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The indoor localization of people is the key to realizing “smart city” applications, such as smart homes, elderly care, and an energy-saving grid. The localization method based on electrostatic information is a passive label-free localization technique with a better balance of localization accuracy, system power consumption, privacy protection, and environmental friendliness. However, the physical information of each actual application scenario is different, resulting in the transfer function from the human electrostatic potential to the sensor signal not being unique, thus limiting the generality of this method. Therefore, this study proposed an indoor localization method based on on-site measured electrostatic signals and symbolic regression machine learning algorithms. A remote, non-contact human electrostatic potential sensor was designed and implemented, and a prototype test system was built. Indoor localization of moving people was achieved in a 5 m × 5 m space with an 80% positioning accuracy and a median error absolute value range of 0.4–0.6 m. This method achieved on-site calibration without requiring physical information about the actual scene. It has the advantages of low computational complexity and only a small amount of training data is required.

In this article, a multi-layer convolutional neural network (ResNet-18) and Long Short-Term Memory Networks (LSTM) model is proposed for dynamic gesture recognition. The Soli dataset is based on the dynamic gesture signals collected by millimeter-wave radar. As a gesture sensor radar, Soli radar has high positional accuracy and can recognize small movements, to achieve the ultimate goal of Human-Computer Interaction (HCI). A set of velocity-range Doppler images transformed from the original signal is used as the input of the model. Especially, ResNet-18 is used to extract deeper spatial features and solve the problem of gradient extinction or gradient explosion. LSTM is used to extract temporal features and solve the problem of long-time dependence. The model was implemented on the Soli dataset for the dynamic gesture recognition experiment, where the accuracy of gesture recognition obtained 92.55%. Finally, compare the model with the traditional methods. The result shows that the model proposed in this paper achieves higher accuracy in dynamic gesture recognition. The validity of the model is verified by experiments.

Learning from the robust mechanism of the biological nervous system is critical for creating reliable neuromorphic hardware. The homeostatic inhibition plasticity rule is a robust biological mechanism to balance Hebbian plasticity and resist external environmental disturbances and local damage. It plays an essential role in maintaining the homeostatic sparse firing patterns of the nervous system. This paper imitates this mechanism and provides a fast homeostatic inhibitory plasticity rule circuit with a memristive synapse. Firstly, the design method and principle of the circuit are demonstrated. Secondly, the function of the circuit was verified in PSpice© using a commercial Knowm memristor as a synapse. The PSpice© simulation results show that the circuit can achieve a weight update curve similar to the biological homeostatic inhibitory plasticity rule, and the time scale of the circuit is improved by a factor of 1000 compared to that of the biological nervous system. Furthermore, the circuit has wide applicability due to the tunable qualities of the homeostatic learning window, scaling factor, and homeostatic factor. This study provides new opportunities for building fast and reliable neuromorphic hardware.

As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems.