Traffic flow prediction is crucial for intelligent transportation and aids in route planning and navigation. However, existing studies often focus on prediction accuracy improvement, while neglecting external influences and practical issues like resource constraints and data sparsity on edge devices. We propose an online transfer learning (OTL) framework with a multi-layer perceptron (MLP)-assisted graph convolutional network (GCN), termed OTL-GM, which consists of two parts: transferring source-domain features to edge devices and using online learning to bridge domain gaps. Experiments on four data sets demonstrate OTL’s effectiveness; in a comparison with models not using OTL, the reduction in the convergence time of the OTL models ranges from 24.77% to 95.32%.

Plasticity is key to the trainability of neural networks and has long been a focus in the field of brain-inspired research. Currently, neuromorphic networks primarily achieve plasticity through synaptic and myelin structures. However, these two are often studied separately, limiting further enhancement of neuronal node plasticity. This paper proposes a neuron model that incorporates both synapses and myelin, designs the corresponding neuronal circuit, and introduces a method for quantifying its discharge characteristics. Through theoretical analysis, simulations, and physical experiments, we validate the effectiveness of this quantification method. Furthermore, we summarize the formation mechanisms of synaptic and myelin plasticity, clarify the differences in their respective plasticity effects, and use the quantification method to compute the response speed, power consumption, and spike firing frequency of neuronal circuits. We also analyze the impact of synaptic and myelin plasticity and their synergistic effects on these three factors. Results demonstrate that the plasticity of synapses and myelin, as well as their synergistic interaction, can significantly optimize the performance of neuron nodes: the response duration is reduced to 2.9% of its initial value, the energy consumption per spike decreases to 38.4%, and the spike firing frequency increases to 1982.6% of the baseline level. This synergy contributes to improving the computational efficiency and energy management capabilities of neuromorphic networks.

Memristive neuromorphic networks have great potential and advantage in both technology and computational protocols for artificial intelligence. Efficient hardware design of biological neuron models forms the core of research problems in neuromorphic networks. However, most of the existing research has been based on logic or integrated circuit principles, limited to replicating simple integrate-and-fire behaviors, while more complex firing characteristics have relied on the inherent properties of the devices themselves, without support from biological principles. This paper proposes a memristor-based neuron circuit system (MNCS) according to the microdynamics of neurons and complex neural cell structures. It leverages the nonlinearity and non-volatile characteristics of memristors to simulate the biological functions of various ion channels. It is designed based on the Hodgkin-Huxley (HH) model circuit, and the parameters are adjusted according to each neuronal firing mechanism. Both PSpice simulations and practical experiments have demonstrated that MNCS can replicate 24 types of repeating biological neuronal behaviors. Furthermore, the results from the Joint Inter-spike Interval(JISI) experiment indicate that as the background noise increases, MNCS exhibits pulse emission characteristics similar to those of biological neurons.

The high cost, low yield, and low stability of nanomaterials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However, these emulators lack nonvolatile characteristics, limiting their scope of application. This article proposes an innovative nonvolatile memristor circuit based on complementary metal–oxide–semiconductor (CMOS) technology, expanding the horizons of memristor emulators. The proposed memristor is fabricated in a reconfigurable array architecture using the standard CMOS process, allowing the connection between memristors to be altered by configuring theon–offstate of switches. Compared to nanomaterial memristors, the CMOS nonvolatile memristor circuit proposed in this article offers advantages of low manufacturing cost and easy mass production, which can promote the application of memristors. The application of the reconfigurable array is further studied by constructing an echo state network for short-term load forecasting in the power system.

Matrix operations are widely used in practical engineering, but the traditional processing methods rely on the loop iterations and neural network algorithm on the software, requiring a long time to calculate. To address such problem, this paper proposes full hardware in-memory computing circuits based on programmable memristor unit array that can solve combinatorial matrix operations of any order in just one step. First, two basic circuit modules are introduced, which can respectively solve matrix multiplication and matrix equation. Further, the basic modules can be linked to solve combinatorial matrix operations with different forms. It’s worth noting that every module can parallel program the value of each memristor in the memristor unit array and complete one-step computation by hardware. Then, some matrix operations are given in the paper as examples to prove the high accuracy of proposed method, where the average accuracy rate achieves 99%. The PSPICE simulation results demonstrate that the processing speed is improved enormously according to the comparison of hardware and software. Moreover, the proposed method has broad application prospect in practical engineering, such as using designed combinational circuit to solve domain shift problem in zero-shot learning, which greatly accelerates the training process of zero-shot learning.

Discrete Hartley transform is a core component of digital signal processing because of its advantages of fast computing speed and less power consumption. Traditional FPGA-based implementation methods have the disadvantage of high latency, which cannot meet the needs of energy-efficient computing in the Internet of Things era. Therefore, A programmable analog memory computing circuit is proposed to accelerate FHT and IFHT calculations for large-scale one-step matrix computation. By adjusting the weight of memristor, different scales of FHT calculation can be achieved. PSPICE simulation results show that the average accuracy of the proposed circuit can reach 99.9%, and the speed can also reach the level of 0.1 μs. The robustness analysis shows that the circuit can tolerate a certain degree of programming error and resistance tolerance. The designed analog circuit is applied to image compression processing, and the image compression accuracy can reach 99.9%.

With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones (FZs) on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors, such as weather and holidays on traffic is an unsolved problem. This article proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing FZs, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into FZs, calculate the interaction between FZs and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show the better prediction performance of the proposed model compared with other existing methods in a complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in a complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.

Emotional intelligence plays an important role in artificial intelligence. The brain circuitry of emotion mainly includes the prefrontal cortex, the amygdala, hippocampus andet al.Many brain emotional learning (BEL) models were proposed in recent years, the existing BEL models failed to consider the contextual information in practical applications, and do not discuss the corresponding circuit implementation. In this article, a context-dependent emotional learning network (CD-ELN) and its memristive circuit implementation are introduced. The added context-dependent module is used to process the contextual information, which makes the network context dependent when receiving the same input signals. For circuit implementation, the memristive circuit design mainly contains the amygdala module and orbitofrontal cortex module, which imitates the emotion learning process in the brain. Besides, a multi-input multioutput memristive circuit of the context-dependent emotional network is applied to multitask classification. PSPICE simulation results verified the adaptability and flexibility of the CD-ELN.

The analog circuit design of the memristive neural network (MNN), which can automatically perform the online learning algorithm, is an open question. In this article, a memristive self-learning neuron circuit for implementing the online least mean square (LMS) algorithm is designed. Extending on the designed neuron circuit, the circuit implementation of the monolayer and multilayer neural network is proposed. The proposed neural network can automatically converge the output to the set target according to the input. The application-level validations of the circuits are done using pattern recognition and license plate detection. The performances of the designed MNN circuits and the effect of memristive variation are analyzed through PSPICE simulations. The learning accuracy of the proposed circuit for license plate detection can reach 93%. Circuit simulation results reveal that the proposed MNN circuits can accelerate the training speed and have the tolerance to the variations of the memristor.

Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) are core components in the field of signal processing. However, in the existing research, there is no fully analog circuit that can realize the one-step calculation of FFT. Therefore, in this paper, an analog circuit that can calculate FFT and its inverse transform IFFT in one-step is proposed. First, a circuit that can realize complex number operations is designed. On the basis of this structure, a fully analog circuit that can realize fast and efficient computing of FFT and IFFT in one-step is proposed. In addition, different coefficient matching can be obtained to achieve arbitrary points of FFT and IFFT by adjusting the resistance value of the memristor, which has good programmability. Specific examples are given in the paper to evaluate the proposed method. The PSPICE simulation results show that the average accuracy is above 99.98%. More importantly, the calculation speed has been greatly improved compared with MATLAB simulation. Finally, the proposed circuit can be used to quickly solve convolution operation, and the average accuracy can reach 99.95%.

A large number of studies have shown that astrocytes can be combined with the presynaptic terminals and postsynaptic spines of neurons to constitute a triple synapse via an endocannabinoid retrograde messenger to achieve a self-repair ability in the human brain. Inspired by the biological self-repair mechanism of astrocytes, this work proposes a self-repairing neuron network circuit that utilizes a memristor to simulate changes in neurotransmitters when a set threshold is reached. The proposed circuit simulates an astrocyte-neuron network and comprises the following: 1) a single-astrocyte-neuron circuit module; 2) an astrocyte-neuron network circuit; 3) a module to detect malfunctions; and 4) a neuron PR (release probability of synaptic transmission) enhancement module. When faults occur in a synapse, the neuron module becomes silent or near silent because of the low PR of the synapses. The circuit can detect faults automatically. The damaged neuron can be repaired by enhancing the PR of other healthy neurons, analogous to the biological repair mechanism of astrocytes. This mechanism helps to repair the damaged circuit. A simulation of the circuit revealed the following: 1) as the number of neurons in the circuit increases, the self-repair ability strengthens and 2) as the number of damaged neurons in the astrocyte-neuron network increases, the self-repair ability weakens, and there is a significant degradation in the performance of the circuit. The self-repairing circuit was used for a robot, and it effectively improved the robots' performance and reliability.

Family farm is the product of promoting the development of modern agriculture. It has the characteristics of large-scale operation, specialization and marketization. It is the representative of the new agricultural mode in China's agricultural modernization reform. However, due to the lack of funds, the development of most family farms is restricted, and the financing channels are blocked, which is an important problem to be solved. Therefore, this paper puts forward an innovative research on the “cloud financing” mode of family farms from the perspective of agricultural industry chain. This paper makes an in-depth investigation on the main financing modes and main demands of family farms in China. According to the survey results, family farms have a large demand for modern agricultural machinery, and there is a large funding gap. However, the interest rate of existing financing channels is too high and the financing cost is too high. In view of this situation, this paper introduces “cloud financing” which has been widely used in recent years into family farms, and analyzes it from the perspective of agricultural industry chain. This paper believes that through the cloud financing mode, driven by the Internet and information technology, it can effectively integrate idle resources and establish network financing platform under the support of policies. This mode simplifies the operation mode of traditional financing mode, optimizes the structure of financing mode, and plays a positive role in promoting the development of the upper and lower industrial chain of family farms.

Inhomogeneous linear ordinary differential equations (ODEs) and systems of ODEs can be solved in a variety of ways. However, hardware circuits that can perform the efficient analog computation to solve them are rarely in the literature. To address such problems, this paper proposes a general method of using a memristor-capacitor (M-C) circuit to solve inhomogeneous linear ODEs and systems of ODEs of any order in initial value problems. The M-C circuit can match the coefficients of the equations sought by adjusting the memristor resistance value according to the coefficient formula proposed in the paper, which has higher programmability. Then, some ODEs and systems of ODEs are given in the paper as examples to evaluate the proposed method. According to the comparison results based on MATLAB software simulation and the simulation based on OrCAD software, the designed M-C circuit has an effective improvement in speed and the accuracy exceeds 99.95% in software simulation. Based on practical verification, this paper gives the actual M-C circuit experiment based on PCB. Moreover, the proposed method can be used to quickly solve the object motion state in the spring mass damping system in actual engineering, and the accuracy can reach 99.98%.

The application of graph convolutional network in short-term traffic flow forecasting of road network has effectively improved the prediction accuracy. The key point of this method is to construct the Laplacian matrix through extracting spatial features among nodes of the road network. However, most available methods mainly rely on the spatial distance among nodes to construct Laplacian matrix, then optimized the Laplacian matrix by other methods, which limits the wide application of the model. In this paper, we propose a dynamic spatial-temporal graph attention graph convolutional network (GAGCN) method to improve the generality of the model. The Laplacian matrix in this model is constructed directly by the dependencies among the nodes hidden in the traffic data which are identified by the graph attention networks, and can be dynamic adjust over time, the information of spatial distance among nodes and human intervention are not required in the process. Experimental results of two real-world datasets show that both the generality and prediction accuracy of the proposed model had been significantly improved.