Honey bees are one of the most important insects on the planet since they play a key role in the pollination services of both cultivated and spontaneous flora. Recent years have seen an increase in bee mortality which points out the necessity of intensive beehive monitoring in order to better understand this phenomenon and try to help these important insects. In this scenario, this work presents an algorithm for sound-based classification of honey bee activity reporting a preliminary comparison between various extracted features used separately as input to a convolutional neural network classifier. In particular, the orphaned colony situation has been considered using a dataset acquired in a real situation. Different experiments with different setups have been carried out in order to test the performance of the proposed system, and the results have confirmed its potentiality.

Digital audio equalization is a very common procedure in the acoustic field that allows to improve the listening experience by adjusting the auditory frequency response. The design of graphic equalizers introduces several problems related to the necessity of implementing high performing filters with linear phase, usually characterized by high computational complexity. This paper proposes an innovative linear-phase graphic equalizer based on interpolated finite impulse response (IFIR) filters. IFIR filters seem to be suitable for a graphic equalizer thanks to their properties. In fact, they can reach very narrow transition bands, however with low computational complexity and linear phase, avoiding ripple between adjacent bands. The proposed IFIR equalizer has been compared with some state-of-the-art methods in terms of frequency response, distortion and computational cost. The experimental results have proved the effectiveness of the proposed equalizer, that has shown a considerable reduction on the computational complexity, meanwhile preserving the performances in terms of audio quality.

An open problem in room impulse response (RIR) measurement is the effect of nonlinearities, especially those with memory, present in the measurement system, specifically in the power amplifier and in the loudspeaker. The nonlinearities can corrupt the measurement introducing artifacts. The paper discusses a RIR measurement method that is robust towards these nonlinearities. The proposed methodology allows measuring the RIR using the cross-correlation method, i.e., computing the cross-correlation between the output signal and an appropriate sequence. In contrast to other cross-correlation based methods, the proposed approach directly estimates the first-order kernel of the Volterra filter modeling the measurement systems, i.e., the system impulse response for small signals. The proposed approach exploits the concepts of orthogonal periodic sequences, recently proposed in the literature. The input signal can be any periodic persistently exciting sequence and can also be a quantized sequence. Measurements performed both on an emulated scenario and in real environments illustrate the validity of the approach and compare it with other competing RIR measurement methods.

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.

Head-up displays (HUDs) have become common equipment in aircraft cockpits. One of the uses of HUDs is to provide a specific visual interface for pilots in the form of what is called a "tunnel-in-the-sky" (i.e. 3D geometry for the navigation path displayed on a flat screen). According to recent studies the "tunnel-in-the-sky" approach does not provide crucial advantages in comparison with more traditional methods of presenting navigation information to pilots. Our research considers a stereoscopic version of the 3D "tunnel-in-the-sky" realized as an augmented reality (AR) pocket-size system with see-through light-weight AR glasses. The system consists of low-cost items and does not suffer from the drawbacks tied with existing synthetic/enhanced vision systems for pilots. The results of the experiments with desktop simulators of different AR pilot's interfaces (2D, 3D and stereo 3D conditions) proved the effectiveness of the proposed stereo AR solution. A flight test of the prototype of the developed AR system was carried out on Cessna 172 aircraft and is showed in the accompanying video.