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A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers' interference, and security issue. Copyright © 2006 John Wiley & Sons, Ltd.

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

Abstract. Self-nonself model makes a lot of sense in the mechanisms of self versus nonself recognition in the immune system but it failed to explain a great number of findings. Some new immune theory is proposed to accommodate incompatible new findings, including Pattern Recognition Receptors (PRRs) Model and Danger Theory. Inspired from the PRRs model, a novel approach called Conserved Self Pattern Recognition Algorithm (CSPRA) is proposed in this paper. The algorithm is tested using the famous benchmark Fisher's Iris data. Preliminary results demonstrate that the new approach lowers the false positive and thus enhances the efficiency and reliability for anomaly detection without increase in complexity comparing to the classical Negative Selection Algorithm (NSA).

T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns.