Search Results (4,116)

As interest in transparent electronics increases, ensuring the reliability of transparent RRAM (T-RRAM) devices, which can be used to construct transparent electronics, has become increasingly important. However, defects and traps within these T-RRAM devices can degrade their reliability. In this study, we investigated the improvement of transparency and reliability of T-RRAM devices with an AZO/HfO₂/Ti structure through rapid thermal annealing (RTA) at 450 °C for 60 s in a nitrogen atmosphere. The device without RTA exhibited a low transmittance of 30%, whereas the device with RTA showed a significantly higher transmittance of over 75%. Furthermore, the device operated at lower current levels after RTA, which resulted in a reduction in its operating voltages, and the forming, setting, and reset voltages changed from 3.3, 2.4, and −5.1 V, respectively, to 2, 1, and −2.7 V. This led to an improvement in the endurance characteristics of the device, which thereby suggests that these improvements can be attributed to a reduction in the defects and trap density within the T-RRAM device caused by RTA. Full article

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments. Full article

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed. Full article

Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8–10 flights (R² = 0.91–0.94; RMSE = 1.8–1.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season. Full article

The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines. Full article

Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, a low-power and lightweight design of the HAR system is essential. In this paper, we propose a low-power and lightweight HAR system using point-cloud data collected by radar. The proposed HAR system uses a pillar feature encoder that converts 3D point-cloud data into a 2D image and a classification network based on depth-wise separable convolution for lightweighting. The proposed classification network achieved an accuracy of 95.54%, with 25.77 M multiply–accumulate operations and 22.28 K network parameters implemented in a 32 bit floating-point format. This network achieved 94.79% accuracy with 4 bit quantization, which reduced memory usage to 12.5% compared to existing 32 bit format networks. In addition, we implemented a lightweight HAR system optimized for low-power design on a heterogeneous computing platform, a Zynq UltraScale+ ZCU104 device, through hardware–software implementation. It took 2.43 ms of execution time to perform one frame of HAR on the device and the system consumed 3.479 W of power when running. Full article

This study proposes a swarm-based Unmanned Aerial Vehicle (UAV) system designed for surveillance tasks, specifically for detecting and tracking ground vehicles. The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. Consequently, faster one-stage models, such as the tested YOLOv8 architectures, appear to be a more viable option for real-time operations. Notably, the swarm-based approach enables these faster algorithms to achieve an accuracy level comparable to that of slower models. Overall, the experimentation analysis demonstrates how larger YOLO architectures exhibit longer processing times in exchange for superior tracking success rates. However, the inclusion of additional UAVs introduced in the system outweighed the choice of the tracking algorithm if the mission is correctly configured, thus demonstrating that the swarm-based approach facilitates the use of faster algorithms while maintaining performance levels comparable to slower alternatives. However, the perspectives provided by the included UAVs hold additional significance, as they are essential for achieving enhanced results. Full article

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society. Full article

Microplastics (MPs) are pervasive environmental contaminants in aquatic systems. Due to their small size, they can be ingested by aquatic biota, and numerous negative effects have been documented. Determining the risks to aquatic organisms is reliant on characterizing the environmental presence and concentrations of MPs, and developing efficient ways to do so over wide scales by means of aerial remote sensing would be beneficial. We conducted a systematic literature review to assess the state of the science of aerial remote sensing of aquatic MPs and propose further research steps to advance the field. Based on 28 key references, we outline three main approaches that currently remain largely experimental rather than operational: remote sensing of aquatic MPs based on (1) their spectral characteristics, (2) their reduction of water surface roughness, and (3) indirect proxies, notably other suspended water constituents. The first two approaches have the most potential for wide-scale monitoring, and the spectral detection of aquatic MPs is seemingly the most direct approach, with the fewest potential confounding factors. Whereas efforts to date have focused on inherently challenging detection in coarse-resolution satellite imagery, we suggest that better progress could be made by experimenting with image acquisition at much lower altitudes and finer spatial and spectral resolutions, which can be conveniently achieved using drones equipped with high-precision hyperspectral sensors. Beyond developing drone-based aquatic MP monitoring capabilities, such experiments could help with upscaling to satellite-based monitoring for global coverage. Full article

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections. Full article

Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to perform shallow-water surveys easily and at accessible costs. This work presents a simple, low-cost, and highly portable platform that allows gathering sequential photos and echosounder depth values of shallow-water sites (up to 5 m depth). The photos are then analysed in conjunction with photogrammetric techniques to obtain digital bathymetric models and orthomosaics of the seafloor. The workflow was tested on four repeated surveys of the same area in the Western Mediterranean and allowed obtaining digital bathymetric models with centimetric average accuracy and precision and root mean square errors within a few decimetres. The platform presented in this work can be employed to obtain first-order bathymetric products, enabling the contextual establishment of the depth accuracy of the final products. Full article

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively Full article

For drone-based detection tasks, accurately identifying small-scale targets like people, bicycles, and pedestrians remains a key challenge. In this paper, we propose DV-DETR, an improved detection model based on the Real-Time Detection Transformer (RT-DETR), specifically optimized for small target detection in high-density scenes. To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. Experimental results on the VisDrone2019 dataset show that DV-DETR achieves an [email protected] of 50.1%, a 1.7% improvement over the baseline model, while increasing detection speed from 75 FPS to 90 FPS, meeting real-time processing requirements. These improvements not only enhance the model’s accuracy and efficiency but also provide practical significance in complex, high-density urban environments, supporting real-world applications in UAV-based surveillance and monitoring tasks. Full article

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training. Full article

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance. Full article