With the increasingly fierce competition of electromagnetic spectrum, developing intelligent communication systems that can reconfigure its waveform can effectively improve the communication system's ability to adapt to the complex electromagnetic environment. In this paper, an adaptive modem based on a deep autoencoder network (DAN) is designed, and the demodulation performance that is close to, consistent with, or better than traditional MPSK or QAM is achieved. This DAN can be trained by a unified loss function and a unified optimization algorithm to implement a variety of modems. For high-order modems, the constellation diagrams generated by the DAN are radically different from the traditional modulation and very difficult to distinguish linearly, which is beneficial to improve the anti-interception ability of the communication systems. Additionally, based on the trained DAN, a convolutional neural network (CNN) with a single convolutional layer is incorporated to suppress a variety of interferences. By using transfer learning, the new deep learning (DL) model with the CNN converges fast with a few epochs of training. Training and test results verify that the new DL model can improve the anti-interference ability of the communication systems by learning and suppressing the interferences in both frequency and power domains.
Aiming at the difficulty of the deep neural network (DNN) adapting to channel changes in communication systems, a channel synchronization deep neural network (CSDNN) based on deep learning (DL) is designed for realizing carrier synchronization, bit timing synchronization, and automatic gain control (AGC). By introducing a frequency‐domain cyclic convolution (FDCC) layer, the network transformed the time‐domain triangle activation into frequency domain linear activation taking FFT and IFFT matrixes as the activation function, solved the reverse gradient transmission‐blocking problems in training the time‐domain carrier synchronization neural network, effectively overcome the FFT inherent “fence” effect, and accurately compensated carrier frequency offset; By introducing a time‐domain cyclic convolution (TDCC) layer and the special frame structure design containing repetitive training sequence, the network training was completed to realize bit timing synchronization under the condition of the uncertain corresponding relationship between training data and labels. Combining phase inverse rotation dense (PIRD) layer, the network can be trained with very little training data to complete fast carrier synchronization and timing synchronization, at the same time adjust the received signal gain and suppress the jamming, which makes it is possible to train the channel synchronization deep neural network online under jamming environment, and provide a feasible way of realizing the intelligent communication system.
The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R²) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.
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