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Nowadays, low-temperature co-fired ceramic (LTCC) technology has become one of the main forms of manufacturing electronic devices. However, a majority of microwave dielectric ceramics are not suitable as LTCC materials due to their high sintering temperatures. Developing novel LTCC materials with good microwave dielectric properties is extremely urgent. In this paper, an LiF sintering aid was added to Li2Ti0.8(Co1/3Nb2/3)0.2O3 (LTCN) ceramics to explore new LTCC materials. The sintering behavior, microstructure and microwave dielectric properties of LTCN + x wt% LiF ceramics were investigated in detail. The results indicated that the addition of LiF increased the degree of disorder in the LTCN matrix, transforming it from a monoclinic to a cubic crystal system. The ceramics exhibited relatively dense and homogeneous microstructures at the sintering temperature of 950 °C as the LiF doping amount was not less than 2 wt%. By LiF doping, the quality factor (Q × f) value was significantly enhanced due to the improved microstructure. Meanwhile, the temperature coefficient of resonant frequency (τf) of LTCN ceramics was successfully regulated to the near zero value owing to the negative τf characteristic of LiF. Excellent microwave dielectric properties of dielectric constant (εr) = 19.01, Q × f = 144,890 GHz, τf = −1.52 ppm/°C were obtained when the sample doped 3 wt% LiF was sintered at 950 °C for 3 h. Furthermore, the good chemical compatibility of the LTCN-3 wt% LiF ceramic with silver electrodes suggested that the ceramic was a potential material for LTCC applications. [ABSTRACT FROM AUTHOR]

Gesture recognition plays an important role in smart homes, such as human–computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require a large number of human participants to train, and are not robust to the recognition environment. To address this problem, we propose a WiFi signal-based gesture recognition system with matched averaging federated learning (WiMA). Since there are differences in the distribution of WiFi signal changes caused by the same gesture in different environments, the traditional federated parameter average algorithm seriously affects the recognition accuracy of the model. In WiMA, we exploit the neuron arrangement invariance of neural networks in parameter aggregation, which can improve the robustness of the gesture recognition model with heterogeneous CSI data of different training environments. We carried out experiments with seven participant users in a distributed gesture recognition environment. Experimental results show that the average accuracy of our proposed system is up to 90.4 % , which is very close to the accuracy of state-of-the-art approaches with centralized training models. [ABSTRACT FROM AUTHOR]