Maintenance strategies based on structural health monitoring can provide effective support in the optimization of scheduled repair of existing structures, thus enabling their lifetime to be extended. With specific regard to reinforced concrete (RC) structures, the state of the art seems to still be lacking an efficient and cost-effective technique capable of monitoring material properties continuously over the lifetime of a structure. Current solutions can typically only measure the required mechanical variables in an indirect, but economic, manner, or directly, but expensively. Moreover, most of the proposed solutions can only be implemented by means of manual activation, making the monitoring very inefficient and then poorly supported. This paper proposes a structural health monitoring system based on a wireless sensor network (WSN) that enables the automatic monitoring of a complete structure. The network includes wireless distributed sensors embedded in the structure itself, and follows the monitoring-based maintenance (MBM) approach, with its ABCDE paradigm, namely: accuracy, benefit, compactness, durability, and easiness of operations. The system is structured in a node level and has a network architecture that enables all the node data to converge in a central unit. Human control is completely unnecessary until the periodic evaluation of the collected data. Several tests are conducted in order to characterize the system from a metrological point of view and assess its performance and effectiveness in real RC conditions.

In this work, two fully-textile wearable devices, to be used as chipless identification tags in identification and tracking applications are presented. For the fabrication of the fully-textile tags, a layer of fleece was used as a substrate, while an adhesive non-woven conductive fabric was employed for the conductive parts. To allow radio-frequency identification of these chipless tags, two alternative techniques were used. One relies on associating a binary code with the resonance frequency of resonant devices: the presence/absence of the resonance peaks in the transmission scattering parameter, $|S_{21}|$ , of a set of resonators is used to encode a string of bits. The second technique for accomplishing radio-frequency identification of the chipless tags resorts to a frequency-shift coding technique, which is implemented by modifying the configuration of a hairpin resonator. The obtained numerical and experimental results confirm the suitability of the proposed strategies for obtaining entirely-textile, wearable chipless tags for identification and tracking purposes, which can be particularly useful, especially in the industrial sector. In this field, in fact, the proposed solutions would guarantee a seamless integration with clothes and would facilitate the user’s interaction with the IoT infrastructure. In this regard, one of the envisaged application scenarios related to the tracking of hides in the leather industry is also presented.

Reducing or optimizing electrical power consumption is one of the most fundamental goals within the current frameworks of smart energy management. This approach would be spread from the industrial plants, characterized by high consumptions often well distributed throughout the day, down to the domestic utilities, typically discontinuous and with limited consumption at the single user level. More specifically, it is desirable for the latter case to be able to control in a simple and effective way the power consumption of typical household appliances by means of technologies that are already used and spread (such as tablets and smartphones) to become aware of their actual impact, both economic and environmental. To this aim, the authors present the proof-of-principle of user-friendly monitoring system for power consumption awareness based on the recent technologies of Internet of Things (IoT) and Augmented Reality (AR). In particular, common devices such as smartphones associated along with appropriate measurement nodes and a suitable app, developed to the purpose, allow consumers to view in AR environment electrical consumption of their domestic electrical loads to consciously decide whether to switch them off. Performance of both sensor nodes and AR environment were preliminarily assessed in either laboratory experiments or actual household context, highlighting the promising effectiveness of the proposed approach.

Brain-Computer Interfaces (BCIs) are an integration of hardware and software communication systems that allow a direct communication path between the human brain and external devices. Among the existing BCI paradigms, Steady-State Visually Evoked Potentials (SSVEPs) have gained momentum in the development of non-invasive BCI applications as they are characterized by adequate signal-to-noise ratio and information transfer rate. In recent years, the adoption of Augmented Reality (AR) head-mounted displays to render the flickering stimuli necessary for SSVEPs elicitation has become an attractive alternative to traditional computer screens. In fact, the increase in system wearability anticipates the possibility of adopting BCIs in contexts other than research laboratory. This has contributed to a steadily-increasing interest in BCIs, as also confirmed by the recent literature dedicated to the topic. In this evolving scenario, this review intends to provide a comprehensive picture of the current state-of-the-art in relation to the latest advancement of wearable BCIs based on SSVEPs classification and AR technology. The goal is to provide the reader with a systematic comparison of different technological solutions realized over the last years, thus making future research in this direction more efficient.

In this work, an innovative system for structural health monitoring of metallic pipes is presented. The proposed system relies on using the pipeline as a waveguide for the propagation of an electromagnetic (EM) signal: possible anomalies in the pipe provoke the partial reflection of the propagating EM signal. Differently from previous works that address similar applications, in this work, the EM test signal is injected in the pipeline/waveguide through a coaxial/waveguide transition that is made on the pipe surface (rather than on a cross-section as generally done). In practical applications, this strategy would allow to connect more easily the test equipment to the operating, buried pipes. Additionally, because reflections caused by anomalies may be difficult to identify with adequate accuracy, the authors have developed a dedicated processing strategy for the analysis of the reflections caused by anomalies. More specifically, by analyzing the measured reflected signal and by correlating it to the ideal reflected signal, it is possible to improve the localization of anomalies along the pipe. The proposed system, along with the processing and operating strategies were validated through full-wave simulations and experimental tests carried out on a steel pipe in presence of intentionally-provoked anomalies.

This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.