Face-based recognition systems have been increasingly used in many different applications in today's society, starting from surveillance and access control to the authentication for banking activities. Therefore, in the last few years an increasing interest in the performance characterization and improvement of such systems can be found in the scientific community. Most of the methodologies for testing the performance of such systems are based on the evaluation of recognition reliability indexes that are generally related to the probability of a false positive and/or of a false negative.

The paper describes the implementation of an instrument fault detection, isolation, and accommodation (IFDIA) system developed for real-time automotive applications. The IFDIA architecture, which is based on artificial neural networks, was used to locate and accommodate faults that could occur in the main sensors involved in managing engine operation. Numerous online tests carried out in a variety of engine operating and vehicle drive conditions have confirmed the validity of the diagnostic procedure and its onboard applicability

Having become aware of how limited all natural resources are, the water leakage problem in piping systems has become a stimulating topic. This problem increased over the last few years even though innovative tools and techniques appeared in the literature and in the consumer market. Identifying water leaks at the nearest point, the household level, is still an unsolved problem because most water meters are mechanical and, therefore, cannot detect leaks. While the issue is not important for water service providers since consumption is charged to the user, the resolution is crucial due to the increasingly relevant concern of saving natural resources. The detection of small but continuous leaks of drinking water in domestic systems is addressed in this work. Machine learning approaches enabled image processing techniques also in uncontrolled environments, overcoming the classical methods but introducing new challenges like power consumption. Using a combination of Convolutional Neural Networks (CNNs) and Long Short–Term Memory Networks (LSTM) within an adaptive under–sampling strategy, it is possible to process the images captured from the mechanical water meter dial and identify the Period With Null Consumption (PWNC) or the consumption class. The presented solution can classify the water flow into four different classes, and, in the case of absence or small flow, its function becomes to detect leakages. Analyzing images from a mechanical water meter quadrant, it has been possible to identify PWNC and detect small water leakages in the domestic environment under common consumer flow profiles. In addition to the confusion matrices, the synthetic parameters of Sørensen–Dice coefficient (DSC) and Jaccard Index have been utilized and presented to quantify the performance of the proposed DNN. The conducted experiments on static and dynamic water flow demonstrated the applicability of this approach and the possibility of an increase in PWNC identification thanks to the adaptative increase in the sampling time. Moreover, the reduction of sampling time allows for the reduction of computational load and power consumption in embedded scenarios where limited energy is available.

Over the last two decades, the craft beer industry has significantly developed with the emergence of thousands of microbreweries all over the world. These are mostly small companies that cannot afford the cost of the process monitoring systems that are usually embedded in the machinery used by industrial breweries, but they need to monitor and control the production process in order to guarantee a constant quality of beer. The development of low-cost systems for monitoring the production process would help microbreweries obtain the desired product quality consistency. In this paper, the authors propose a low-cost system for the real-time identification of the different phases of the alcoholic fermentation of beer. The first results prove the ability of the proposed system to monitor the fermentation and to detect anomalies in the process promptly.