Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this, we take the cue from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture tailored for point cloud analysis. By leveraging the unique characteristics of point clouds, our design combines discrete and continuous encoding, replacing traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Our approach substantially improves the performance of Brain-Inspired Neural Networks on point analysis tasks and maintaining performance comparable to state-of-the-art methods. Furthermore, DC-CCNN exhibits enhanced robustness against various point cloud deformations and corruptions. Our experimental results demonstrate that DC-CCNN achieves competitive performance on benchmark datasets, making it a promising alternative to traditional deep learning methods for point cloud analysis. With its high efficiency and robustness, DC-CCNN has the potential for widespread adoption in 3D computer vision, robotics, and autonomous systems.

Long-term video understanding in intelligent transportation systems (ITS) has advanced significantly with the integration of large language models (LLMs) and vision foundation models. However, existing LLM-based multimodal approaches are limited by context length and memory constraints, restricting their effectiveness to short video scenarios. To address these challenges, we propose AS-Memory, a novel framework that combines adaptive sparse memory with LLMs for efficient and scalable long-term video understanding. AS-Memory introduces a plug-and-play memory bank, a lightweight module designed to seamlessly integrate with existing multimodal LLMs. This memory bank stores and retrieves historical video content, enabling long-term analysis while mitigating context length and GPU memory limitations. To further enhance efficiency, we propose a sparse adaptive mechanism that dynamically compresses redundant features and retains critical information, ensuring effective management of streaming video data. Comprehensive evaluations on long-term video understanding benchmarks demonstrate that AS-Memory consistently outperforms state-of-the-art methods in terms of accuracy. The source code and trained models will be made available to the public.

Event cameras are bio-inspired vision sensors that encode visual information with high dynamic range, high temporal resolution, and low latency. Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity-changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation.

The rapid advancement of large language models (LLMs) has led to the widespread adoption of video-language models (VLMs) across various domains. However, VLMs are often hindered by their limited semantic discrimination capability, exacerbated by the limited diversity and biased sample distribution of most video-language datasets. This limitation results in a biased understanding of the semantics between visual concepts, leading to hallucinations. To address this challenge, we propose a Multi-level Multimodal Alignment (MMA) framework that leverages a text encoder and semantic discriminative loss to achieve multi-level alignment. This enables the model to capture both low-level and high-level semantic relationships, thereby reducing hallucinations. By incorporating language-level alignment into the training process, our approach ensures stronger semantic consistency between video and textual modalities. Furthermore, we introduce a two-stage progressive training strategy that exploits larger and more diverse datasets to enhance semantic alignment and better capture general semantic relationships between visual and textual modalities. Our comprehensive experiments demonstrate that the proposed MMA method significantly mitigates hallucinations and achieves state-of-the-art performance across multiple video-language tasks, establishing a new benchmark in the field.

ABSTRACT Bearings are a critical part of various industrial equipment. Existing bearing fault detection methods face challenges such as complicated data preprocessing, difficulty in analysing time series data, and inability to learn multi‐dimensional features, resulting in insufficient accuracy. To address these issues, this study proposes a novel bearing fault diagnosis model called multi‐channel deep pulse‐coupled net (MC‐DPCN) inspired by the mechanisms of image processing in the primary visual cortex of the brain. Initially, the data are transformed into greyscale spectrograms, allowing the model to handle time series data effectively. The method introduces a convolutional coupling mechanism between multiple channels, enabling the framework can learn the features on all channels well. This study conducted experiments using the bearing fault dataset from Case Western Reserve University. On this dataset, a 6‐channel (adjustable to specific tasks) MC‐DPCN was utilized to analyse one normal class and three fault classes. Compared to state‐of‐the‐art bearing fault diagnosis methods, our model demonstrates one of the highest diagnostic accuracies. This method achieved an accuracy of 99.96% in normal vs. fault discrimination and 99.89% in fault type diagnosis (average result of ten‐fold cross‐validation).

With the advent of Vehicular Ad Hoc Networks (VANETs), Vehicular Edge Computing (VEC) facilitates the execution of vehicular tasks through the Internet. In the VEC architecture, vehicles request task offloading, and a central decision center allocates resources. Effective task offloading algorithms provide optimal and equitable decisions based on objectives such as task latency and system overhead; however, current task offloading algorithms for VEC face challenges in adapting to complex and dynamic road environments. This paper proposes a task-offloading algorithm based on deep reinforcement learning to address the challenges of task offloading in vehicular edge computing. During the task offloading process, the privacy of vehicular task data may be compromised. This study introduces a novel task-offloading algorithm for Vehicular Edge Computing (VEC) that employs differential privacy principles to safeguard the confidentiality of vehicular tasks during the offloading process. The proposed algorithm introduces noise in accordance with the privacy budget during the training process. The study provides a theoretical analysis of privacy, and experimental results based on Attari demonstrate that the proposed differential privacy-based reinforcement learning algorithm exhibits superior convergence compared to existing algorithms. Veins-based simulation experiments on VEC demonstrate that the proposed differential privacy-based task-offloading algorithm can achieve practical offloading while preserving privacy.

Deep generative models, particularly diffusion probabilistic models, have recently shown promise in medical ultrasound image segmentation due to their powerful denoising and detail restoration capabilities. However, most existing generative models focus primarily on image enhancement, with limited consideration for segmentation-specific challenges. To address this, we propose CTrans-SegDiff, a novel segmentation framework that integrates a denoising diffusion probabilistic model with a Transformer-enhanced dynamic conditioning mechanism. Specifically, we design a dual-channel dynamic conditioning module to jointly capture lesion-specific semantics and global contextual dependencies, and a Gaussian Distribution Fusion Module (GDFM) to harmonize the fusion of conditioning features with diffusion-encoded representations. Extensive experiments on two ultrasound datasets demonstrate that our method effectively suppresses noise, enhances structural clarity, and achieves superior segmentation performance compared to existing approaches.

Dunhuang mural inpainting aims to fill in the missing regions of damaged murals with realistic content. Denoising probabilistic diffusion model (DDPM) has made great strides in semantic generation and shown promising results in image inpainting. However, three potential challenges prevent existing diffusion-based methods from restoring the Dunhuang murals: 1) effective visual information cannot be accurately extracted due to historical reasons, with most of the pixels being faded; 2) there are semantic discrepancy between damaged and visible regions in the inpainting results; and 3) the original structure and style of the damaged regions cannot be adequately restored. To this end, we propose a novel adversarial diffusion model for mural inpainting, which consists of: 1) a mural enhancement module named pixel-enhanced fire-controlled pulse-coupled neural network (PEFCPCNN), designed to enhance faded pixels to accurately extract the visual features of the mural; 2) a novel adversarial diffusion framework that optimizes the sampling prediction of mural over time steps; and 3) line drawing and different loss functions to constrain the reconstructed content to approximate the structure and style of original mural. The variational transform layer (VTL) and multi-scale contextual feature aggregation (MCFA) module are proposed to reconstruct content that is structurally coherent and texturally reasonable. Experiments on the Dunhuang mural dataset demonstrate that the proposed method outperforms state-of-the-art methods in terms of both the semantic reasonableness and global semantic consistency of inpainting content.

The advancement of the Internet of Vehicles (IoV) has facilitated the integration of intelligent vehicles with Internet connectivity, providing access to a wide range of services that significantly enhance vehicular applications. However, this connectivity also brings about an increased vulnerability to cyber attacks from the internet. Given the limited computing and communication resources available in vehicles, existing intrusion detection methods are ill-suited for vehicular networks. In this paper, we propose a lightweight vehicular intrusion detection method based on Edge Intelligence. The proposed method utilizes edge intelligence to achieve real-time intrusion detection in vehicles. To ensure efficient intrusion detection, we design a lightweight Convolutional Neural Networks (CNN) intrusion detection model and incorporate Auxiliary Classifier Generative Adversarial Networks (ACGAN) for model training. The CNN component will be offloaded to the Edge Cloud to further enhance intrusion detection performance. To address the task offloading optimization problem in edge computing, we introduce a deep reinforcement learning-based task offloading algorithm to allocate the resources of edge cloud for vehicles with limited computing resources. Simulation experiments demonstrate the superiority of proposed vehicular intrusion detection method over existing state-of-the-art methods. The simulation experiments by Veins also show the efficiency of the proposed vehicular intrusion detection.

Exploring and establishing artificial neural networks with electrophysiological characteristics and high computational efficiency is a popular topic that has been explored for many years in the fields of pattern recognition and computer vision. Inspired by the working mechanism of the primary visual cortex, pulse-coupled neural networks (PCNNs) can exhibit the characteristics of synchronous oscillation, refractory period, and exponential decay. These characteristics empower the PCNN model to group pixels with similar spatiality and gray values and to process digital images without training. However, electrophysiological evidence shows that the neurons exhibit highly complex nonlinear dynamics when stimulated by external periodic signals. This chaos phenomenon, also known as the ‘butterfly effect,” cannot be explained by all PCNN models. In this work, we analyze the main obstacle preventing PCNN models from imitating a real primary visual cortex. We consider neuronal excitation as a stochastic process. We then propose a novel neural network of the primary visual cortex, called a continuous-coupled neural network (CCNN). Theoretical analysis indicates that the dynamic behavior of the CCNN is distinct from the PCNN. Numerical results show that the CCNN model exhibits periodic behavior under a DC stimulus, and exhibits chaotic behavior under an AC stimulus, which is consistent with the testing results of primary visual cortex neurons. Furthermore, the image and video processing mechanisms of the CCNN model are analyzed. For image processing tasks, this model encodes the pixel intensity as the frequency of output signals so that it can group pixels with similar gray values. This image processing method can reduce the local gray level difference of the image, and compensate for small local discontinuities in the image. For video processing tasks, the CCNN encodes changing pixels as non-periodic chaotic signals, and it encodes static pixels as periodic signals. It thusachieves the purpose of moving target object recognition by distinguishing the dynamic states corresponding to different neuron clusters in the video. Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.

This work describes the simplest chaotic system with a hyperbolic sine non‐linearity, accompanied by analysis of Lyapunov exponents, bifurcations, and stability. The corresponding simple chaotic circuit using only diodes and linear components is designed and implemented. Finally, an application of the system to spread spectrum communication based on differential chaos shift keying (DCSK) is presented. Since the hyperbolic sine is an odd function of its argument, the system is antisymmetric and exhibits symmetry breaking where the attractors split or merge as some bifurcation parameter is changed. The proposed system is especially simple both from the structure of the equations and in its electronic circuit realisation. Compared with the traditional DCSK scheme of a Chebyshev sequence, the system can reduce the bit error rate in the presence of noise.