Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibratio...
The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such top...
The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also co...
Presenting real-world paths in property graphs is a complex challenge of identifying and representing the properties of routes and their environments. These property graphs serve as foundational datasets for generating smart sports training routes, where route features such as terrain, bends, and hills critically influence the route design. This pa...
Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial p...
This study presents a multimodal data fusion system to identify and impact rocks in mining comminution tasks, specifically during the crushing stage. The system integrates information from various sensory modalities to enhance data accuracy, even under challenging environmental conditions such as dust and lighting variations. For the strategy selec...
Published in Environmental Research Letters
Particulate matter poses significant risks to respiratory and cardiovascular health. Monitoring ambient particulate matter concentrations can provide information on potential exposures and inform mitigation strategies, but ground-based measurements are sparse. Data fusion approaches that integrate data from multiple sources can complement existing ...
Digital twin is an attractive technology for the representation of objects due to its ability to produce precise measurements and their geovisualisation. Of special interest is the application and fusion of various remote sensing techniques for shallow river and inland water areas, commonly measured using conventional surveying or multimodal photog...
Published in Measurement Science and Technology
With the advancement of artificial intelligence technology, fault diagnosis methods based on deep learning have been extensively studied due to their ability to automatically extract fault latent features and develop end-to-end diagnostic models. However, the existing methods focus on achieving high accuracy while neglecting model complexity. There...
Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification has been applied in the field of ground category recognition. However, existing methods still perform poorly in extracting high-dimensional features and elevation information, resulting in insufficient data classification accuracy. To address this challenge,...
