Development and Application of Fiber-Optic Sensing Technology for Monitoring Soil Moisture Field

Accurate acquisition of the moisture field distribution in in situ soil is of great significance to prevent geological disasters and protect the soil ecological environment. In recent years, rapidly developed fiber-optic sensing technology has shown outstanding advantages, such as distributed measurement, long-distance monitoring, and good durability, which provides a new technical means for soil moisture field monitoring. After several years of technical research, the authors’ group has made a number of new achievements in the development of fiber-optic sensing technology for the soil moisture field, that is, two new fiber-optic sensing technologies for soil moisture content, including the actively heated fiber Bragg grating (AH-FBG) technology and the actively heated distributed temperature sensing (AH-DTS) technology, and a new fiber-optic sensing technology for soil pore gas humidity are developed. This paper systematically summarizes the three fiber-optic sensing technologies for soil moisture field, including sensing principle, sensor development and calibration test. Moreover, the practical application cases of three fiber-optic sensing technologies are introduced. Finally, the development trend of fiber-optic sensing technology for soil moisture field in the future is summarized and prospected.

Augmenting Social Science Research with Multimodal Data Collection: The EZ-MMLA Toolkit

While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people’s behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users’ attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications.

Multi-Temporal InSAR for transport infrastructure monitoring: Recent trends and challenges

Worldwide, transport infrastructure is increasingly vulnerable to ageing-induced deterioration and climate-related hazards. Oftentimes inspection and maintenance costs far exceed available resources, and numerous assets lack any rigorous structural evaluation. Space-borne Synthetic Aperture Radar Interferometry (InSAR) is a powerful remote-sensing technology, which can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown the InSAR accuracy to be comparable with traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility to support inspection planning and maintenance prioritisation, while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in reality. This paper identifies and discusses the challenges of using InSAR for the purpose of structural monitoring, with a specific focus on bridges and transport networks. Examples are presented to illustrate current practical limitations of InSAR; possible solutions and promising research directions are identified. The aim of this study is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges.

GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.