Location Estimation Method Research Articles

Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical.

Detection of delamination using laser-generated Lamb waves with improved wavenumber analysis

Abstract Delamination is a typical failure mode in multilayered materials, which may potentially threaten the safety and reliability of the materials if not detected promptly. This paper proposes a novel quantitative detection method for delamination in multilayered materials based on laser-generated Lamb waves with improved wavenumber analysis. First, a three-dimensional(3D) finite element model is established to simulate the interaction between delamination and laser-generated Lamb waves. The propagation characteristics of Lamb waves in multilayered materials without defects and with delamination of three different shapes, i.e., rectangular, circular and triangular are analyzed. And the mechanism that delamination can lead to the appearance of scattered wave and new wavenumber components is investigated, facilitating the quantitative detection of delamination. Then, an improved wavenumber analysis method for quantitative detection of delamination is proposed. The new wavenumber components caused by delamination are extracted by broadband adaptive wavenumber filtering to reduce imaging interference and artifacts caused by irrelevant components, and the spatial wavenumber spectrum is obtained by fast broadband local wavenumber estimation algorithm to enhance the delamination imaging accuracy and spatial resolution. The experimental results indicate that, across various experimental scenarios, the proposed method achieves superior imaging precision compared to traditional wavenumber filtering or local wavenumber estimation methods, and can quantitatively identify delamination defects of various shapes and sizes.

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Do FDI inflows bring both capital and CO2 emissions? Evidence from non-parametric modelling for the G7 countries

Foreign direct investment profoundly influences environmental conditions while promoting economic growth. This study examines the effects of FDI on CO2 emissions across the G7 countries from 1971 to 2020. Utilizing panel data, we employ a local linear dummy variable estimation method to analyze the non-linear and time-varying relationships between FDI and CO2 emissions. Our findings reveal a negative correlation between FDI and CO2 emissions during the period 1971–1995. In contrast, from 2000 to 2015, a significant positive correlation is observed. After 2015, no significant relationship is detected. Furthermore, we investigate the moderating effect of trade openness, urbanization level, and energy consumption to gain a deeper understanding of how the relationship between FDI and CO2 emissions is influenced under different national contexts. These findings underscore the dynamic nature of the relationship between FDI and CO2 emissions, offering significant implications for the advancement of a low-carbon economy.

Investigating the dynamic effects of service value added on CO2 emissions: Novel insights from a non-parametric approach

Carbon dioxide (CO2) emissions are the primary contributors to climate change. Addressing and mitigating climate change necessitates the effective management and utilization of renewable energy consumption, which poses a substantial challenge for the forthcoming decades. This study explores the dynamic effects of service value added (SVA) and renewable energy on environmental quality, particularly focusing on CO2 emissions. Unlike previous studies, we employ a non-parametric modeling approach to uncover the time-varying influence of service sector growth on CO2 emissions. Specifically, we apply the local linear dummy variable estimation (LLDVE) method to a panel of the 17 highest-emitting nations over the period 1980–2021. Our study uncovers a non-linear relationship between CO2 emissions and SVA. From 1980 to 2003, we observe a negative correlation. However, starting from 2005 to 2020, we witness a shift towards a positive correlation, indicating a rise in energy consumption within the service sector. The results indicate that significant emitter economies have yet to achieve sustainability, with the service sector continuing to contribute to pollution. Addressing this issue necessitates more robust climate change policies and increased investment in clean energy, specifically targeting the service sector, including buildings and transport.

Optimization study of high-dimensional varying coefficient partially linear model based on elastic network

In this paper, we study the high-dimensional varying coefficient partially linear model and proposes a variable parameter selection method combined with elastic network. The two-stage estimation method is adopted and the alternating direction multiplier method (ADMM) is used to solve the model. Firstly, based on the varying coefficient partially linear model, we use local polynomial estimation method to estimate the non-parametric part of the model and convert it into a parametric model. Secondly, the elastic network regularization term is introduced into the model for variable selection, and the alternating direction multiplier method (ADMM) is used to solve the model. Then, the convergence of the algorithm is proved under certain conditions. Finally, the proposed method has been validated to have good effectiveness and certain practicality through numerical simulation of two types of high-dimensional datasets and example analysis.

Improved Particle Filter in Machine Learning-Based BLE Fingerprinting Method to Reduce Indoor Location Estimation Errors

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method.

An infrastructure‐less emergency location detection system using basic cellular device‐to‐device connection

AbstractOne of the most time‐sensitive and critical tasks for first responders in the aftermath of a disaster is to locate people for effective and successful rescue operations. The existing solutions for large‐scale location detection of citizens depend on expensive equipment that may not be readily available in many urban areas and also cannot cover a vast area quickly. To address this challenge, the present study proposes a novel location estimation method by creating a Mobile Ad Hoc Network (MANET) using citizens' mobile phones. The proposed solution can accurately estimate the location of mobile devices in a MANET based on their Received Signal Strength (RSS) and communicate the estimated location coordinates to first responders without any dependencies on telecommunication infrastructures. This system will help first responders locate and map mobile devices in the affected area in a matter of seconds to improve the efficiency and effectiveness of postdisaster rescue operations. The envisioned solution offers unique capabilities that differentiate it from previously existing methods. The proposed method directly estimates device locations using their basic cellular signal strength without any dependency on GPS and relies only on basic cellphone signals. Therefore, nonsmartphones can also join and function in the envisioned MANET system. It also does not impose any computational operations or requirements for installing an app on citizens' phones. The performance of the proposed system was empirically evaluated using a simulation analysis. The results indicated that the method could efficiently and accurately discover more than 93% of mobile devices and estimate their locations.

An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

农业环境高精度三维点云地图构建方法研究

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.

Enhancing endoscopic measurement: validating a quantitative method for polyp size and location estimation in upper gastrointestinal endoscopy

BackgroundAccurate measurement of polyps size is crucial in predicting malignancy, planning relevant intervention strategies and surveillance schedules. Endoscopists’ visual estimations can lack precision. This study builds on our prior research, with the aim to evaluate a recently developed quantitative method to measure the polyp size and location accurately during a simulated endoscopy session.MethodsThe quantitative method merges information about endoscopic positions obtained from an electromagnetic tracking sensor, with corresponding points on the images of the segmented polyp border. This yields real-scale 3D coordinates of the border of the polyp. By utilising the sensor, positions of any anatomical landmarks are attainable, enabling the estimation of a polyp’s location relative to them. To verify the method’s reliability and accuracy, simulated endoscopies were conducted in pig stomachs, where polyps were artificially created and assessed in a test–retest manner. The polyp measurements were subsequently compared against clipper measurements.ResultsThe average size of the fifteen polyps evaluated was approximately 12 ± 4.3 mm, ranging from 5 to 20 mm. The test–retest reliability, measured by the Intraclass Correlation Coefficient (ICC) for polyp size estimation, demonstrated an absolute agreement of 0.991 (95% CI 0.973–0.997, p < 0.05). Bland & Altman analysis revealed a mean estimation difference of − 0.17 mm (− 2.03%) for polyp size and, a mean difference of − 0.4 mm (− 0.21%) for polyp location. Both differences were statistically non-significant (p > 0.05). When comparing the proposed method with calliper measurements, the Bland & Altman plots showed 95% of size estimation differences between − 1.4 and 1.8 mm (− 13 to 17.4%) which was not significant (p > 0.05).ConclusionsThe proposed method of measurements of polyp size and location was found to be highly accurate, offering great potential for clinical implementation to improve polyp assessment. This level of performance represents a notable improvement over visual estimation technique used in clinical practice.

Innovation, financial permeation, and income inequality: From a dynamic perspective in China

Innovation is a driving force of wealth distribution. To explore its time-varying effect on income inequality, we propose a nonparametric model using the local linear dummy variable estimation (LLDVE) method. Based on province-level panel data from China spanning from 2006 to 2020, we find that innovation initially reduces income disparity until 2009, then exacerbates it from 2013 to 2016, and alleviates inequality again over 2018–2020. We further verify that financial permeation serves as a catalyst in the inequitable income distribution driven by innovation. However, this moderating effect reverses the relationship between green innovation and income inequality. This suggests that we should enhance the financial service towards all aspects of innovation beyond its support of green innovation.

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

AbstractIndoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix‐based RSSI indoor positioning (M‐RIP) for initial location estimation and feature‐based image indoor positioning (F‐IIP) for position determination via image feature matching. Furthermore, extended area‐based post‐processing (EA‐PP) is employed to augment M‐RIP's precision and minimize image matching computation in F‐IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

Location of Faults in Six Phase Transmission Line using a Neuro Fuzzy System

Fault location plays an important role in power systems. It is frequently used in transmission lines to reduce the system damage for growing electrical energy demand. However, the location estimation method itself is easy to be disturbed by different parameters and gives estimation errors. To solve this problem, this paper introduces a scheme that uses neuro fuzzy system (NFS) to locate all types of faults such as shunt faults, transforming faults, series faults and simultaneous faults in six phase overhead line (SPOL). This scheme has been performed on a 68 km, 138 kV, 60Hz, SPOL for all fault types by employing MATLAB software Simulink. Here, Haar wavelet transforms (HWT) are used to extract the current signals in order to evaluate the behavior and characteristics of higher frequency components. Then, the obtained HWT currents data are employed to construct the fault location scheme in SPOL based on NFS. The obtained experimental results confirm that by using the NFS, the maximum fault location error (MFLE) can be reduced by more than 10%. It can further reduce the maximum response time (MRT) for the operator to address the faults and ensure the power system reliability in the future. The test result executed in this investigation indicates that the NFS is resilient to wide changes in fault conditions, and it shows good performance and huge potential for location of faults in SPOL.

Unraveling the Dynamics of Inflation Persistence: A Long-Memory Analysis of Headline Inflation Convergence and Divergence in European Economies

This comprehensive study delves into the Harmonised Consumer Price Index (HCPI) behavior across European economies from the early 2000s to the post-pandemic period. By analyzing the patterns of convergence and divergence in HCPI persistence, the study aims to uncover the common external and country-specific factors driving these dynamics. Utilizing the local Whittle estimation method, the research examines the long-memory properties of headline inflation through monthly data from January 1996 to January 2023. The study expands upon existing knowledge of inflation persistence by exploring its changes and the various patterns of inflation persistence in Europe. The findings reveal significant differences in the persistence of inflation and the underlying factors behind it. As such, the study emphasizes the need for tailored policy approaches and a deeper understanding of inflation's true nature, expectation formation, price-setting behavior, and monetary policy in shaping inflation persistence. Given its important policy implications, this research is highly relevant to the European Central Bank (ECB) and national governments within the Eurozone.

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on map-model fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Q-score. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at http://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

Accurate road user localization in aerial images captured by unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become increasingly popular for traffic data collection. However, the depth relief of road users and the perspective distortion of the onboard camera induce nonnegligible measurement errors while exploiting UAVs to localize road users. To address this issue, this paper presents a method for accurate road user location estimation in aerial images. First, a deep-learning-based method was employed to detect road users in aerial images using oriented bounding boxes. Then, the localization error induced by the depth relief and perspective distortion was examined and modeled, based on which an error compensation scheme was developed to offset the localization error for each road user so that higher localization accuracy is attainable. Field experiments were conducted to evaluate the proposed method's performance. The results demonstrated a promising accuracy in estimating the location of road users, signifying the method's potential to improve the credibility of UAVs in traffic applications.

A local correlation estimation surrogate-assisted bi-objective evolutionary algorithm for heterogeneous objectives

Most existing evolutionary algorithms for solving bi-objective optimization problems are based on the assumption that both objectives can be evaluated once at the same time. However, in real-world optimization problems, there is a significant difference in function evaluation time between objectives, and these problems are defined as the problems with heterogeneous objectives. To utilize the latency of objectives, we propose a local correlation estimation based surrogate-assisted bi-objective evolutionary algorithm for problems with heterogeneous objectives. In the proposed algorithm, surrogate models are employed to approximate the objective functions. The proposed local correlation estimation (LCE) method is used to analyze the correlation between objectives within a local region, guiding the search direction for one objective while identifying promising solutions for the other objective. Finally, the ablation experiment for the LCE method validates the effectiveness of the proposed strategy, and the comparative results on various expensive bi-objective test problems demonstrate that the proposed algorithm is promising on heterogeneous bi-objective optimization problems.

Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix

Existing SNP-heritability estimators that leverage summary statistics from genome-wide association studies (GWAS) are much less efficient (i.e., have larger standard errors) than the restricted maximum likelihood (REML) estimators which require access to individual-level data. We introduce a new method for local heritability estimation—Heritability Estimation with high Efficiency using LD and association Summary Statistics (HEELS)—that significantly improves the statistical efficiency of summary-statistics-based heritability estimator and attains comparable statistical efficiency as REML (with a relative statistical efficiency >92%). Moreover, we propose representing the empirical LD matrix as the sum of a low-rank matrix and a banded matrix. We show that this way of modeling the LD can not only reduce the storage and memory cost, but also improve the computational efficiency of heritability estimation. We demonstrate the statistical efficiency of HEELS and the advantages of our proposed LD approximation strategies both in simulations and through empirical analyses of the UK Biobank data.

Causal identification of transit-induced property value uplift in Canada's Waterloo Region: A spatio-temporal difference-in-differences method application

Renewed interest in light-rail transit (LRT) in North America has heightened the need for an improved understanding of transit impacts on land value uplift (LVU). A number of studies have investigated the relationship, with findings varying with local contexts and estimation methods. Most of these studies focus on the aggregate effects of transit using cross-sectional models, but do not examine the spatial and temporal heterogeneity in transit impacts through quasi-experimental approaches. To bridge this gap, we build a set of spatio-temporal difference-in-differences (STDID) models for the causal identification of transit-induced land-value uplift, taking the new LRT line in Canada's Waterloo Region as the case study. The study contributes to the transit-induced LVU literature in several ways. First, we account for the space-time influence of recent comparable sales in price determination, a transaction data-generating process often excluded in hedonic studies. Second, our models reveal the disaggregate effects of transit policies in different station areas and transit phases. Third, we provide a pre-LRT analysis in a car-dependent mid-sized urban area, which offers insight into speculative investment in TOD areas and/or resident anticipation of future accessibility benefits. The findings can provide guidance for value-capture programs and cost-benefit analysis for transit-oriented development in mid-sized cities.