Machine learning (ML)-based models are popular for complex physical system simulation and prediction. Lake is the important indicator in arid and semi-arid areas, and to achieve the proper management of the water resources in a lake basin, it is cruc...
In this paper, I propose a bird eye view image detection method for parking areas and collision risk areas at the same time in parking situations. Deep learning algorithms using area detection and semantic segmentation were used. The main architectur...
Built-up areas are the main gathering place for human activities. The widespread availability of various satellite sensors provides a rich data source for mapping built-up areas. Deep learning can automatically learn multi-level features of targets f...
This study aims to implement and evaluate a methodological proposal using the hologram as a teaching medium for the learning of concepts related to areas and volumes of geometrical bodies. The study has been carried out with a sample of 78 students i...
Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been deve...
Sentinel-2 satellite images allow high separability for mapping burned and unburned areas. This problem has been extensively addressed using machine-learning algorithms. However, these need a suitable dataset and entail considerable training time. Re...
Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and...
Terminal airspace is the convergence area of air traffic flow, which is the bottleneck of air traffic management. With the rapid growth of air traffic volume, the impact of convective weather on flight operations is becoming more and more serious. To...
The latest technologies and communication protocols are arousing a keen interest in automation, in which the field of home area networks is the most prominent area to work upon toward solving the issues and challenges faced by wireless home area netw...
Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-con...
Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural netw...
In the Caribbean basin, particulate matter lower or equal to 10 μm in diameter (PM10) has a huge impact on human mortality and morbidity due to the African dust. For the first time in this geographical area, the theoretical framework of artificial...
To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. The full-process...
A method to efficiently and quantitatively study the delivery of a pesticide-surfactant formulation in a water solution to plant leaves is presented. The methodology of measurement of the surface of the leaf wet area is used instead of the more probl...
Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, f...
Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteoro...
As an emerging direction of multi-agent collaborative control technology, multiple autonomous underwater vehicle (multi-AUV) cooperative area search technology has played an important role in civilian fields such as marine resource exploration and de...
Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most represe...
Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influenc...
University students use various ICT-based media a goal to help them learn. The Chinese government is also increasing the use of ICT tools in the education sector because they relate to university students’ learning outcomes. Several universitie...
Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mi...
Research in Chile, regarding language teaching and learning, has focused on Spanish as a first language (L1). However, due to the growing influx and settlement of non-Spanish-speaking adult migrants, the significance of investigating language educati...
Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physi...
The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study,...
Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogge...
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffe...
Accurate inflation forecasting is of central importance for monetary authorities, governments, and businesses, as it shapes economic decisions and policy responses. While most studies focus on headline inflation, this paper analyses the Harmonised In...
Semiconducting materials require stringent design specifications that make their fabrication more difficult and prone to flaws that are costly and damaging to their computing and electrical properties. Area-selective atomic layer deposition is a proc...
The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It e...
Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterog...
An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and...
Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification...
The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productiv...
Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the L...
Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility...
A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes in...
The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML)...
In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse respon...
The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and...
The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, a...
With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban...
Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need to study marine water quality. The use of remote sensing for water quality inversion offers a precise, timely, and comprehensive way to...
The prediction of the daily crop leaf area index (LAI) plays a crucial role in forecasting crop growth trends and guiding field management decisions in the realm of scientific research. However, research on the daily prediction of LAI is scarce, and...
Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (...
Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven an...
Accurate extraction of urban landscape features in the historic district of China is an essential task for the protection of the cultural and historical heritage. In recent years, deep learning (DL)-based methods have made substantial progress in lan...
The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long...
The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be t...
This paper investigates the sustainable inheritance of contemporary lacquer art in the Guangzhou area. Based on the traditional teaching mode dominated by folk inheritance and university education, this paper develops the advantage of contemporary in...
Background: the Skill, Rule, and Knowledge (S-R-K) model is a framework used to describe and analyze human behaviour and decision-making in complex environments based on the nature of the task and kind of cognitive control required. The S-R-K model i...
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