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To ensure the confidentiality and integrity of image data and prevent unauthorized data tampering and privacy leaks. This study proposes a new color image encryption scheme based on the Mackey–Glass time-delay chaotic system and quantum random walk. This approach fully leverages the unpredictability of quantum random walks to generate random values. It combines the differences in Hamming distance between the three RGB channels of color images to create a highly complex and random key. The overall image and the three independent RGB channels are arranged in ascending order using Logistic-tent chaotic mapping and the Mackey–Glass time-delay chaotic system to obfuscate the image data. The deformed fractional-order Lorenz chaotic system is introduced, integrated with DNA encoding and decoding technology, and XOR operations are performed to achieve encryption at the spatial and pixel levels, thereby increasing the complexity of decryption. Through extensive experimental research, this solution has demonstrated excellent results in tests such as adjacent pixel correlation, information entropy, and key sensitivity. It has an excellent ability to protect the privacy of images and provides a reliable guarantee for the security of image data.

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Quantum image processing, which merges classical image processing techniques with quantum computing, provides exceptional storage capacity and unparalleled parallel computing power. In this study, we present a quantum color image watermarking scheme that employs quantum error correction codes to address issues such as pixel loss and image distortion during watermark embedding and extraction. By utilizing the least significant bit method to embed the color values of the watermark image into those of the carrier image, we improve the scheme’s robustness. We also address the error correction capabilities of channel coding for phase-flip errors and follow the majority principle, resulting in more accurate extraction of the watermark image’s color and enhancing the watermarking scheme’s reliability and integrity. Our experimental simulations demonstrate that the proposed watermarking scheme boasts high security, strong robustness, and excellent concealment.

Multimodal hash technology maps high-dimensional multimodal data into hash codes, which greatly reduces the cost of data storage and improves query speed through the Hamming similarity calculation. However, existing unsupervised methods still have two key obstacles: (1) With the evolution of large multimodal models, how to efficiently distill the multimodal matching relationship of large models to train a powerful student model? (2) Existing methods do not consider other adjacencies between multimodal instances, resulting in limited similarity representation. To address these obstacles, called Unsupervised Graph Reasoning Distillation Hashing (UGRDH) is proposed. The UGRDH approach uses the CLIP as the teacher model, thus extracting fine-grained multimodal features and relations for teacher–student distillation. Specifically, the multimodal features of the teacher are used to construct a similarity–complementary relation graph matrix, and the proposed graph convolution auxiliary network performs feature aggregation guided by the relation graph matrix to generate a more discriminative hash code. In addition, a cross-attention module was designed to reason potential instance relations to enable effective teacher–student distilled learning. Finally, UGRDH greatly improves search precision while maintaining lightness. Experimental results show that our method achieves about 1.5%, 3%, and 2.8% performance improvements on MS COCO, NUS-WIDE, and MIRFlickr, respectively.

Precipitation forecasting has important applications in meteorological research. Accurate forecasting is of great significance for reducing the impact of floods, optimizing crop planting plans, rationally allocating water resources, and ensuring traffic safety. However, the factors affecting precipitation are complex and nonlinear, and have spatiotemporal variability, making rainfall forecasting extremely challenging. In response to these challenges, this paper proposes a hybrid model based on temporal convolutional network, quantum long short-term memory network (QLSTM), and random forest regression (RFR) to achieve more accurate rainfall forecasting. The hyperparameters of the model are optimized using the Bayesian optimization algorithm to obtain the best performance. Experiments are conducted on meteorological datasets from Seattle and Ukraine, and the results are verified using mean absolute error (MAE), root mean square error (RMSE), and bias evaluation indicators. The results show that the proposed hybrid model outperforms traditional models such as RFR, support vector machine, K-nearest neighbor, LSTM, and QLSTM in terms of MAE, RMSE, and bias. The proposed model achieves improvements of 1.89 % MAE, 2.65 % RMSE, and 31 % Bias, respectively. These results highlight the improved forecast accuracy and robustness of the proposed hybrid model. This research provides a new approach to weather forecasting and demonstrates the potential of combining quantum computing with traditional machine learning techniques.

In the field of image watermarking technology, it is very important to balance imperceptibility, robustness and embedding capacity. In order to solve this key problem, this paper proposes a new color image adaptive watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). In order to improve the security of the watermark, we use Lorenz hyperchaotic mapping to encrypt the watermark image. We adaptively determine the embedding factor by calculating the Bhattacharyya distance between the cover image and the watermark image, and combine the Alpha blending technique to embed the watermark image into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. The experimental results show that the average PSNR of our scheme is 45.9382 dB, and the SSIM is 0.9986. Through a large number of experimental results and comparative analysis, it shows that the scheme has good imperceptibility and robustness, indicating that we have achieved a good balance between imperceptibility, robustness and embedding capacity.

Infrared images have good anti-environmental interference ability and can capture hot target information well, but their pictures lack rich detailed texture information and poor contrast. Visible image has clear and detailed texture information, but their imaging process depends more on the environment, and the quality of the environment determines the quality of the visible image. This paper presents an infrared image and visual image fusion algorithm based on deep learning. Two identical feature extractors are used to extract the features of visible and infrared images of different scales, fuse these features through specific fusion methods, and restore the features of visible and infrared images to the pictures through the feature restorer to make up for the deficiencies in the various photos of infrared and visible images. This paper tests infrared visual images, multi-focus images, and other data sets. The traditional image fusion algorithm is compared several with the current advanced image fusion algorithm. The experimental results show that the image fusion method proposed in this paper can keep more feature information of the source image in the fused image, and achieve excellent results in some image evaluation indexes.

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarmand following activities, meanwhile using the adaptive parameters, to avoid it falling into localextremum of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.

In this paper, numerical methods for solving fractional differential equations by using a triangle neural network are proposed. The fractional derivative is considered Caputo type. The fractional derivative of the triangle neural network is analyzed first. Then, based on the technique of minimizing the loss function of the neural network, the proposed numerical methods reduce the fractional differential equation into a gradient descent problem or the quadratic optimization problem. By using the gradient descent process or the quadratic optimization process, the numerical solution to the FDEs can be obtained. The efficiency and accuracy of the presented methods are shown by some numerical examples. Numerical tests show that this approach is easy to implement and accurate when applied to many types of FDEs.

In this paper, a quantum neural network with multilayer activation function is proposed by using multilayer Sigmoid function superposition and learning algorithm to adjust quantum interval. On this basis, the quasiuniform stability of fractional quantum neural networks with mixed delays is studied. According to the order of two different cases, the conditions of quasi uniform stability of networks are given by using the techniques of linear matrix inequality analysis, and the sufficiency of the conditions is proved. Finally, the feasibility of the conclusion is verified by experiments.