As the demand for multi-dimensional ground and air information acquisition increases on modern battlefields, ground-air cooperation has become a crucial method to enhance operational efficiency and decision-making. In the complex and changeable battlefield environment, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) can leverage their strengths to maximize exploration efficiency. The multi-level collaborative path planning method for UGV-UAV systems (MLCPP-UUs) is proposed in this paper, comprising three layers: single-UAV, multi-UGV and interactive planning. To optimize the total cost of the model, multiple strategies are used to improve the global exploration and local search of the walrus optimizer (WO). In search-enhanced walrus optimizer (SEWO), a dynamic step size adjustment is introduced during migration based on terrain steepness to avoid blind random search and improve search space coverage. In the later iteration, a nonlinear decreasing search factor is used to accelerate the convergence speed. For high-quality solutions, the simplex method is used to complete the “secondary exploitation" by efficiently searching the neighborhood of the solution space. To evaluate the performance of SEWO, three reference terrains from real digital elevation models (DEM) are generated with obstacle scenarios and interaction modes. The results show that the proposed algorithm can plan the collaborative paths satisfying the constraints efficiently, proving its effectiveness in the ground-air cooperative planning problem.

Most neural networks based on memristor only consider the relationship between emotion and memory, but ignore the relationship between emotion and directional forgetting. This article presents a neural network circuit based on memristors, which can achieve targeted forgetting with self-correlation and emotional memory. The designed circuit is mainly composed of memory module, self-correlation module, amygdala module, emotion module, and association neuron module. Memory module can dynamically adjust the time for memory formation and the time for memory decay according to the requirements of the task. Association between learning materials and the characteristics of the individual can be achieved through the self-correlation module. The learning material signals are transformed into positive learning material signal and negative learning material signal through the amygdala module. Emotion module and the association neuron module are used to determine whether the emotionality of the learning materials is consistent with one's own emotionality. The feasibility of the function of the proposed circuit is verified through Simulation Program with integrated circuit emphasis. This circuit provides more references for the further development of brain-like intelligence.

With the continuous advancements in computer technology and industrial technology, ensuring the security of industrial information is becoming more crucial. To safeguard against the exposure of sensitive industrial information, research into industrial image encryption technology is crucial. In this paper, a controllable multistable memristor model is presented. The multi-stability of the memristor is analyzed and described by mathematical model. The chaotic system of coupled multistable memristor is constructed using the Hopfield neural network (HNN) and the Hindmarsh-Rose neuron (HR). The intricate dynamic behavior of the HNN-HR chaotic system is uncovered through dynamic analysis and numerical simulations. The equivalent circuit of the HNN-HR chaotic system has been constructed, and the accuracy of the numerical results has been validated. The HNN-HR chaotic system has multi-stability and tunability of initial conditions, which can be used for industrial image encryption. The chaotic sequences generated by the HNN-HR system are utilized for encrypting industrial images by cyclic shift algorithm and bi-directional DNA diffusion algorithm. This paper provides an encryption scheme for the industrial Internet of things (IIoT). The research results indicate that the encryption schemes provide enhanced resistance to attacks. The encryption scheme shows great potential in the field of industrial image encryption and enhances the security of industrial image transmission.

Path planning for unmanned combat aerial vehicles (UCAV) has evolved into a multiconstrained, high-dimensional and multimodal optimization problem in complex combat environments. To solve the global optimal path planning problem of UCAV in a variety of complex terrain and multiple obstacles, human evolution optimization algorithm (HEOA) based on multistrategy is proposed in this article. In developed HEOA (DHEOA), a parallel population division combined with the double reverse learning strategy is employed to balance human exploration and development. Subsequently, the update strategies of the ball-rolling dung beetle and the thief dung beetle in dung beetle optimizer (DBO) are integrated into the human exploration stage. The ability for search is enhanced and convergence accuracy is improved. Finally, a variation strategy inspired by the natural development process is designed. The goal is to capture and activate the cycle of changes in population diversity. To evaluate the performance of DHEOA, four reference terraforms are generated from the real digital elevation model (DEM) and three different scenarios of each terraform are simulated. A series of path planning simulation experiments in a complex 3-D environment are carried out. The results show that the proposed algorithm can plan a path satisfying the constraints stably and efficiently. It has better results in UCAV path planning problems.

Operant conditioning (OC) and secondary conditioning are two important mechanisms for organisms to adapt to external environments. However, most memristor-based neural network circuits only consider a single OC. The secondary conditioned reflex process that occurs on the basis of OC during actual animal training are ignored. On the basis of memristors, the operant conditioning neural network circuit with emotion transmission and secondary conditioned reflex is designed. After OC learning, organisms respond to the first conditioned stimulus. On this basis, another conditioned stimulus is introduced and secondary conditioned reflex is indirectly established. Additionally, the phenomenon of emotion transmission and the effect of emotion on associative memory are considered. The designed circuit mainly consists of delay module, voltage control module, emotion transmission module and synaptic module. The implementation of the secondary conditioned reflex process based on OC is achieved through the delay module, voltage control module and synaptic module. Emotion transmission and the influence of emotion on associative memory are realized by emotion transmission module and voltage control module. The PSPICE simulation results confirm the implementation of the above functions. This circuit provides more references for the further development of brain-like technology.

Current memristive circuits only focus the impact of simple rewards and punishments on biological behaviors, without considering the consequences of sustained stimuli and the occurrence of secondary behaviors. In this article, a memristor-based reward and punishment neural network circuit with approach and inhibition is designed, secondary behaviors are taken into account. The designed circuit is mainly composed of thalamus module, reward pathway, punishment pathway, amygdala module, feature module, and prefrontal cortex module. The signal processing in the brain is simulated by reward and punishment neural network, where signals of different intensities are produced to generate different overshadowing effects. Continuous stimulus is generated by external signal, producing different emotions and affecting memory. Approach and inhibition behaviors are initial outcomes, followed by secondary behaviors by competition between systems. The feasibility of the circuit is verified by PSpice, the proposed circuit provides a reference for biomimetic robots in neurocomputing and industrial applications.

The traditional association theory maintains that associations between cues can change only in trials where the cue is actually presented. However, the retrospective revaluation (RR) studies the phenomenon that responses to a cue can change even when the cue is not actually presented. A hardware memristor-based neural network circuit with an RR effect is proposed in this article. The neural network circuit successfully demonstrates various phenomena of RR, including the impact of deflation and inflation of companion cue associations on target cue, higher order RR, and context dependence. The correctness of the circuit design is verified by Pspice simulation. The key feature of this design lies in its ability to learn cue associations even in training trials, where the target cues are absent. This distinctive attribute offers a fresh perspective for the creation of more intricate, brain-inspired information processing systems with enhanced integration capabilities.

Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the application scenarios of neural networks. In this article, bionic associative memory and temporal order memory circuits are constructed by DNA strand displacement (DSD) reactions. First, a temporal logic gate is constructed on the basis of DSD circuit and extended to a three-input temporal logic gate. The output of temporal logic gate is used for the weight species of associative memory. Second, the forgetting module and output module based on the DSD circuit are constructed to realize some functions of associative memory, including associative memory with simultaneous stimulus, associative memory with interstimulus interval effect, and the facilitation by intermittent stimulus. In addition, the coding, storage, and retrieval modules are designed based on the analysis and memory capabilities of temporal logic gate for temporal information. The temporal order memory circuit is constructed, demonstrating the temporal order memory ability of DNA circuit. Finally, the reliability of the circuit is verified through Visual DSD software simulation. Our work provides ideas and inspiration to construct more complex DNA bionic circuits and intelligent circuits by using DSD technology.

The omega-K algorithm ($\omega $KA) is an accurate imaging method for squinted synthetic aperture radar (SAR) with linear trajectory. However, for SARs with curved trajectory, since it does not allow the radar velocity to vary with range, the imaging quality would degrade severely. To address this issue, an improved$\omega $KA is proposed in this letter. We design a special Stolt mapping to avoid the complex additional range cell migration (RCM) and the skew of the data support region. Then, the RCM correction (RCMC) and azimuth compression are operated in the range Doppler domain. With these improvements, the radar velocity is allowed to vary with range, which greatly reduces the phase error in the case of large range swaths. Numerical results validate the proposed algorithm.

Operant conditioning is an important learning mechanism for organisms, as well as a basic theory for reinforcement learning in artificial intelligence. Although there are already some memristive neural circuits for operant conditioning, they can only process a single stimulus and cannot handle multiple inputs simultaneously. This article proposes a multi-input operant conditioning neural network that incorporates blocking and competing effects. This network can achieve the blocking and overshadowing effects in the presence of multiple inputs and learn efficiently in complex environments. In addition, it incorporates time differences between signals and excitations, random exploration, feedback learning, experience memory, decision-making based on experience, and adaptive learning in low-reward environments. Finally, the feasibility of the proposed circuit function is verified through PSPICE simulation. This work provides an implementation idea for the hardware implementation of artificial intelligence.

The clutter suppression methods based on post-Doppler space time adaptive processing technology is a hot search topic in recent years. However, to coherently integrate a target’s energy of each channel, these methods require searching target’s velocities, which may introduce a large computation load. This paper proposes a method for calculating the optimal velocity search step of adaptive clutter suppression for the commonly used three-channel synthetic aperture radar (SAR) ground moving target indication (GMTI) systems. First, the signal models in the range Doppler domain are developed. Then, based on the fact that the signal-to-clutter-noise ratio loss of clutter suppression and the coherent integration gain loss are all introduced by the mismatch of the steer vector, the way to obtain the analytical expression for calculating the optimal velocity search step is developed. Experimental results validate the propose method.

Classical three-variable chaotic system coupling synchronization has been implemented in previous work based on DNA strand displacement (DSD). Herein, by using DSD reactions as the foundation, a proportional integral (PI) controller for chaotic system is introduced to realize the hybrid projective synchronization for different four-variable chaotic systems. DSD-based chaotic systems are composed of catalysis modules, annihilation modules and degradation modules for realizing the construction of chaotic attractors. PI controllers are consist of catalysis, annihilation and adjust DSD modules that are easy to modify and can be added to chaotic system for achieving hybrid projective synchronization. Our work can be acted as the reference for the investigation of chaos synchronization.

This paper proposed an efficient imaging method for medium-Earth-orbit (MEO) synthetic aperture radar (SAR) ground moving target indication (GMTI) systems. MEO SAR is an attractive tool for GMTI applications, because of its advantages of large coverage, short revisit time and strong damage resistance. In this paper, first, the third-order Taylor-approximated range model of a moving target for MEO SAR is developed. Then, based on the proposed range model, the target's 2-D spectrum is derived and a novel imaging method is proposed. The proposed imaging method implements focusing without a priori knowledge of targets' motion parameters and position parameters. Finally, numerical results validate the proposed method.

This paper presents a study on signal modeling for airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems with relatively high resolution. High resolution is an important trend of synthetic aperture radar (SAR), which means higher accuracy requirement of range history and longer synthetic aperture time. It's known that targets are probably experience accelerations as the synthetic aperture time increases. Therefore, compared with the systems of lower resolution, the target signal modeling of high-resolution systems becomes more challenging. Besides, the coupling relationship among a target's motion and position parameters becomes more complicated due to the existence of accelerations. In this paper, a fourth-order range history model of a moving target is developed. Moreover, the target signal model is established, and the along-track interferometric (ATI) phase history is derived to reveal the coupling relationship among a target's motion and position parameters.