Search Results (3,390)

GNSS integrity is an essential component for ensuring the reliability of safety-critical applications using Global Navigation Satellite Systems (GNSSs). These applications, such as use in aviation and autonomous vehicles, demand high precision and dependability. There are two major GNSS integrity monitoring techniques, namely prior and posterior integrity monitoring. The principles of the two approaches, however, differ significantly, each influencing the GNSS positioning system’s continuity and accuracy performance in unique ways. In this study, we conduct a thorough evaluation and comparison of these two approaches to integrity monitoring, focusing on their effects on continuity and accuracy performance. We assess the probability of false alarms and continuity risks associated with posterior integrity monitoring by defining specific geometric spheres, both inside and outside the contours of the parity set, where the integrity risk requirement is satisfied. By using these defined spheres, we determine the lower and upper bounds for the probability of false alarms and continuity risks in posterior integrity monitoring. These spheres provide a novel and effective framework for comparing the continuity performance between the Chi-squared residual-based prior and posterior integrity monitoring. Our analysis highlights that, under fault-free scenarios, posterior integrity monitoring offers superior accuracy compared with the Chi-squared residual-based prior integrity monitoring approach. This finding underscores the critical importance of selecting an appropriate integrity monitoring strategy to enhance GNSS positioning system performance, particularly in environments where safety and precision are paramount. The insights gained from this study contribute to the advancement of GNSS technologies, supporting their implementation in an increasingly wide range of safety-critical applications. Full article

Wearable inertial sensor technology affords opportunities to record the physical activity of young children in their natural environments. The interpretation of these data, however, requires validation. The purpose of this study was to develop and establish the criterion validity of a method of quantifying active and sedentary physical activity using an inertial sensor for pre-ambulatory children with cerebral palsy. Ten participants were video recorded during 30 min physical therapy sessions that encouraged gross motor play activities, and the video recording was behaviorally coded to identify active and sedentary time. A receiver operating characteristic curve identified the optimal threshold to maximize true positive and minimize false positive active time for eight participants in the development dataset. The threshold was 0.417 m/s² and was then validated with the remaining two participants; the percent of true positives and true negatives was 92.2 and 89.7%, respectively. We conclude that there is potential for raw sensor data to be used to quantify active and sedentary time in pre-ambulatory children with physical disability, and raw acceleration data may be more generalizable than the sensor-specific activity counts commonly reported in the literature. Full article

Background/Objectives: Cervical cancer primarily disseminates through the lymphatic system, with the metastatic involvement of pelvic and para-aortic lymph nodes significantly impacting prognosis and treatment decisions. Sentinel lymph node (SLN) mapping is critical in guiding surgical management. However, resource-limited settings often lack advanced detection tools like indocyanine green (ICG). This study evaluated the feasibility and effectiveness of SLN biopsy using alternative techniques in a high-risk population with a high prevalence of large tumours. Methods: This prospective, observational study included 42 patients with FIGO 2018 stage IA1–IIA1 cervical cancer treated between November 2019 and April 2024. SLN mapping was performed using methylene blue alone or combined with a technetium-99m radiotracer. Detection rates, sensitivity, and false-negative rates were analysed. Additional endpoints included tracer technique comparisons, SLN localization patterns, and factors influencing detection success. Results: SLNs were identified in 78.6% of cases, with bilateral detection in 57.1%. The combined technique yielded higher detection rates (93.3% overall, 80% bilateral) compared to methylene blue alone (70.4% overall, 40.7% bilateral, p < 0.05). The sensitivity and negative predictive values were 70% and 93.87%, respectively. Larger tumours (>4 cm), deep stromal invasion, and prior conization negatively impacted detection rates. False-negative SLNs were associated with larger tumours and positive lymphovascular space invasion. Conclusions: SLN biopsy is feasible in resource-limited settings, with improved detection rates using combined tracer techniques. However, sensitivity remains suboptimal due to a steep learning curve and challenges in high-risk patients. Until a high detection accuracy is achieved, SLN mapping should complement, rather than replace, pelvic lymphadenectomy in high-risk cases. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

UAV moving object detection focuses on identifying moving objects in images captured by UAVs, with broad applications in regional surveillance and event reconnaissance. Compared to general moving object detection scenarios, UAV videos exhibit unique characteristics, including foreground sparsity and varying target scales. The direct application of conventional background modeling or motion segmentation methods from general settings may yield suboptimal performance in UAV contexts. This paper introduces an unsupervised UAV moving object detection network. Domain-specific knowledge, including spatiotemporal consistency and foreground sparsity, is integrated into the loss function to mitigate false positives caused by motion parallax and platform movement. Multi-scale features are fully utilized to address the variability in target sizes. Furthermore, we have collected a UAV moving object detection dataset from various typical scenarios, providing a benchmark for this task. Extensive experiments conducted on both our dataset and existing benchmarks demonstrate the superiority of the proposed algorithm. Full article

Background: Pressure injuries (PIs) are increasing worldwide, and there has been no significant improvement in preventing them. Traditional assessment tools are widely used to identify a patient at risk of developing a PI. This study aims to construct a novel fused multi-channel prediction model of PIs in adult hospitalized patients using machine learning algorithms (MLAs). Methods: A multi-phase quantitative approach involving a case–control experimental design was used. A first-hand dataset was collected retrospectively between March/2022 and August/2023 from the electronic medical records of three hospitals in Palestine. Results: The total number of patients was 49,500. A balanced dataset was utilized with a total number of 1110 patients (80% training and 20% testing). The models that were developed utilized eight MLAs, including linear regression and support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting (GB), K-nearest neighbor (KNN), decision tree (DT), and extreme gradient boosting (XG boosting) and validated with five-fold cross-validation techniques. The best model was RF, for which the accuracy was 0.962, precision was 0.942, recall was 0.922, F1 was 0.931, area under curve (AUC) was 0.922, false positive rate (FPR) was 0.155, and true positive rate (TPR) was 0.782. Conclusions: The predictive factors were age, moisture, activity, length of stay (LOS), systolic blood pressure (BP), and albumin. A novel fused multi-channel prediction model of pressure injury was developed from different datasets. Full article

This study investigated the influence of non-landslide sampling strategies on landslide susceptibility assessment (LSA) performance and explored approaches to minimizing uncertainty in model selection. Five non-landslide sampling strategies were evaluated using the random forest (RF) model to generate landslide susceptibility maps (LSMs) for each scenario. To assess the impact of these strategies, this study employed a receiver operating characteristic (ROC) curve, a confusion matrix, and various statistical indicators. Additionally, the mean susceptibility indices derived from the gradient boosting decision tree (GBDT), support vector machine (SVM), and RF models were analyzed to evaluate their effectiveness in reducing the uncertainty during model selection. The GBDT, SVM, and RF were selected for their ability to handle complex, nonlinear relationships in the data, superior generalization capability, effective mitigation of overfitting risks, high predictive performance, and robustness. The findings revealed that selecting non-landslide samples from slope units without landslides enhances accuracy and averaging across models mitigated the uncertainty associated with landslide susceptibility models. Furthermore, this study demonstrated that the non-landslide sample selection method significantly improved prediction accuracy, particularly when samples were drawn from very-low-susceptibility zones identified by pre-classified machine learning models. These results highlight the importance of refining sample selection strategies and integrating multiple machine learning models to improve the reliability and accuracy of landslide susceptibility assessments. This approach provides valuable insights for future research and practical applications in risk mitigation and disaster management by offering a more precise depiction of low-susceptibility areas, thereby reducing the occurrence of false positives in landslide prediction. Full article

Computer-aided detection (CADe) of masses in digital breast tomosynthesis (DBT) is crucial for early breast cancer diagnosis. However, the variability in the size and morphology of breast masses and their resemblance to surrounding tissues present significant challenges. Current CNN-based CADe methods, particularly those that use Feature Pyramid Networks (FPN), often fail to integrate multi-scale information effectively and struggle to handle dense glandular tissue with high-density or iso-density mass lesions due to the unidirectional integration and progressive attenuation of features, leading to high false positive rates. Additionally, the commonly indistinct boundaries of breast masses introduce uncertainty in boundary localization, which makes traditional Dirac boundary modeling insufficient for precise boundary regression. To address these issues, we propose the CU-Net network, which efficiently fuses multi-scale features and accurately models blurred boundaries. Specifically, the CU-Net introduces the Cross-Attention Adaptive Feature Pyramid Network (CA-FPN), which enhances the effectiveness and accuracy of feature interactions through a cross-attention mechanism to capture global correlations across multi-scale feature maps. Simultaneously, the Breast Density Perceptual Module (BDPM) incorporates breast density information to weight intermediate features, thereby improving the network’s focus on dense breast regions susceptible to false positives. For blurred mass boundaries, we introduce Uncertainty Boundary Modeling (UBM) to model the positional distribution function of predicted bounding boxes for masses with uncertain boundaries. In comparative experiments on an in-house clinical DBT dataset and the BCS-DBT dataset, the proposed method achieved sensitivities of 89.68% and 72.73% at 2 false positives per DBT volume (FPs/DBT), respectively, significantly outperforming existing state-of-the-art detection methods. This method offers clinicians rapid, accurate, and objective diagnostic assistance, demonstrating substantial potential for clinical application. Full article

The Advanced Message Queuing Protocol (AMQP) is a widely used communication standard in IoT systems due to its robust and reliable message delivery capabilities. However, its increasing adoption has made it a target for various cyber threats, including Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and brute force attacks. This study presents a comprehensive analysis of AMQP-specific vulnerabilities and introduces a statistical model for the detection and classification of malicious activities in IoT networks. Leveraging a custom-designed IoT testbed, realistic attack scenarios were simulated, and a dataset encompassing normal, malicious, and mixed traffic was generated. Unique attack signatures were identified and validated through repeated experiments, forming the foundation of a signature-based detection mechanism tailored for AMQP networks. The proposed model demonstrated high accuracy in detecting and classifying attack-specific traffic while maintaining a low false positive rate for benign traffic. Notable results include effective detection of RST packets in DDoS scenarios, precise classification of MitM attack patterns, and identification of brute force attempts on AMQP systems. This research highlights the efficacy of signature-based approaches in enhancing IoT security and offers a benchmark for future machine learning-driven detection systems. By addressing AMQP-specific challenges, the study contributes to the development of resilient and secure IoT ecosystems. Full article

This paper provides a comparative study of unsupervised machine learning (ML) methods for anomaly detection in flight data monitoring (FDM). The study applies various unsupervised ML techniques to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in industry. The results are validated by the industrial experts. The study finds that a hybrid Local Outlier Factor (LOF) approach provides significant advantages compared to the current state of the art and other ML techniques because it requires less hyperparameter tuning, reduces the number of false positives, provides an ability to establish trends amongst the entire fleet and has an ability to investigate anomalies at each timestep within every flight. Finally, the study provides an in-depth review for some of the cases highlighted by the hybrid LOF and discusses the particular cases providing insights from an academic and flight safety/operational point of view. The analysis conducted by the human expert regarding the outcomes produced by an ML technique is predominantly absent in scholarly research, thereby offering extra value. The study presents a compelling argument for transitioning from the current approach, based on analyzing occurrences through the exceedances of a threshold value, towards an ML-based method which provides a proactive nature of data analysis. The study shows that there is an untapped opportunity to process flight data and achieve valuable information for enhancing air transport safety and improved aviation operations. Full article

Background/Objectives: Critical congenital heart disease (CCHD) is among the major causes of global neonatal morbidity and mortality. While the incidence of CCHD appears to vary across populations, much of this variation may stem from differences in detection and reporting capabilities rather than true prevalence. In Jordan, recent data revealed a congenital cardiac disease incidence of 17.8/1000 live births, much higher than international averages. Diagnosis is largely dependent upon echocardiography, which is difficult to obtain in low-resource settings where prenatal screening modalities are limited. Screening for CCHD with pulse oximetry offers a potential method to identify patients earlier and contribute to improved outcomes. Methods: This prospective cohort study evaluated 20,482 neonates screened using pulse oximetry at Al-Bashir Hospital between January 2022 and May 2024. Demographic data, pulse oximetry measurements, and echocardiogram findings were collected during the screening process after obtaining ethical approval from the Jordanian Ministry of Health. Results: Pulse oximetry screening identified 752 neonates (3.7%) requiring further evaluation by echocardiography. An abnormality was detected in 240 neonates (31.9%), which included cardiac anomalies and pulmonary hypertension. Screening led to the identification of 138 infants with CCHD, including 80 with a previously unknown diagnosis, and an additional 247 infants with conditions requiring increased monitoring or treatment. Among those with CCHD, hypoplastic left heart syndrome and Tetralogy of Fallot were the most common conditions, 3.1%, and 2.4%, respectively. The overall false positive rate was 1.8% and was higher among those screened at less than 24 h of life compared to those screened at or after 24 h of life (2.3% [95%CI 2.1–2.6] vs. 0.8% [95%CI 0.6–1.0], p < 0.001). Conclusions: Pulse oximetry screening successfully led to the early detection of CCHD among Jordanian neonates. There was a high prevalence of CCHD compared to other reported cohorts. This highlights the importance of implementing national screening protocols to improve early diagnosis and intervention. Future studies will inform the feasibility and cost-effectiveness of national implementation in this setting. Full article

Accurately localizing individual trees and identifying species distribution are critical tasks in forestry remote sensing. Visual Simultaneous Localization and Mapping (visual SLAM) algorithms serve as important tools for outdoor spatial positioning and mapping, mitigating signal loss caused by tree canopy obstructions. To address these challenges, a semantic SLAM algorithm called LPD-SLAM (Line-Point-Distance Semantic SLAM) is proposed, which integrates stereo cameras with an inertial measurement unit (IMU), with contributions including dynamic feature removal, an individual tree data structure, and semantic point distance constraints. LPD-SLAM is capable of performing individual tree localization and tree species discrimination tasks in forest environments. In mapping, LPD-SLAM reduces false species detection and filters dynamic objects by leveraging a deep learning model and a novel individual tree data structure. In optimization, LPD-SLAM incorporates point and line feature reprojection error constraints along with semantic point distance constraints, which improve robustness and accuracy by introducing additional geometric constraints. Due to the lack of publicly available forest datasets, we choose to validate the proposed algorithm on eight experimental plots, which are selected to cover different seasons, various tree species, and different data collection paths, ensuring the dataset’s diversity and representativeness. The experimental results indicate that the average root mean square error (RMSE) of the trajectories of LPD-SLAM is reduced by up to 81.2% compared with leading algorithms. Meanwhile, the mean absolute error (MAE) of LPD-SLAM in tree localization is 0.24 m, which verifies its excellent performance in forest environments. Full article

The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region. Full article

With the increasing number of human space activities, space surveillance systems need to be developed to reduce the risk of collisions between space assets and space debris. In this context, optical surveillance systems have gradually become a significant means of space surveillance due to their various advantages. Generally, the sidereal tracking mode is used to search for unknown moving targets, which appear as streaks in the star image generated by the optical surveillance system. Typical matched filtering can detect faint streak-like targets in star images, but it generates more false alarms and must traverse all potential filters. In this paper, the layering approach is used to improve the environment for detecting faint targets, in which dual-threshold segmentation is proposed to separate bright objects while maintaining the completeness of faint targets. Second, a streak-like matched filter unit and a dual-step search approach are recommended to lower the computational cost of matched filtering. Finally, perpendicular cross filtering is provided to further eliminate false positives. Experiments performed with both simulated and real data demonstrate that the proposed method has excellent detection performance for detecting multiple faint streak-like targets in a single star image. Full article

In response to the increasing volume of network traffic and the growing sophistication of cyber threats, this study examines the use of deep learning-based intrusion detection systems (IDSs) in large-scale network environments. Traditional IDS face challenges such as high false positive rates, complex feature engineering, and class imbalances in datasets, all of which impede accurate threat detection. To overcome these limitations, we implement various deep learning models, including multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM), alongside traditional machine learning algorithms such as logistic regression, naive Bayes, random forest, K-nearest neighbors, and decision trees. A significant contribution of this study is the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance, enhancing the representativeness of the learning process. Additionally, we conduct a comprehensive performance comparison of the models, incorporating correlation-based feature selection and hyperparameter tuning to maximize detection accuracy. Our results indicate that deep learning models, particularly CNN and LSTM, outperform traditional machine learning approaches in cyber threat detection, achieving accuracy rates of 98%. However, random forest achieves the highest accuracy at 99.9%, demonstrating its effectiveness in structured intrusion detection tasks. Moreover, we evaluate computational efficiency and practical deployment considerations, discussing trade-offs between accuracy and resource consumption. These findings highlight the potential of deep learning-based IDS for large-scale network security applications while addressing key challenges such as interpretability and computational overhead. The study provides actionable insights for selecting the most suitable IDS models based on specific network environments and security requirements. Full article