In this paper, we present a new method for improving the efficiency of Indoor Localization algorithms, in terms of running time and error rate, using the KD-tree data structure. One of the main challenges of Indoor Localization algorithms in large environments is the high processing overhead of these algorithms due to the high volume of input data and lack of processing resources in users' mobile devices. In the proposed method in this paper, we first cluster the fingerprint database. Then, with the help of a newly proposed method, a modified KD-tree is implemented according to the conditions of the clusters. This tree is a decision-making structure to select one specific cluster where the user stands there. Finally, when a user entered, using a few simple comparisons in the KD-tree, the desired cluster is found and only information about that cluster is passed to the Localization algorithm, to compare and predict the user’s location. The results of the implementation of this method on the fingerprint data set of the Faculty of Engineering at Arak University show that the proposed method reduces the running times and errors to less than half the values, compared to the time of not using the proposed method.

Accurate 3D Localization is very important for a wide range of applications, such as Indoor navigation, industrial robotics, and motion tracking. This research focuses on Indoor 3D positioning systems using ultra-wideband (UWB) devices.  Two Localization experiments were conducted using the Least Squares Trilateration method. In the first experiment, anchors were at the same height, while in the second, they were at varying heights. The lowest percentage errors in the first experiment were 0% at the x-axis, 0.21% at the y-axis, and 19.75% at the z-axis. In the second experiment, the lowest percentage errors in the experiment were 1.98% at the x-axis, 0.68% at the y-axis, and 17.86% at the z-axis, demonstrating improved accuracy with varied anchor heights at the axis. This work shows the z-axis measurements are unreliable and noisy due to the limited intersection of signal waves of each anchor in a same height anchors setup.

In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the advancement of methods have made accurate position estimation essential. This study evaluates the effectiveness of a fuzzy-based adaptive unscented Kalman filter (FAUKF) for improving the state estimation accuracy for mobile robot Localization. The proposed approach leverages the FAUKF algorithm to address noise uncertainty effectively by adaptively adjusting the covariance of the measurement noise based on a defined adaptation law. The Mamdani Fuzzy Inference System (FIS) serves as an observer, enhancing the matching law and improving overall system performance. The findings of this study demonstrate that the FAUKF algorithm provides superior position estimation accuracy compared to conventional Unscented Kalman Filter (UKF) methods. Furthermore, the research introduces an innovative navigation framework for mobile robots by integrating the Random Tree Routing algorithm with Rapidly exploring Random Tree Star (RRT*) for optimal path planning in Indoor environments. The RRT* integration aims to generate efficient and optimal paths while addressing safety considerations and environmental constraints. By combining the prediction and update phases of the Kalman filter, the proposed methodology effectively minimizes the propagation of uncertainty during the Localization process, thereby enabling precise Localization and robust path planning for designated targets. The simulation results confirmed the effectiveness of this method in maintaining constant uncertainty levels in Localization over time. The proposed adaptive method enables efficient navigation in complex environments. Path planning is a critical element in robotics applications, and the RRT*-based approach presented herein offers a comprehensive solution for generating optimal and efficient paths. By providing an up-to-date perspective, this research contributes to the evolving landscape of mobile robot Localization methods. The proposed method highlights the importance of utilizing adaptive algorithms and advanced path-planning techniques to enhance navigation capabilities in Indoor environments.

In this paper, we focus to find desired node position in Indoor environments using a sequence of observations and user movement records. For this purpose, we first record the user's movements in Indoor environments by defining a set of states and several matrices, which are Viterbi inputs. To record the fingerprints of the environment, we move across the entire coordinates of the building to collect and record the fingerprints of different places. In the online phase, we use the Weighted K-Nearest Neighbors (WKNN) algorithm in parallel to check the accuracy of both WKNN and Viterbi algorithms and to correct the WKNN behavior by Viterbi. During this phase, an experimental node is inserted into the environment and moves in the desired direction by determining the destination. The proposed method calculates the current location of the node and its most probable location in the next step. The results of the implementation and testing of the proposed algorithm in the Faculty of Engineering, Arak University, show the optimal performance of the proposed idea for predicting the location and path of the node.

In this paper, a new algorithm based on ESPRIT is proposed for the estimation of central angle and angular extension of Incoherently Distributed (ID) sources. The central angles are estimated using TLS-ESPRIT. The covariance matrix is approximated using a finite Taylor series expansion which leads to the formulation of covariance matrix in terms of central moments of the angular power distribution. The extension widths are estimated using the central moments of distribution. The algorithm can be used for sources with different angular distributions and has low computational cost.

Introduction: Myxomas are benign mesenchymal neoplasms which arise mainly in the heart. The laryngeal Localization is very rare. We aim to describe the clinical, histological and therapeutic features of this condition. Case Report: We report two cases of laryngeal myxomas occurred in male and female patients, presenting with a history of prolonged hoarseness. Laryngoscopy revealed a polypoid mass on the true vocal folds. The lesions were excised with cold instruments. One patient presented a recurrence 4 years after the first surgery. Conclusions: Laryngeal myxoma should be considered in case of a benign looking vocal fold lesion, especially a vocal cord polyp. Histologic exam is the only tool to confirm the diagnosis. It is treated by surgical resection. In the literature, recurrence is rare in laryngeal site, but patients need to be kept on close follow-up.

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