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.

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.

Electrostatic discharge is a common phenomenon in daily life, and the electromagnetic pulse radiation generated during discharge can cause great harm to power, communication, sensing, and other equipment, resulting in systems not working properly. To verify the safety and reliability of unmanned vehicle handling systems in complex electromagnetic environments, the interference effect of electrostatic discharge, a common source of electromagnetic interference in life, on unmanned vehicle systems was studied. According to the electrostatic discharge interference mechanism, typical electrostatic discharge mode, and discharge model, an unmanned vehicle handling system was tested for electrostatic discharge according to the ISO10605-2008 standard. Based on the measured data, the effect of electrostatic discharge on the safety and functionality of the unmanned vehicle handling system and its sensors was analyzed, and threshold values for the failure of the unmanned vehicle handling system under different discharge methods, discharge models, and discharge polarity were obtained. When the electrostatic discharge voltage amplitude is only 2 kV, the vehicle’s LiDAR data sensor cannot work normally due to the echo reception circuit, and the failure rate of LiDAR continues to increase with increasing discharge voltage. When the discharge voltage is only 4 kV, the millimeter-wave radar is disconnected from the vehicle module due to the electrostatic discharge interference of the transmission cable, and when the discharge voltage is 12 kV, the unmanned vehicle is unable to provide stable and accurate environmental information to avoid collisions. This study provides a reference for the design of electromagnetic protection of unmanned equipment and will have a guiding role in enhancing the construction of reliable, safe, and intelligent equipment in complex electromagnetic environments.

Autonomous platforms have been widely adopted in both civilian and military contexts, achieving notable success. However, their energy resources, computational power, payload capacity, and cost are constrained and interdependent. These limitations prevent the integration of advanced threat detection systems, such as high-speed cameras and radar. Consequently, they are unable to detect high-velocity projectiles, significantly undermining their survivability in hostile environments. Developing low-power, cost-effective, and computationally efficient threat detection methods is of critical importance. This letter proposes a method for perceiving the flight trajectories of charged objects based on the principle of electrostatic induction. A physical model for electrostatic detection is established, and the effects of flight speed and trajectory on induction signals are analyzed. Inspired by sharks, a four-element array testing system with specific orientation and spatial distribution characteristics is designed. An indoor experimental setup is developed to simulate and test the flight of charged objects, generating a comprehensive dataset by varying parameters such as flight speed, incident angle, and testing distance. This dataset is then used to train a symbolic regression machine learning model, resulting in a mathematical model capable of predicting the incoming direction of charged objects with an error margin of less than 10 degrees. The model's generalization ability and the impact of the number of sensors are also discussed.

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.

Noncontact surface potential measurement approaches with high spatiotemporal resolution are suitable for insulating and conducting materials and have the advantages of noninvasiveness and high sensitivity. These methods show promising application prospects in fields, such as material surface charge imaging. However, reduced electrode size leads to issues such as induction signal attenuation and interference between electrode arrays, limiting the improvement in the spatial resolution and test accuracy. Inspired by the visual principle of lateral inhibition, we propose a biomimetic measurement method that addresses these issues by utilizing the weighted summation of multichannel induction signals and lateral inhibition spatial functions. A lateral inhibition biomimetic circuit is designed, and a circuit parameter calculation method is established. A biomimetic sensor is implemented, and a prototype experimental system is constructed. The proposed method is experimentally validated using test samples featuring line and dot matrix pattern potential distributions. The results indicate that appropriately selected lateral inhibition weights can effectively improve surface potential imaging quality. Optimal weights can increase the structural similarity between the test and simulation results from 0.71 to 0.83 and the peak signal-to-noise ratio from 23.63 to 25.70 dB. The Gershon lateral inhibition function demonstrates higher validity and generality than other inhibition functions.