Automatic Identification System Research Articles

The hypothesis of fingerprint individuality continues to be debated due to limited empirical verification, impacting the scientific foundation of fingerprint identification. This study proposed a quantitative model for fingerprint individuality and investigated the three-dimensional (3D) distribution of minutiae. This model considered the position and direction of minutiae as 3D feature variables. We extracted 3D feature data from 56,812,114 known fingerprints based on the automatic fingerprint identification system (AFIS). Following data calibration, translation, and error correction, we statistically analyzed the distribution density of minutiae. We developed the algorithm to calculate the individuality score of a single fingerprint through the individuality model. The experimental results showed that the minutiae distribution followed distinct patterns. The distribution density of minutiae exhibited symmetry between corresponding fingers on left/right hands. Significant variations in minutiae distribution density and central point distribution were observed across the five pattern types (whorl, left loop, right loop, arch, accidental). Minutiae with different directions exhibited symmetry along the Y-axis in both positional and quantitative distribution. Minutiae within diagonally opposite angular ranges showed similar distribution trends. The individuality scores were robust to distinguish different fingerprints. We preliminarily applied the individuality score to provide a basis for modifying the AFIS scoring mechanism, and we found that the individuality score of same-source fingerprints was greater than that of close nonmatches (CNMs). This work provides novel insights into fingerprint individuality and establishes a statistical foundation for refining AFIS scoring mechanisms and likelihood-ratio evidence evaluation frameworks.

To address the limitations of single-sensor perception in inland vessel monitoring and the lack of robustness of traditional tracking methods in occlusion and maneuvering scenarios, this paper proposes a hierarchical multi-target tracking framework that fuses Light Detection and Ranging (LiDAR) data with Automatic Identification System (AIS) information. First, an improved adaptive LiDAR tracking algorithm is introduced: stable trajectory tracking and state estimation are achieved through hybrid cost association and an Adaptive Kalman Filter (AKF). Experimental results demonstrate that the LiDAR module achieves a Multi-Object Tracking Accuracy (MOTA) of 89.03%, an Identity F1 Score (IDF1) of 89.80%, and an Identity Switch count (IDSW) as low as 5.1, demonstrating competitive performance compared with representative non-deep-learning-based approaches. Furthermore, by incorporating a fusion mechanism based on improved Dempster–Shafer (D-S) evidence theory and Covariance Intersection (CI), the system achieves further improvements in MOTA (90.33%) and IDF1 (90.82%), while the root mean square error (RMSE) of vessel size estimation decreases from 3.41 m to 1.97 m. Finally, the system outputs structured three-level tracks: AIS early-warning tracks, LiDAR-confirmed tracks, and LiDAR-AIS fused tracks. This hierarchical design not only enables beyond-visual-range (BVR) early warning but also enhances perception coverage and estimation accuracy.

Traditional waterway traffic information services and management primarily depend on equipment such as Automatic Identification Systems (AIS), shore-based radar, and video surveillance. However, these traditional methods suffer from temporal and spatial latency, as well as insufficient precision in heterogeneous data fusion, making it challenging to satisfy refined situational awareness demands in waterways. This study introduces a novel end-to-end multi-source data fusion framework for fine-grained maritime traffic perception, addressing the limitations of traditional methods in integrating heterogeneous data. We enhance an Edge-SAM instance segmentation model with low-rank matrix optimisation and a ship-sensitive attention mechanism, achieving pixel-level segmentation of waterway targets. A regional matching method is proposed to deeply fuse discrete AIS information with detected objects from surveillance videos, effectively tackling challenges such as temporal alignment and coordinate mapping. Experiments on a real-world inland waterway dataset demonstrate significant improvements over baseline models, with 21% higher IoU, 14% higher Dice, and 23% higher Precision. The method achieves robust data fusion accuracy and real-time processing capability, offering a reliable solution for intelligent waterway regulation and collision risk assessment. This approach not only enhances vessel segmentation accuracy but also provides a viable technical pathway for the efficient operation of intelligent maritime supervision systems.

The ever-increasing volume of maritime freight increases the risk of ship collisions with devastating consequences. This study conducts a hotspot analysis to address this safety concern. Using Automatic Identification System data in the Bass Strait waters, Australia, the main results are as follows: First, the Getis-Ord general Gi* statistic shows that most of the Bass Strait waters have a low collision risk on a monthly basis, which can be classified into five clusters. Second, the spatial hotspot analysis identifies the Sydney-Melbourne route as the major shipping route with the highest collision risk, followed by the Melbourne-West Coast of Tasmania route, and the Melbourne-Devonport route. Third, ship collision risk maps for different time periods visualize the Port of Melbourne and Devonport as high-risk areas due to their persistently high Getis-Ord Gi* statistics. Finally, ship collision risk in the Bass Strait waters shows clear monthly and hourly trends as well as seasonal and day-night variations. These results provide valuable insights for enhancing vessel maneuverability and strategic channel coordination, thereby reducing the likelihood of ship collisions.

This study discusses the integration of Convolutional Neural Network (CNN) and K-Nearest Neighbors (KNN) for the identification of coffee bean quality as an effort to increase the competitiveness of local commodities. CNN is used as a feature extractor to produce an information-rich representation of coffee bean images, while KNN acts as a classifier to classify quality into two classes, namely Good and Defective. The dataset is divided into training, validation, and test data, with a total of 1,190 images obtained from the manual annotation process. The research stages include (1) pre-processing of data in the form of cropping based on bounding boxes, resize to 224×224 pixels, normalization, and data augmentation; (2) feature extraction using pretrained CNN (ResNet18) by eliminating the final classification layer to obtain a 512-dimensional feature vector; and (3) classification using KNN with variations in k values (3, 5, and 7) as well as Euclidean distance metrics. The results of the experiment showed that the CNN+Softmax baseline resulted in an accuracy of 86%, while the CNN+KNN method provided better performance. The k=5 configuration was proven to be optimal with an accuracy of 93.4%, precision, recall, and a balanced F1-score in both classes. The confusion matrix shows that most samples can be classified correctly with a low error rate. These findings are in line with previous research that emphasized the effectiveness of CNN in the extraction of visual features and the advantages of KNN on limited datasets. Thus, this approach can be a practical solution to support an automatic, accurate, and consistent coffee bean quality identification system to increase the competitiveness of local coffee commodities in the global market.

Shipping expansion, offshore energy generation, fish farming, and construction work radiate high levels of underwater noise, which may critically stress marine ecosystems. Tools for simulating, analyzing, and forecasting underwater noise can be of great help in understanding the impact of underwater radiated noise both on the environment and on man-made equipment, such as underwater communication and telemetry systems. To address this challenge, we developed a web-based Marine Noise Analysis Tool (MNAT) that models, simulates, and predicts underwater radiated noise levels. To reproduce realistic shipping conditions, MNAT combines real-time Automatic Identification System data with environmental data using broadly accepted underwater acoustic propagation models, including Bellhop and RAM. Moreover, MNAT can simulate other kinds of noise sources, such as seismic airguns. It features an intuitive interface enabling real-time tracking, noise impact assessment, and interactive visualizations. MNAT’s noise modeling capabilities allow the user to design resilient communication systems in different noise conditions, analyze maritime noise data, and forecast future noise levels, with potential contributions to the design of noise-resilient systems, to the optimization of environmental monitoring device deployments, and to noise mitigation policymaking. MNAT has been made available for the community at a public GIT repository.

ABSTRACT In inland waterway maritime, AIS and VHF voice communication, due to overlapping speakers, strong non-stationary noise hinders automation and intelligent supervision. This study proposes a method for predicting the berthing behaviour intentions of ships by integrating AIS data with maritime VHF voice feature selection and recognition. The method employs threshold-based screening and preprocessing to remove invalid audio components, followed by denoising and enhancement using a denoising convolutional neural network. An integrated attention mechanism enables the model to focus on key information related to vessel intentions. Experimental results show that the proposed approach reduces the character error rate for keyword recognition from 70% to 41.27% after AIS big data integration denoising, and to 20.54% with domain-specific training—substantially outperforming traditional methods. This research significantly enhances the automation and accuracy of vessel traffic service operations, offering robust technical support for the safety of maritime communications management. Abbreviations: AIS: automatic identification system; CASA: computational auditory scene analysis; CER: character error rate; CNN: convolutional neural network; DnCNN: denoising convolutional neural network; DNN: deep neural network; FM: frequency modulation; IMO: International Maritime Organization; LoRA: low-rank adaptation; PEFT: parameter-efficient fine-tuning; ReLU: rectified linear unit; RNN: recurrent neural network; SGD: stochastic gradient descent; SNR: signal-to-noise ratio; UNCTAD: United Nations Conference on Trade and Development; VHF: very high frequency; VTS: vessel traffic service; WNNM: weighted nuclear norm minimization

As a navigational aid integrating advanced communication technologies, real Automatic Identification System Aids to Navigation (AIS AtoN) play an indispensable role in emerging fields such as offshore wind farm marking, digital waterway construction, and bridge collision prevention. Currently, a series of standards related to AIS AtoN have been published both domestically and internationally, but there remains a gap in China regarding standards for the performance and testing requirements of real AIS AtoN. This paper introduces real AIS AtoN, its application scenarios and issues, an overview of domestic and international standards research, and related performance indicators of real AIS AtoN, highlighting the necessity for China to establish standards for the performance and testing methods of real AIS AtoN.

"Radiobiometrics": Deep-learning radiograph biometrics for patient identification.

The sea area adjacent to ports features a dense network of intricate access routes. Existing route modeling methods exhibit limitations in accurately capturing these complex routes and effectively representing the diverse handling behavior patterns of ships within them. To address this issue, this paper proposes a maritime route modeling method incorporating ship handling behavior (MARSHB) to accurately identify port channels with diverse traffic flows and enabling a multi-dimensional model of heterogeneous vessel behaviors along these channels. Numerical experiments using extensive automatic identification system (AIS) data from the Bohai Sea show that the proposed method reduces the computational time by 49.75% for route extraction compared to the traditional method. For route modeling, MARSHB covers 88.31% of 95% high-density traffic areas, with safety boundaries exhibiting a higher accuracy of conformity with historical trajectory data.

BACKGROUND The study aimed to evaluate the economic and operational efficiency of maritime navigation systems worldwide, considering Georgia’s maritime sector specifics to improve safety and reduce shipping costs. MATERIALS AND METHODS The analysis used simulation modelling based on hypothetical operational scenarios, verified with industry report data. RESULTS Results showed that global positioning systems (with a 150% return on investment (ROI), minimal implementation costs of USD 10,000, and maintenance costs of USD 1,000) and automatic identification systems (ROI of 120-125% with a 15-18% cost reduction) offered the highest economic efficiency. Satellite communication provided a balanced cost-benefit ratio (ROI of 110%, 14% cost reduction). Integrated bridge systems, despite the potential to cut costs by 18%, were limited by high implementation costs (USD 100,000) and low adoption in Georgia (30%). In Georgia’s context, automatic identification reduced incidents by 20%, saving USD 6,000 annually in insurance costs (ROI of 40%), while satellite communication reduced incidents by 15%, saving USD 4,500 (ROI of 22.5%). Electronic chart systems and radar systems showed moderate efficiency, but their use was restricted by complex interfaces and high training costs (USD 5,000-USD 12,000). The vulnerability of systems to cyberattacks underscored the need for stronger cybersecurity. CONCLUSION The practical importance of these findings is in developing recommendations for shipping companies and regulators, which could be used by shipping firms, authorities, and educational institutions to modernize navigation infrastructure and train crews within Georgia’s maritime industry.

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines-including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception.

The Barito River is a strategic shipping lane facing various safety challenges. The use of navigation technology such as the Automatic Identification System (AIS) can improve shipping safety and efficiency, but its adoption among ship operators is still limited. This study analyzes the acceptance of navigation technology in the Barito River Channel using the Technology Acceptance Model (TAM), which involves five variables: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitude Toward Using (ATU), Behavioral Intention (BI), and Actual System Use (AU). The results of the analysis using PLS-SEM show that PEU has a positive effect on PU (t-statistic = 10.056, p-value = 0), PEU has a significant effect on ATU (t-statistic = 4.131, p-value = 0), and ATU has a significant effect on BI (t-statistic = 5.059, p-value = 0). PU has a significant effect on BI (t-statistic = 5.875, p-value = 0) and BI has a positive effect on AU (t-statistic = 8.898, p-value = 0). The results of the hypothesis test indicate that most of the relationships between variables in the TAM are significant, with positive influences between PEU and PU, PEU and ATU, ATU and BI, and BI and AU. However, the relationship between PU and ATU does not show significance, indicating that perceived usefulness does not directly influence attitudes towards technology use. This acceptance contributes to the development of policies and strategies for the acceptance of navigation technology in the shipping sector.

This study proposes a novel system for automatic cognitive workload identification from EEG signals. It overcomes limitations of traditional methods by integrating a pre-trained CNN with advanced features. The model processes a functional connectivity matrix, generated using the corrected imaginary phase locking value (ciPLV) method, with the Xception network. The CNN's output is then combined with nonlinear dynamic features. A feedforward neural network uses these combined vectors for classification, achieving high accuracy (over 98% for two workloads and 92.50% for three), demonstrating the promise of this integrated deep learning approach.

Abstract Melioidosis is an emerging infectious disease in India which presents with febrile illness ranging from septicemia to localized abscess formation. We present a case of a 61-year-old male who presented with fever of almost 2 months' duration, persistent pneumonia, and liver and splenic abscesses. Aspiration of hepatic fluid collection and subsequent culture yielded Gram-negative bacilli, which was identified as Burkholderia pseudomallei. He was treated successfully with surgical drainage of abscess and prolonged course of intravenous and oral antibiotics. So, in cases of pyogenic liver abscess not responding to conventional antibiotics, B. pseudomallei should always be thought as a possible cause, which can be identified by its characteristic appearance on culture and microscopy and accurately identified by automated identification systems.

The high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making.

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO2, 20,500 tons of NOx, 4250 tons of SO2, 656 tons of PM10, and 603 tons of PM2.5, with anchorage activities contributing 497,000 tons of CO2, 7010 tons of NOx, 1520 tons of SO2, 232 tons of PM10, and 214 tons of PM2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies.

Maritime transport accounts for approximately 80% of international cargo volume; however, it confronts significant threats from typhoons or hurricanes. Existing typhoon-avoidance strategies are often challenged by uncertainties in the forecast of typhoon intensity and trajectory. This study analyzes the case of a bulk carrier navigating from Australia to China during Typhoon “Doksuri” in 2023. Automatic Identification System (AIS) data and high-resolution meteorological datasets were ultilized, along with an improved Dijkstra algorithm, to construct a navigable typhoon-avoidance route map. A time-minimization objective function incorporating a penalty mechanism for violating typhoon safety distances was established. The bulk carrier adopted four sequential avoidance strategies to evade Typhoon “Doksuri”: reducing speed, front-crossing avoidance, stopping and drifting, and resuming sailing under safe conditions. As a result of these adjustments, the Distance of Closest Point of Approach (DCPA) increased from 101 nautical miles to 183 nautical miles. The maximum encountered wind force was reduced from Beaufort scale 11 to 8, and the significant wave height decreased from 9 meters to 6.5 meters. The research indicates that timely and accurate typhoon forecasts, refined weather and ocean condition forecasts, and precise shore-based meteorological routing services are critical factors for ships to avoid typhoons effectively.

With the global increase in maritime cargo volume and vessel size, accurate anchor circle determination is crucial for safe and efficient anchorage management. This study proposes a new grid-based extension method that integrates vessel-specific characteristics with Automatic Identification System (AIS) data to determine anchor circles. Experiments with five vessels at Busan Port demonstrated high accuracy, with a maximum radius error of 9 m, significantly outperforming current Vessel Traffic Service (VTS) systems, which overestimate required radii by 1.7–2.1 times. The method enables immediate field application using existing VTS data, contributing to efficient anchorage management and smart port development.

Spatiotemporal evolution and prediction of Arctic shipping black carbon emissions based on AIS data 2016 to 2022.