This paper describes an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle. The sensor is based on the measurement of the time of flight of an ultrasonic pulse, which is reflected by the ground. A constrained optimization technique is employed to obtain reflected pulses that are easily detectable by means of a threshold comparator. Such a technique, which takes the frequency response of the ultrasonic transducers into account, allows a sub-wavelength detection to be obtained. Experimental tests, performed with a 40 kHz piezoelectric-transducer based sensor, showed a standard uncertainty of 1 mm at rest or at low speeds; the sensor still works at speeds of up to 30 m/s, although at higher uncertainty. The sensor is composed of only low cost components, thus being apt for first car equipment in many cases, and is able to self-adapt to different conditions in order to give the best result

Objective. Processing strategies are analyzed with respect to the classification of electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor imagery (MI). A review of literature is carried out to understand the achievements in MI classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery-based BCIs. Article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85%–100% range for the binary case and in the 83%–93% range for multi-class one. Associated uncertainties are up to 6% while repeatability for a predetermined dataset is up to 8%. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analyzed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a BCI. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of the results reproducibility.

Evolution and cost of measurement equipment, continuous training, and distance learning make it difficult to provide a complete set of updated workbenches to every student. For a preliminary familiarization and experimentation with instrumentation and measurement procedures, the use of virtual equipment is often considered more than sufficient from the didactic point of view, while the hands-on approach with real instrumentation and measurement systems still remains necessary to complete and refine the student's practical expertise. Creation and distribution of workbenches in networked computer laboratories therefore becomes attractive and convenient. This paper describes specification and design of a geographically distributed system based on commercially standard component

Environmental monitoring is extremely important to ensure a safe and wealthy life of both humans and artifacts. Monitoring requirements are extremely different depending on the environment, leading to ad hoc implementations that lack flexibility. This paper describes an implementation that can be adapted to many different applications and embeds the flexibi- lity required to be deployed and upgraded without the necessity of arranging complex infrastructures. The solution is based on small autonomous wireless sensor nodes, small wireless receivers connected to the Internet, and a cloud architecture which provides data storage and delivery to remote clients. The solution permits supervisors on-site not only to have an immediate idea of the current situation by using their smart-phones but also to monitor remote sites through the Internet. All measurements are redundantly stored at different concentration levels to guarantee a safe back-trace and to provide quality assurance also in case of network failure or unavailability. The sensing nodes have small impact, with dimensions which can be of less than 2.5 cm x 1.5 cm when the nodes have to acquire only temperature and relative humidity, and a low cost that enables using them in a set-and-forget way for intervals in excess of one year.

This paper deals with a wireless sensor network that was specifically designed to monitor temperature-sensitive products during their distribution with the aim of conforming to the cold-chain assurance requirements. The measurement problems and the constraints that have been encountered in this application are initially highlighted, and then, an architecture that takes such problems into account is proposed. The proposed architecture is based on specifically designed measuring nodes that are inserted into the products to identify their behavior under real operating conditions, e.g., during a typical distribution. Such product nodes communicate through a wireless channel with a base station, which collects and processes the data sent by all the nodes. A peculiarity of the product nodes is the low cost, which allows the information on the cold-chain integrity to be provided to the final customer. The results that refer to the functional tests of the proposed system and to the experimental tests performed on a refrigerated vehicle during a distribution are reported.