Wood surface defect inspection is a key step in production-line quality control. However, supervised detectors typically require large-scale annotations and often generalize poorly to unseen defect types. In this paper, we formulate wood defect inspection as an unsupervised anomaly detection problem and propose CoMemRet, a framework based on multiview contrastive learning and memory bank retrieval. CoMemRet trains a multiview contrastive encoder in the Lab color space to decouple luminance structure from chromatic variation, and introduces a patch-level contrastive constraint to enhance representations of fine-grained texture anomalies. The extracted features are further refined by feature gating and fusion modules to obtain more stable and compact embeddings. During anomaly detection, CoMemRet constructs a memory bank from a coreset of normal features and computes anomaly scores from the k-NN distances between test features and the normal-feature coreset. A clustering-guided coreset subsampling strategy is further introduced to improve the efficiency of coreset construction and k-NN retrieval while preserving coverage of normal patterns. Experiments on the newly collected Wood Surface Anomaly Detection Dataset (WSAD) and the public MVTec AD (wood) benchmark demonstrate competitive performance and improved generalization, suggesting suitability for production-line inspection.

Detection of wood surface defects is one critical step in the plywood process. YOLO series detection models need to find a good tradeoff between detection speed and accuracy. Therefore, this paper improves the YOLOv8 model by replacing the backbone, neck, and head with the FasterNet, Enhanced Feature Pyramid Network (EFPN), and Multi-Scale Feature Decoupling Head (MSDH), respectively. The FasterNet reduces the number of model parameters by using Partial Convolution (PConv). To remedy the accuracy loss caused by the FasterNet, the EFPN adds auxiliary branches to reduce information loss between different feature layers and uses Dilated Reparameter Modules (DRM) to extract more multi-scale features. Besides, the MSDH reorganizes the features on different scales to enhance their semantic relationships. Experimental results show that FasterNet reduces the number of parameters by 43%, the EFPN and the MSDH increase the mAP (mean Average Precison) by 2.2% and 1.4%, respectively, compared to the original Yolov8 model.

The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual Learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.

The Global Navigation Satellite System Network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a ‘Multiple Reception’ strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the ‘Multiple Reception’ method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS.

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.