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Major hurdles for Microsoft's HoloLens as a tool in medicine have been accessing tracking data, as well as a relatively high-localisation error of the displayed information; cumulatively resulting in its limited use and minimal quantification. The following work investigates the augmentation of HoloLens with the proprietary image processing SDK Vuforia, allowing integration of data from its front-facing RGB camera to provide more spatially stable holograms for neuronavigational use. Continuous camera tracking was able to maintain hologram registration with a mean perceived drift of 1.41 mm, as well as a mean sub 2-mm surface point localisation accuracy of 53%, all while allowing the researcher to walk about a test area. This represents a 68% improvement for the later and a 34% improvement for the former compared with a typical HoloLens deployment used as a control. Both represent a significant improvement on hologram stability given the current state-of-the-art, and to the best of the authors knowledge are the first example of quantified measurements when augmenting hologram stability using data from the RGB sensor.

Accurate understanding of surgical tool-tip tracking error is important for decision making in image-guided surgery. In this Letter, the authors present a novel method to estimate/model surgical tool-tip tracking error in which they take pivot calibration uncertainty into consideration. First, a new type of error that is referred to as total target registration error (TTRE) is formally defined in a single-rigid registration. Target localisation error (TLE) in two spaces to be registered is considered in proposed TTRE formulation. With first-order approximation in fiducial localisation error (FLE) or TLE magnitude, TTRE statistics (mean, covariance matrix and root-mean-square (RMS)) are then derived. Second, surgical tool-tip tracking error in optical tracking system (OTS) frame is formulated using TTRE when pivot calibration uncertainty is considered. Finally, TTRE statistics of tool-tip in OTS frame are then propagated relative to a coordinate reference frame (CRF) rigid-body. Monte Carlo simulations are conducted to validate the proposed error model. The percentage passing statistical tests that there is no difference between simulated and theoretical mean and covariance matrix of tool-tip tracking error in CRF space is more than 90% in all test cases. The RMS percentage difference between simulated and theoretical tool-tip tracking error in CRF space is within 5% in all test cases.

•We provide a tutorial and evaluation of MNE-type and beamforming methods.•We highlight the importance of resolution matrix, point-spread and cross-talk.•We present intuitive resolution metrics to evaluate and compare methods.•We applied these tools to five MNE-type methods and two beamformers.•Point-spread localization error can be low but cross-talk is fundamentally limited. The spatial resolution of EEG/MEG source estimates, often described in terms of source leakage in the context of the inverse problem, poses constraints on the inferences that can be drawn from EEG/MEG source estimation results. Software packages for EEG/MEG data analysis offer a large choice of source estimation methods but few tools to experimental researchers for methods evaluation and comparison. Here, we describe a framework and tools for objective and intuitive resolution analysis of EEG/MEG source estimation based on linear systems analysis, and apply those to the most widely used distributed source estimation methods such as L2-minimum-norm estimation (L2-MNE) and linearly constrained minimum variance (LCMV) beamformers. Within this framework it is possible to define resolution metrics that define meaningful aspects of source estimation results (such as localization accuracy in terms of peak localization error, PLE, and spatial extent in terms of spatial deviation, SD) that are relevant to the task at hand and can easily be visualized. At the core of this framework is the resolution matrix, which describes the potential leakage from and into point sources (point-spread and cross-talk functions, or PSFs and CTFs, respectively). Importantly, for linear methods these functions allow generalizations to multiple sources or complex source distributions. This paper provides a tutorial-style introduction into linear EEG/MEG source estimation and resolution analysis aimed at experimental (rather than methods-oriented) researchers. We used this framework to demonstrate how L2-MNE-type as well as LCMV beamforming methods can be evaluated in practice using software tools that have only recently become available for routine use. Our novel methods comparison includes PLE and SD for a larger number of methods than in similar previous studies, such as unweighted, depth-weighted and normalized L2-MNE methods (including dSPM, sLORETA, eLORETA) and two LCMV beamformers. The results demonstrate that some methods can achieve low and even zero PLE for PSFs. However, their SD as well as both PLE and SD for CTFs are far less optimal for all methods, in particular for deep cortical areas. We hope that our paper will encourage EEG/MEG researchers to apply this approach to their own tasks at hand.

A wireless indoor positioning system using white LED lights is proposed. The time difference of arrival technique is employed and the phase differences between the received signals are determined to develop a positioning algorithm which can estimate the receiver location with a mean localisation error as low as 1 mm in a room of dimensions 5 × 5 × 3 m. Through simulations, it is identified that the optimum receiver height where localisation error gets minimised is between 2.5 and 3 m from the ceiling which corresponds well with the typical dimensions of a room.

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

Research in translation studies addressing the corporate website text genre has reported some content loss that occurs in localized versions when compared to the original versions. This study focuses on the contrastive analysis of a corpus of homepages and products sections from corporate websites from the Andalusian agri-food sector, studying their original versions (in Spanish) and the versions localised into English and French, according to a localization error taxonomy. The analysis focuses on assessing content loss in French versions, given that France is one of the main export destinations of the Andalusian agri-food industry. It is worth noticing that English versions tend to have less shortcomings than those localised into other languages, which may due to the international status of this language. Results show mainly shortcomings related to content omissions, untranslated text, and English text in versions localised into French, which puts French versions at a disadvantage in relation to English versions.

Indoor localization is hindered by GPS signal weakening in indoor environments. This research formulates machine learning with Multi-layer Perceptron Regression (MLPR) algorithm supported by two metaheuristic optimizers, namely, Gold Rush Optimizer (GRO) and Pelican Optimizer (POA), to yield hybrid models MLGR and MLPO to forecast Average Localization Error (ALE). The dataset organized in a structured form was of size 107 samples with six significant features as follows: anchor ratio, transmission range, node density, trainings, standard deviation of ALE, and ALE as objective. The dataset was split into training (70%), validation (15%), and testing (15%) subsets. Experimental analysis in three prediction layers reveals that MLGR outperformed MLPO and MLPR models in every prediction layer. MLGR exhibited maximum performance at the third test layer with an RMSE of 0.036 and R² of 0.993, whereas MLPO and MLPR attained RMSE of 0.059 and 0.080 and values of R² of 0.981 and 0.966, respectively. The findings establish the validity of the introduced hybrid optimization technique to increase accuracy and convergence rate of prediction of ALE in wireless sensor networks.

This paper presents a machine learning (ML)-based framework to predict performance degra- dation in Wi-Fi indoor localization systems (ILSs) under varying moving human crowd densities. While indoor localization can be performed in both mobile and fixed wireless settings, the majority of prior research emphasizes mobile devices in motion. In contrast, this study adopts a fixed-wireless configuration, where a smartphone node was held stationary while moving human density varied around it. This design particularly isolates the effect of human crowd-induced interference on re- ceived signal strength indicator (RSSI) fluctuations, enabling a controlled evaluation of ML-based error compensation, which is a perspective rarely explored in the literature. Accelerometer-derived motion features were integrated with RSSI measurements, and baseline localization errors were calculated using the conventional Weighted Least Squares (WLS) indoor localization algorithm. Three main ML regression models namely Random Forest, CatBoost, and XGBoost were trained and evaluated. Among them, CatBoost demonstrated the best performance, achieving a root mean squared error (RMSE) of 0.331 m compared to the WLS baseline error of 1.405 m, corresponding to a 76.47% improvement in localization accuracy. The evaluation was intentionally limited to a single indoor layout with a stationary device to isolate crowd-induced RSSI distortions, and multi- position validation and mobile-user scenarios are reserved for future work. The findings confirm that smartphone sensor-fused ML models can anticipate human crowd-induced localization errors and enhance the robustness of multilateration-based ILSs.

Wireless Sensor Networks (WSNs) used in modern applications like environmental monitoring, smart cities, and healthcare systems depend on accurate sensor node localization. However, attaining accurate localization is challenging due to dynamic environmental conditions. Varying network densities and the interdependence of parameters such as anchor ratio, transmission range, and node density increase the Average Localization Error (ALE) in WSNs. Existing methodologies, including regression-based models, heuristic approaches, and optimization-driven methods, struggle to generalize across dynamic environments due to their reliance on static parameter configurations. Machine learning-based approaches have improved localization accuracy but require extensive labeled datasets and often lack adaptability to real-time variations. Traditional optimization techniques tend to converge with local optima, limiting their effectiveness in dynamically changing network topologies. To overcome these limitations, a novel Multi-Agent Reinforcement Learning (MARL) algorithm is proposed in this research, combined with Golden Jackal Optimization (GJO). The proposed optimized MARL framework dynamically learns optimal parameter adjustments through a reward mechanism, minimizing localization error and its variability even under dynamic network conditions. The GJO algorithm fine-tunes the hyperparameters of MARL to improve generalization across different WSN configurations. The proposed model is evaluated using a benchmark dataset, and performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) are analyzed. Experimental results demonstrate that the proposed model significantly outperforms existing methods such as Grid Search RF, Bayesian Optimized RF, Gradient Boosting, and Deep Neural Networks. The proposed approach achieves an MSE of 0.02, MAE of 0.11, RMSE of 0.14, R² of 0.88, and MAPE of 2.5%, reflecting its ability to adapt dynamically and improve localization accuracy compared to static or heuristic models.

The evolutionary growth of Wireless Sensor Networks (WSN) exploits a wide range of applications. To deploy the WSN in a larger area, for sensing the environment, the accurate location of the node is a prerequisite. Owing to these traits, the WSN has been effectively implemented with devices. Using various localization techniques, the information related to node location is obtained for unknown nodes. Recently, node localization has employed the standard bio-inspired algorithm to sustain the fast convergence ability of WSN applications. Thus, this paper aims to develop a new hybrid optimization algorithm for solving the node localization problems among the unknown nodes in WSN. This hybrid optimization scheme is developed with two efficient heuristic strategies of Black Widow Optimization (BWO) and Honey Badger Algorithm (HBA), named as Hybridized Black Widow-Honey Badger Optimization (HBW-HBO) to achieve the objective of the framework. The main objective of the developed heuristic-based node localization framework is to minimize the localization error between the actual locations and detected locations of all nodes in WSN. For validating the developed heuristic-based node localization scheme in WSN, it is compared with different existing optimization strategies using different measures. The experimental analysis proves the robust and consistent node localization performance in WSN for the developed scheme than the other comparative algorithms.