Enhance Localization Accuracy Research Articles

New Strategies for Boosting Localization Accuracy in Wireless Sensor Nodes

Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. The method evaluates node trust using Dempster-Shafer Evidence Theoryto secure localization against rogue nodes and optimizes the localization process through trilateral and multilateration systems. The SABWP-NL approach demonstrates superior performance in terms of localized nodes and localization error compared to existing techniques like BWP, ROA, and AO. Results show that SABWP-NL achieves the highest number of localized nodes and the lowest localization error, making it a promising solution for efficient and secure node localization in WSNs.

State-of-the-Art on the Impact of Bimodal Acoustic Stimulation on Speech Perception in Noise in Adults: A Systematic Review.

Bimodal stimulation (BS), which combines the use of a cochlear implant (CI) in one ear and a hearing aid (HA) in the opposite ear, is an established strategy to treat hearing loss by exploiting the unique capabilities of each device. CIs stimulate the auditory nerve by bypassing damaged hair cells, while HAs amplify sounds by requiring a functional hearing residual. The aim of this systematic review is to investigate the advantages and disadvantages of BS such as speech perception in noise. We examined clinical studies published from October 2020 to July 2024, following the PRISMA guidelines, focusing on the advantages and disadvantages of BS on speech perception in noise in adulthood. BS in adult patients significantly improves speech perception in quiet and noisy environments, especially for those with increased residual hearing. Unilateral CIs and BS perform similarly in quiet conditions, but BS significantly improves speech discrimination in noisy environments if loudness between the two devices is appropriately balanced. Directional microphones and programming software are new technologies that succeed in reducing environmental noise and improving verbal perception outcomes, although their features in the literature are controversial. In addition, the individuals using BS may face temporal mismatches mainly due to differing device latencies, affecting sound localization. Compensating for these mismatches can enhance localization accuracy. However, modulated noise remains a significant obstacle to verbal perception in noise. Valuable assessment tools such as music tests provide further information on hearing performance and quality of life. More research is needed to define certain selection criteria.

Efficient Optimized YOLOv8 Model with Extended Vision.

In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative improvement measures and strategies. First, we propose a multi-branch group-enhanced fusion attention (MGEFA) module and integrate it into YOLO-EV, which significantly boosts the model's feature extraction capabilities. Second, we enhance the existing spatial pyramid pooling fast (SPPF) layer by integrating large scale kernel attention (LSKA), improving the model's efficiency in processing spatial information. Additionally, we replace the traditional IOU loss function with the Wise-IOU loss function, thereby enhancing localization accuracy across various target sizes. We also introduce a P6 layer to augment the model's detection capabilities for multi-scale targets. Through network structure optimization, we achieve higher computational efficiency, ensuring that YOLO-EV consumes fewer computational resources than YOLOv8s. In the validation section, preliminary tests on the VOC12 dataset demonstrate YOLO-EV's effectiveness in standard object detection tasks. Moreover, YOLO-EV has been applied to the CottonWeedDet12 and CropWeed datasets, which are characterized by complex scenes, diverse weed morphologies, significant occlusions, and numerous small targets. Experimental results indicate that YOLO-EV exhibits superior detection accuracy in these complex agricultural environments compared to the original YOLOv8s and other state-of-the-art models, effectively identifying and locating various types of weeds, thus demonstrating its significant practical application potential.

DLCH-YOLO: An Object Detection Algorithm for Monitoring the Operation Status of Circuit Breakers in Power Scenarios

In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.

Enhancing Indoor Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Integrating Hierarchical Structure Poly Particle Swarm Optimization and Teaching–Learning-Based Optimization (HSPPSO-TLBO)

Background and Objective: Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching– Learning-Based Optimization (TLBO). Methods: The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision. Results: Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime. Conclusion: The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the costfree advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy. other: N/A

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.

Impact of Ventilator Settings on Pulmonary Nodule Localization Accuracy in a Hybrid Operating Room: A Single-Center Study.

Background: Pulmonary nodule localization in a hybrid operating room (OR) followed by thoracoscopic operation presents a viable alternative for early lung cancer treatment, potentially supplanting conventional two-stage preoperative computed tomography-guided localization. This hybrid OR technique enables lesion localization under positive ventilation, contrasting with the traditional method requiring concurrent respiratory motion. This study aimed to evaluate our experience with different ventilator settings and the accuracy of pulmonary nodule localization. Methods: We retrospectively analyzed 176 patients with multiple pulmonary nodules who had localization procedures in our hybrid operating room. Ninety-five patients were assigned to the traditional ventilator setting group (tidal volume 8-10 mL/kg) and 81 to the lung-protective strategy group (tidal volume < 8 mL/kg). Localization accuracy was assessed via hybrid computed tomography imaging, ensuring that the needle-to-lesion distance was ≤5 mm. Between-group differences were assessed using the chi-squared test, Fisher's exact test, and the Mann-Whitney U test, as appropriate. Results: Pathological findings revealed primary lung malignancy in 150 patients, inclusive of invasive adenocarcinoma, adenocarcinoma in situ, and minimally invasive adenocarcinoma. Multivariate regression analysis identified tidal volume, nodule count, and localization depth as significant predictors of localization accuracy. Conclusions: This study demonstrated that ventilator settings with a tidal volume of 8-10 mL/kg significantly enhanced localization accuracy and slightly improved patient oxygenation. However, additional randomized controlled trials are warranted to validate these findings and establish definitive guidelines for future interventions.

Atrial pace mapping using intracardiac pattern matching: feasibility and accuracy

Abstract Introduction Pace mapping of atrial arrhythmia is a method to localize the origin of usually focal arrhythmias that are unstable or not persisting during EP-study and, therefore not eligible for LAT-Mapping. The technique applies pattern matching of p-waves from surface-electrocardiogram (sEKG) as well as intracardiac signals during atrial tachycardia and pacing from the site of suspected origin. The CARTO® 3 system Version 7.2 applies an automated algorithm for intracardiac pace mapping (ICPM) using the unipolar signal of the reference electrode, usually located in the coronary sinus. Methods We examined 12 consecutive patients undergoing RF pulmonary vein isolation (PVI). We performed local pacing at the anterior and posterior wall of the left atrium (LA) and the posterior and lateral wall of the right atrium (RA). The first pacing point was defined as the reference pacing site (RPS). The ICPM algorithm was applied to calculate the similarity of the IC pattern of the RPS to the IC pattern across various distances. The maps, including mesh, point locations and IC pattern similarity of each point, were exported for further analysis in the Python environment. The areas were calculated for the 5% similarity strata. Results The study assessed areas with a 95% or higher pattern similarity in different atrial regions, revealing notable differences in pattern similarity. The median area of pattern similarity was 1.48 cm² (IQR: 0.76 - 2.01 cm²) in the LA posterior, 0.62 cm² (IQR: 0.15 - 1.06 cm²) in the LA anterior, 0.60 cm² (IQR: 0.38 - 1.19 cm²) in the RA posterior, and 5.68 cm² (IQR: 2.89 - 11.1 cm²) in the RA lateral wall. Significant differences were found between the posterior and anterior walls of the LA and the RA lateral wall, as well as between the RA posterior and RA lateral wall, indicating variability in intracardiac pattern matching accuracy. Patterns below 2 cm² are acceptable for catheter ablation. An outlier was noted in the RA posterior region. Conclusion The intracardiac pattern matching for atrial arrhythmias employing the CARTO-ICPM algorithm is feasible with acceptable accuracy except for the lateral regions of the right atrium. This might be explained by the larger distance to the referent catheter in the coronary sinus and the preferred conduction direction in the crista terminalis. Enhancing localization accuracy may necessitate the adjunctive use of surface ECG-pace mapping or the deployment of an additional multipolar intracardiac reference catheter in the lateral RA.A: IC Pattern, B: Box plots similarity

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects.

Universal Relocalizer for Weakly Supervised Referring Expression Grounding

This article introduces the Universal Relocalizer, a novel approach designed for weakly supervised referring expression grounding. Our method strives to pinpoint a target proposal that corresponds to a specific query, eliminating the need for region-level annotations during training. To bolster the localization precision and enrich the semantic understanding of the target proposal, we devise three key modules: the category module, the color module, and the spatial relationship module. The category and color modules assign respective category and color labels to region proposals, enabling the computation of category and color scores. Simultaneously, the spatial relationship module integrates spatial cues, yielding a spatial score for each proposal to enhance localization accuracy further. By adeptly amalgamating the category, color, and spatial scores, we derive a refined grounding score for every proposal. Comprehensive evaluations on the RefCOCO, RefCOCO+, and RefCOCOg datasets manifest the prowess of the Universal Relocalizer, showcasing its formidable performance across the board.

Hybrid TOA/AOA localization for indoor multipath-assisted next-generation wireless networks

The next-generation wireless communication systems (NGWCs) necessitate integrating effective communication and localization techniques. However, the multipath badly affects both communication and localization, especially in indoor NGWCs. Therefore, we propose a novel hybrid localization method based on time and angle-based ranging estimation to address this issue. This method employs an improved matrix enhancement matrix pencil algorithm (IMEMP) to obtain the time of arrival (TOA) and corresponding angle of arrival (AOA) of each path. Two distinct localization methods are presented: First arrival path-based maximum likelihood (ML) and scatterer-assisted. Both methods are designed to mitigate the adverse effects of serious non-line-of-sight (NLOS) scenarios. The proposed scatterer-assisted method demonstrates a noteworthy 20% improvement in performance compared to scenarios where only the first path is considered, particularly in specific areas. These findings highlight the potential of the proposed approach to enhance localization accuracy in challenging wireless environments, offering valuable insights for the advancement of NGWCs.

A MOBILE-BASED LOCALIZATION SYSTEM FOR INFRASTRUCTURE-INDEPENDENT EMERGENCY COMMUNICATION

After natural or human-made disasters, determining the locations of individuals needing urgent assistance is critical and time-sensitive. This paper introduces a novel ad-hoc infrastructure-less communication system for emergency scenarios, relying solely on basic mobile device capabilities. The proposed approach estimates mobile device coordination using multilateration, incorporating path loss theory to transform receiving signal strength into distance. A least square optimization algorithm is also applied to enhance localization accuracy. The system facilitates coordination and communication among citizens and first responders by strategically positioning a few emergency mobile devices as temporary base stations. Mobile devices search for neighboring base stations and transform into temporary base stations upon finding at least four. Those temporary base stations assist farther mobile devices in localization, creating a cascading effect until no more mobile devices are discovered. Simulation demonstrates robustness with a 97% discovery rate, maintaining accuracy even in environmental noise. While promising, real-world validation is essential to assess performance under diverse conditions and practical feasibility.

A MOBILE-BASED LOCALIZATION SYSTEM FOR INFRASTRUCTURE-INDEPENDENT EMERGENCY COMMUNICATION

After natural or human-made disasters, determining the locations of individuals needing urgent assistance is critical and time-sensitive. This paper introduces a novel ad-hoc infrastructure-less communication system for emergency scenarios, relying solely on basic mobile device capabilities. The proposed approach estimates mobile device coordination using multilateration, incorporating path loss theory to transform receiving signal strength into distance. A least square optimization algorithm is also applied to enhance localization accuracy. The system facilitates coordination and communication among citizens and first responders by strategically positioning a few emergency mobile devices as temporary base stations. Mobile devices search for neighboring base stations and transform into temporary base stations upon finding at least four. Those temporary base stations assist farther mobile devices in localization, creating a cascading effect until no more mobile devices are discovered. Simulation demonstrates robustness with a 97% discovery rate, maintaining accuracy even in environmental noise. While promising, real-world validation is essential to assess performance under diverse conditions and practical feasibility.

Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includesfour steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor startsbroadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possiblelocation where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing themeasuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchorsaccording to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts ininitialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it forfuture use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational modeattaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, withlower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar MeanAbsolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer amongthe neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared toMTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers aneffective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressingthe correlation between wireless device positions and signal intensities to improve the localization process. Theobtained results and provided justification emphasize the significance and value of the model in the field oflocalization in WSNs. The model represents a valuable contribution to the development of localization techniquesand improving their accuracy to meet the needs of various applications. The model opens up opportunities for itsutilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disastermanagement, enhancing its practical applications and practical significance.

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

FMCW Radar on LiDAR map localization in structural urban environments

AbstractMultisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

A Semantics-Guided Visual Simultaneous Localization and Mapping with U-Net for Complex Dynamic Indoor Environments

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.

Cooperative localization for master–salve multi-AUVs based on range measurements

Underwater localization has consistently remained a prominent technical challenge for autonomous underwater vehicles (AUVs). The advent of cooperative localization techniques has emerged as a novel avenue for enhancing localization accuracy. The master–slave cooperative localization mode has gained widespread adoption due to its cost-effectiveness in implementation. In view of the complexity of underwater noise characteristics, in the multi-AUVs cooperative localization system, this paper addresses scenarios involving distance-dependent noise in a master–slave-based multi-AUVs cooperative localization system. To tackle the negative impact of distance-dependent noise and the non-linearity of the distance function, a two-step algorithm is proposed that combines maximum likelihood estimation and the Gaussian belief propagation algorithm (ML-GBP) to estimate the positions of AUVs. The maximum likelihood estimation is employed to cope with the interference caused by distance-dependent noise, and subsequently, the Gaussian belief propagation algorithm, based on range observations and reference information, is used to achieve accurate estimation of AUV positions and implement position correction. Simulation results demonstrate that the proposed ML-GBP algorithm outperforms traditional extended Kalman filter (EKF) and nonparametric belief propagation (NBP) methods by enhancing the localization accuracy of the system while exhibiting superior performance in terms of computational complexity and system communication overhead.

Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.