Matching Estimation Research Articles

Accurate TDOA estimation using matching cumulative estimator for LFM signal with small samples

This paper introduces two matching cumulative estimators designed for achieving gridless time difference of arrival (TDOA) estimation for linear frequency modulated (LFM) signals with limited samples. A designed matching basis function, customized to the source signal waveform, is employed to calculate with the received data. Time delay matching is then utilized to formulate a monotonic objective function related to the estimated TDOA, enabling gridless estimation. To enhance robustness, especially in scenarios characterized by small samples and low signal to noise ratios (SNRs), a nonlinear cumulative transformation, comprised of a series of strongly convex operations, is incorporated to mitigate the impact of noise on the peak calculation of the objective function. Simulation results validate the efficacy of the two proposed TDOA estimators, particularly under challenging conditions involving small samples and low SNRs.

Performance Gap between Best-bet System of Rice Intensification and Farmers’ Practices in Zamfara State, Nigeria: An Additive-Multiplicative Translog Stochastic Frontier Profit Function

Inadequate information about the performance of a technology can hinder its uptake. The aim of this paper was to provide a thorough examination of the performance gap between the best bet system of rice intensification (BB-SRI) and farmers’ practices (FP) in Zamfara State, Nigeria. Using a multi-stage sampling technique, a sample of 300 rice farmers were selected from the Bakolori Irrigation Scheme, Zamfara State, among which 40 farmers (13%) used the BB-SRI while the remaining 260 farmers (87%) used FP. The data were analyzed using descriptive statistics, farm budget technique, additive-multiplicative stochastic frontier translog profit function and propensity score matching (PSM) estimator. The findings revealed that yield increased from 5,285 kg/ha with FP to 12,735 kg/ha with BB-SRI, an increase of 141%. Rice production was profitable under both FP and BB-SRI with return on investment (ROI) increasing from 2.64 using FP to 6.66 under BB-SRI, an increase of 152%. There was evidence of profit inefficiency in rice farming since the profit could still be raised by 7% and 21% using FP and BB-SRI, respectively. The yield, ROI and profit efficiency gaps between the BB-SRI and FP ranged from 7,452 kg/ha to 7,510 kg/ha, 4.02 to 4.05, and from 0.08 to 0.09, respectively. We recommend therefore that important investments and training in SRI should be considered as a top priority for the transformation and sustainability of the rice sector in order to ensure its successful promotion, uptake and diffusion across other rice producing States in Nigeria.

An Improved TransMVSNet Algorithm for Three-Dimensional Reconstruction in the Unmanned Aerial Vehicle Remote Sensing Domain.

It is important to achieve the 3D reconstruction of UAV remote sensing images in deep learning-based multi-view stereo (MVS) vision. The lack of obvious texture features and detailed edges in UAV remote sensing images leads to inaccurate feature point matching or depth estimation. To address this problem, this study improves the TransMVSNet algorithm in the field of 3D reconstruction by optimizing its feature extraction network and costumed body depth prediction network. The improvement is mainly achieved by extracting features with the Asymptotic Pyramidal Network (AFPN) and assigning weights to different levels of features through the ASFF module to increase the importance of key levels and also using the UNet structured network combined with an attention mechanism to predict the depth information, which also extracts the key area information. It aims to improve the performance and accuracy of the TransMVSNet algorithm's 3D reconstruction of UAV remote sensing images. In this work, we have performed comparative experiments and quantitative evaluation with other algorithms on the DTU dataset as well as on a large UAV remote sensing image dataset. After a large number of experimental studies, it is shown that our improved TransMVSNet algorithm has better performance and robustness, providing a valuable reference for research and application in the field of 3D reconstruction of UAV remote sensing images.

Vitamin D supplementation may be beneficial in improving the prognosis of patients with chronic obstructive pulmonary disease in the intensive care unit: a retrospective study.

Vitamin D is a crucial fat-soluble vitamin that has garnered significant attention due to its potential impact on respiratory health. It is noteworthy that many patients with chronic obstructive pulmonary disease (COPD) often experience deficiencies or insufficiencies of vitamin D. To address this issue, our retrospective study aimed to explore the potential association between serum 25-hydroxyvitamin D concentration and the prognoses of COPD patients in the Intensive Care Unit (ICU). This study utilised data from the Medical Information Marketplace in Intensive Care IV (MIMIC-IV), a database of patients admitted to the Intensive Care Unit at Beth Israel Deaconess Medical Center (BIDMC) in the United States of America, with a focus on patients with a diagnosis of COPD. These patients were categorized into two groups: those who received vitamin D supplementation during their ICU stay and those who did not. We assessed in-hospital mortality and ICU mortality outcomes. Our analysis involved various analytical tools, including Kaplan-Meier survival curves, Cox proportional risk regression models, and subgroup analyses, to investigate the relationship between vitamin D supplementation and these outcomes. Additionally, we employed propensity-score matching (PSM) to enhance the reliability of our findings. The study included a total of 3,203 COPD patients, with 587 in the vitamin D group and 2,616 in the no-vitamin D group. The Kaplan-Meier survival curve demonstrated a significant difference in survival probability between the two groups. After adjusting for potential confounders using Cox regression models, the vitamin D group exhibited a substantially lower risk of in-hospital and ICU mortalities compared to the no-vitamin D group. The hazard ratios for in-hospital and ICU mortalities in the vitamin D group were 1.7 (95% CI: 1.3, 2.3) and 1.8 (95% CI: 1.2, 2.6), respectively. Propensity-score matching (PSM) estimation yielded consistent results. Furthermore, in the subgroup analysis, female patients who received vitamin D supplementation showed a reduced risk of in-hospital mortality. The study suggests that vitamin D supplementation may be linked to a reduction in in-hospital and ICU mortalities among COPD patients in the ICU. Of particular note is the potential benefit observed in terms of in-hospital mortality, especially for female patients.

Point cloud registration with quantile assignment

Point cloud registration is a fundamental problem in computer vision. The problem encompasses critical tasks such as feature estimation, correspondence matching, and transformation estimation. The point cloud registration problem can be cast as a quantile matching problem. We refined the quantile assignment algorithm by integrating prevalent feature descriptors and transformation estimation methods to enhance the correspondence between the source and target point clouds. We evaluated the performances of these descriptors and methods with our approach through controlled experiments on a dataset we constructed using well-known 3D models. This systematic investigation led us to identify the most suitable methods for complementing our approach. Subsequently, we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework. Finally, we tested our framework on indoor and outdoor benchmark datasets and compared our results with state-of-the-art point cloud registration methods.

Robust score matching for compositional data

The restricted polynomially-tilted pairwise interaction (RPPI) distribution gives a flexible model for compositional data. It is particularly well-suited to situations where some of the marginal distributions of the components of a composition are concentrated near zero, possibly with right skewness. This article develops a method of tractable robust estimation for the model by combining two ideas. The first idea is to use score matching estimation after an additive log-ratio transformation. The resulting estimator is automatically insensitive to zeros in the data compositions. The second idea is to incorporate suitable weights in the estimating equations. The resulting estimator is additionally resistant to outliers. These properties are confirmed in simulation studies where we further also demonstrate that our new outlier-robust estimator is efficient in high concentration settings, even in the case when there is no model contamination. An example is given using microbiome data. A user-friendly R package accompanies the article.

Publicly funded health insurance schemes and demand for health services: evidence from an Indian state using a matching estimator approach.

Using Demographic and Health Survey data (2015-16) from the state of Andhra Pradesh, we estimate the differential probability of hysterectomy (removal of uterus) for women (aged 15-49 years) covered under publicly funded health insurance (PFHI) schemes relative to those not covered. To reduce the extent of selection bias into treatment assignment (PFHI coverage) we use matching methods, propensity score matching, and coarsened exact matching, achieving a comparable treatment and control group. We find that PFHI coverage increases the probability of undergoing a hysterectomy by 7-11 percentage points in our study sample. Sub-sample analysis indicates that the observed increase is significant for women with lower education levels and higher order parity. Additionally, we perform a test of no-hidden bias by estimating the treatment effect on placebo outcomes (doctor's visit, health check-up). The robustness of the results is established using different matching specifications and sensitivity analysis. The study results are indicative of increased demand for surgical intervention associated with PFHI coverage in our study sample, suggesting a need for critical evaluation of the PFHI scheme design and delivery in the context of increasing reliance on PFHI schemes for delivering specialised care to poor people, neglect of preventive and primary care, and the prevailing fiscal constraints in the healthcare sector.

The Effects of Body-Worn Cameras on Police Efficiency: A Study of Local Police Agencies in the United States

Research on body-worn cameras (BWCs) has burgeoned in recent years. However, much of this research has not examined the effects of this technology on police performance. In this study, we created a novel dataset on police organizational inputs and outputs and assessed the effects that BWCs have on police efficiency. Using a multiple-stage approach, in the first stage, we estimated the efficiency of local police agencies using a robust order- m efficiency model. In the second stage, we assessed the effects of BWCs using a range of matching estimators and an instrumental variable (IV) model. The first-stage results show that police agencies could improve their efficiency by 2% (from a mean efficiency score of 0.98 to 1). The second-stage matching and IV estimates suggest BWC effects on efficiency range between four and six percentage points in our preferred model. The introduction of this significant technological advance has brought many benefits to police agencies. Our findings show that BWCs can help improve police efficiency, yielding significant cost savings to law enforcement.

Triple line-scan camera measurement for efficient and accurate narrow-space 3-D scanning with motion estimation.

Vision-based three-dimensional (3-D) shape measurement plays a crucial role in the inspection of vehicles and trains in the field of transportation. Consequently, the demand for more comprehensive narrow-space inspection has become an inevitable necessity and presents a great challenge to conventional vision methods. We propose the following efficient and accurate narrow-space 3-D scanning method based on triple line-scan cameras. First, the structure of coplanar dual line-scan cameras with a narrow reflector is proposed, making it easy for the optical path to enter a narrow space and obtain 3-D shape information. Efficient in-motion measurement with a large field of view is thereby achieved without the worries that often accompany narrow-space constraints. Secondly, an additional line-scan camera is attached to the coplanar dual cameras and creates a time-space constraint in point cloud stitching direction through the triple line-scan camera structure. With an efficient algorithm template including matching and motion estimation, accurate point cloud stitching is ensured. Lastly, point cloud texture mapping and light source optimization are realized. Our experimental results prove that the method realizes low-distortion in-motion reconstruction in narrow space despite motion variation.

Climate smart agriculture impact on food and nutrition security in Ethiopia

IntroductionThe objective of this study was to analyze how innovations in Climate-smart agriculture (CSA) contribute to improving the food and nutrition security of smallholder households, thereby promoting sustainable food systems.MethodsA cross-sectional household survey was conducted among a multi-stage sample of 424 smallholder farmers drawn from five different agroecosystems. To examine households’ food and nutrition security, we used food consumption score (FCS) and modified household dietary diversity score (HDDS) in propensity score matching (PSM) and endogenous switching regression (ESR) estimation models.ResultsPSM results showed that crop residue management, compost, and agroforestry have a significant effect on improving households’ food and nutrition security by 21.3, 13.6, and 16.6%, respectively, whereas Soil and water conservation (SWC) has reduced adopters’ food security by 12.9%. However, the conditional average treatment effect, or ESR result, reveals that households’ food and nutrition security has improved as a result of the adoption of crop residue management, compost, SWC, and agroforestry.DiscussionAlthough the impact of crop residue management, compost, and agroforestry is positive, the effect of SWC on household food security has been inconclusive. Hence, it is important to upscale the adoption of multiple CSA innovations to improve smallholder household’s food security in the face of climate change.

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

Channel Parameter Estimation of mmWave MIMO System in Urban Traffic Scene: A Training Channel-Based Method

In frequency selective channel environment, channel estimation in hybrid precoding millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a challenge issue. To solve this problem, we propose an effective channel estimation scheme for frequency selective channel, which is based on the training channel model in urban traffic environment. Considering that the practical mmWave MIMO channel is sparsity and the subcarrier multi-channels have the same sparse structure, we regard the channel estimation problem as the sparse channel recovery, and propose a multipath simultaneous matching tracking estimation method. It is assumed that the noise between the practical channels has a certain correlation, and the noise correlation has an impact on the selection of the optimal atomic support set in the process of channel recovery. Therefore, noise weighting is introduced in our proposed method. The simulation results prove the validity of this proposed method in frequency selective mmWave MIMO channel. Without increasing the complexity of the algorithm, the proposed method can achieve better local performance than the traditional classical methods.

A high-precision image registration method for multi-channel single molecule localization

Single-molecule localization technology has been widely used in single-particle tracking and super-resolution imaging of biological samples, as it can bypass the diffraction limit of optical systems. Multi-channel single-molecule localization uses multiple imaging channels to simultaneously track different targets or perform multi-color super-resolution imaging, and can also improve the axial depth of single-particle tracking or achieve higher localization precision and density for super-resolution imaging. However, the difference between images in each channel can affect collaborative localization or quantitative analysis, so image registration is a key step in its image data preprocessing. Moreover, due to the high precision of single-molecule localization, its requirements for multi-channel image registration accuracy are also high. Existing technologies generally use control point-based registration methods and often use complicated and precise methods to obtain fiducial images for locating control point pairs to achieve high-precision image registration, which involves high sample or experimental equipment requirements and is difficult to directly extend to other systems. Therefore, developed in this work, is a high-precision image registration method that can directly use randomly distributed fluorescent beads as fiducial samples based on local nonlinear transformation and elimination of mismatched points. By monitoring and iteratively filtering control points in the process of feature matching and transformation model parameter estimation to eliminate control point pairs that are not accurately matched due to inaccurate or poor precision of single-molecule localization, the adverse effects on accurate acquisition and precise matching of control points when using randomly distributed fluorescent beads as fiducial samples are eliminated. At the same time, a second-order polynomial fitting based on local weighted mean is used for estimating the transformation model parameter to better adapt to the existence of local nonlinear deformation between different channels. The results show that using this method only requires three iterations to find and eliminate control point pairs that are not accurately located and matched, thereby achieving more accurate transformation model parameter and improving the registration accuracy by an order of magnitude, achieving a registration accuracy of about 6 nm in a complex dual-channel single-molecule localization imaging system based on orthogonal astigmatism.

Evaluating disparity of subjective cognitive decline between male veterans and non-veterans in the United States using propensity score matching estimation: A behavioral risk factor surveillance system survey cross-sectional study.

Subjective cognitive decline (SCD) is one of the most important early onset symptoms of Alzheimer's Disease. Previous studies consistently reported that SCD is associated with quality of life, socio-economic factors, and related health comorbidities. However, the impact of veteran status on SCD has been little investigated. This study conducted a cross-sectional study to address disproportionate effects in subjective cognitive decline between veterans and non-veterans in the United States. Propensity score matching (PSM) was applied in this observational study to achieve covariate balancing and reduce selection bias, providing a more accurate estimate of the isolated effect of veteran status on SCD. Our study utilized 32,431 forty-five years or older non-institutionalized White, Black or African-American, and Hispanic or Latin-American male population from the 2019 Behavioral Risk Factor Surveillance System data. We first identified 10,685 paired PSM samples for the binary veteran status using the preselected covariates. Next, we performed a logistic regression for modeling the relationship between the veteran status and the SCD status using the PSM samples along with the covariates selected by a BIC-based stepwise selection. Our analyses revealed a statistically significant causal association between veteran status and SCD after PSM (odds ratio (OR): 1.16 and 95% confidence interval (CI): 1.06-1.27). We obtained a similar result before PSM with an OR of 1.20 and 95% CI of 1.10-1.31. When we focused on a minority group (Black or African-American males), we found a significantly increased veterans' risk of SCD, especially after propensity score matching (OR: 1.69, 95% CI: 1.16-2.45). We also found several factors such as employment status, difficulty dressing/walking/running errands, general health status, physical health status, unaffordability of medical costs, mental health status, and comorbid conditions including stroke, blindness, high cholesterol, and arthritis as statistically significantly associated with SCD (P-value < 0.05). Similar to post-traumatic stress disorder and traumatic brain injury, our study demonstrated a causal association between SCD and military-related activities in the United States, which has a disproportionate impact on the minority population. This study sets the groundwork to further research in this domain to diagnose neurological diseases early among veterans.

Consumer Food Safety Awareness and Demand for Fluid Milk: A Case Study from India

ABSTRACT: Despite significant improvements in consumers’ food safety awareness, the literature provides little or no evidence of the impact of food safety awareness on the quantity of fluid milk consumers purchased. This is especially true for India’s consumers, where the economy is changing rapidly regarding food marketing, incomes, urbanization, and increased demand for food safety.The study is based on primary data from a survey of consumers from urban and peri-urban districts in India. The survey collected information on socioeconomic characteristics, dairy consumption, purchasing patterns, and food safety awareness. This study first creates a food safety awareness (FSA) index to measure consumers’ awareness of food safety attitudes and perceptions. It investigates the factors affecting consumers’ food safety awareness and FSA’s impact on fluid milk demand. The study uses a newly developed impact evaluation method, continuous treatment matching estimation, and dose-response functions (DRFs) to assess the impact of FSA on consumers’ fluid milk purchases. Findings show that educational attainment, sources of information on food safety, age, and gender of the head of the household significantly influenced consumers’ food safety awareness. Further, the study finds that food safety awareness positively impacts the demand for fluid milk. After controlling for milk prices and family income, Indian consumers aware of and adopt seven or more food safety habits or practices are likely to demand more fluid milk. Results are robust to specifications, income levels, and the location of households. The link between food safety awareness and the quantity of milk purchased implies a latent consumer demand for food safety. Any consumer awareness programs and policies supporting milk and its products’ hygienic production will likely increase demand for milk and dairy products. At a policy level, credible systems can be put in place for food certification and labeling, which enhance the availability of safe and hygienic food.

A comparative study of pose estimation algorithms for visual navigation in autonomous robots

Autonomous navigation is a research field that gives mobile robots the capacity to perform various tasks without human assistance. Autonomous navigation based on visual sensors can be used in GPS denied environments. Vision-based navigation performs feature detection, matching, and pose estimation using camera images. This paper presents a new approach to autonomous navigation for mobile robots using a Color-based Image Segmentation and Centroid Detection algorithm instead of traditional feature detection algorithms. The algorithm intentionally matches features across images using known feature points and uses conventional techniques such as Epipolar Geometry and Perspective-N-Points algorithms for camera pose estimation. The study includes camera calibration to estimate intrinsic parameters and their physical unit conversion ensuring accurate measurements. Experimental datasets are used to analyze the performance of the Epipolar geometry, Perspective-3-Point, and Efficient-Perspective-n-Point based pose estimation algorithms. To enhance accuracy, the P3P algorithm is modified to consider combinations of image points for pose estimation. The paper concludes with a comparative analysis between the original P3P algorithm and the modified version, providing valuable insights into their respective performances. Overall, the paper aims to provide a comparison of different pose estimation algorithms used for visual navigation. Keywords: pose estimation, visual navigation, camera calibration, perspective-n-point, efficient pnp, epipolar geometry Abbreviations PnP, perspective-n-point; P3P, perspective-3-point; EPnP, efficient perspective-n-point; SLAM, simultaneous localization and mapping; VO, visual odometry; GPS, global positioning system; SURF, speeded up robust features; SIFT, scale-invariant feature transform; EKF, extended kalman filter; RGB, red-green-blue; HSV, hue- saturation-value; CNN, convolutional neural network

Does the belt and road initiative boost Chinese automobile exports? A staggered adoption approach

ABSTRACT We examine whether China’s ambitious Belt and Road Initiative assisted its export performance in the final automobile sector, a market that has been challenging for the Chinese economy. We use the recent econometric technique of heterogeneity robust difference-in-difference estimators for staggered timing. We also control anticipation effects and potential BRI selection endogeneity using IVs and matching estimation. Using data from 2005 to 2019, our estimates find that China’s final automobile exports to the BRI partner destination countries after these countries became signatories almost doubled relative to that of the export to non-BRI partner destinations, and the effect for net-exports is an increase of about 59%. Sub-sample analyses indicate that the positive effect is stronger for China’s export to lower-income partners compared to its exports to relatively higher-income destinations, though the effects in the automobile industry is more balanced between high-income and low-income destinations, compared to overall exports where BRI’s impact. The results are robust across alternate methods and specifications.

The effect of remittances on financial literacy in Mexico

This article studies the impact of remittances on financial literacy and its components using data for ENIF 2021 in Mexico. Using instrumental variables, random matching estimators and non-parametric estimations that control for the endogeneity of remittances and other econometric challenges, the study is the first to find that remittances increase the average level of financial literacy, and its components of financial knowledge, behavior, and attitudes. The effect is not monotonic, since the study finds that remittances reduce the probability of showing high levels of literacy, as well as that of its components of financial knowledge, behavior, and attitudes. Non-parametric estimations show that remittances move the distribution of financial literacy outcomes towards the center of the distribution, which explains why average values of financial literacy outcomes are above the average. These results may indicate that households acquire financial literacy through a process of learning by doing, which shows decreasing returns. They highlight the need for specific policies to increase the level of financial literacy among households that receive remittances. The limitation of the study is the lack of panel data.

Where there’s a will, there’s a way: Border walls and refugees

Over the last decade, there has been a notable surge in the movement of refugees across international borders, posing significant challenges for the international community. In response, various policy measures have been implemented, including the construction of border walls, with the aim of impeding refugee influx. However, scholars have expressed doubts regarding the effectiveness of these fortifications, suggesting that walls merely redirect migrants to alternative routes, discourage return migration, or alter migrants’ cost–benefit calculations. Despite these concerns, there has been a lack of rigorous testing to support or refute these claims beyond case-specific evidence. This article addresses this research gap by thoroughly examining the arguments surrounding the impact of border fencing on refugee flows. We conduct a systematic, cross-national test of these arguments with a two-way fixed-effects estimator, an equivalence test, and a recently developed matching estimator designed for use on time-series cross-sectional data. Our results strongly support those who are skeptical of the impact of walls. We consistently demonstrate either that border fencing has not had any causal impact on refugee flows between 1970 and 2017 or that the statistical state-of-the-art is incapable of discerning that true effect. In either scenario, the evidence suggests that border fences fail to deliver the anticipated outcomes. These findings hold significant implications as violence-driven refugee flows persist, underscoring that while walls may serve as politically attractive tools for populist leaders, their actual deterrent effects are highly questionable at best.