, the fault detection rates exhibit an increase of more than 29.32% and 13.56%, respectively. Throughout the fault, the filtering accuracy demonstrates an increase of more than 16.52% and 15.47%, respectively. The algorithm effectively improves the measurement accuracy of the GNSS/SINS integrated navigation system.

Enhanced frame synchronization and carrier recovery in coherent FSO communication: a pseudo-random and cyclic QPSK approach

To alleviate the impact of atmospheric turbulence on receiver carrier recovery in free-space optical communication, this study introduces a novel frame synchronization and carrier recovery method. This method employs a blend of pseudorandom sequences and specific cyclic quadrature phase shift keying (QPSK) training sequences, facilitating frame synchronization and frequency offset (FO) estimation. The research designs a time-series metric curve to counteract the effect of side peaks, enhancing the accuracy of frequency offset estimation via QPSK spectrum features. Furthermore, the method incorporates a two-stage frequency offset estimation process, utilizing the inherent sign characteristics of the x and y polarization states. Applied to the quadrature amplitude modulation system under atmospheric turbulence conditions, the proposed algorithm demonstrates high precision under both strong and weak turbulence conditions. Furthermore, we confirmed the universality of the algorithm across multiple modulation formats.

An image-processing toolkit for remote photoplethysmography

AbstractObjective. Image-processing-based remote photoplethysmography algorithms are usually composed of steps where different methods are used, and often, researchers perform these steps using methods that are not necessarily the best for their application. With our toolkit, we want to provide easy and fast access to different state-of-the-art methods for the most common image-processing steps in remote photoplethysmography algorithms. Methods. Our toolkit was programmed in Python and was developed with sequential workflow in mind, making it versatile and easy to use in interactive environments. It also includes tools so the users can modify or extend it if they want to, and will be updated as new methods for the different steps are published. Results. Our use case examples and validation show an effective approach and how the toolkit can be used for exhaustive evaluation and ablation studies in a simple way. We also show how choosing different methods can affect the final heart rate estimation accuracy at the cost of computation time. Conclusion. With this toolkit we are providing researchers with a versatile, easy-to-use tool, with access to different methods for the most common steps in remote photoplethysmography algorithms. Significance. Our toolkit is a relevant tool for researchers in the remote photoplethysmography field due to their versatility, ease of use, and adaptability. (It will be available onhttps://github.com/Montyro/rppgtkgithub upon acceptance).

Cramér-Rao lower bounds on parameter estimation for a circular binary of supermassive black holes in gravitational wave observations using pulsar timing array

Abstract Pulsar timing arrays, as an effective low frequency gravitational source identification tool, show excellent applicability in circular supermassive binary black hole systems. In order to evaluate the efficiency and accuracy of the array, we introduce the Cramer-Rao lower bound to estimate the detection efficiency of the pulsar timing array. We use CRLB to estimate the parameters of Virgo source under both evolutionary and non-evolving search conditions, as well as full term and Earth term search conditions. The results show that the estimation accuracy of the two parameters is lower in the evolutionary condition than in the non evolving condition because the inclination Angle and the gravitational wave strain participate in the evolving process. Under the condition of Full term and Earth term search, because the Earth term search does not involve pulsar distance, the estimation accuracy of the whole parameter is higher than that of full term search. This results in a significant reduction in the uncertainty of pulsar parameters, enabling more accurate gravitational wave detection. In addition, in the case of relatively dense pulsars, the detection accuracy of gravitational wave 
direction will also be significantly reduced. In general, our method provides a reference for searching the lower limit of parameters under different objective conditions, and helps to evaluate whether the parameter estimation of the gravitational wave detection algorithm 
reaches the optimal level. This provides a theoretical basis for the progress in the field of gravitational waves and the optimization of pulsar timing arrays.