The increasing spread of pervasive technology has led to the fast development of human-centered connected systems, such as cloud-based voice services, assisted driving systems, domotics control systems, personal digital assistants. The user interacts with these systems by speaking to an artificial intelligence, which interprets the speaker's requests and takes decision accordingly. In such scenario, the real-time collection of personal information from the speaker's voice is a key-function to develop in order to offer personalized services. Gender is part of the basic information needed to customize the user experience. Furthermore, knowledge about the sex of the speaker also proves useful in automatic speaker recognition and voice-based identity recognition systems, since it restricts the search space to individuals of one gender, thus speeding up the system response. Therefore, gender recognition techniques through speech analysis have largely attracted the researchers' attention. Speech analysis is usually performed by extracting some features from the speech signal that can be affected by additional factors other than the gender: emotional state of the speaker, for example, is conveyed in the speech by altering some parameters that take part to the gender recognition process. At the same time, the outcome of emotion recognition systems based on speech analysis can be affected by the speaker's gender. This paper briefly summarizes the techniques used to perform gender recognition through speech analysis and proposes a practice to take gender into account in emotion recognition methods.

The interest in graphene-based electronics is due to graphene’s great carrier mobility, atomic thickness, resistance to radiation, and tolerance to extreme temperatures. These characteristics enable the development of extremely miniaturized high-performing electronic devices for next-generation radiofrequency (RF) communication systems. The main building block of graphene-based electronics is the graphene-field effect transistor (GFET). An important issue hindering the diffusion of GFET-based circuits on a commercial level is the repeatability of the fabrication process, which affects the uncertainty of both the device geometry and the graphene quality. Concerning the GFET geometrical parameters, it is well known that the channel length is the main factor that determines the high-frequency limitations of a field-effect transistor, and is therefore the parameter that should be better controlled during the fabrication. Nevertheless, other parameters are affected by a fabrication-related tolerance; to understand to which extent an increase of the accuracy of the GFET layout patterning process steps can improve the performance uniformity, their impact on the GFET performance variability should be considered and compared to that of the channel length. In this work, we assess the impact of the fabrication-related tolerances of GFET-base amplifier geometrical parameters on the RF performance, in terms of the amplifier transit frequency and maximum oscillation frequency, by using a design-of-experiments approach.

The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common musculoskeletal disorders related to the persistence of incorrect sitting postures during work hours. This work proposes a low-cost IoT measurement system for monitoring the sitting posture symmetry and generating a visual alert to warn the worker when an asymmetric position is detected. The system employs four force sensing resistors (FSR) embedded in a cushion and a microcontroller-based read-out circuit for monitoring the pressure exerted on the chair seat. Java-based software performs the real-time monitoring of the sensors’ measurements and implements an uncertainty-driven asymmetry detection algorithm. The shifts from a symmetric to an asymmetric posture and vice versa generate and close a pop-up warning message, respectively. In this way, the user is promptly notified when an asymmetric posture is detected and invited to adjust the sitting position. Every position shift is recorded in a web database for further analysis of the sitting behavior.

Among the various applications of carbon nanotubes (CNTs) that have been investigated since the discovery of their exceptional potential in the electronic field, great interest has been directed towards the creation of carbon-based materials capable of replacing Indium Tin Oxide (ITO) as a transparent electrode. Such transparent conductive films find application in touch panels, LCD screens, OLED displays, photovoltaic cells, and many others. This review presents a collection of techniques that have been proposed during the last decade for the modeling of carbon nanotube-based materials by means of equivalent electrical networks. These networks represent the electrical properties of CNT-based conductive thin films in a way that can be easily included in circuit simulators for the simulation-assisted design of the different devices under static and dynamic conditions.

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt.