Nowadays, lithium-ion is among the most used chemistry for batteries and shows an increasing market growth rate. However, to reduce failure or safety risks, the battery State-of-Charge (SoC) must be accurately monitored and predicted by a suitable Battery Management System (BMS). Artificial Intelligence (AI) techniques have been extensively applied to this field with good results. Typically, AIs are trained on dynamic profile data, emulating battery charging and discharging cycles related to the application under test. In this paper, a novel approach is presented: application independent constant current profiles are used to train a Support Vector Regression (SVR) algorithm. To enhance the estimation accuracy, the output of the obtained SVR model was post-processed. Finally, an error correction algorithm was applied to further reduce the estimation error. The system is validated over test cycles representing different application scenarios for the battery cell operations. For the development of the proposed approach, a total of 105 constant current discharge profiles for the training and 20 realistic test cycles for the validation have been considered, including standard automotive cycles and a generic battery powered power tool. The performance in the SoC estimation resulted in a Root Mean Square Error (RMSE) of 0.94% and a Mean Absolute Error (MAE) of 0.75% over all the test cycles. Error metrics are comparable to those obtained for SoC estimation AI algorithms based on traditional approaches using application dependent battery profiles for the training phase.

Advanced automotive applications like Active Noise Cancellation (ANC) and Individual Listening Zones (ILZ) require a high number of transducers (i.e., microphones, accelerometers, and loudspeakers) usually arranged as arrays. Transducer arrays are widely employed in several applications besides automotive field, such as teleconferencing systems, industrial and civil monitoring of noise and vibrations. Automotive Audio Bus ( $\text{A}^{2}\text{B}$ ) is an audio transport protocol that solves the latest requirements of automotive and industrial fields. $\text{A}^{2}\text{B}$ allows transporting up to 32 channels in a multi-node daisy chain network and guarantees synchronization and low deterministic latency. This paper aims to develop a clock propagation model of an $\text{A}^{2}\text{B}$ network composed by transducer arrays. This model will be useful to evaluate the impact of the bus on the array performance. Firstly, a theoretical description of the $\text{A}^{2}\text{B}$ protocol and jitter analysis is provided. It follows a description of the jitter measures carried out on the clocks distributed along the $\text{A}^{2}\text{B}$ network. Lastly, latency introduced by nodes of the network is investigated.

Microphone arrays of various sizes and shapes are currently employed in consumer electronics devices such as speakerphones, smart TVs, smartphones, and headphones. In this paper, a full-digital, planar microphone array is presented. It makes use of digital Micro Electro-Mechanical Systems (MEMS) microphones, connected through the Automotive Audio Bus (A2B). A clock propagation model for A2B networks, developed in a previous work, was employed to estimate the effects of jitter and delay on microphone arrays. It will be shown that A2B allows for a robust data transmission, while ensuring deterministic latency and channels synchronization, thus overcoming the signal integrity issues which usually affect MEMS capsules. The microphone positioning is also discussed since it greatly affects the spatial accuracy of beamforming. Numerical simulations were performed on four regular geometries to identify the optimal layout in terms of number of capsules and beamforming directivity. An A2B planar array with equilateral triangle geometry and four microphones, three in the vertices and one in the center, was built. Experimental measurements were performed, obtaining an excellent matching with numerical simulations. Finally, the concept of an array of arrays (meta-array) is presented, designed by combining several triangular units and analyzed through numerical simulations.

In this paper, open-circuit voltage decay measurements of carrier lifetimes on 4H silicon carbide ion implanted planar p-i-n diodes of circular shape and different diameters are performed at increasing bias currents. The measured ambipolar carrier lifetimes have shown to be dependent on the carrier injection levels, quickly increasing at low-bias current up to reaching a saturation value, τHL, when the value of the average carrier density within the base exceeds the intrinsic-region doping. The τHL measured in diodes of different area also demonstrate a marked dependence on the diode dimension, with longer lifetimes being typical of larger-diameter diodes, suggesting that a great contribution of recombination comes from the diode periphery. A bulk ambipolar lifetime τHL_Vol = 320 ns has been extracted from the area-dependent measured lifetimes.

An experimental BallistoCardioGram (BCG) measurement and analysis system is presented, featuring accelerometers placed between the mattress and bed slats. The system can sample BCG waveforms at 500 Hz and can perform unsupervised BCG heartbeat detection and heartbeat interval measurements. The approach is validated on an experimental dataset consisting in 14 subjects recorded while lying in three different positions: supine, left side and right side. The overall performance is good, comparing to other works in literature. In fact, the average sensitivity and precision across subjects and positions is 98.2% and 98.0%, respectively; similarly, an $R^{2}$ score of 98.2% was achieved between BCG and reference ECG measurements, while Mean Absolute Error and Root Mean Squared Error are as low as 3.9 ms and 5.6 ms. The presented methodology is shown to be resilient to different sleeping positions, as confirmed by Kruskal-Wallis statistical tests $(p\approx 0.91$ for sensitivity, $p\approx 0.73$ for precision, $p\approx 0.81$ for $R^{2},\ p\approx 0.26$ for MAE, $p\approx 0.20$ for RMSE). Moreover, results are in line and comparable to those already achieved on a different measurement scenario featuring a different bed structure. This further proves the stability of the presented BCG measurement and analysis system.

The open-circuit voltage decay (OCVD) method is a well-known technique for conducting electrical measurements of carrier lifetime: the main advantages lie in the simple setup and the possibility of carrying out measurements in commercial devices without the need of removing the package, as for optical methods. Despite several researchers having reported carrier lifetimes measured by the OCVD method in different devices, there has been little discussion about the potential effect of the experimental setup on the obtained results. By comparing the outputs of the experimental measurements with those of numerical simulations, this study investigates the overlooked effect of the OCVD measurement setup on the former. Due to the growing importance of SiC-based devices, the analysis is applied to a 4H-SiC p-i-n diode. Two main points are addressed: 1) the effect of circuit setup on the ambipolar lifetime is discussed and a method, originally developed for improving the estimate of low-level carrier lifetime in OCVD measurements, is used to correct the measured lifetime for this influence; 2) the origin of the local minimum eventually appearing in the lifetime versus time curves is also investigated. It is found that the minimum can also be related to the time constant of the experimental setup, giving rise to doubts about the usual interpretation of this minimum as the minority carrier lifetime. A method is thus proposed to help discriminate between the two interpretations.