Location Estimation Research Articles

On the estimation of hip joint centre location with incomplete bone ossification for foetus-specific neuromusculoskeletal modeling

Childbirth is a complex physiological process in which a foetal neuromusculoskeletal model is of great importance to develop realistic delivery simulations and associated complication analyses. However, the estimation of hip joint centre (HJC) in foetuses remains a challenging issue. Thus, this paper aims to propose and evaluate a new approach to locate the HJC in foetuses. Hip CT-scans from 25 children (F = 11, age = 5.5 ± 2.6 years, height = 117 ± 21 cm, mass = 26 kg ± 9.5 kg) were used to propose and evaluate the novel acetabulum sphere fitting process to locate the HJC. This new approach using the acetabulum surface was applied to a population of 57 post-mortem foetal CT scans to locate the HJC as well as to determine associated regression equations using multiple linear regression. As results, the average distance between the HJC located using acetabulum sphere fitting and femoral head sphere fitting in children was 1.5 ± 0.7 mm. The average prediction error using our developed foetal HJC regression equations was 3.0 ± 1.5 mm, even though the equation for the x coordinate had a poor value of R2 (R2 for the x coordinate = 0.488). The present study suggests that the use of the acetabulum sphere fitting approach is a valid and accurate method to locate the HJC in children, and then can be extrapolated to get an estimation of the HJC in foetuses with incomplete bone ossification. Therefore, the present paper can be used as a guideline for foetus specific neuromusculoskeletal modelling.

Multi-faceted deep learning framework for dynamics modeling and robot localization learning

Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc.

Transient sounds of hydrothermal vents

Transient acoustic signals were recently detected at the Main Endeavour Hydrothermal Vent Field which are believed to be generated by both geological and biological sources, including vent chimney collapse, impulsive geological signals, fish grunts, and snapping. These signals provide an opportunity for long-term passive acoustic monitoring of hydrothermal vent activity and ecology. This method offers advantages of longevity and robustness compared with other monitoring techniques, as the sensor can remain a safe distance away from the high-temperature, caustic vent fluid. Utilizing recordings from a bottom-mounted hydrophone on Ocean Networks Canada’s NEPTUNE observatory, a detector was implemented to identify and classify these signals in more than one year of acoustic data after 2016. While only a single hydrophone is available at this site, an array of three seismic accelerometers also on the NEPTUNE observatory was used to localize transient events when sufficient signal-to-noise ratio was available to provide confidence in the location estimate. Correlation of the transient sounds with seismic activity at the vent field was also evaluated, suggesting that passive acoustic monitoring can augment seismic records to provide additional information regarding geological activity at hydrothermal vent sites.

The biogeographic history of neosuchian crocodiles and the impact of saltwater tolerance variability.

Extant neosuchian crocodiles are represented by only 24 taxa that are confined to the tropics and subtropics. However, at other intervals during their 200 Myr evolutionary history the clade reached considerably higher levels of species-richness, matched by more widespread distributions. Neosuchians have occupied numerous habitats and niches, ranging from dwarf riverine forms to large marine predators. Despite numerous previous studies, several unsolved questions remain with respect to their biogeographic history, including the geographical origins of major groups, e.g. Eusuchia and Neosuchia itself. We carried out the most comprehensive biogeographic analysis of Neosuchia to date, based on a multivariate K-means clustering approach followed by the application of two ancestral area estimation methods (BioGeoBEARS and Bayesian ancestral location estimation) applied to two recently published phylogenies. Our results place the origin of Neosuchia in northwestern Pangaea, with subsequent radiations into Gondwana. Eusuchia probably emerged in the European archipelago during the Late Jurassic/Early Cretaceous, followed by dispersals to the North American and Asian landmasses. We show that putative transoceanic dispersal events are statistically significantly less likely to happen in alligatoroids. This finding is consistent with the saltwater intolerant physiology of extant alligatoroids, bolstering inferences of such intolerance in their ancestral lineages.

Dual Random Noise Suppression Strategy for Permanent Magnet Synchronous Motor Based on Random Pulse Width Modulation and Random High-frequency Injection Method

This study proposes an innovative dual control strategy to tackle the challenge of electromagnetic noise in the sensorless low-speed operation of Permanent Magnet Synchronous Motors (PMSM). The strategy combines random Pulse Width Modulation (PWM) technology with a stochastic high-frequency injection approach. By incorporating two distinct randomized frequency switching techniques into the high-frequency injection circuit, the method integrates two-state Markov chains into both PWM technology and the random high-frequency injection method. Subsequently, a mathematical model for signal processing is developed, and extensive simulation and experimental verification are conducted. The experimental findings affirm the effectiveness of this approach in effectively mitigating electromagnetic noise while ensuring accurate location estimation within an acceptable range.

Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT

Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we propose a novel localization technique assisted by a single base station (BS) in 3D IIoT scenarios. Our approach employs the MUltiple SIgnal Classification (MUSIC) algorithm to jointly estimate the angle of arrival (AoA) in azimuth and elevation, as well as the time of arrival (ToA). Compared to conventional multi-BS-assisted or MUSIC-based algorithms, our technique offers flexibility, easy implementation, and low computational cost. To improve performance, we integrate the Taylor-series into the iterative process after a MUSIC-based joint azimuth, elevation angle and delay estimation (JAEDE), resulting in a significant 99% reduction in computational complexity compared to a two-step MUSIC-based approach utilizing coarse-fine grid searching. Through numerical simulations, we compare our algorithm with three other MUSIC-based joint or separate estimation approaches, demonstrating its superior performance in azimuth angle of arrival (AoAz), elevation angle of arrival (AoAe), TOA, and overall location estimation across varying signal-to-noise ratio (SNR) conditions.

Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min.

Localization of underground pipe jacking machinery: A reliable, real-time and robust INS/OD solution

Accurate positioning is important for guidance control in pipeline construction, but traditional approaches fail in GPS-denied environments. This paper addresses the underground localization problem for the pipe jacking machine by presenting an Inertial Navigation System (INS)/ODometry (OD) scheme. It is a loosely coupled navigation system that combines an INS and an OD to provide reliable, real-time, and robust location estimation in an enclosed and restricted space. The combination is achieved by a multi-sensor fusion algorithm to avoid the divergence of standalone INS. Compared with the traditional localization technologies, the advantage of the INS/OD scheme is that there is no performance degradation or service failure despite severe blockage of the thick soil layer due to the selection of dead reckoning-based sensors. Moreover, an Unbiased Finite Impulse Response (UFIR) filter serves as the fusion mechanism to improve the robustness against a prior knowledge of noise, considering unknown and time-varying noise statistics in practice. The INS/OD scheme is verified through a numerical simulation and employed on-site in a real underground sewage pipeline construction project. Despite the construction site being more than 10 meters deep underground, the INS/OD scheme continues to perform reliably and robustly in real-time, significantly benefiting the engineering guidance enterprise.

Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms

Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object’s geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object’s location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE ibeacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN.

Preoperative estimation of retinal hole location using ultra-wide-field imaging.

Accurate localization of retinal holes is essential for successful scleral buckling (SB) surgery. We aimed to verify the feasibility of using ultra-wide-field (UWF) imaging for preoperative estimation of retinal hole location. We observed 21 eyes from 21 patients with rhegmatogenous retinal detachment (RRD) who underwent successful SB. They were treated at the Department of Ophthalmology of the Second Hospital of Hebei Medical University between November 2020 and November 2021. UWF fundus photography using an Optos device was performed at different steering positions 1 day before, 1 day after, and 1 month after SB. Using the preoperative fundus images, we measured the transverse diameter of the optic disc (D1) and the distance from the centre of the retinal holes to the ora serrata (D2). The accurate transverse diameter of the optic disc (Dd) was measured preoperatively using optical coherence tomography. The same surgeon measured the scleral chord lengths intraoperatively from the limbus to the located retinal hole marked on the sclera using an ophthalmic calliper. Statistical software was used to analyze the consistency of scleral chord length between the retinal hole and the limbus, which was estimated by preoperative UWF imaging and was measured using an ophthalmic calliper intraoperatively. There was no statistically significant difference in the scleral chord length between the retinal holes and the limbus, which was estimated by preoperative UWF fundus photography and was measured by the calliper during surgery. It is feasible to locate retinal holes using UWF fundus photography before SB, which is helpful for quick localization, thereby reducing the learning curve of SB surgery.

FAULT DETECTION AND LOCATION BASED SVM FOR THREE PHASE TRANSMISSION LINES APPLYING POSITIVE SEQUENCE FAULT COMPONENTS

Transmission lines are an imperative element of the modern power systems. Any faults in them can cause an undesirable interruption in power supply. Precise analysis of these faults is important in-order to ensure an incessant supply of power. For this purpose, fault detection and classification are needed to clear any such faults and re-establish the system to maintain its normal operation. In this paper, a novel integrated approach of protective relaying with enhanced support vector machine algorithm has been adopted for detecting faults and its location estimation in long transmission line. The proposed scheme is successfully able to detect and classify different symmetrical and unsymmetrical faults along with some peculiar cases related to High Impedance Faults (HIF) and evolving faults, current transformer (CT) saturation/ capacitive voltage transformer (CVT) transient, close-in faults, swing condition, source strength variation, etc. The comparative analysis with recent proposed techniques declared the potentiality and robustness of the scheme

Emulation of seismic-phase traveltimes with machine learning

SUMMARY We present a machine learning (ML) method for emulating seismic-phase traveltimes that are computed using a global-scale 3-D earth model and physics-based ray tracing. Accurate traveltime predictions based on 3-D earth models are known to reduce the bias of event location estimates, increase our ability to assign phase labels to seismic detections and associate detections to events. However, practical use of 3-D models is challenged by slow computational speed and the unwieldiness of pre-computed lookup tables that are often large and have prescribed computational grids. In this work, we train a ML emulator using pre-computed traveltimes, resulting in a compact and computationally fast way to approximate traveltimes that are based on a 3-D earth model. Our model is trained using approximately 850 million P-wave traveltimes that are based on the global LLNL-G3D-JPS model, which was developed for more accurate event location. The training-set consists of traveltimes between 10 393 global seismic stations and randomly sampled event locations that provide a prescribed, distance-dependent geographic sample density for each station. Prediction accuracy is dependent on event-station distance and whether the station was included in the training set. For stations included in the training set the mean absolute deviation (MAD) of the difference between traveltimes computed using ray tracing through the 3-D model and the ML emulator for local, regional, and teleseismic distances are 0.090, 0.125 and 0.121 s, respectively. For tested station locations not included in the training set, MAD values for the three distance ranges increase to 0.173, 0.219 and 0.210 s, respectively. Empirical traveltime residuals for a global reference data are indistinguishable when ML emulation or the 3-D model is used to compute traveltimes. This result holds regardless of whether the recording station is used in ML training or not.

Risk-aware autonomous localization in harsh urban environments with mosaic zonotope shadow matching

Urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of which street or which side of the street the user is located. 3D mapping-aided GNSS uses grid-based GNSS shadow matching and ranging alongside data-driven line-of-sight (LOS) classifiers to improve localization accuracy. However, previous work on shadow matching has not considered the needs of risk-aware autonomous systems. Our prior work introduced a fully set-based version of shadow matching that proposed set-valued maps and localization with computationally efficient set representations. In this work, we extend our prior work to propose Mosaic Zonotope Shadow Matching (MZSM), which provides a mathematical framework to begin addressing risk awareness in 3D mapping-aided GNSS. We employ a classifier-agnostic polytope mosaic architecture to provide risk-aware set-based bounds on urban positioning. We formulate a recursively expanding binary tree that, through set operations, refines an initial set-based location estimate into a mosaic of smaller polytopes. We assess our framework with a 3D building map of San Francisco and emulated classifiers to validate our algorithm's risk-aware improvements. We demonstrate that MZSM provides set-based localization bounds across varied data-driven LOS classifiers and yields a more precise understanding of the uncertainty and risks in 3DMA-GNSS over existing methods. We validate that our tree-based construction is efficient and tractable, computing a mosaic from 14 satellites in 0.63 seconds and growing quadratically in the satellite number.

Two-Leak Case Diagnosis Based on Static Flow Model for Liquid Transmission Pipelines.

The article deals with a diagnosis of multiple leaks from liquid transmission pipelines using analytical methods. Such solutions, based on advanced mathematical models of pipeline flow dynamics, usually turn out to be very complex and time-consuming. However, under certain operating conditions, a simpler approach may also be useful. Such an idea is presented in this paper, proposing two simplified methods for diagnosing double leakages. In principle, these methods apply to both simultaneous and non-simultaneous leaks. The first one uses a static model of a pipeline involving two leaks and takes advantage of the minimization of the objective function defined as the squared deviation of the modeled pressures from the pressures measured on the pipeline. The second method uses a pipeline flow model of a static type in combination with a gradient indicator aimed at detecting leaks and employing algorithms assigned to determining the location and size of leaks. The results of methods' validation, based on tests carried out with the use of measurement data obtained from an experimental water pipeline, were also presented. The outcomes of the performed tests proved the methods' effectiveness in terms of detection, isolation, localization, and intensity estimation of both simultaneous and non-simultaneous double leakages.

Computer Vision Based Planogram Compliance Evaluation

Arranging products in stores according to planograms, optimized product arrangement maps, is an important sales enabler and necessary for keeping up with the highly competitive modern retail market. Key benefits of planograms include increased efficiency, maximized retail store space, increased customer satisfaction, visual appeal, and increased revenue. The planograms are realized into product arrangements by humans, a process that is prone to mistakes. Therefore, for optimal merchandising performance, the planogram compliance of the arrangements needs to be evaluated from time to time. We investigate utilizing a computer vision problem setting—retail product detection—to automate planogram compliance evaluation. Retail product detection comprises product detection and classification. The detected and classified products can be compared to the planogram in order to evaluate compliance. In this paper, we propose a novel retail product detection pipeline combining a Gaussian layer network product proposal generator and domain invariant hierarchical embedding (DIHE) classifier. We utilize the detection pipeline with RANSAC pose estimation for planogram compliance evaluation. As the existing metrics for evaluating the planogram compliance evaluation performance assume unrealistically that the test image matches the planogram, we propose a novel metric, called normalized planogram compliance error (EPC), for benchmarking real-world setups. We evaluate the performance of our method with two datasets: the only open-source dataset with planogram evaluation data, GP-180, and our own dataset collected from a large Nordic retailer. Based on the evaluation, our method provides an improved planogram compliance evaluation pipeline, with accurate product location estimation when using real-life images that include entire shelves, unlike previous research that has only used images with few products. Our analysis also demonstrates that our method requires less processing time than the state-of-the-art compliance evaluation methods.

Routing Optimization of Unmanned AVS for Localization of Wireless Emitters in Outside Locations

To control illegal radio waves, methods of locating unknown emitters are used. Location methods using ground sensors suffer from location accuracy degradation in environments where the distance between the emitter and the sensor is not line of sight (NLoS). Therefore, research is being done to improve location accuracy by using Unmanned Aerial Vehicles (UAVs) as sensors to ensure a line-of-sight (LoS) condition. However, UAVs can fly freely in the sky, making it difficult to optimize flight paths based on particle swarm optimization (PSO) for efficient and accurate localization. This article examines the optimization of UAV flight paths to achieve highly efficient and accurate external localization of unknown emitters through two approaches, a circular orbit and a free path, respectively. Our numerical results reveal the improved location estimation error performance of our proposed approach. In particular, when evaluating at the 90th percentile of the cumulative error distribution function (CDF), the proposed approach can achieve an error of 28.59 m with circular orbit and 12.91 m with free path orbit, compared to the case of the conventional fixed sensor. whose location estimation error is 55.02 m.

Coral Detection, Ranging, and Assessment (CDRA) algorithm-based automatic estimation of coral reef coverage

Climate change poses significant threats to the health of coral reefs. The detection of coral coverage is a quantitative method for assessing the health of coral reefs. Valuable insights into the effects of climate change on coral reef ecosystems and the trends in coral reef development can be gained through the monitoring of coral coverage. However, traditional manual methods for estimating coral coverage are time-consuming and labor-intensive, limiting the scalability and efficiency of coral surveys. The field of automated processing for coral video imagery is currently limited, impeding the development of effective techniques for comprehensive coral monitoring and further hindering progress in coral ecosystem monitoring. To cope with these challenges, there is a crucial need for automated systems to efficiently handle large volumes of video data, thereby transforming imaging devices, such as underwater robots, into autonomous sensors capable of conducting reliable and comprehensive surveys. In response to this pressing issue, this study presents a novel video analysis approach, termed the Coral Detection, Ranging, and Assessment (CDRA) algorithm, which combines cutting-edge computer vision techniques including You Only Look Once version 5 (YOLOv5) object detection, binocular stereo vision, and tracking algorithms. The CDRA algorithm aims to analyze continuous video segments of coral reefs, enabling the identification, counting, and estimation of size and location of individual coral colonies. Experimental evaluations conducted in controlled pool environments demonstrated the high accuracy of the proposed method, with an average accuracy of 90.7% in estimating coral coverage at different locations. Moreover, field trials conducted in Xidao Island of China verified the effectiveness and robustness of the CDRA algorithm under varying water quality and lighting conditions. The findings of this study represent a significant step towards the development of reliable and automated techniques for coral reef detection, contributing to the advancement of coral reef conservation efforts.

Event detection in a noisy time series data using static smoothening and gradient variation analytics

Data-driven prognostic industrial asset health management is essential to improve reliability and availability of industrial machineries. Industrial sensors capture multiple phenomena of underlying assets like environment impact, system degradation, process variation, instrument noise, control system response or user induced actions. Some of these phenomena have distinct signature and have impact on component life and remaining useful life. Capturing these events’ signature help to apply advanced AI algorithm to categorize various failure modes and early detection. For Ex: Some of transient events can contribute to thermal cycling of component and in turn reduces life of high temperature and low thermal mass components. Due to random and nonstandard nature of these events, it is extremely challenging to detect and extract these events. Existing change point detection algorithms have limitations to detect sudden variations, which is common due to process or control actions. The noisy signal adds additional challenge to differentiate between important event and noise. In this work of time series analysis, we propose a new approach for consistent estimation of numbers and locations of the change-points. With this tunable algorithm combined with event labelling and pattern search, we can detect events of our functional needs and use them as a feature for our prediction models. This methodology has opened exciting opportunity to further analyse these events with development of classification system and time to fail prediction models and also apply large language models for time series data.

Efficient Localization Method Based on RSSI for AP Clusters.

The localization accuracy is susceptible to the received signal strength indication (RSSI) fluctuations for RSSI-based wireless localization methods. Moreover, the maximum likelihood estimation (MLE) of the target location is nonconvex, and locating target presents a significant computational complexity. In this paper, an RSSI-based access point cluster localization (APCL) method is proposed for locating a moving target. Multiple location-constrained access points (APs) are used in the APCL method to form an AP cluster as an anchor node (AN) in the wireless sensor network (WSN), and the RSSI of the target is estimated with several RSSI samples obtained by the AN. With the estimated RSSI for each AN, the solution for the target location can be obtained quickly and accurately due to the fact that the MLE localization problem is transformed into an eigenvalue problem by constructing an eigenvalue equation. Simulation and experimental results show that the APCL method can meet the requirement of high-precision real-time localization of moving targets in WSN with higher localization accuracy and lower computational effort compared to the existing classical RSSI-based localization methods.

Time-reversal technique for pipeline defect detection.

In this paper, time-reversal (TR) technique is proposed for the detection of discrete defects (e.g., leaks and blockages) in pressurized fluid-filled pipelines using active transient waves. The proposed technique is based on the one-dimensional wave equation and decouples the search for the location from that of the size of the defect. Another key feature of this method is that the measured pressure head signal can be truncated so as to eliminate undesirable reflections from unknown or uncertain parts of the pipe system so as not to compromise the accuracy and robustness of the detection. The proposed method is tested through numerical simulations for a single leak, multiple leaks, and multiple mixed-type of defects (i.e., leaks and blockages) in elastic as well as visco-elastic pipe systems. The proposed method is also validated through laboratory experiments wherein leaks and blockages of various sizes are considered. The sensitivity analysis of the method with respect to wave speed and signal-to-noise ratio (SNR) is also performed. In all cases, the method has shown accurate results in terms of localization, classification, and size estimation of the defects even for SNR as low as 0 dB.