The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO ${}_2$ inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.

Recent years have seen a worsening in the decline of honey bees (Apis mellifera L.) colonies. This phenomenon has sparked a great amount of attention regarding the need for intense bee hive monitoring, in order to identify possible causes, and design corresponding countermeasures. Honey bees have a key role in pollination services of both cultivated and spontaneous flora, and the increase in bee mortality could lead to an ecological and economical damage. Despite many smart monitoring systems for honey bees and bee hives, relying on different sensors and measured quantities, have been proposed over the years, the most promising ones are based on sound analysis. Sounds are used by the bees to communicate within the hive, and their analysis can reveal useful information to understand the colony health status and to detect sudden variations, just by using a simple microphone and an acquisition system. The work here presented aims to provide a review of the most interesting approaches proposed over the years for honey bees sound analysis and the type of knowledge about bees that can be extracted from sounds.

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.

The multiple-variance method is a cross-correlation method that exploits input signals with different powers for the identification of a nonlinear system by means of the Volterra series. It overcomes the problem of the locality of the solution of traditional nonlinear identification methods that successfully approximate only for inputs having approximately the same power of the identification signal. The multiple-variance method improves the model performance in case of inputs with high...

The paper introduces a novel family of deterministic periodic signals, the orthogonal periodic sequences (OPSs), that allow the perfect identification on a finite period of any functional link polynomials (FLiP) filter with the cross-correlation method. The class of FLiP filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. The novel sequences share many properties of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of FLiP filters with the cross-correlation method. But, while PPSs exist only for orthogonal FLiP filters, the OPSs allow also the identification of non-orthogonal FLiP filters, as the Volterra filters. In OPSs, the modeled system input can be any persistently exciting sequence and can also be a quantized sequence. Moreover, OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller that PPSs. The provided experimental results, involving the identification of real devices and of a benchmark model, highlight the potentialities of the proposed OPSs in modeling unknown nonlinear systems.

The ability to produce accurate models of nonlinear systems has grown enormously thanks to the increase in the available computing power, thus making possible to use such models in a wide range of applications. Adopting these techniques enables to design much more efficient systems, but also increases the need for accurate and straightforward methods to monitor distortion levels, even out of the testing laboratories. In this context, efficient estimators of total harmonic distortion, harmonic distortion of order n, and intermodulation distortion are needed, which are among the most widely adopted indicators of non-linearity. In this paper, we focus on the intermodulation distortion, which is a robust feature indicator within the whole systems operating bandwidth, whose measurement is based on the effects produced by the nonlinear system when a composite signal feeds its input. We propose an efficient procedure to measure the intermodulation distortion allowing the simultaneous evaluation of harmonic distortion of order n and of total harmonic distortion. The proposed technique relies and extends the synchronized exponential swept-sine technique for the characterization of non-linear systems by introducing a suitable double exponential chirp input signal. The method estimates all the distortion level indicators by processing the response to a single, short-duration signal, showing a moderate computational cost and low sensitivity to ambient noise. These advantages make the application of the proposed method extremely rapid and convenient, even in the measurement of operating field devices. Tests have been carried out both in simulated and real-world scenarios, confirming the proposed approach validity.