Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.

There is a huge gap between the output and demand of soybean in China. How to improve the seed vigor of soybean has always been a research focus. Low temperature plasma (LTP) is a new green technology, which is widely used in crop seed treatment. Corona plasma is a typical discharge mode of plasma, which can affect the vigor of seeds. The effect of different discharge power on the soybean seed vigor by plasma treatment was experimentally investigated. Plasma discharge characteristic wavelength and spatial distribution were analysed. It shows that the corona discharge spectrum mainly exhibits the strong ultraviolet radiation and 90% of the spectral intensity focused in the center of discharge region. Water absorption and germination index of seeds and the fresh weight of seedlings were used to characterize the specific effects caused by different plasma powers. The results show that plasma treatment has a significant effect on the early stage of germination and can significantly affect the soybean seed vigor and growth. Overdose treatment will cause inhibiting effect. This study provides an experimental basis for the practical agriculture application of corona plasma seed treatment.

Semantic segmentation, as the pixel level classification with dividing an image into multiple blocks based on the similarities and differences of categories (i.e., assigning each pixel in the image to a class label), is an important task in computer vision. Combining RGB and Depth information can improve the performance of semantic segmentation. However, there is still a problem of the way to deeply integrate RGB and Depth. In this paper, we propose a cross-modal feature fusion RGB-D semantic segmentation model based on ConvNeXt, which uses ConvNeXt as the skeleton network and embeds a cross-modal feature fusion module (CMFFM). The CMFFM designs feature channel-wise and spectral-wise fusion, which can realize the deeply feature fusion of RGB and Depth. The in-depth multi-modal feature fusion in multiple stages improves the performance of the model. Experiments are performed on the public dataset of SUN-RGBD, showing the best segmentation by our proposed model ConvNeXt-CMFFM with the highest mIoU score of 53.5% among the nine comparative models. The outstanding performance of ConvNeXt-CMFFM is also achieved on our self-built dataset of RICE-RGBD with the highest mIoU score and pixel accuracy among the three comparative datasets. The ablation experiment on our rice dataset shows that compared with ConvNeXt (without CMFFM), the mIoU score of ConvNext-CMFFM is increased from 71.5% to 74.8% and its pixel accuracy is increased from 86.2% to 88.3%, indicating the effectiveness of the added feature fusion module in improving segmentation performance. This study shows the feasibility of the practical application of the proposed model in agriculture.

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared ($R^2$) compared to the baseline model CNN-LSTM.

As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application.