The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants’ feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

Effective electromagnetic interference (EMI) filters are required by switching converters to comply with conducted emission regulations. However, these filters often pose significant challenges, being expensive, bulky, and heavy. Such an issue is further aggravated with the latest generation of wide band gap (WBG)-based power converters. Reduction of the filter size can be achieved through conducted EMI mitigation techniques that address emissions at the source, particularly at low frequencies. Spread-spectrum modulation (SSM) has gained increasing interest to mitigate EMI, as it does not require any hardware alterations. This article investigates the effectiveness of the SSM in case of WBG traction inverters, where the switching frequency remains in the tens of kilohertz. In addition, the use of an additional switching leg to reduce the EMI was also investigated. A comparative analysis of these two techniques and their effectiveness in the common mode (CM) conducted EMI suppression is carried out. Experimental results are provided demonstrating the feasible CM conducted EMI reduction. It was found that the EMI peak at 160 kHz decreased by 2 dB in the SSM case and by 25 dB in the additional leg case.

Active gate drivers lend themselves well to reducing over- and undervoltages during the commutations of hard switched power transistors, as well as to damping resonances. However, their control strategy is a major challenge, as it should account for variations of operating condition, parameter spread, and nonlinearities of the driven transistor. This article proposes an effective control method to reduce overshoots and undershoots in a power transistor driven by an active gate driver. The modulation pattern is modified on-the-fly and none a priori characterization is required. The presented method modifies the timing parameter to attain almost zero over- and undervoltages with the lowest power losses. This is achieved by combining a low-complexity active gate driver with the measurements of peak values of the drain–source voltage. The technique was experimentally assessed for a 48–12 V dc–dc converter, and resulted in better switching performance than standard solutions and open-loop control.

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.

Fast power transistors require a tight control of their switching trajectory to exploit them at full speed, without degrading reliability and delivered electromagnetic emission figures of merit. By using active gate drivers in place of conventional ones, the switching trajectory can be shaped to reduce current and voltage overshoots, as well as the amplitude of oscillations. However, the tuning of such drivers is still an open issue. This paper investigates the damping of oscillations by means of active gate drivers, and proposes a method to tune them a-priori . The target switching waveforms are evaluated by means of non-linear damping elements in simulation, easing the tuning procedure. The proposed method was assessed on a digital active gate driver, providing the initial set-point for the adaptive controller. The convergence time measured experimentally was found to be as low as 50for the overall load current range, whilst not affecting the conversion efficiency of the dc-dc converter exploited as test case.

Condition monitoring techniques have been successfully applied to detect damaged bearings. However, the signal acquisition and the subsequent processing are typically outsourced to expensive data acquisition boards and complex software, resulting in expensive solutions. As a side effect, the integration of condition monitoring systems in wireless sensor networks can be tough to achieve. Aiming to overcome such issues, a low-cost and small-size electronic module to be placed in the proximity of the bearing to be monitored was developed. The acoustic signal delivered by the bearing is acquired, and the corresponding frequency spectrum is evaluated on-board. Based on that, the developed module automatically detects the presence of defects and notifies the remote controller via a wireless connection only when a fault is detected, thus avoiding the use of data cables whilst minimizing the amount of transferred data. Experimental tests carried out on the proposed system assessed the accuracy of the evaluated frequency spectrum, resulting in an amplitude error within $\pm 0.6\%$, as well as the fault detection capability in the presence of environmental acoustic noise.