Localization Algorithm Research Articles

Monte Carlo simulations of glass-forming liquids beyond Metropolis.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

Research on SLAM Localization Algorithm for Orchard Dynamic Vision Based on YOLOD-SLAM2

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

Position estimation of acoustic elements based on improved delay estimation algorithm

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

Optimization of the Diagnosis of Central Nervous System Infections in Vietnamese Hospitals: Results From a Retrospective Multicenter Study.

Central nervous system infections pose significant health challenges, particularly in low- and middle-income countries, because of high morbidity and mortality rates. Rapid and accurate diagnosis is essential for effective treatment to prevent adverse outcomes. Traditional culture-based diagnostics are often slow and lack specificity. This study evaluates the BioFire FilmArray Meningitis/Encephalitis (FAME) Panel against standard diagnostics in Vietnam to assess its clinical impact and suitability for local epidemiology. We conducted a prospective study involving 330 patients with suspected central nervous system infections at 4 hospitals in northern Vietnam from July 2022 to April 2023. Cerebrospinal fluid samples were analyzed using routine culture methods and FAME. We compared pathogen detection rates and assessed the potential clinical impact of FAME results on patient management. Of the 330 cerebrospinal fluid specimens, 64 (19%) were positive by either conventional diagnostics (n = 48) and/or FAME (n = 33). The agreement between FAME and conventional diagnostics was 87%. Key pathogens Mycobacterium tuberculosis (n = 7), Klebsiella pneumoniae (n = 5), Streptococcus suis (n = 5), Epstein-Barr virus (n = 3), Acinetobacter baumannii (n = 1), and Trichosporon asahii (n = 1) were not detected by FAME. Classical meningitis parameter clinical symptoms, altered glucose, protein, and pleocytosis were good predictors of FAME positivity, indicating their utility in optimizing local diagnostic algorithms. FAME complements traditional diagnostics by offering rapid and broad pathogen detection, crucial for timely and appropriate therapy. However, its effectiveness varies with local epidemiology, and it should not replace conventional methods entirely. Tailoring diagnostic panels to regional pathogen prevalence is recommended to enhance diagnostic accuracy and clinical outcomes in low- and middle-income countries.

A non-intrusive multiscale framework for 2D analysis of local features by GFEM — A thorough parameter investigation

This work comprehensively investigates key parameters associated with a recently proposed non-intrusive coupling strategy for multiscale structural problems. The IGL-GFEMgl combines the Iterative Global Local Method and the Generalized Finite Element Method with global–local enrichment, GFEMgl. Different scales of the problem are solved using distinct finite element codes: the commercial software Abaqus and a research in-house code. An Iterative Global–Local non-intrusive algorithm is employed to couple the solutions provided by the two solvers, with the process accelerated by Aitken’s relaxation. Slight modifications have been introduced, and the resulting accuracy and computational performance are discussed using numerical examples. The problems investigated explore the coupling strategy within the context of 2D linear elastic problems, which include voids and crack propagation described at the local scale solved by the in-house code. A noteworthy trade-off between reducing iterations and increasing the time to solve the local problems is observed. Despite the high accuracy achieved, the two versions of the coupling strategy, namely the monolithic and staggered algorithms, exhibit different computational performances when the GFEMgl parameters, such as the number of global–local cycles and the size of the buffer zone, are evaluated for the crack propagation simulation.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow.

Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optical flow thread estimates optical flow and introduces a novel approach to capture the optical flow of moving objects by leveraging optical flow residuals. Following this, an optical flow consistency method is implemented to assess the dynamic nature of rigid object mask regions, classifying them as either moving or stationary rigid objects. To mitigate errors caused by missed detections or motion blur, a motion frame propagation method is employed. Lastly, a dense mapping thread is incorporated to filter out non-rigid objects using semantic information, track the point clouds of rigid objects, reconstruct the static background, and store the resulting map in an octree format. Experimental results demonstrate that the proposed method surpasses current mainstream dynamic VSLAM techniques in both localization accuracy and real-time performance.

Service Selection under Uncertainty

Cloud computing has emerged as a popular computing paradigm, providing scalable and pay-per-use services to execute a variety of tasks. However, selecting the most suitable cloud services for a client can be challenging, as it involves taking into account the service characteristics and a set of client restrictions. In this paper, we formalize the problem of Service Selection under Uncertainty (SSuU) as an optimization problem. Our goal is to allocate tasks to appropriate cloud services while considering the probability of Service Level Agreement (SLA) violations and a set of client restrictions. We introduce an efficient dynamic programming approach to calculate the probability of SLA violations incrementally over time. We prove that the SSuU problem is strongly NP-complete, and propose an Integer Linear Programming (ILP) formulation and an Iterated Local Search-based algorithm for tackling it. To facilitate evaluation, we also introduce a set of benchmark instances for the SSuU problem. We extensively evaluate our proposed solutions on 94 input instances and compare them to the exact method (i.e., which always produces the optimal solution). Our results demonstrate that the metaheuristic approach is significantly faster and leads to exact solutions in the great majority of the evaluation scenarios.

Improved DV-Hop Localization Algorithm Based on Variable Communication Radius for Wireless Sensor Networks

Improved DV-Hop Localization Algorithm Based on Variable Communication Radius for Wireless Sensor Networks

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A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold.

To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of -9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.

Evaluating the efficacy of local anesthetic techniques during scalp microneedling.

An increasing interest in minimally invasive procedures hassled to a demand for an effective local anesthetic algorithm. The scalp presents a challenge in achieving effective anesthesia due to the presence of hair shafts. This study aims to evaluate the efficacy of different methods during a microneedling procedure, including 25 mg lidocaine and 25 mg prilocaine cream, skin spray with 10% lidocaine, and cold gel compresses. Sixty-two men aged between 20 and 50 years underwent three microneedling treatments, each using a different method of anesthesia. The treatment area was divided into two equal parts, with one part exposed to a specific anesthetic method. Patients were asked to rate their pain on a 0-10 verbal analog scale. An attempt was made to objectify the results using algometry. A negative correlation was observed between the algometry results and the VAS score after the application of the cream and cold compresses. When choosing monotherapy, it is recommended to use cold gel compresses for scalp microneedling after considering the advantages and disadvantages of different methods.

A hybrid improved compressed particle swarm optimization WSN node location algorithm

The improvement of positioning accuracy in Wireless Sensor Networks (hereafter, WSN) is crucial to develop advanced Internet of Things (IOT, for short) applications. However, the conventional distance vector-hop (DV-Hop) localization algorithm has shortcomings such as low accuracy and weak stability. To overcome these shortcomings, this paper proposes a hybrid improved compressed particle swarm optimization algorithm (HICPSO), which consists of a scheme of linearly decreasing inertia weights, compressed velocity vectors, population Gaussian variants and optimal boundary selection. Then, HICPSO is integrated with DV-Hop to gradually reduce the distance error of least squares method (LSM) estimated with the efficient search advantage of HICPSO. Our simulation results show that the HICPSO algorithm possesses better computational accuracy and search performance on the 22 benchmark test functions compared with the algorithms such as the Improved Adaptive Genetic Algorithm (IAGA) and Adaptive Weighted Particle Swarm Optimizer (AWPSO). Meanwhile, compared with IAGA and AWPSO, the positioning accuracy of HICPSO-based positioning algorithm is improved by 4.28% and 4.76% respectively, and the stability is improved by one order of magnitude.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

High-precision docking of wheelchair/beds through LIDAR and visual information.

To address the low docking accuracy of existing robotic wheelchair/beds, this study proposes an automatic docking framework integrating light detection and ranging (LIDAR), visual positioning, and laser ranging. First, a mobile chassis was designed for an intelligent wheelchair/bed with independent four-wheel steering. In the remote guidance phase, the simultaneous localization and mapping (SLAM) algorithm was employed to construct an environment map, achieving remote guidance and obstacle avoidance through the integration of LIDAR, inertial measurement unit (IMU), and an improved A* algorithm. In the mid-range pose determination and positioning phase, the IMU module and vision system on the wheelchair/bed collected coordinate and path information marked by quick response (QR) code labels to adjust the relative pose between the wheelchair/bed and bed frame. Finally, in the short-range precise docking phase, laser triangulation ranging was utilized to achieve precise automatic docking between the wheelchair/bed and the bed frame. The results of multiple experiments show that the proposed method significantly improves the docking accuracy of the intelligent wheelchair/bed.

IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, an all-sky gamma-ray monitor launched on 2020 December 10. We developed a multifunctional trigger and localization software operating in the CPU of the GECAM Electronic Box. This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for both short and long bursts, and low time latency of the trigger information which is downlinked through the Global Short Message Communication service of the global BeiDou navigation system. This paper provides a detailed description of the design and development of the trigger and localization software system for GECAM. It covers the general design, workflow, the main functions, and the algorithms used in the system. The paper also includes on-ground trigger tests using simulated gamma-ray bursts generated by a dedicated X-ray tube, as well as an overview of the performance for real celestial bursts during its in-orbit operation.

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

Indoor fingerprint localization algorithm based on WKNN and LightGBM-GA

Abstract WiFi-based indoor fingerprint localization is widely used in indoor localization owing to its high accuracy and low deployment costs. Changes in the indoor signal environment directly affect localization accuracy. To improve localization accuracy and stability, this paper proposes a novel indoor fingerprint localization algorithm based on Weighted K-Nearest Neighbors (WKNN) and an enhanced Light Gradient Boosting Machine (LightGBM). First, in the offline phase, Gaussian filtering and K-Nearest Neighbors-Random Forest information completion algorithm with fusion of Euclidean and Manhattan distances are used to remove outliers from the fingerprint database dataset and fill in missing fingerprint information, ensuring the integrity of the fingerprint database. During the online phase, the fingerprint database is divided into training and testing sets. The LightGBM algorithm is used for modeling. Additionally, Genetic Algorithm (GA) is use d to optimize the parameters of LightGBM algorithm to find the best parameters by fitness evaluation. Then, the nearest neighbor set found by the WKNN algorithm is introduced into the LightGBM-GA model. Combining the predictions from the standalone LightGBM algorithm and performing weighted fusion yields the final predicted coordinates. The experiments are conducted in 8 m × 10 m laboratory containing 5 access points and 80 reference points to collect the Received Signal Strength Indication values of 5 WiFi hotspots. The experimental results show that the average localization error of the proposed algorithm is 1.11 m, which is reduced by 6.7%–38.3% compared to K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), LightGBM, KNN + XGBoost, WKNN + LightGBM, and WKNN + XGBoost-GA localization algorithms. The localization curve is smoother, and the cumulative distribution function converges faster. Moreover, the localization time is reduced by 13.3%–36.7%, effectively enhancing localization accuracy and decreasing localization time.