With the rapid adoption of Internet of Things (IoT) and artificial intelligence (AI), lithium-ion battery state-of-health (SOH) estimation plays an important role in guaranteeing the secure and stable functioning of various domains. However, the majority of the existing methods are constrained by factors, such as transmission latency, computational energy, and computing speed. To address these challenges, we develop a time-frequency hybrid neuromorphic computing architecture for battery SOH estimation. Specifically, an eco-friendly, biodegradable memristor crossbar array is designed, enabling high-energy efficiency and high-performance density in the proposed system. To improve the understanding of the designed time-frequency hybrid neuromorphic computing system, a local information extraction module, a time-frequency feature fusion module, and a global information perception module are proposed. Furthermore, the proposed system is validated on two publicly available battery ageing data sets (i.e., the CALCE-CS2 data set and the National Aeronautics and Space Administration data set). The experimental results show that the system exhibits superior performance to that of the state-of-the-art (SOTA) methods in terms of estimation accuracy (highest estimation accuracy), time consumption (approximately 8–12 times faster), and transmission latency (approximately 10 times faster). This study is expected to promote the advancement and evolution of next-generation computing systems, enabling the realization of low-power consumption and high-density information processing in IoT scenarios.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

The combination of the Metaverse and intelligent transportation systems (ITS) holds significant developmental promise, especially for visual perception tasks. However, the acquisition of high-quality scene data poses a challenging and expensive endeavor. Meanwhile, the visual disparity between the Metaverse and the physical world poses an impact on the practical applicability of the visual perception tasks. In this paper, a Metaverse Intelligent Traffic Visual Framework, MITVF, is developed to guide the implementation of visual perception tasks in the physical world. Firstly, a two-stage metadata optimization strategy is proposed that can efficiently provide diverse and high-quality scene data for traffic perception models. Specifically, an element reconfigurability strategy is proposed to flexibly combine dynamic and static traffic elements to enrich the data with a low cost. A diffusion model-based metadata optimization acceleration strategy is proposed to achieve efficient improvement of image resolution. Secondly, a Meta-Physical adaptive learning method is proposed, and further applied to visual perception tasks to compensate for the visual disparity between the Metaverse and the physical world. Experimental results show that MITVF achieves a 10 $\times$ acceleration in optimization speed, ensuring the image quality and reconstructing diverse. Further, MITVF is applied to the traffic object detection task to verify the effectiveness and validity. The performance of the model trained with 5k real data exceeded that of the model trained with 200k real data, with AP $_{50}$ reaching 67.7%.

Artificial neural networks (ANN) have shown remarkable performance in intelligent transportation systems (ITS), especially for the traffic object detection. However, as the ITS is applied to a wider range of traffic scenarios, the increasing demand for the trade-off between detection performance and power resources has become inevitable. A biologically interpretable spike-driven traffic object detector for challenging scenarios is proposed in this paper, named SpikeTOD, achieving the trade-off between the accuracy and power consumption. Firstly, the spike neural network (SNN) is employed to realize energy-efficient object detection in traffic scenarios. And a local modulation-based integrate-and-fire (IF) neuron is designed, which provides an efficient way to convert the traffic detection model from ANN to SNN. Secondly, a biology-inspired detail-guided context-aware network (DCNet) is proposed to improve the detection performance. The integration of detail coherence and global priors is leveraged to selectively emphasize object features and improve the detection capabilities within challenging conditions. As far as we know, this is the first application of SNN in traffic object detection tasks. SpikeTOD achieved a mAP@50 of 46.11% on the BDD100K dataset with a power consumption of 4.73E-03J, demonstrating a more efficient trade-off in detection accuracy and power consumption. Notably, SpikeTOD maintained an average missed detection rate of 44.56%, further contributing to its overall efficacy in traffic object detection. Further, we conducted on road test by deploying SpikeTOD on Jetson Xavier NX and Loihi to demonstrate that model achieves a better balance between accuracy and power consumption.

Emerging cloud-native distributed databases rely on local NVMe SSDs to provide high-performance and highavailable data services to many cloud applications. However, the database clusters suffer from low utilization of local storage because of the imbalance between CPU and storage capacities within each node. For instance, the OceanBase distributed database cluster, with hundreds of PB local storage capacity, only utilizes around 40% of its local storage. Although disaggregated storage (EBS) can enhance storage utilization by provisioning the CPU and storage independently on demand, they suffer from performance bottlenecks and high costs. In this paper, we propose LightPool, a high-performance and lightweight storage pool architecture large-scale deployed in the OceanBase clusters, enhancing storage resource utilization. The key idea of LightPool is aggregating cluster storage into a storage pool and enabling unified management. In particular, LightPool adopts NVMe-oF to enable high-performance storage resource sharing among cluster nodes and integrate the storage pool with Kubernetes to achieve flexible management and allocation of storage resources. Furthermore, we design the hot-upgrade and hot-migration mechanisms to enhance the availability of LightPool. We have deployed LightPool on over 8500 nodes in production clusters. Statistics show that LightPool can improve storage resource utilization from about 40% to 65%. Experimental results show that the extra latency from LightPool is only about 2.1 μs compared to local storage. Compared to OpenEBS, LightPool enhances bandwidth up to 190.9% in microbenchmarks and throughput up to 6.9% in real-world applications. LightPool is the best practice to deploy NVMe-oF (NVMe/TCP) in the production environment. We also discuss important lessons and experiences learned from the development of LightPool.

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON.