Location Estimation Method Research Articles

Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies.

Local genetic correlation evaluates the correlation of additive genetic effects between different traits across the same genetic variants at a genomic locus. It has been proven informative for understanding the genetic similarities of complex traits beyond that captured by global genetic correlation calculated across the whole genome. Several summary-statistics-based approaches have been developed for estimating local genetic correlation, including $\rho$-hess, SUPERGNOVA and LAVA. However, there has not been a comprehensive evaluation of these methods to offer practical guidelines on the choices of these methods. In this study, we conduct benchmark comparisons of the performance of these three methods through extensive simulation and real data analyses. We focus on two technical difficulties in estimating local genetic correlation: sample overlaps across traits and local linkage disequilibrium (LD) estimates when only the external reference panels are available. Our simulations suggest the likelihood of incorrectly identifying correlated regions and local correlation estimation accuracy are highly dependent on the estimation of the local LD matrix. These observations are corroborated by real data analyses of 31 complex traits. Overall, our findings illuminate the distinct results yielded by different methods applied in post-genome-wide association studies (post-GWAS) local correlation studies. We underscore the sensitivity of local genetic correlation estimates and inferences to the precision of local LD estimation. These observations accentuate the vital need for ongoing refinement in methodologies.

An Intelligent Method for Fault Location Estimation in HVDC Cable Systems Connected to Offshore Wind Farms

Large and remote offshore wind farms (OWFs) usually use voltage source converter (VSC) systems to transmit electrical power to the main network. Submarine high-voltage direct current (HVDC) cables are commonly used as transmission links. As they are liable to insulation breakdown, fault location in the HVDC cables is a major issue in these systems. Exact fault location can significantly reduce the high cost of submarine HVDC cable repair in multi-terminal networks. In this paper, a novel method is presented to find the exact location of the DC faults. The fault location is calculated using extraction of new features from voltage signals of cables’ sheaths and a trained artificial neural network (ANN). The results obtained from a simulation of a three-terminal HVDC system in power systems computer-aided design (PSCAD) environment show that the maximum percentage error of the proposed method is less than 1%.

Construction path tracking and pose estimation of unmanned bulldozer

The vigorous development of automatic control technology and AI has generated considerable interest in driverless systems. Presently, research on driverless systems is mainly focused on the automotive field, but it also has immense scope in engineering machinery. In the field of unmanned bulldozers, path tracking and pose estimation are the foundation of construction quality and safety. This article focuses on this field. A construction path tracking method was designed, wherein motion control commands were generated according to the course angle deviation combined with verification of steering gain and saturation characteristics. The proposed method can track the construction path of an unmanned bulldozer with high accuracy. The improved ORB-SLAM3 pose estimation method fused with GNSS and the local map reloading pose estimation method are used to estimate the bulldozer pose to ensure construction safety. The two methods are superior to the general SLAM system in terms of precision and dynamic performance.

A novel stratification clustering algorithm based on a new local density estimation method and an improved local inter-cluster distance measure

A novel stratification clustering algorithm based on a new local density estimation method and an improved local inter-cluster distance measure

A method for detecting outliers in linear-circular non-parametric regression.

This study proposes a robust outlier detection method based on the circular median for non-parametric linear-circular regression in case the response variable includes outlier(s) and the residuals are Wrapped-Cauchy distributed. Nadaraya-Watson and local linear regression methods were employed to obtain non-parametric regression fits. The proposed method's performance was investigated by using a real dataset and a comprehensive simulation study with different sample sizes, contamination, and heterogeneity degrees. The method performs quite well in medium and higher contamination degrees, and its performance increases as the sample size and the homogeneity of data increase. In addition, when the response variable of linear-circular regression contains outliers, the Local Linear Estimation method fits the data set better than the Nadaraya Watson method.

Infrared Small Target Detection Based on Isolation Constraint Under Complex Background

In the development of infrared search and track (IRST) system, small target detection in complex background is a challenging task due to the low signal-to-noise ratio (SNR) of small targets and interference of irregular clutter. In this paper, a detection method based on isolation constraint is presented. Firstly, the suspected region with certain contrast is obtained by multilayer gray difference (MGD). Then we calculate the isolation index of the suspected region using the eigenvalues of Hessian matrix of the raw image to suppress the irregular clutter. In order to get accurate size of the target, a local scale estimation method is proposed. Experiment results demonstrate that the proposed method is accurate and robust, and superior to baseline methods in signal-to-clutter gain (SCRG) and receiver operating characteristic (ROC) curve.

Effective estimation of nonlinear errors-in-variables models

Model variables are inevitably affected and interfered by measurement errors in statistical models inference. In this paper, we investigate the effective estimators of unknown operators in the nonlinear errors-in-variables models. By combining the maximum quasi-likelihood approach and the nonparametric local estimation method, a modified approximate maximum quasi-likelihood estimators are applied to evaluate the unknown parameters of the models. Under some mild assumptions, the consistency and asymptotic normality of the corresponding estimators are established. In order to further illustrate the advantages of the modified approach, we utilize simulation examples and actual data of monozygotic twins for analysis. The results visually show the robustness of the modified estimator as well as higher precision and accuracy.

Abstract NG03: Multiomics in disparities research: Identifying African ancestry-specific mechanisms in the triple negative breast cancer tumor microenvironment

Abstract NG03: Multiomics in disparities research: Identifying African ancestry-specific mechanisms in the triple negative breast cancer tumor microenvironment

LSOS: An FG Position Method Based on Group Phase Ranging Ambiguity Estimation of BeiDou Pseudolite

Due to the influence of indoor space environments, the carrier phase information obtained by the BeiDou pseudo-satellite often has the issue of cycle slips, which makes the user unable to carry out high-precision positioning. Aiming at the problem of ambiguity resolution (AR) and location in large-scale occluded space (LSOS), a factor graph (FG) position method based on group phase ranging ambiguity estimation of BeiDou pseudolite is proposed. Firstly, by introducing the principle of group phase period quantization and utilizing the multi-frequency characteristic of the BeiDou pseudo-satellite, the carrier phase propagation ambiguity of the BeiDou pseudo-satellite can be estimated quickly. On this basis, by introducing the shuffled frog leading algorithm (SFLA) assisted factor graph optimization location estimation method, the BeiDou pseudo-satellite positioning process in LSOS is realized. The experimental results show that the proposed method can solve the problem of fast estimation of ranging ambiguity of BeiDou pseudolite in LSOS, and the ranging accuracy can be improved to two wavelength ranges. In the further location experiment, it is found that the algorithm can not only guarantee the real-time location output but also improve the location precision to sub-meter level under the multi-frequency combination; the optimal location test precision is 9 cm, the maximum positioning error is 50 cm. This method successfully solves the problem wherein the BeiDou pseudo-satellite cannot provide real-time, continuous, and high-precision positioning in LSOS.

Target location and velocity estimation with the multistatic MU‐MIMO‐OFDM modulation signal

AbstractIn passive radar and joint communication and radar sensing (JCRS), target sensing with the multiuser multiple input multiple output orthogonal frequency division multiplexing (MU‐MIMO‐OFDM) modulation signal is gaining increasing interest. Multiple transmit nodes emitting the MU‐MIMO‐OFDM modulation signals at the same carrier frequency and one receiver collecting the target‐reflected signals for target location and velocity estimation are considered. This is a typical scenario when using the fifth‐generation (5G) communication network signal for target sensing. In this scenario, the echo signals corresponding to different transmit nodes are not resolved in the receiver, and the modulated data symbols cannot be removed from the received signals. Most traditional parameter estimation methods in passive radar and JCRS may not be suitable here. A location and velocity estimation method with the received echo signals is proposed. Specifically, the location parameters are extracted directly from the received echo signals. The location estimation is cast into a block sparse vector reconstruction problem. The variational Bayesian sparsity learning (VBSL) method is exploited for the reconstruction of the block sparse vector. Accelerated VBSL methods are developed for improving the computational efficiency. Simulations verify the effectiveness of the proposed methods.

CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning

Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called “half maps”. Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at https://cryores.zhanglab.net.

Multifidelity data augmentation for data driven passive impact location and force estimation in composite structures under simulated environmental and operational conditions

Data driven methods for impact location and severity estimation have great potential for real life application due to their ability to construct meta models that are not affected by changes in structural complexity (e.g. stiffeners and cut-outs). However, to do so, an initial reference database containing known input and output pairs is required to form an accurate meta model. The requirement to collect this data (mainly in the form of experimental tests) is considered by many to be not feasible and hinders the application of data driven methods in large scale, real life structures. Here a new multifidelity approach is presented to reduce the “cost” of constructing the reference database necessary for data driven impact location and severity estimation methods. The proposed cokriging approach is used to combined “cheap” low fidelity FE (Finite Element) simulation data with a limited amount of accurate but “expensive” experimental data to construct a reference database that requires less experimental data sampling (thus lower “cost”) but with similar levels of accuracy compared to reference databases that were constructed from pure experimental data. Using previously developed impact location (kriging based localisation) and maximum force estimation (maximum impact force gradient method) methods, the effect of different reference databases with varying amounts of experimental data samples are tested. The results obtained on a CFRP (Carbon Fibre Reinforced Plastic) coupon and stiffened panel showed that using pure FE data for the reference database yielded poor results. By adding a limited number of experimental points, it was shown that the accuracy increases significantly approaching levels achieved using pure experimental data with significantly less samples. The wider context of data fusion is also explored, highlighting the possibility to combine various sources of data (including inspection history as well as data from sister assets) which can possibly be used to create a data driven framework that requires less initial data whilst also being robust and self-improving.

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader-follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios.

A model-independent method for local blur estimation and its application to edge detection

A model-independent method for local blur estimation and its application to edge detection

Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user's photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).

Time-varying impact of information and communication technology on carbon emissions

To investigate the time-varying net environmental impact of Information and Communication Technology (ICT), we apply the local linear dummy variable estimation (LLDVE) method using a panel data consisting of 63 countries for the period 1995–2017. Our analysis reveals that ICT increases CO2 emissions until 2004, while reducing them after 2008, regardless of the national income level. We further uncover that the positive environmental impact of ICT on high-income countries is about 10 times greater than that on middle-income countries over time. These findings indicate that the development of ICT should be encouraged to alleviate carbon emissions on a global scale, especially for middle-income countries, given the benefits of an improved technology absorption rate on the mitigation effect in high income countries.

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person’s position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

BayesTICS: Local temporal image correlation spectroscopy and Bayesian simulation technique for sparse estimation of diffusion in fluorescence imaging.

The dynamics and fusion of vesicles during the last steps of exocytosis are not well established yet in cell biology. An open issue is the characterization of the diffusion process at the plasma membrane. Total internal reflection fluorescence microscopy (TIRFM) has been successfully used to analyze the coordination of proteins involved in this mechanism. It enables to capture dynamics of proteins with high frame rate and reasonable signal-to-noise values. Nevertheless, methodological approaches that can analyze and estimate diffusion in local small areas at the scale of a single diffusing spot within cells, are still lacking. To address this issue, we propose a novel correlation-based method for local diffusion estimation. As a starting point, we consider Fick's second law of diffusion that relates the diffusive flux to the gradient of the concentration. Then, we derive an explicit parametric model which is further fitted to time-correlation signals computed from regions of interest (ROI) containing individual spots. Our modeling and Bayesian estimation framework are well appropriate to represent isolated diffusion events and are robust to noise, ROI sizes, and localization of spots in ROIs. The performance of BayesTICS is shown on both synthetic and real TIRFM images depicting Transferrin Receptor proteins.

Joint localization and channel estimation in flow-assisted molecular communication systems

In this paper, we present a joint localization and channel parameter estimation method in the presence of signal-dependent noise and inter-symbol interference for diffusive molecular communication systems. The joint parameter estimation of the nanomachine can provide reliable communication in a generic diffusive molecular communication system. In particular, an iterative maximum likelihood estimation (MLE) approach for jointly estimating locations, flow velocity, and diffusion coefficient is carried out. The Cramer–Rao lower bound on the variance of channel parameters and location is derived. The normalized estimation error is marginally higher for unknown parameters case than that of some known parameters. The individual result (location estimation or channel parameters estimation) outperforms the existing methods.

Bayesian location estimation of mobile devices using a signal strength model

Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.