Underground mines require continuous airflow monitoring, yet cabling or frequent battery replacement for distributed sensors is costly and hazardous. In this study, we present a piezoelectric wind energy harvester (PWEH) that also functions as a self-powered sensor for monitoring ventilation airflow conditions in underground mines. A shallow groove machined into the windward face of a D‑shaped bluff body boosts harvested power by 29.3% at a wind speed of 4.5 m/s compared with an ungrooved benchmark. The resulting voltage waveform supports two embedded sensing functions without external power: (i) ventilation fault diagnosis based on a stacking ensemble that integrates one-dimensional convolutional neural network (1D CNN), random forest (RF) and transfer-learned AlexNet classifier, enhanced by manifold‑based data augmentation, to identify 16 fault scenarios in a laboratory mine ventilation layout with up to 97.4% accuracy; and (ii) wind speed estimation using Random Forest that delivers a median mean absolute error below 0.12 m/s and worst‑case error under 0.5 m/s across 1.5–5.5 m/s airflow speeds, matching the performance of commercial anemometers. Laboratory trials in a scaled mine ventilation layout validate these capabilities across 16 duct networks with fan speeds ranging from 200–700 rpm, while energy measurements show the device produces a root mean square power output of 5.2 μW at a 4.5 m/s wind speed, which is sufficient to run a Bluetooth Low Energy-based SensorTag for fog data transmission every 50 seconds. By combining wind energy harvesting and airflow sensing in a single device, the proposed PWEH offers a battery-free and low-maintenance path to safer and smarter Mine Internet of Things deployments.

Internet of Things (IoT) technology has been widely used for real-time monitoring of the environment, safety and production in underground mines. This paper presents the basic structure of a Mine Internet of Things (MIoT) system based on a widely used three-layer IoT architecture, classifies types of sensors commonly used in underground mines by specific application, and introduces available wired and wireless communication technologies and network topologies that can be applied in underground mines. This paper provides a comprehensive review of recent developments in IoT applications in underground mines to monitor various environmental parameters, including mine gas and dust concentrations, temperature, humidity and airflow, groundwater, ground support and seismic activity. MIoT applications for fire and hazard detection, personnel and equipment positioning, and production safety management have also been investigated. This paper highlights key challenges for the broad application of IoT technology in underground mines such as operation disruption, additional investment, limited battery life, poor quality of underground communication, and difficulty in data management. Further research on novel advanced techniques, such as self-powered sensors, MIoT standardization and underground wireless communication technologies, is essential to improve the applicability and effectiveness of IoT applications in underground mines.

A steerable parametric source is designed to steer an audio beam without mechanically rotating the source. To achieve this without the generation of grating lobes requires an ultrasonic array with interelement spacing that is less than half the wavelength of the carrier ultrasound because of the spatial Nyquist criterion. However, ultrasonic wavelengths are typically smaller than the size of an ultrasonic transducer and this generates grating lobes in the radiation pattern, which is known as the spatial aliasing effect. This work proposes a method to suppress sidelobes including these grating lobes by optimizing the position and weight coefficients of the array elements by using a sparse random array technique. This is achieved by using the peak sidelobe level as the objective function and the simulated annealing algorithm for the optimization. Both simulation and experimental results demonstrate the sidelobe level can be effectively suppressed when the average interelement spacing is two wavelengths. It is also found that Westervelt directivity has a significant effect on the spatial aliasing, because it serves as a spatial filter on the product directivity of the ultrasound. Accordingly, the sidelobe suppression performance deteriorates at low audio frequencies and high ultrasound frequencies where Westervelt directivity tends to be broader. However, this deterioration in performance can be addressed by increasing the number of the array elements.

In this paper, the audio sound field generated by a focusing parametric array loudspeaker (PAL) is investigated both numerically and experimentally. A computationally efficient method is developed to calculate the quasilinear solution of both the Westervelt and Kuznetsov equations. The accuracy of generated sound field using Westervelt equation is initially examined, and the results are then compared with those obtained using the Kuznetsov equation. It is found that the focusing on the true focal location deteriorates the prediction accuracy using the Westervelt equation due to the strong and complicated local effects. To validate the numerical results against experimental data, a 37-channel focusing PAL prototype is constructed by using a field programmable gate array, metal-oxide semiconductor field-effect transistor drivers and the pulse train signal technique. Both the simulation and experimental results show that the audio sound pressure around the true focal location is increased, and the increment becomes larger at low audio frequencies and when the focal point moves close to the radiation surface, which is attributed to the strong local effects in the near field. It has been shown that a focusing PAL can improve the poor low frequency response for a conventional PAL without focusing, and the generated audio sound decays rapidly with the distance in the far field when compared to that without focusing.