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MIMO over-the-air computation (MIMO-AirComp) is a recently proposed technique that leverages the superposition property of the multiple access channel to compute the target multifunction of various applications. This article presents how the MIMO-AirComp principle can be applied to the state estimation problem using distributed sensing data. The representative target function is explicitly formulated as a nomographic function matched to the structure of the multiple access channel with the proper processing function. The proposed framework efficiently computes the target multifunction by coordinating local preprocessing at each node, aggregation through the wireless channel, and postprocessing at the fusion center. We analyze and demonstrate that the proposed approach significantly improves the computation throughput for the distributed estimation application. Specifically, the proposed MIMO-AirComp framework outperforms the conventional separated communication and computation approach when the network system relies on noisy measurements obtained by the densely deployed sensors.

Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time&ndash, frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.

In this work, we report on a flexible triboelectric generator (TEG) with a multilayer polymer structure, consisting of a poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) layer sandwiched by polydimethylsiloxane (PDMS) layers for the performance enhancement of TEGs. We confirmed that the output performance of the TEG is closely dependent on the structure and polarization direction of the PVDF-TrFE layer. In addition, the PDMS layer serves as the electron trapping layer and suppresses the discharging of the surface charges, boosting the output performance. Furthermore, the polarized PVDF-TrFE layer in the preferred direction contributes to increasing the surface potential during the contact–separation motion. The interaction between these two polymer layers synergistically leads to the boosted output performance of TEGs. Specifically, the maximum peak-to-peak output voltage and current density of 420 V and 50 μA/cm2 generated by the proposed architecture, representing approximately a fivefold improvement compared with the TEG with a single layer, even though the same friction layers were used for contact electrification.