Accurate Localization Research Articles

Arthroscopically assisted accurate location of the bone tunnel entrance for lateral ankle ligament reconstruction may be a better choice for patients with chronic ankle instability: a retrospective study

Arthroscopically assisted accurate location of the bone tunnel entrance for lateral ankle ligament reconstruction may be a better choice for patients with chronic ankle instability: a retrospective study

Enhanced Typhoon Center Localization Using Geostationary Satellite Imagery

AbstractAn accurate center localization in near real‐time is critical for tropical cyclone (TC) monitoring and forecasting. This study presents a robust algorithm for localizing typhoon centers using the Chinese geostationary (GEO) meteorological satellite. The results using the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun‐4A (FY‐4A) satellite data, achieving a mean absolute error (MAE) of 29.4 km across various typhoon intensities in the Western North Pacific, superior to other baseline methods. By harnessing the multi‐spectral imagery from the FY‐4A and incorporating an attention mechanism, it significantly boosts the deep learning convolutional neural network's ability to identify typhoon cloud features and their centers, even during their initial and weakest stages, which is laudable because these are the most difficult for center fixing even for human analysts. Remarkably, it requires just a single moment satellite imagery to locate the center of typhoon, enabling automated updates of the typhoon centers in near real‐time applications.

Impact localization of CFRP laminate based on FBG sensing and GWO-BP neural network

Carbon fiber reinforced plastic (CFRP) structures are susceptible to external energy impact during service, resulting in invisible damage. Therefore, accurate localization of these impact sites is crucial for the maintenance and safety of CFRP structures. In this paper, the strain data of the impact response of the CFRP laminate is obtained by using the fiber grating (FBG) sensor, and the data are mapped to the impact location by BP neural network. However, the traditional BP neural network is susceptible to local minima and slow convergence speed. Therefore, Grey Wolf optimization algorithm (GWO) is introduced in this study to optimize the BP neural network, which improves the convergence speed and positioning accuracy of the model. The experimental results show that the GWO-BP neural network model has excellent performance in impact positioning, with the maximum positioning error of 17.01 mm and the average positioning error of 13.05 mm. Compared with other machine learning methods, it has higher accuracy and efficiency.

Global Semantic Localization from Abstract Ellipse-Ellipsoid Model and Object-Level Instance Topology

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based on an ellipse-ellipsoid model, combined with object-level instance topology and alignment. First, we develop a CNN-based (Convolutional Neural Network) ellipse prediction network, DEllipse-Net, which integrates depth information with RGB data to estimate the projection of ellipsoids onto images. Second, we model environments using 3D (Three-dimensional) ellipsoids, instance topology, and ellipsoid descriptors. Finally, the detected ellipses are aligned with the ellipsoids in the environment through semantic object association, and 6-DoF (Degree of Freedom) pose estimation is performed using the ellipse-ellipsoid model. In the bounding box noise experiment, DEllipse-Net demonstrates higher robustness compared to other methods, achieving the highest prediction accuracy for 11 out of 23 objects in ellipse prediction. In the localization test with 15 pixels of noise, we achieve ATE (Absolute Translation Error) and ARE (Absolute Rotation Error) of 0.077 m and 2.70∘ in the fr2_desk sequence. Additionally, DEllipse-Net is lightweight and highly portable, with a model size of only 18.6 MB, and a single model can handle all objects. In the object-level instance topology and alignment experiment, our topology and alignment methods significantly enhance the global localization accuracy of the ellipse-ellipsoid model. In experiments involving lighting changes and occlusions, our method achieves more robust global localization compared to the classical bag-of-words based localization method and other ellipse-ellipsoid localization methods.

Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.

CBCT Guidance for Removing Foreign Object from the Jawbone: A Case Study

AbstractForeign objects lodged in the mandibular bone often result from trauma or dental procedures. Accurate localization of these foreign objects is crucial for guided surgical removal, especially when patients present with persistent pain. Conventional imaging modalities like periapical and panoramic radiographs may not provide sufficient detail for precise localization. Cone beam computed tomography (CBCT) has emerged as a valuable tool for guided surgical interventions owing to its superior imaging capabilities. We present two cases of foreign objects lodged in the mandible, where patients complained of prolonged pain, soreness, and numbness in the right mandible extending to the head and behind the ear. Both patients had undergone previous right mandibular tooth extractions and received treatment from a neurologist without resolution of symptoms. Both cases showed the control improvement in complaints, and no paresthesia or postoperative complications were found. CBCT-guided surgical removal was performed in both cases, revealing a metal specimen measuring 6 × 3 × 1 mm3 in the first patient and a remaining root measuring 5 × 3 × 2 mm3 in the second patient. Diagnosing foreign objects in the mandible poses challenges due to their varied size, composition, and proximity to vital structures. CBCT offers superior imaging resolution, enabling precise localization and assessment of anatomical relationships, such as the distance to the inferior alveolar nerve and surrounding boundaries. CBCT emerges as the preferred imaging modality for diagnosing and guiding the surgical removal of foreign objects in the mandible. Its advantages include accurate localization, low radiation exposure, and cost-effectiveness. Compared with CT scans, CBCT also offers faster scanning times, making it a valuable tool in clinical practice for managing such cases

FastSLAM-MO-PSO: A Robust Method for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings.

Distributed Passive Positioning and Sorting Method for Multi-Network Frequency-Hopping Time Division Multiple Access Signals

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods.

BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization.

Localizing the bronchoscope in real time is essential for ensuring intervention quality. However, most existing vision-based methods struggle to balance between speed and generalization. To address these challenges, we present BronchoTrack, an innovative real-time framework for accurate branch-level localization, encompassing lumen detection, tracking, and airway association. To achieve real-time performance, we employ benchmark light weight detector for efficient lumen detection. We firstly introduce multi-object tracking to bronchoscopic localization, mitigating temporal confusion in lumen identification caused by rapid bronchoscope movement and complex airway structures. To ensure generalization across patient cases, we propose a training-free detection-airway association method based on a semantic airway graph that encodes the hierarchy of bronchial tree structures. Experiments on 11 patient datasets demonstrate BronchoTrack's localization accuracy of 81.72%, while accessing up to the 6th generation of airways. Furthermore, we tested BronchoTrack in an in-vivo animal study using a porcine model, where it localized the bronchoscope into the 8th generation airway successfully. Experimental evaluation underscores BronchoTrack's real-time performance in both satisfying accuracy and generalization, demonstrating its potential for clinical applications.

Metabolic Acidity/H2O2Dual-Cascade-Activatable Molecular Imaging Platform toward Metastatic Breast Tumor Malignancy.

Fluorescence imaging in the second near-infrared window (NIR-II) is crucial for accurate tumor diagnosis, offering superior resolution and penetration capabilities. Current NIR-II probes are limited by either being "always on" or responding to one stimulus, leading to low signal-to-noise ratios and potential false positives. We introduced a dual-lock-controlled probe, HN-PBA, activated by both H2O2 and tumor acidic environment. This dual response ensures bright fluorescence at tumor sites, leading to higher tumor-to-normal tissue ratios (T/NT) compared to conventional "always on" probes and probes activated only by H2O2. This strategy allows precise tumor identification and removal of primary and metastatic tumors, achieving superior T/NT ratios (24.3/6.4 for orthotopic and lung metastasis, respectively). Our probe also effectively detected lung metastatic foci as small as ≤ 0.7 mm and showed the capability for accurate lesion localization in clinical breast cancer specimens. This dual-stimuli-responsive strategy could aid future diagnostic probe design.

Accurate Acupoint Localization in 2D Hand Images: Evaluating HRNet and ResNet Architectures for Enhanced Detection Performance

Introduction: This research assesses HRNet and ResNet architectures for their precision in localizing hand acupoints on 2D images, which is integral to automated acupuncture therapy. Objectives: The primary objective was to advance the accuracy of acupoint detection in traditional Korean medicine through the application of these advanced deep-learning models, aiming to improve treatment efficacy. Background: Acupoint localization in traditional Korean medicine is crucial for effective treatment, and the study aims to enhance this process using advanced deep-learning models. Methods: The study employs YOLOv3, YOLOF, and YOLOX-s for object detection within a top-down framework, comparing HRNet and ResNet architectures. These models were trained and tested using datasets annotated by technicians and their mean values, with performance evaluated based on Average Precision at two IoU thresholds. Results: HRNet consistently demonstrated lower mean distance errors across various acupoints compared to ResNet, particularly at a 256x256 pixel resolution. Notably, the HRNet-w48 model surpassed human annotators, including medical experts, in localization accuracy. Conclusion: HRNet's superior performance in acupoint localization suggests its potential to improve the precision and efficacy of acupuncture treatments. The study highlights the promising role of machine learning in enhancing traditional medical practices and underscores the importance of accurate acupoint localization in clinical acupuncture.

A novel damage localization method of Circular Phased Array using Minimum Variance Distortionless Response Beamforming with Autocorrelation Matrix Diagonal Loading

Damage localization methods based on Acoustic Emission (AE) can be classified into time-based and waveform-based. However, the former requires a large number of sensors while the latter is limited to 2D plane localization. In order to address the challenge of achieving more accurate 3D localization using a reduced number of sensors, this paper proposes a Circular Phased Array using Minimum Variance Distortionless Response (MVDR) Beamforming with Autocorrelation Matrix Diagonal Loading (AMDL) method. Firstly, a sparse circular array is utilized to form multiple beamforming for coherent shear wave signals, decomposing the original 3D localization problem into Direction Of Arrival (DOA) estimation. Secondly, azimuth angle, elevation angle and autocorrelation matrix diagonal loading methods are introduced, working in conjunction with the MVDR beamforming algorithm. Finally, spatial integration is performed through matrix decomposition to solve geometric overdetermined equations. The effectiveness of the proposed method is validated through numerical simulations and experimental verifications under various damage conditions. Results indicate that estimation errors for azimuth and elevation angles are both less than 2 %, while 3D damage source localization errors remain within a range of less than 3 %. This proposed method extends beamforming technology from 2D plane localization to 3D localization, significantly reducing the complexity of sensor arrangement and lowering the cost of structural health monitoring systems by utilizing a small number of sensors.

Sensitive imaging of actinide materials in shielded radioactive waste

This paper reports on the development of a method for enhanced non-destructive assay (NDA) of radioactive waste using the novel technique neutron-gamma emission tomography (NGET). The technique relies on the detection of correlated fast neutrons and gamma rays emitted in spontaneous or induced fission. It is based on fast organic scintillators and enables sensitive detection and three-dimensional (3D) localization of the fission events. The technique is passive and does not require moving components. In this work, we apply the NGET technique to the category of radioactive waste which is often referred to as historic or legacy waste. This can include mixed wastes encased in shielded containers many decades ago, before the advent of detailed waste description criteria. These low or intermediate level wastes are often associated with lacking, limited or conflicting documentation. This poses a challenge when assigning the waste to the proper disposal route as well as in deciding whether the waste needs to undergo sorting and conditioning to fulfil waste acceptance criteria both with regards to safe interim storage and to its ultimate disposal. Actinides, such as isotopes of uranium and plutonium, with their typically long half-lives and decay chains are of special interest in this regard since they may challenge the long-term safety assessment in repositories predicated on mainly shorter half-life radionuclides if undetected. Accurate identification and localisation of actinides is also important from a safeguards perspective, especially since they are generally difficult to detect and localise by established passive means due to their relatively weak radiation emissions, in particular in shielded containments and in the presence of strong radiation fields from other radioactive materials. In this paper we present findings of measurements on shielded containments of long-lived radioactive waste performed at the Studsvik site in Sweden, as well as measurements on a laboratory assembly simulating a grouted waste drum. Similarities and differences between the novel NGET technique and a commercially available gamma imaging system are also briefly discussed.

Volcán de Fuego (Guatemala) monitoring with the Normalized Hotspot Indices (NHI) tool

Abstract. Volcán de Fuego is a stratovolcano located in Guatemala among the most active in the world. In this work, we investigate its eruptive activity from space by means of the Normalized Hotspot Indices (NHI) algorithm. The latter runs operationally under the Google Earth Engine (GEE) platform providing information on active volcanoes at global scale by means of daytime Sentinel 2 MSI (Multispectral Instrument) and Landsat 8/9 OLI/OLI2 (Operational Land Imager) data. In study, we present the results of a time series analysis performed by investigating 40 years of Landsat observations through the NHI algorithm. Results show that during the major periods of thermal activity (e.g., 2000−2003; 2012−2013; 2015−2018) the Volcán de Fuego generated extended lava flows, which were well identified and mapped by satellite. In addition, periods of reduced thermal activity (e.g., 2022−May 2024) marked by NHI were in good agreement with information provided by independent sources. The accurate localization and mapping of high-temperature features, and the characterization of different eruptive phases, demonstrate that the NHI algorithm, through the GEE-App available online (https://sites.google.com/view/nhi-tool/nhi-tool-for-volcanoes), may support scientists also in the monitoring of frequently active volcanoes such as the Volcán de Fuego, providing information about changes of thermal volcanic activity that could precede future and more dangerous eruptions.

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

Preoperative Ultrasound-Guided Marking, Mammogram, and Peroperative Use of Image Intensifier: A Cost-Effective Technique in Clipped Non-Palpable Breast Cancer Lesions to Achieve Adequate Surgical Margins.

To measure the effectiveness of localisation and removal of impalpable target lesions without compromising patient safety in a resource-limited setup using preoperative ultrasound and mammography with peroperative use of C-arm image intensifier. Descriptive study. Place and Duration of the Study: Department of Breast Surgery, Ittefaq Hospital (Trust), Lahore, Pakistan, from 25th October 2011 to 17th February 2023. All the breast cancer patients who achieved complete clinical response after neoadjuvant systemic treatment and underwent breast conservation surgery during the study period were included. Tumour / clip localisation was done using preoperative ultrasound or image-guided marking, a 2-view mammogram in all cases and the use of an image intensifier to confirm the presence of clips in the excised specimen. The primary outcome was the accurate localisation and removal of the index lesion, while the secondary outcome included the reoperation rate for positive margins and early local recurrence. Data from 144 patients were reviewed. Successful localisation was done in all the patients; only one patient had a positive margin for ductal carcinoma-in situ (DCIS), achieving a 99.3% clear margin rate. Local recurrence within two years after primary operation was seen in one patient only. Conclusion: By a combined approach of preoperative ultrasound-guided marking, a 2-view mammogram, and the use of image intensifier, successful localisation of an impalpable breast lesion is possible without compromising oncological and aesthetic principles. Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

Preoperative Ultrasound-Guided Marking, Mammogram, and Peroperative Use of Image Intensifier: A Cost-Effective Technique in Clipped Non-Palpable Breast Cancer Lesions to Achieve Adequate Surgical Margins.

To measure the effectiveness of localisation and removal of impalpable target lesions without compromising patient safety in a resource-limited setup using preoperative ultrasound and mammography with peroperative use of C-arm image intensifier. Descriptive study. Place and Duration of the Study: Department of Breast Surgery, Ittefaq Hospital (Trust), Lahore, Pakistan, from 25th October 2011 to 17th February 2023. All the breast cancer patients who achieved complete clinical response after neoadjuvant systemic treatment and underwent breast conservation surgery during the study period were included. Tumour / clip localisation was done using preoperative ultrasound or image-guided marking, a 2-view mammogram in all cases and the use of an image intensifier to confirm the presence of clips in the excised specimen. The primary outcome was the accurate localisation and removal of the index lesion, while the secondary outcome included the reoperation rate for positive margins and early local recurrence. Data from 144 patients were reviewed. Successful localisation was done in all the patients; only one patient had a positive margin for ductal carcinoma-in situ (DCIS), achieving a 99.3% clear margin rate. Local recurrence within two years after primary operation was seen in one patient only. Conclusion: By a combined approach of preoperative ultrasound-guided marking, a 2-view mammogram, and the use of image intensifier, successful localisation of an impalpable breast lesion is possible without compromising oncological and aesthetic principles. Breast conservation surgery, Localisation, Non-palpable, Margins, Image intensifier.

Method for Locating Secondary Circuit Faults in Substations Based on Graph Neural Networks

To improve the accuracy and portability of fault location in secondary circuits of smart substations, a fault location method for secondary circuits based on graph neural networks is proposed. First, a fault analysis of the secondary circuits in smart substations is conducted, and a graph database model of the secondary circuits is constructed, revealing the connection relationships between the secondary devices. Then, the fault location problem in secondary circuits is defined as a graph classification problem, and a fault graph generation model is proposed based on a representation method for fault information in secondary circuits. Finally, a fault location model for secondary circuits based on graph neural networks is constructed, and a performance optimization method based on binary cross-entropy is proposed to achieve accurate fault location in substation secondary circuits. Using the secondary circuits of a 220 kV smart substation as a reference, different case studies of secondary circuit faults are generated by altering the network topology, connection relationships, and network configurations through the fault graph generation model. Experiments comparing the proposed location method with other models show that this method has higher location accuracy and robustness.

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Distributed Backface Strain Sensing of Composite Adhesively Bonded Joints under Mode II Fatigue Loading

When investigating the fracture behaviour of adhesive joints, accurate crack tip localization is under mode II loading is particularly challenging due to the absence of crack opening. The crack front often has a curved shape, so methods that inspect the side of the specimen cannot capture the crack length throughout its width. In this study, distributed backface strain sensing using Optical Backscatter Reflectometry fibre sensors is used to monitor fatigue crack growth in adhesively bonded composite End Notched Flexure Specimens. The obtained backface strain profiles were compared with analytical and Finite Element solutions to understand their relationship with the crack length. Results show that the crack tip position can be found by a local extremum in the first derivative of the strain profile. The proposed monitoring technique was compared with more established techniques for crack length measurement, such as Visual Testing, Compliance Monitoring, and Phased Array Ultrasonic Testing, and was shown to provide good accuracy. The evaluation of these techniques not only plays a crucial role in ensuring accurate testing, but can also contribute to the development of effective strategies for inspection and monitoring of adhesive joints.