ABSTRACT
The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of the IoT networks in numerous digital applications. To counter physical threats in these systems, automatic modulation classification (AMC) has emerged as an effective approach for identifying the modulation format of signals in noisy environments. However, identifying those threats can be particularly challenging due to the scarcity of labeled data, which is a common issue in various IoT applications, such as anomaly detection for unmanned aerial vehicles (UAVs) and intrusion detection in the IoT networks. Few-shot learning (FSL) offers a promising solution by enabling models to grasp the concepts of new classes using only a limited number of labeled samples. However, prevalent FSL techniques are primarily tailored for tasks in the computer vision domain and are not suitable for the wireless signal domain. Instead of designing a new FSL model, this work suggests a novel approach that enhances wireless signals to be more efficiently processed by the existing state-of-the-art (SOTA) FSL models. We present the semantic-consistent signal pretransformation (ScSP), a parameterized transformation architecture that ensures signals with identical semantics exhibit similar representations. ScSP is designed to integrate seamlessly with various SOTA FSL models for signal modulation recognition and supports commonly used deep learning backbones. Our evaluation indicates that ScSP boosts the performance of numerous SOTA FSL models, while preserving flexibility.
ABSTRACT
OBJECTIVE: Timely acquisition of comprehensive and accurate lymph node information from surgically resected specimens of gastric cancer is an important indicator for determining the accuracy of pathological staging and predicting the prognosis of patients. METHODS: Use the sorting and analysis system to perform lymph node sorting, image data collection and storage, and intelligent software analysis for gastric cancer lymphoid tissue immediately during or after surgery. RESULTS: The number of lymph nodes obtained by the system was significantly higher than that obtained by the traditional method, the sorting time was shortened, the omission rate of lymph nodes sorting was reduced, the sorting efficiency was improved. CONCLUSIONS: The clinical application of sorting and analysis system can effectively improve the coincidence rate of gastric cancer staging, plays a very important role in the prognosis evaluation of patients and the selection of the best treatment plan.
