Maintaining wheat moisture content within a safe range is of critical importance for ensuring the quality and safety of wheat. High-precision, rapid detection of wheat moisture content is a key factor in enabling effective control processes. A microwave detection system based on metasurface lens antennas was proposed in this study, which facilitates accurate, non-invasive, and contactless measurement of wheat moisture content. The system measures the attenuation characteristics of wheat with varying moisture content from 23.5 GHz to 24.5 GHz in the frequency range. A linear regression equation (coefficient of determination R²=0.9946) was established by using the measured actual moisture content obtained through the standard drying method, and was used as the prediction model for wheat moisture. Totally, 72 wheat samples were selected for moisture content prediction, yielding a root mean square error (RMSE) of 0.193%, mean absolute error (MAE) of 0.16%, and maximum relative error (MRE) of 5.25%. The results indicate that the proposed microwave detection system, based on metasurface lens antennas, provides an effective method for detecting wheat moisture content.

Radar warning receivers (RWRs) are critical for swiftly and accurately identifying potential threats in complex electromagnetic environments. Numerous methods have been developed over the years, with recent advances in artificial intelligence (AI) significantly enhancing RWR capabilities. This study presents a machine learning-based approach for emitter identification within RWR systems, leveraging a comprehensive radar signal library. Key parameters such as signal frequency, pulse width, pulse repetition frequency (PRF), and beam width were extracted from pulsed radar signals and utilized in various machine learning algorithms. The preprogramming phase of RWRs was optimized through the application of multiple classification algorithms, including k-Nearest Neighbors (KNN), Decision Tree (DT), the ensemble learning method, support vector machine (SVM), and Artificial Neural Network (ANN). These algorithms were compared against conventional methods to evaluate their performance. The machine learning models demonstrated a high degree of accuracy, achieving over 95% in training phases and exceeding 99% in test simulations. The findings highlight the superiority of machine learning algorithms in terms of speed and precision when compared to traditional approaches. Furthermore, the flexibility of machine learning techniques to adapt to diverse problem sets underscores their potential as a preferred solution for future RWR applications. This study suggests that the integration of machine learning into RWR emitter identification not only enhances the operational efficiency of electronic warfare (EW) systems but also represents a significant advancement in the field. The increasing relevance of machine learning in recent years positions it as a promising tool for addressing complex signal processing challenges in EW.

Swarm intelligence (SI) has emerged as a transformative approach in solving complex optimization problems by drawing inspiration from collective behaviors observed in nature, particularly among social animals and insects. Ant Colony Optimization (ACO), a prominent subclass of SI algorithms, models the foraging behavior of ant colonies to address a range of challenging combinatorial problems. Originally introduced in 1992 for the Traveling Salesman Problem (TSP), ACO employs artificial pheromone trails and heuristic information to probabilistically guide solution construction. The artificial ants within ACO algorithms engage in a stochastic search process, iteratively refining solutions through the deposition and evaporation of pheromone levels based on previous search experiences. This review synthesizes the extensive body of research that has since advanced ACO from its initial ant system (AS) model to sophisticated algorithmic variants. These advances have both significantly enhanced ACO's practical performance across various application domains and contributed to a deeper theoretical understanding of its mechanics. The focus of this study is placed on the behavioral foundations of ACO, as well as on the metaheuristic frameworks that enable its versatility and robustness in handling large-scale, computationally intensive tasks. Additionally, this study highlights current limitations and potential areas for future exploration within ACO, aiming to facilitate a comprehensive understanding of this dynamic field of swarm-based optimization.

This study explores hybrid deep learning architectures for the classification of financial sentiment, focusing on the integration of the Convolutional Neural Network (CNN) with the Support Vector Machine (SVM) and the Random Forest (RF). CNN, with its powerful feature extraction capabilities, was combined with SVM’s ability to handle non-linear decision boundaries, while RF enhanced model generalization through ensemble learning. The proposed hybrid frameworks addressed two fundamental challenges in sentiment analysis: overfitting and class imbalance. These challenges were mitigated, resulting in improved model accuracy and reliability compared to standalone methods. Empirical evaluations demonstrated that the CNN-SVM model achieved competitive or superior validation accuracy and loss, indicating its suitability for precise financial sentiment classification. By enabling more accurate sentiment categorization, the model provides actionable insights for financial analysts and investors, thereby supporting better market assessment and investment decision-making. Future work is suggested to incorporate advanced techniques such as adversarial training and domain-specific pre-trained models to further enhance model performance.

In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be damaged, such as image loss, noise interference, or a combination of both. Using Low-Rank Tensor Completion (LRTC) techniques to recover missing or corrupted tensor data has become a hot research topic. In this paper, we adopt a tensor coupled total variation (t-CTV) norm based on t-SVD as the minimization criterion to capture the combined effects of low-rank and local piecewise smooth priors, thus eliminating the need for balance parameters in the process. At the same time, we utilize the Non-Local Means (NLM) denoiser to smooth the image and reduce noise by leveraging the nonlocal self-similarity of the image. Furthermore, an Alternating Direction Method of Multipliers (ADMM) algorithm is designed for the proposed optimization model, NLM-TCTV. Extensive numerical experiments on real tensor data (including color, medical, and satellite remote sensing images) show that the proposed method has good robustness, performs well in noisy images, and surpasses many existing methods in both quality and visual effects.