Localization Algorithm Research Articles

A Graph Multi-separator Problem for Image Segmentation

We propose a novel abstraction of the image segmentation task in the form of a combinatorial optimization problem that we call the multi-separator problem. Feasible solutions indicate for every pixel whether it belongs to a segment or a segment separator, and indicate for pairs of pixels whether or not the pixels belong to the same segment. This is in contrast to the closely related lifted multicut problem, where every pixel is associated with a segment and no pixel explicitly represents a separating structure. While the multi-separator problem is np-hard, we identify two special cases for which it can be solved efficiently. Moreover, we define two local search algorithms for the general case and demonstrate their effectiveness in segmenting simulated volume images of foam cells and filaments.

Comparative investigation on the performance of spot positioning sensors in point-autofocus microscopy

As products evolve towards greater precision and intelligence, their components also develop towards miniaturisation and increasing complexity. In the era of intelligence, the demand for complex small components continues to grow. The point-autofocus microscopy (PAM), with its attributes such as small measurement spots and high vertical resolution, holds a comprehensive advantage in instruments for measuring the geometry of complex small components. The performance of the spot positioning sensor directly determines the capability of the PAM to measure complex small components. This study introduces the basic measurement principles of the PAM and commonly used spot positioning sensors. The analysis explored the feasibility of applying different sensors to the PAM. An automatic segmentation and weighted localisation algorithm of the spot based on the centroid method was proposed. Using this algorithm, the spot positioning error of the complementary metal–oxide–semiconductor (CMOS) sensor can be controlled within 0.48 μm, with a measurement uncertainty of no more than ±0.19 μm. This study focuses on comparing the performance of CMOS and position-sensitive detector sensors with the performance of seven indicators. In addition, a measurement system of the PAM was constructed, and measurements of the test object were conducted using both types of sensors. The experimental results show that when the measurement system employs the CMOS sensor, it achieves higher measurement accuracy. The maximum measurement error was approximately 0.22 μm, with a measurement uncertainty of no more than ±0.10 μm.

A Survey of Visible-Light-Communication-Based Indoor Positioning Systems.

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.

Integration of attention mechanism and CNN-BiGRU for TDOA/FDOA collaborative mobile underwater multi-scene localization algorithm

The aim of this study is to address the issue of TDOA/FDOA measurement accuracy in complex underwater environments, which is affected by multipath effects and variations in water sound velocity induced by the challenging nature of the underwater environment. To this end, a novel cooperative localisation algorithm has been developed, integrating the attention mechanism and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) with TDOA/FDOA and two-step weighted least squares (ImTSWLS). This algorithm is designed to enhance the accuracy of TDOA/FDOA measurements in complex underwater environments. The algorithm initially makes use of the considerable capacity of a convolutional neural network (CNN) to extract profound spatial and frequency domain characteristics from multimodal data. These features are of paramount importance for the characterisation of underwater signal propagation, particularly in complex environments. Subsequently, through the use of a bidirectional gated recurrent unit (BiGRU), the algorithm is able to effectively capture long-term dependencies in time series data. This enables a more comprehensive analysis and understanding of the changing pattern of signals over time. Furthermore, the incorporation of an attention mechanism within the algorithm enables the model to focus more on the signal features that have a significant impact on localisation, while simultaneously suppressing the interference of extraneous information. This further enhances the efficiency of identifying and utilising the key signal features. ImTSWLS is employed to resolve the position and velocity data following the acquisition of the predicted TDOA/FDOA, thereby enabling the accurate estimation of the position and velocity of the mobile radiation source. The algorithm was subjected to a series of tests in a variety of simulated underwater environments, including different sea states, target motion speeds and base station configurations. The experimental results demonstrate that the algorithm exhibits a deviation of only 2.88 m/s in velocity estimation and 2.58 m in position estimation when the noise level is 20 dB. The algorithm presented in this paper demonstrates superior performance in both position and velocity estimation compared to other algorithms.

Multimodal Massage Localization Algorithm for Human Acupoints

As the aging population issue worsens, people are experiencing increasing life pressures, and the elderly are unable to receive sufficient care, intensifying the demand for home-based intelligent massage healthcare robots. Addressing the challenges faced by users in expressing preferences for specific areas like the back through gestures or voice during the use of massage robots, and the issue of elderly individuals expressing imprecisely, which hinders effective communication of the desired massage target location to the robot, this paper proposes a new human-machine interaction mode for massage robots. The main innovations include: achieving precise control of the massage area through the representation of a virtual human body on the screen; proposing a virtual-to-real mapping algorithm based on human acupoints to address the consistency issue in human virtual-to-real mapping; introducing a multimodal intent understanding algorithm based on dynamic information entropy to address the current limitations of a single interaction mode and low intent comprehension rates in massage robot interactions. Experimental results demonstrate that the proposed multimodal massage localization algorithm achieves good massage intent recognition effects. By combining natural human-machine interaction with intent understanding, it not only accurately captures user massage intentions and assists in completing massage tasks but also reduces user psychological and cognitive loads, leading to more desirable interaction outcomes.

Distributed vibration and temperature sensing system by multiplexed fiber scattering spectra

A new, to the best of our knowledge, distributed optical fiber vibration and temperature hybrid sensing system is proposed and experimentally demonstrated. The proposed system only employs two signal channels, which is more compact and practical. Based on the structure of the optical time domain reflectometer (OTDR), the Rayleigh scattering light and the Raman anti-Stokes scattering light is extracted for vibration and temperature sensing, respectively. For vibration sensing, a new differential location algorithm based on polarization state analysis of the Rayleigh scattering light is proposed to locate the vibration events. It first rectifies the original OTDR traces by fiber attenuation compensation to make each position in it with the same pulse power level. And then, by difference of adjacent traces and threshold discrimination, the vibration positions are identified and located. For temperature sensing, a temperature calibration unit and algorithm are adopted to dynamically correct the trace data and reduce the temperature measurement error caused by the instability of the pulse laser source. The experiment is conducted with a fiber range of about 12 km and laser pulse width of 60 ns, and the experimental results show that the maximum error range for temperature measurement is −0.7∘C to 1.3°C, with a root mean square (RMS) error of 0.85°C in the entire temperature measurement range. Additionally, the spatial resolution (SR) for both vibration and temperature sensing is 6 m.

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Abstract In order to rapidly identify and locate the weld seam initial point in robotic automated welding, we established a binocular vision system and proposed a weld seam initial point localization algorithm named WIPL-Net. Built upon the Fully Convolutional One-Stage object detection network, WIPL-Net introduces a lightweight ResNext as its backbone network and incorporates channel attention and enhanced feature fusion mechanisms to enhance feature detection and extraction capabilities. Subsequently, WIPL-Net is utilized to obtain the weld seam’s initial point, and its three-dimensional coordinates are determined through trigonometric measurements. To further estimate the robot’s posture at the initial point, we performed sparse three-dimensional reconstruction of the local region centered on the weld seam initial point based on You Only Look At Coefficients of Tensors instance segmentation and feature point matching. Finally, we conducted comparative experiments on WIPL-Net and conducted weld seam initial point localization experiments in real welding scenarios. The results demonstrate that our proposed method achieves a positioning error of less than 1.2 mm for the weld seam’s initial point and a pose error of less than 10 degrees for the robot, meeting the requirement for real-time positioning of the weld seam’s initial point.

A fast local search algorithm for minimum sum coloring problem on massive graphs

The minimum sum coloring problem (MSCP) is an important extension of the graph coloring problem with wide real-world applications. Compared to the classic graph coloring problem, where lots of methods have been developed and even massive graphs with millions of vertices can be solved well, few works have been done for the MSCP, and no specialized MSCP algorithms are available for solving massive graphs. This paper explores how to solve MSCP on massive graphs, and then proposes a fast local search algorithm for the MSCP based on three main ideas including a coarse-grained reduction method, two kinds of scoring functions and selection rules as well as a novel local search framework. Experiments are conducted to compare our algorithm with several state-of-the-art algorithms on massive graphs. The proposed algorithm outperforms previous algorithms in almost all the massive graphs and also improves the best-known solutions for some conventional instances, which demonstrates the performance and robustness of the proposed algorithm.

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

Olfactory sampling volume for pheromone capture by wing fanning of silkworm moth: a simulation-based study

Odours used by insects for foraging and mating are carried by the air. Insects induce airflows around them by flapping their wings, and the distribution of these airflows may strongly influence odour source localisation. The flightless silkworm moth, Bombyx mori, has been a prominent insect model for olfactory research. However, although there have been numerous studies on antenna morphology and its fluid dynamics, neurophysiology, and localisation algorithms, the airflow manipulation of the B. mori by fanning has not been thoroughly investigated. In this study, we performed computational fluid dynamics (CFD) analyses of flapping B. mori to analyse this mechanism in depth. A three-dimensional simulation using reconstructed wing kinematics was used to investigate the effects of B. mori fanning on locomotion and pheromone capture. The fanning of the B. mori was found to generate an aerodynamic force on the scale of its weight through an aerodynamic mechanism similar to that of flying insects. Our simulations further indicate that the B. mori guides particles from its anterior direction within the ~ 60° horizontally by wing fanning. Hence, if it detects pheromones during fanning, the pheromone can be concluded to originate from the direction the head is pointing. The anisotropy in the sampling volume enables the B. mori to orient to the pheromone plume direction. These results provide new insights into insect behaviour and offer design guidelines for robots for odour source localisation.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Exploring publication networks with a local cohesion-maximizing algorithm

Abstract Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications, and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed by either interacting with a fixed network data set or querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a data set of AMO physics illustrate the properties of MALBA and its potential.

An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems

As one of the most classical scheduling problems, flexible job shop scheduling problems (FJSP) find widespread applications in modern intelligent manufacturing systems. However, the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms, which are complex and time-consuming. In this paper, we propose a fast effective single-solution based local search algorithm with an innovative adaptive weighting technique (AWLS) for solving FJSP. The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration. AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity. Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency, despite of its simplicity. Specifically, AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s. As a strongly Non-deterministic Polynomia (NP)-hard problem which has been extensively studied for nearly half a century, breaking the records on these classic instances is an arduous task. Nevertheless, AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.

Location method of ill-conditioned microseismic source and its engineering application

Microseismic event location is one of the core parameters in microseismic monitoring, and the accuracy of localization will directly affect the effectiveness of engineering applications. However, limited by spatial factors, the geometry of the sensor installation will be close to linear, which makes the localization equation suffer from the pathological problem, and the localization accuracy is greatly reduced. To address this problem, the reasons for the pathological problem are analyzed from the perspective of the objective function residuals and coefficient matrix. The pathological problem is caused by the combined effect of the poorer sensor array and data errors, and its residual isosurface shows a conical distribution, and as the residual value decreases, the apex of the isosurface gradually extends to the far side, and the localization results do not converge. For this reason, an improved regularized Newton downhill localization algorithm is proposed. In this method, firstly, the Newtonian downhill method is improved so that the magnitudes of the seismic source parameters are the same, and the condition number of the coefficient matrix is reduced; then, the L-curve method is used to calculate the regularization factor for the pathological equations, and the coefficient matrix is improved; finally, the pathological equations are regularized, and the seismic source coordinates are obtained by the improved Newtonian downhill method. The results of engineering applications show that compared with the traditional algorithm based on automatic of P-arrival picking, the number of effective microseismic events calculated by the proposed localization algorithm is increased by 194.7 %, and the localization accuracy is substantially improved. The proposed algorithm reduces the problem of low accuracy of S-arrival picking and allows localization using only P-wave arrival. The method reduces the quality requirements of the data and significantly improves the utilization of microseismic events and positioning accuracy.

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.

Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs.

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

A Resolution and Localization Algorithm for Closely-Spaced Objects Based on Improved YOLOv5 Joint Fuzzy C-Means Clustering

Sound source localization based on data and neural network model

Abstract In this study, the integration of sound source localization with neural network techniques is proposed. Firstly, the development history and basic principle of neural networks are introduced. Combined with the characteristics of sound source location, the residual neural network is analyzed, and the residual neural network is used as the neural network model. The sound signal received by the microphone array is computed by PHAT-SCOT and the output is trained as the input feature of ResNet. Finally, the results of indoor sound source location are obtained in the test set. Experimental findings demonstrate that the neural network-based localization algorithm exhibits robust localization accuracy across diverse environmental conditions.

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning

A Hybrid Recommendation Model for Tourist Using Evolutionary Algorithm Combined with Local Search Algorithm for Trip Planning