Guilei Ma

Human body static electricity is one of the major hazards in electrostatic-sensitive industrial environments. Existing noncontact measurement methods often rely on explicit physical modeling and generally require accurate prior knowledge of the sensor layout, target motion state, and scene geometry, which limits their applicability in complex dynamic scenarios. To address this issue, this paper proposes a neural network-based data-driven method for estimating human body electrostatic potential from noncontact electrostatic measurements. The proposed method uses four-channel noncontact electrostatic sensor signals as inputs and the synchronously measured reference body potential as the target output. Sixteen neural network architectures, including recurrent neural networks, convolutional neural networks, attention-based networks, and hybrid models, are systematically evaluated. The Gray Wolf Optimizer is further used to optimize key hyperparameters of each model. A 5 m × 5 m experimental scene is established, and 36 groups of synchronized time-series signals are collected from three subjects under two motion states. Training and validation datasets are constructed using a sliding time-window method, and the effects of model architecture, window length, and subject–motion condition on inversion performance are analyzed. The results show that the proposed method can effectively estimate human body electrostatic potential from noncontact measurements. Among the evaluated models, Trans-LSTM achieves the best performance. With a window length of 1 s, it obtains a validation normalized root-mean-square error of approximately 0.139 and a coefficient of determination of approximately 0.72. Compared with a representative existing method under the same coverage area and sensor layout, the proposed approach reduces the NRMSE from 0.22 to 0.139. These results demonstrate that the proposed method provides a feasible approach for remote, real-time, and noncontact monitoring of human body electrostatic potential in complex environments.

Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. In this context, spiking neural networks show the ability to resist Gaussian noise, spike interference, and AC electric field interference by adjusting synaptic plasticity. The anti-interference ability to spike neural networks has become an important direction of electromagnetic protection bionics research. Methods: Therefore, this research constructs two types of spiking neural network models with LIF model as nodes: VGG-SNN and FCNN-SNN, and combines pruning algorithm to simulate network connection damage during the training process. By comparing and analyzing the millimeter wave radar human motion dataset and MNIST dataset with traditional artificial neural networks, the anti-interference performance of spiking neural networks and traditional artificial neural networks under the same probability of edge loss was deeply explored. Results: The experimental results show that on the millimeter wave radar human motion dataset, the accuracy of the spiking neural network decreased by 5.83% at a sparsity of 30%, while the accuracy of the artificial neural network decreased by 18.71%. On the MNIST dataset, the accuracy of the spiking neural network decreased by 3.91% at a sparsity of 30%, while the artificial neural network decreased by 10.13%. Conclusions: Therefore, under the same network connection damage conditions, spiking neural networks exhibit unique anti-interference performance advantages. The performance of spiking neural networks in information processing and pattern recognition is relatively more stable and outstanding. Further analysis reveals that factors such as network structure, encoding method, and learning algorithm have a significant impact on the anti-interference performance of both.

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.

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.

Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.

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.

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.

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.

Noise permeates every level of the nervous systems, from single neurons to the whole system. However, the noise seems not to take so much trouble to neural information processing in contrast to the electronic system. The robustness of neural information processing inspired us to study the method of neuromorphic hardware systems to resist electromagnetic interference. The dynamics of the neuron model can reflect the inherent mechanism of neural information processing. In this work, we take the Maeda-Makino (MM) hardware neuron as an example to study the robustness mechanism of biological neuron information encoding based on symbolic dynamics. MM hardware neuron is a neuromorphic neuron circuit with biological plausibility. The simulation results show that MM hardware neuron can encode the order of current stimulus periods, even with parameter disturbance and noisy input signals which simulate the effects of electromagnetic interference on neuronal cell membrane permeability, sodium ion channels, potassium ion channels, and action potentials. Choosing a robust hardware neuron is the basis for building a neuromorphic hardware system that can resist electromagnetic interference. This letter not only verifies the robustness of using symbol sequence encoding but also provides an optional hardware neuron solution for neuromorphic hardware designed for anti-electromagnetic interference.