Spotting a significant number of drones flying near the entrance of a beehive during late Spring could indicate the occurrence of swarming mood, as the the surge in drone presence is related to an overcrowded hive. Swarming refers to a natural reproductive process witnessed in honey bees, wherein half of the bee colony departs from their hive alongside the aging queen. In the paper, we propose an early swarming detection mechanism that relies on the behavior of the drones. The proposed method is based on audio signals registered in a close proximity to the beehive entrance. A comparative study was performed to find the most effective preprocessing method for the audio signals for the detection problem. We have compared the results for three different power spectrum density coefficients estimation methods, which are used as an input of an autoencoder neural network to discriminate drones from worker bees. Through simulations employing real-life signals, it has been demonstrated that drone detection based solely on audio signals is indeed feasible. The attained level of detection accuracy enables the creation of an efficient alarm system for beekeepers.

This paper discusses the estimation of flow velocity from a multi-sensor scenario. Different estimation methods were used, which allow the effective measurement of the actual Doppler shift in a noisy environment, such as water with air bubbles, and on this basis the estimation of the flow velocity in the pipe was calculated. Information fusion is proposed for the estimates collected. The proposed approach focuses on the density of the fluid. The proposed method is capable of determining the flow velocity with high accuracy and small variations. Simulation results for plastic and steel (both galvanized and non-galvanized) pipes show the possibility of accurate fluid flow measurements without the need for sensors inside the pipe.