Currently pressure sensors utilized for water pressure measurement need batteries for direct power supply. However, batteries are lifespan-limited and not so reliable in the buried water pipe environment. The maintenance work due to battery failures leads to high cost to utility owners. Wireless passive surface acoustic wave (WP-SAW) sensors do not need direct power supply from batteries and can work in harsh environment. They are low-cost and compatible with microelectromechanical systems (MEMS) technologies. This study investigates a temperature-compensated WP-SAW bidirectional reflective delay line (RDL) pressure sensor and its feasibility in improving water pressure measurement. The linear temperaturecompensated pressure sensing functional model between the output phase shifts and the pressure change is established theoretically and verified by experiments. An experimental framework for testing the sensor node is built. The water pressure sensing adaptor is proposed. Experimental results: the experimental data show good linearities, which fits the established functional relationship; the numerical functional relationship has been derived and expressed; the sensor node has a good performance in the range of pressure difference from 0 to 0.5 MPa, which meets the normal 28-meter water pressure sensing requirements; the accuracy of this sensor is 7.22 kPa, which can be utilized for the water pressure sensing tasks in water distribution systems.

This work experimentally explores the post-impact behaviour of thin-ply angle-ply pseudo-ductile carbon fibre laminates subjected to tensile load. Indentation and low-speed impact tests were performed on standard tensile test specimens. Non-destructive tests were used to investigate the damage propagation. Digital Image Correlation (DIC) was adopted to detect the strain distribution during tensile tests. Post-damage pseudo-ductile behaviour was retained in angle-ply hybrid composites subjected to tensile loading conditions.

Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.

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