Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study.

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.

Indoor Localization Method of Personnel Movement Based on Non-Contact Electrostatic Potential Measurements.

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.

Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM.

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.

Effects of acute exposure to ultra-wideband pulsed electromagnetic fields on the liver and kidneys of mice.

The biosafety of ultra-wideband (UWB) pulses, which are characterized by simultaneously high power and a high bandwidth ratio, has gained increasing attention. Although there is substantial prior literature on the biological effects of UWB pulses on both cells and animals, an explicit, unequivocal and definite pattern of the corresponding biological responses remains elusive, and the systemic secondary consequences are also still not fully understood. In this study, we found that exposing mice to UWB pulses resulted in the alteration of several biochemical blood parameters, which further prompted us to investigate changes in the liver and kidneys of mice exposed to UWB pulses with different field intensities and different durations. The data demonstrated that exposure to UWB pulses significantly increased the levels of ALT and AST, increased oxidative stress, and could even induce the accumulation of lipid droplets in hepatocytes. The total number of pulses under the tested acute exposure regiment contributed most to the observed hepatic and rental dysfunction. Notably, the physiological and molecular changes recovered approximately 72 hours after exposure. These results imply the potential risk of acute exposure to UWB pulses, and highlight the meaningful targets for further long-term study of chronic exposure.

Research on action behavior of neuron system in case of single pulse stimulus.

Facing on the complex electromagnetic environment of electrical equipment, based on the bio-anti-interference characteristics of neuron system, the bio-inspired electromagnetic protection is proposed in order to improve and assist the traditional electromagnetic protection method. In order to analyze the dynamical characteristics of electrical signal transfer process of neuron system, Hodgkin-Huxley (HH) model is adopted to calculate the action potential of single neuron. The initial value problem used in the parameters of Hodgkin-Huxley model is studied in order to satisfy the physiological phenomenon. The stability of HH model is analyzed to assess the dynamic stable performance of neuron. Based on the investigation of single neuron, a simple neuron system consisted of two neurons and one synapse is studied. The compassion between the action potential of posterior neuron and different synapse is performed, which explores how the mathematic models of different synapses influence the action potential. The relationship between action potential of posterior neuron and coupling strength of simplified synapse is calculated to explain the diversity of electrical signal output of neuron system. These numerical results enable to provide some datum for deeply developing the bio-inspired electromagnetic protection and well designing the bio-inspired circuit.

Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network.

Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity - and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics.