Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

Due to high costs and power consumptions, fully digital baseband precoding schemes are usually prohibitive in millimeter-wave massive MIMO systems. Therefore, hybrid precoding strategies become promising solutions. In this paper, we present a novel real-time yet high-performance precoding strategy. Specifically, the eigenvectors corresponding to the larger eigenvalues of the right unitary matrix after singular value decomposition on an array response matrix are used to abstract the angle information of an analog precoding matrix. As the obtained eigenvectors correspond to the larger singular values, the major phase information of channels is captured. In this way, the iterative search process for obtaining the analog precoding vectors is avoided, and thus the hybrid precoding can be realized in parallel. To further improve its spectral-efficiency, we enlarge the resultant vector set by involving more relevant vectors in terms of their correlation values with the unconstrained optimal precoder, and a hybrid precoder is thus produced by using the vector set. The simulation results show that our proposed scheme achieves near the same performance as the orthogonal matching pursuit does, whereas it costs much fewer complexities than the OMP, and thus can be realized in parallel. INDEX TERMS Millimeter wave communication, MIMO, wireless communication, hybrid precoding. I. INTRODUCTION

Abstract:In this paper, a multimode precoder design for spacetime block code (STBC) is investigated, which varies the number of streams depending on the channel condition. We develop a design criterion of minimizing the vector symbol error rate and derive an efficient offline algorithm to generate precoders. Simulation results show that the multimode precoded STBC (MM-STBC) provides substantial performance improvements compared with the single mode precoded orthogonal STBC (SM-STBC) for a fixed data-rate. It also outperforms the multimode precoded multiple-input multiple-output (MM-MIMO) system when feedback rate is very limited.

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