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Background: Accurate preoperative localization is a challenge in thoracoscopic surgery for multiple pulmonary nodules. In this study, we aimed to assess the accuracy and feasibility of electromagnetic navigation (EN)-guided percutaneous localization. Methods: We enrolled 50 patients with multiple pulmonary nodules for EN-guided (EN group) or CT-guided (CT group) localization. The primary outcome was the localization accuracy, and the primary analysis was to assess the noninferiority (noninferiority margin of 5 mm) of EN-induced localization deviation compared with that of CT-induced deviation. The secondary outcomes included the procedural duration, anxiety score, and incidence of complications. Results: Among the 50 patients randomized to the EN- and CT-guided groups, 24 patients (53 nodules) underwent EN-guided preoperative marking, and 25 patients (54 nodules) underwent CT-guided preoperative marking. The demographic, clinical, and radiological characteristics did not differ significantly between the groups (P > 0.05). Among these patients, the EN group was noninferior in terms of localization deviation compared with the CT group (9.0 [6.5] vs. 7.5 [6.0] mm; P = 0.33; absolute difference 0.9 [95% CI] 0.03–1.77]). Furthermore, the procedural duration was 16.3 (4.2) minutes for the EN group and 22.3 (8.2) minutes for the CT group (P = 0.002). Additionally, the EN group exhibited significant improvements compared with the CT group on the basis of the Amsterdam Preoperative Anxiety and Information Scale, particularly in relation to the S and C subscales' cumulative scores. Conclusions: EN was found to be noninferior to CT in terms of localization accuracy, as it significantly decreased the procedural duration and relieved psychological stress for patients who underwent simultaneous surgery for multiple pulmonary nodules. Clinical Trial Registration: Chinese Clinical Trial Registry Identifier: ChiCTR2200056734. [ABSTRACT FROM AUTHOR]

Abstract Background Accurate preoperative localization is a challenge in thoracoscopic surgery for multiple pulmonary nodules. In this study, we aimed to assess the accuracy and feasibility of electromagnetic navigation (EN)-guided percutaneous localization. Methods We enrolled 50 patients with multiple pulmonary nodules for EN-guided (EN group) or CT-guided (CT group) localization. The primary outcome was the localization accuracy, and the primary analysis was to assess the noninferiority (noninferiority margin of 5 mm) of EN-induced localization deviation compared with that of CT-induced deviation. The secondary outcomes included the procedural duration, anxiety score, and incidence of complications. Results Among the 50 patients randomized to the EN- and CT-guided groups, 24 patients (53 nodules) underwent EN-guided preoperative marking, and 25 patients (54 nodules) underwent CT-guided preoperative marking. The demographic, clinical, and radiological characteristics did not differ significantly between the groups (P > 0.05). Among these patients, the EN group was noninferior in terms of localization deviation compared with the CT group (9.0 [6.5] vs. 7.5 [6.0] mm; P = 0.33; absolute difference 0.9 [95% CI] 0.03–1.77]). Furthermore, the procedural duration was 16.3 (4.2) minutes for the EN group and 22.3 (8.2) minutes for the CT group (P = 0.002). Additionally, the EN group exhibited significant improvements compared with the CT group on the basis of the Amsterdam Preoperative Anxiety and Information Scale, particularly in relation to the S and C subscales’ cumulative scores. Conclusions EN was found to be noninferior to CT in terms of localization accuracy, as it significantly decreased the procedural duration and relieved psychological stress for patients who underwent simultaneous surgery for multiple pulmonary nodules. Clinical Trial Registration Chinese Clinical Trial Registry Identifier: ChiCTR2200056734.

Abstract Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

Phase-sensitive optical time domain reflectometry (Φ-OTDR) plays a crucial role in localizing and monitoring seismic waves, underwater structures, etc. Accurate localization of external perturbations along the fiber is essential for addressing these challenges effectively. The conversion coefficient, which links the detected phase signal to the perturbation signal on the fiber, has a significant impact on localization accuracy. This makes the characteristic of parameters relative to the conversion coefficient in Φ-OTDR a subject of deep research. Based on the coherent Φ-OTDR mathematical model, parameters like the modulus, the statistical phase, the phase change, and the peak difference are analyzed with and without the static region, respectively. When perturbations are homogeneously distributed along the fiber, the absence of static region on the phase change-fiber length plane leads to a nonlinear phase change relationship. This deviation from the expected linear relationship in the presence of static region means that the static region is essential for higher localization accuracy. The absence of static region results in a standard deviation of 0.042263 m for the localization deviation value, which could be theoretically reduced by a new sensor design with a static region. These findings underscore the importance of the conversion coefficient and the relevance of the static region in Φ-OTDR to achieving accurate and effective localization. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.

Magnetoencephalography (MEG) non-invasively provides important information about human brain electrophysiology. The growing use of optically pumped magnetometers (OPM) for MEG, as opposed to fixed arrays of cryogenic sensors, has opened the door for innovation in system design and use cases. For example, cryogenic MEG systems are housed in large, shielded rooms to provide sufficient space for the system dewar. Here, we investigate the performance of OPM recordings inside of a cylindrical shield with a 1 × 2 m2 footprint. The efficacy of shielding was measured in terms of field attenuation and isotropy, and the value of post hoc noise reduction algorithms was also investigated. Localization accuracy was quantified for 104 OPM sensors mounted on a fixed helmet array based on simulations and recordings from a bespoke current dipole phantom. Passive shielding attenuated the vector field magnitude to 50.0 nT at direct current (DC), to 16.7 pT/√Hz at power line, and to 71 fT/√Hz (median) in the 10–200 Hz range. Post hoc noise reduction provided an additional 5–15 dB attenuation. Substantial field isotropy remained in the volume encompassing the sensor array. The consistency of the isotropy over months suggests that a field nulling solution could be readily applied. A current dipole phantom generating source activity at an appropriate magnitude for the human brain generated field fluctuations on the order of 0.5–1 pT. Phantom signals were localized with 3 mm localization accuracy, and no significant bias in localization was observed, which is in line with performance for cryogenic and OPM MEG systems. This validation of the performance of a small footprint MEG system opens the door for lower-cost MEG installations in terms of raw materials and facility space, as well as mobile imaging systems (e.g., truck-based). Such implementations are relevant for global adoption of MEG outside of highly resourced research and clinical institutions. [ABSTRACT FROM AUTHOR]

Summary: Most of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In this article, we propose a Processing‐in‐Memory (PIM) based localization approach for multilateration using a memristor crossbar array for performing faster matrix multiplication. The experiments were conducted on real‐time data collected through ultra‐wideband (UWB) chip. The simulation results demonstrate that the proposed model is around 50% faster than the fastest known localization algorithm considered for evaluation and exhibits an average of 55% improvement in localization accuracy. [ABSTRACT FROM AUTHOR]

To estimate the accurate position of the target, the geodesic curvature of the transmitter sensor is proposed in the present work. The geodesic curvature of the transmitter sensor traces the motion of a sensor as the shortest distance along a curved path, which measures the optimal distance on a curved path, accurate measurement of moving targets in an environment with complex geodesics, enhances the realism of the simulation, and underwater surveillance. Theorem 2.1 describes the geodesic curvature between the transmitter sensor and the target. A proposed non-cooperative moving target state measurement (NMTSM) algorithm quantifies the degradation of localization performance and mitigates the stratification effect and geodesic spreading losses. The localization error, localization error variance, and localization accuracy with respect to the number of anchor nodes are compared with standard methods. The proposed non-cooperative moving target state measurement (NMTSM) algorithm improves the localization performance by 33.46% and reduces position MSE by 35.46%, and velocity MSE by 38.86% up to 650 meters. It reduces the localization error by 32.23%, the localization error variance by 34.86% and improves the localization accuracy by 42.21%. The proposed algorithm is useful for the sub-aquatic Internet of Underwater Things (IoUT). [ABSTRACT FROM AUTHOR]

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the prevention of groundwater pollution and overexploitation. Moreover, the development of a novel localization strategy project in wireless sensor groundwater networks aims to address the challenge of optimizing sensor location in relation to the monitoring process so as to extract the maximum quantity of information with the minimum cost. In this study, the improved hybrid butterfly artificial gorilla troop optimizer (iHBAGTO) technique is applied to optimize nodes' position and the analysis of the path loss delay, and the RSS is calculated. The hybrid of Butterfly Artificial Intelligence and an artificial gorilla troop optimizer is used in the multi-functional derivation and the convergence rate to produce the designed data localization. The proposed iHBAGTO algorithm demonstrated the highest convergence rate of 99.6%, and it achieved the lowest average error of 4.8; it consistently had the lowest delay of 13.3 ms for all iteration counts, and it has the highest path loss values of 8.2 dB, with the lowest energy consumption value of 0.01 J, and has the highest received signal strength value of 86% for all iteration counts. Overall, the Proposed iHBAGTO algorithm outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Accurate node localization in Wireless Sensor Networks (WSNs) is crucial for applications like environmental monitoring and military surveillance. Traditional localization methods, such as trilateration, often struggle with accuracy due to signal attenuation and environmental obstructions, leading to significant localization errors in practical scenarios. This paper aims to address the limitations of traditional localization methods by developing and evaluating a hybrid localization method that enhances accuracy and robustness in node localization within WSNs. The proposed method integrates the Bounding Box approach with the Harmony Search optimization algorithm, resulting in the Bounding Box Harmony Search (BBHS) method. The BBHS method utilizes the initial geometric constraints provided by the Bounding Box approach and refines these estimates using the global optimization capabilities of the Harmony Search algorithm. Simulation results, obtained using a custom-developed WSN localization simulator, demonstrate that the BBHS method significantly reduces localization errors compared to traditional trilateration and the standalone Bounding Box method. The BBHS method consistently provides enhanced accuracy and robustness across varying network conditions, highlighting its effectiveness in practical deployment scenarios. The advancements presented in this paper suggest that hybrid methods like BBHS represent a significant step forward in WSN localization technologies. By combining geometric constraints with optimization processes, the BBHS method overcomes the drawbacks of earlier techniques, paving the way for more reliable and resilient WSN operations.

Phase-sensitive optical time domain reflectometry (Φ-OTDR) plays a crucial role in localizing and monitoring seismic waves, underwater structures, etc. Accurate localization of external perturbations along the fiber is essential for addressing these challenges effectively. The conversion coefficient, which links the detected phase signal to the perturbation signal on the fiber, has a significant impact on localization accuracy. This makes the characteristic of parameters relative to the conversion coefficient in Φ-OTDR a subject of deep research. Based on the coherent Φ-OTDR mathematical model, parameters like the modulus, the statistical phase, the phase change, and the peak difference are analyzed with and without the static region, respectively. When perturbations are homogeneously distributed along the fiber, the absence of static region on the phase change-fiber length plane leads to a nonlinear phase change relationship. This deviation from the expected linear relationship in the presence of static region means that the static region is essential for higher localization accuracy. The absence of static region results in a standard deviation of 0.042263 m for the localization deviation value, which could be theoretically reduced by a new sensor design with a static region. These findings underscore the importance of the conversion coefficient and the relevance of the static region in Φ-OTDR to achieving accurate and effective localization.

As the importance of reliable multimedia content increases in today's society, image forensics is a growing field of research. The act of copying and pasting specific parts of an image, known as copy‐move forgery, may be utilized for illegal or unethical purposes. Just as with other vision‐related technologies, the accuracy of forensic analysis depends on having an appropriate image representation. Most existing feature extraction techniques do not accurately reflect the underlying image content leading to reduced performance. In this article, to detect the copy‐move forgery attack, the Generic Radial Harmonic Fourier Moment (GRHFM) is proposed for reliable and distinctive image representation. The algorithm has the ability to effectively manipulate the distribution of zeros to emphasize certain image regions. Additionally, the relationships between complex exponentials and trigonometric functions are exploited to efficiently compute and easily implement the transform kernels. The efficacy of the algorithm is illustrated through experiments on dense‐domain‐based matching patterns. Experimental results on five benchmarking databases prove the effectiveness of the proposed approach compared with the state‐of‐the‐art methods. According to the average scores, the proposed method demonstrates superior accuracy in overall localization performance. The F1 score, precision, and recall percentage values obtained are 92.5, 95.44, and 91.96, respectively. Robustness experiments on more challenging attacks are also conducted on FAU dataset. Results show that the proposed framework satisfies invariance to the various image variations, and thus an enhanced robustness compared to the previous methods. Moreover, the advantage of reasonable computational cost implies its potential use in real‐world forensic applications. [ABSTRACT FROM AUTHOR]

Although the conventional differential evolution (DE) localization algorithm provides reasonable localization results, its improvement in localization performance is limited. Considering the distance information between the tags being an effective way to improve localization performance, this paper proposes a novel cooperative DE-based localization algorithm by presenting the localization process and the fitness function, which contains the distance information between the tags. Specifically, our method first utilizes the conventional DE algorithm to provide the initial values of the tags. Then, according to the initial values, the distance information between the tags is fully utilized to improve the localization performance. The simulation results demonstrate our algorithm's effectiveness in improving the localization performance and simultaneously realizing the localization of all tags. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) non-invasively provides important information about human brain electrophysiology. The growing use of optically pumped magnetometers (OPM) for MEG, as opposed to fixed arrays of cryogenic sensors, has opened the door for innovation in system design and use cases. For example, cryogenic MEG systems are housed in large, shielded rooms to provide sufficient space for the system dewar. Here, we investigate the performance of OPM recordings inside of a cylindrical shield with a 1 × 2 m2 footprint. The efficacy of shielding was measured in terms of field attenuation and isotropy, and the value of post hoc noise reduction algorithms was also investigated. Localization accuracy was quantified for 104 OPM sensors mounted on a fixed helmet array based on simulations and recordings from a bespoke current dipole phantom. Passive shielding attenuated the vector field magnitude to 50.0 nT at direct current (DC), to 16.7 pT/√Hz at power line, and to 71 fT/√Hz (median) in the 10–200 Hz range. Post hoc noise reduction provided an additional 5–15 dB attenuation. Substantial field isotropy remained in the volume encompassing the sensor array. The consistency of the isotropy over months suggests that a field nulling solution could be readily applied. A current dipole phantom generating source activity at an appropriate magnitude for the human brain generated field fluctuations on the order of 0.5–1 pT. Phantom signals were localized with 3 mm localization accuracy, and no significant bias in localization was observed, which is in line with performance for cryogenic and OPM MEG systems. This validation of the performance of a small footprint MEG system opens the door for lower-cost MEG installations in terms of raw materials and facility space, as well as mobile imaging systems (e.g., truck-based). Such implementations are relevant for global adoption of MEG outside of highly resourced research and clinical institutions.

ObjectivesThis paper proposes a multi-autonomous underwater vehicle (AUV) cooperative localization algorithm based on an adaptive fading Sage–Husa extended Kalman filter in order to increase the positioning accuracy of mult-AUVs during navigation. MethodsThe algorithm improves the adaptive filter in the filtering algorithm, obtains the estimated value of the innovation covariance through the weighting of the fading memory index, and introduces the fading factor to adjust the prediction error covariance matrix to achieve the purpose of adjusting the filter gain. The multi-AUV cooperative navigation model is established and the basic filtering process is obtained; through the fusion of speed and position information, an accurate estimation of the position state of the follower-AUV is achieved; finally, the algorithm and the expectation maximization-EKF (EM-EKF) and EKF algorithms are used in an AUV cooperative navigation model for simulation comparison.Results The results show that when the noise covariance does not match, the root mean square error (RMSE) of the proposed algorithm is reduced by 17.82% and 24.48% respectively, and the average localization error (ALE) is reduced by 17.87% and 22.54% respectively; when the noise covariance is time-varying, the RMSE of the proposed algorithm is reduced by 42.11% and 51.23% respectively, and the ALE is reduced by 34.87% and 46.90% respectively. Conclusions The proposed algorithm can effectively improve the reliability, accuracy and adaptability of filtering.

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications. [ABSTRACT FROM AUTHOR]

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points. [ABSTRACT FROM AUTHOR]

Indoor localization is one of the emergent technologies in location based services, which find useful for commercial as well as civilian industries. Global position systems are a familiar solution for outdoor localization systems. But the presence of complicated obstacles in buildings poses a major challenge in indoor localization. Though few indoor localization techniques based on ranging and fingerprint based techniques are devised, they are time consuming and laborious. Therefore, this paper devises an efficient deer hunting optimization algorithm with weighted least square estimation (DHOA-WLSE) technique for accurate 3D indoor node localization technique. The proposed DHOA-WLSE technique has the ability to accomplish minimal localization error with least localization time. In DHOA-WLSE technique, the DHOA is used to estimate the primary target location to eliminate the non-line of sight errors. Based on the primary locations attained, the WLSE technique is applied to determine the accurate target's final location. In order to validate the 3D indoor localization performance of the DHOA-WLSE technique, an extensive simulation analysis is performed and the results are investigated in terms of different measures. The simulation outcomes demonstrated the superior performance of the DHOA-WLSE technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]

Abstract One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error.

Underwater wireless sensor networks (UWSNs) play an increasingly important role in monitoring the marine environment. The research of sensor node localization is one of the biggest concerns in UWSNs, but there are still many formidable obstacles to overcome, including localization accuracy, localization rate, and other issues. In contrast to localization accuracy, however, few studies focus on increasing the node localization rate. Therefore, this paper proposes a maximum localization rate (MLR) algorithm for 3D large-scale UWSNs. In view of the number of anchor nodes around the unknown node, the MLR algorithm designs different positioning strategies, such as the triangular cosine method and the dynamic position-assisted localization method. The MLR algorithm also adopts the regular tetrahedron network topology and the collinear judgment mechanism to guarantee localization accuracy. The simulation results demonstrate that excluding isolated nodes, the MLR algorithm can achieve the maximum localization rate while maintaining a certain level of positioning accuracy at the same time.

Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20 % . We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm. [ABSTRACT FROM AUTHOR]

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• Setup error of off-isocenter targets tended to increase in stereotactic radiotherapy. • SyncTraX FX4 achieved similar setup accuracy with cone-beam CT. • Setup accuracy of SyncTraX was within 1 mm for targets within 7 cm. • In the patient study, setup errors during treatment were reduced using SyncTraX. • SyncTraX reduced dosimetric errors caused by intrafractional setup errors. We investigated the localization accuracy of the off-isocenter targets using SyncTraX FX4, a new image registration device. In a phantom study, we used a MultiMet-WL Cube with metal targets at different distances from the isocenter. Image registrations were performed with SyncTraX and cone-beam computed tomography (CBCT). Nineteen fields with different gantry, collimator, and couch angles were delivered to each target. Localization errors of the off-isocenter targets were then evaluated. In a clinical study, localization accuracy was evaluated for 32 patients. First, image registration was performed using SyncTraX, and the accuracy of patient positioning was evaluated using CBCT. Next, positioning corrections were performed for intracranial setup errors exceeding the threshold (0.5 mm/0.5°) in each field. Finally, total setup uncertainty was evaluated using CBCT. Differences in dosimetric errors from planned doses between no patient positioning corrections during treatment and positioning corrections with SyncTraX were also evaluated. In the phantom study, the positioning accuracy on targets up to 7 cm from the isocenter was within 1 mm. In the clinical practice, the localization accuracies of SyncTraX were 0.35 ± 0.39 mm, 0.30 ± 0.24 mm, and 0.03 ± 0.27 mm in the lateral, vertical, and longitudinal directions, respectively. Post-treatment setup errors were reduced by correcting intrafractional setup errors with SyncTraX during treatment. Positioning corrections with SyncTraX reduced the maximum dosimetric error from 1.6% to 1.0%. SyncTraX provides satisfactory localization accuracy for the off-isocenter targets within 7 cm. SyncTraX reduce dosimetric errors caused by intrafractional setup errors during treatment. [ABSTRACT FROM AUTHOR]

Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy.

An object detector based on convolutional neural network (CNN) has been widely used in the field of computer vision because of its simplicity and efficiency. The average accuracy of CNN model detection results in the object detector is greatly affected by the loss function. The precision of the localization algorithm in the loss function is the main factor affecting the result. Based on the complete intersection over union (CIoU) loss function, an improved penalty function is proposed to improve the localization accuracy. Specifically, the algorithm more comprehensively considers matching bounding boxes between prediction with ground truth, using the proportional relationship of the aspect ratio from both bounding boxes. Under the same aspect ratio of the two bounding boxes, the influence factors of the prediction box on localization accuracy were considered. In this way, the function of the penalty function is strengthened, and localization accuracy of the network model improved. This loss function is called Improved CIoU (ICIoU). Experiments on the Udacity, PASCAL VOC, and MS COCO datasets have demonstrated the effectiveness of ICIoU in improving localization accuracy of network models by using the one-stage object detector YOLOv4. Compared with CIoU, the proposed ICIoU improved average precision (AP) by 0.57% and AP75 by 0.12% on Udacity, AP by 0.26% and AP75 by 1.28% on PASCAL VOC, and AP by 0.06% and AP75 by 0.65% on MS COCO.

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error. [ABSTRACT FROM AUTHOR]

Passive localization technology, which intercepts emitter signals and passively determines their positions, has important value in fields such as electronic reconnaissance and search and rescue. The traditional passive localization technology approach, i.e., cross-bearing, time difference of arrival, and frequency difference of arrival, requires two steps to estimate the emitter position—estimating the parameters related to the positions and then solving the emitter positions based on the previously estimated parameters. This process results in loss of information and difficulty with data association, and requires high system sensitivity. In recent years, a Direct Position Determination (DPD) method was developed that obtains the emitter positions directly by processing the original sampled signals and requires no estimation of intermediate parameters. This method is robust, achieves high performance with a low signal-to-noise ratio, and requires no parameter association. In this paper, we present a comprehensive summary of existing research on DPD and an overall introduction of DPD, including typical DPD methods based on different information types, DPD of special signals, high-resolution high-accuracy DPD, fast DPD algorithms, and the calibration technology used to address DPD model errors. We also consider the future outlook for DPD.

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

Fire source localization is of great significance to the firefighting and evacuation. In order to resolve the problem of precise localization of fire source in confined space, a method based on the diffusion law of hot smoke flow in the early stage of fire is proposed in this paper. According to the fire characteristic physical quantity information collected by the sensor array, the relative variability correlation degree is used to obtain the signal time delays between the sensor units. Then, the direction angle of the sensor units and fire source can be determined through the geometric relationship, and the angular localization principle is used to obtain the fire source localization results. Finally, according to the fire source localization results obtained in different sets of time delays, the localization estimation area is selected based on the dynamic clustering, and the center of this area is output as the comprehensive localization result. The testing results show that this method performs well and achieves a high localization accuracy. [ABSTRACT FROM AUTHOR]

Localization is necessary for applications such as warning systems, where the sensed data along with the location of occurrence is essential for decision making in Wireless Sensor Networks (WSNs). Equipping each sensor node with a global positioning system (GPS) unit is infeasible, as it increases both the power consumption and cost of the deployed network. A more practical approach is to employ a single or a few GPS-equipped mobile anchor nodes (MANs). These MANs localize the unknown sensor nodes by broadcasting their current locations periodically while moving in the area of interest. In a realistic environment, obstacles may be present in the deployment area that obstruct the radio connectivity between unknown sensors and the MANs. In this paper, we propose a mechanism to handle irregular obstacles in the path of various existing static trajectories. Also, we have considered an accurate and reliable channel model to provide a realistic evaluation of the proposed method. The results are compared on the basis of four different localization techniques, namely, accuracy priority trilateration (APT), time priority trilateration (TPT), weighted centroid localization (WCL) and weight-compensated weighted centroid localization (WCWCL). Experimental and simulation results show that the proposed method effectively handles regular and irregular obstacles in the deployed environment. [ABSTRACT FROM AUTHOR]

Iris localization is the crucial link of iris recognition and automatic eye tracking. Based on the traditional Hough transform, this paper proposes an accurate pupil detection method combined with ellipse fitting and circular data compensation. We used the minimum gray mean method to approximately determine the inner edge. According to the results, the inner edge image is extracted and finely located by the center compensation method. When locating the outer boundary, a coarse localization is performed based on approximate radius compensation. The fine localization of the outer edge is performed based on approximate circle center compensation. The experimental results show the proposed algorithm improves the accuracy and real‐time performance of the localization compared with the traditional method. It retains the original advantages of Hough transform while reduces the amount of computation and useless information. [ABSTRACT FROM AUTHOR]

With the continuous development of wireless network technology, more and more mobile devices are connected to the network, and the location information of these devices is becoming one of the important basis for analyzing other geographic data in the net. Location is an important problem in spatial information sensing, and the theoretical basis of location is the free space fading feature of wireless signals. In this paper, we proposes WiLocWare, which is a middle ware to understand and modeling the performance of the state-of-the-art and to be proposed algorithms. We evaluated and characterized the correlation between localization accuracy and networks parameters such as signal propagation model, the coverage of wireless radios, the distributions of wireless devices and the density of the anchor nodes. The experimental results show the localization accuracies under different wireless signatures, WiLocWare is also scalable for the performance evaluation for the to be proposed algorithms.

The article describes methods and algorithms related to the analysis of dynamically changing discrete random fields. Time-optimal strategies for the localization of pulsed-point sources having a random spatial distribution and indicating themselves by generating instant delta pulses at random times are proposed. An optimal strategy is a procedure that has a minimum (statistically) average localization time. The search is performed in accordance with the requirements for localization accuracy and is carried out by a system with one or several receiving devices. Along with the predetermined accuracy of localization of a random pulsed-point source, a significant complicating factor of the formulated problem is that the choice of the optimal search procedure is not limited to one-step algorithms that end at the moment of first pulse generation. Moreover, the article shows that even with relatively low requirements for localization accuracy, the time-optimal procedure consists of several steps, and the transition from one step to another occurs at the time of registration of the next pulse by the receiving system. In this case, the situation is acceptable when during the process of optimal search some of the generated pulses are not fixed by the receiving system. The parameters of the optimal search depending on the number of receiving devices and the required accuracy of localization are calculated and described in the paper.

Localization accuracy is a fundamental requirement for Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM (vSLAM) schemes are usually based upon the assumption of static environments, so they do not perform well in dynamic environments. While a number of vSLAM frameworks have been reported for dynamic environments, the localization accuracy is usually unsatisfactory. In this article, we present a novel motion detection and segmentation method using Red Green Blue-Depth (RGB-D) data to improve the localization accuracy of feature-based RGB-D SLAM in dynamic environments. To overcome the problem due to undersegmentation generated by the semantic segmentation network, a mask inpainting method is developed to ensure the completeness of object segmentation. In the meantime, an optical flow-based motion detection method is proposed to detect dynamic objects from moving cameras, allowing robust detection by removing irrelevant information. Experiments performed on the public Technical University of Munich (TUM) RGB-D data set show that the presented scheme outperforms the state-of-art RGB-D SLAM systems in terms of trajectory accuracy, improving the localization accuracy of RGB-D SLAM in dynamic environments. [ABSTRACT FROM AUTHOR]

Purpose: To develop a scheme to quantitatively assess localization accuracy of tomosynthesis‐guided vacuum‐assisted breast biopsy apparatus. Methods: A phantom containing a metallic pellet on a flexible plastic shaft was constructed and was tested in cranio‐caudal (CC) and lateral (LAT) arm biopsy geometries following the standard clinical breast biopsy workflow. Three points were manually digitized on tomosynthesis images including: the center of the target, and the tip of the needle in pre‐ and postfire positions. The needle trajectory was determined and four error metrics were defined: (1) stroke length error (difference between the nominal and measured stroke lengths); (2) Euclidian distance between the target and center of trough (i.e., aperture); (3) longitudinal distance between target and center of trough; and (4) lateral distance between target and needle. The proposed methodology was also evaluated on a breast gel phantom and the complete biopsy procedure, including vacuum‐assisted biopsy was performed. Results: Three biopsy geometries were investigated: (i) LAT arm on a prone table unit (Hologic, Affirm Prone), (ii) CC‐ and (iii) LAT arm in an upright unit (Hologic Affirm Upright). Both biopsy units passed the vendor‐provided daily localization accuracy test, with <1 mm nominal error in each dimension. The aforementioned error metrics (1) to (4) were (0.6, 1.8, 0.4, 1.7) mm, (0.4, 4.2, 4.1, 1.1) mm, and (0.3, 2.4, 0.7, 2.3) mm, respectively, for geometry‐I, ‐II, and ‐III. The gel phantom was tested on the upright unit with lateral arm and the error metrics (1) to (4) were 0.4, 2.5, 0.8, and 2.4 mm respectively. Conclusions: A framework was developed to evaluate the tomosynthesis‐guided breast biopsy localization error, allowing quantitative comparisons between different systems and biopsy configurations. The proposed framework can also be extended to the stereotactic breast biopsy units. We suggest that a quantitative tolerance level for localization accuracy of breast biopsy units be established. [ABSTRACT FROM AUTHOR]

In recent years, urban population growth and the diversity of vehicles have increased. Location prediction in VANETs is extremely necessary for consumer applications such as routing, network management, knowledge dissemination protocols, and road cognition, among others. This could increase the performance of VANETs. In this paper a Kalman filter is used to predict the vehicle's future location. We conducted experiments exploitation each vehicle quality traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural network-based methods. This paper proposes a location prediction algorithm for nonlinear vehicular movement using an Extended Kalman filter (EKF). Evaluation of the ESCL-VNET algorithm with EKF assess the given better results. [ABSTRACT FROM AUTHOR]

For wireless sensor network, the localization algorithm based on Voronoi diagram has been applied. However, the location accuracy node position in wireless sensor network needs to be optimized by the analysis of the literature, a node location algorithm based on Voronoi diagram and support vector machine is proposed in this article. The basic idea of the algorithm is to first divide the region into several parts using Voronoi diagram and anchor node in the localization region. The range of the initial position of the target node is obtained by locating the target node in each region and then the support vector machine is used to optimize the position of the target node accurately. The localization performance of the localization algorithm is analyzed by simulation and real-world experiments. The experimental results show that the localization algorithm proposed in this article is better than the optimal region selection strategy based on Voronoi diagram-based localization scheme and Weighted Voronoi diagram-based localization scheme localization algorithms in terms of localization accuracy. Therefore, the performance of the localization algorithm proposed in this article is verified. [ABSTRACT FROM AUTHOR]

Localization is an essential module for most protocols and applications in Wireless Sensor Networks - Internet of Things(WSN-IoT). Among the well-known approaches available for WSN-IoT localization, the algorithm requires at least one,two, or three beacon nodes-based localization approaches. Many other localization protocols use a small set of beacon nodesfor the localization of sensor nodes. However, still, the authors are not able to provide an accurate and reliable approach inthe field of WSN-IoT. Thus, this work provides an adaptive ensemble localization approach in WSN-IoT. The proposedapproach adaptively uses the concept of available single, two, and three beacons nodes-based localization approachesaccording to the number of available beacon nodes. By comparing available single, two, or three beacons nodes-basedlocalization approaches the simulation results of the proposed work outperformed in terms of fast convergence rate, lesserroneous and higher accuracy with reducing the line of sight problem.

Deep learning has recently shown great potentials in learning powerful features for visual tracking. However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates. What's more, they usually over-rely on the discriminative features in a single frame in the training process. Consequently, they may fail when the discriminative features are occluded or changed in the tracking phase. In this paper, we propose a novel localization-aware meta tracker (LMT) guided with adversarial features to address the above issues. First of all, we design a novel intersection over union guided method to effectively balance the problem of classification and localization accuracy. To further improve the robustness of our classifier, we creatively use adversarial features during offline training phase. Those adversarial features can effectively guide the classifier in learning how to better deal with the situation where the discriminative features are occluded or changed. Finally, benefiting from meta learning, our algorithm only needs to perform one iterative update on the first frame and it can perform well on the tracking sceneries. The extensive experiments demonstrate the outstanding performance of our LMT compared with the state-of-the-art trackers on three benchmarks: OTB-2015, VOT-2016, and VOT-2018.