Finger veins are widely used in various fields due to their high safety. Existing finger vein recognition methods have some shortcomings, such as low recognition accuracy and large model size. To address these shortcomings, a multi-scale feature bilinear fusion network (MSFBF-Net) was designed. First, the network model extracts the global features and local detail features of the finger veins and performs linear fusion to obtain second-order features with richer information. Then, the mixed depthwise separable convolution replaces the ordinary convolution, which greatly reduces the computational complexity of the network model. Finally, a multiple attention mechanism (MAM) suitable for finger veins was designed, which can simultaneously extract the channel, spatial, directional, and positional information. The experimental results show that the method is very effective, and the accuracy of the two public finger vein databases is 99.90% and 99.82%, respectively.

Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sensors. In this paper, we propose a dynamic attention mechanism-based thermal image super-resolution network for infrared sensors. Specifically, the dynamic attention modules adaptively reweight the outputs of the attention and non-attention branches according to features at different depths of the network. The attention branch, which consists of channel- and pixel-wise attention blocks, is responsible for extracting the most informative features, while the non-attention branch is adopted as a supplement to extract the remaining ignored features. The dynamic weights block operates with 1D convolution instead of the full multi-layer perceptron on the global average pooled features, reducing parameters and enhancing information interaction between channels, and the same structure is adopted in the channel attention block. Qualitative and quantitative results on three testing datasets demonstrate that the proposed network can superior restore high-frequency details while improving the resolution of thermal images. And the lightweight structure of the proposed network with lower computing cost can be practically deployed on edge devices, effectively improving the imaging perception quality of infrared sensors.

Currently, infrared object detection is utilized in a broad spectrum of fields, including military applications, security, and aerospace. Nonetheless, the limited computational power of edge devices presents a considerable challenge in achieving an optimal balance between accuracy and computational efficiency in infrared object detection. In order to enhance the accuracy of infrared target detection and strengthen the implementation of robust models on edge platforms for rapid real-time inference, this paper presents LKD-YOLOv8, an innovative infrared object detection method that integrates YOLOv8 architecture with masked generative distillation (MGD), further augmented by the lightweight convolution design and attention mechanism for improved feature adaptability. Linear deformable convolution (LDConv) strengthens spatial feature extraction by dynamically adjusting kernel offsets, while coordinate attention (CA) refines feature alignment through channel-wise interaction. We employ a large-scale model (YOLOv8s) as the teacher to imparts knowledge and supervise the training of a compact student model (YOLOv8n). Experiments show that LKD-YOLOv8 achieves a 1.18% mAP@0.5:0.95 improvement over baseline methods while reducing the parameter size by 7.9%. Our approach effectively balances accuracy and efficiency, rendering it applicable for resource-constrained edge devices in infrared scenarios.

A time-varying process with nonlinearity and time lag is the temperature control of pulsing vacuum steam sterilization. In order to achieve efficient and accurate control requirements, conventional PID temperature control algorithms sometimes display slow response speed, severe overshooting, unstable performance, and other challenges that ultimately affect the sterilizing effect. In order to find the ideal steam sterilization temperature control settings iteratively, this research used the PSO algorithm. Simulating and analyzing the system model is done simultaneously using fuzzy control of the PID parameter adaptive modification. According to the results, there is no overshooting and the response speed approach is faster. This paper presents an approach to fuzzy PID control based on the PSO optimization algorithm. As a result of fuzzy adaptive PID's high control accuracy and quick response time, the PID parameters are also continuously optimized utilizing the PSO approach for steam sterilization temperature control. For the purpose of doing simulation analysis, create and modify a system model. As evidenced by the results, this strategy has a reduced overshoot, a faster response time, and better stability. It may also successfully boost the control effect. Eventually, this method was applied to a self-tuning PID control experiment for sterilizer temperature control, and a relatively optimal control effect was obtained.

In the classification tasks of deep learning, unbalanced data distribution of training samples is a serious problem. In this case, the deep neural networks will bias to the majority-class samples and can not learn data characteristics of the minority-class samples well, making it difficult to identify the minority-class samples. To improve the performance of deep neural networks on minority-class samples, this paper proposes a novel scheme based on SMOTE algorithm, which generates new minority-class samples to balance the training dataset. Compared with the traditional over-sampling operations commonly used in image classification tasks such as copying images simply, image flipping, color adjustment etc, our scheme generates new minority-class samples, which improves the features of minority-class samples and avoids the over-fitting problem in classification. The workflow of image preprocessing and SMOTE over-sampling operation are introduced in this paper. The selection basis of the network model and the comprehensive model indicators are also presented. Comparative experiments are performed by testing models trained respectively on unbalanced dataset and balanced dataset after SMOTE over-sampling operation. Results shows that the proposed scheme is feasible and effective to improve the neural network’s ability of identifying minority-class samples in classification tasks.