Electronic health record (EHR) has recorded the process of occurrence, development, and treatment of diseases. So it has high medical value. Owing to the private and sensitive nature of medical data for patients, the data sharing and privacy preservation are critical issues in EHR. Blockchain technology may be a promising solution for the problems above since it holds the features of decentralization and tamper resistance. In the paper, we propose a medical data sharing and protection scheme based on the hospital’s private blockchain to improve the electronic health system of the hospital. Firstly, the scheme can satisfy various security properties such as decentralization, openness, and tamper resistance. A reliable mechanism is created for the doctors to store medical data or access the historical data of patients while meeting privacy preservation. Furthermore, a symptoms-matching mechanism is given between patients. It allows patients who get the same symptoms to conduct mutual authentication and create a session key for their future communication about the illness. The proposed scheme is implemented by using PBC and OpenSSL libraries. Finally, the security and performance evaluation of the proposed scheme is given.

This paper proposes an optimized tracking control approach using neural network (NN)-based reinforcement learning (RL) for a class of nonlinear dynamic systems, which requires both tracking and optimizing to be performed simultaneously. Generally, for obtaining optimal control solution, HJB (Hamilton- Jacobi-Bellman) equation is expected to be solvable, but, owing to strong nonlinearity, the equation is solved by analytical methods to be very difficult or even impossible. Therefore, adaptive NN approximation based RL is usually considered. In the optimized control design, for driving output state following to the desired trajectory, an error term is split from optimal performance index function, then both actor and critic NNs are built to perform RL algorithm. Actor NN aims to execute control behaviors, and critic NN aims to appraise control performance and make feedback to actor. The proof of stability concludes that the desired control performances are obtained. A numerical simulation is designed and implemented, and the desired results are shown.

The Industrial Internet of Things (IIoT) collects a large amount of data through various types of sensors and intelligently processes this data using cloud computing, which is flexible, efficient, and cost-effective. Since IIoT data is stored on the cloud service provider’s server, the data must be encrypted to protect the user’s privacy. However, the encrypted data faces the search problem, which is usually solved by Public Key Encryption with Keyword Search (PEKS). In addition, most existing PEKS schemes are vulnerable to Inside Keyword Guessing Attacks (IKGA). Recently, some certificateless public key authenticated encryption with keyword search (CLPEKS) schemes have been proposed, which not only avoid the problems of certificate management and key escrow but can also resist IKGA. However, most of them rely on the expensive bilinear pairing. To overcome these problems, in this paper we propose a pairing-free CLPEKS scheme. The security of the proposed scheme is proved in the random oracle model. The analysis results show that the proposed scheme has better overall performance in terms of computational cost, communication cost and security properties.

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.

Implantable medical devices (IMDs) in medical sciences have provided a quantum leap in network transformation. The communication network with IMDs typically has a wireless radio frequency (RF) telemetry or wired connection. IMDs, being devices, have more computing, communication capabilities and decision-making. Furthermore, these devices are being used to improve patients’ quality of life by medicating various chronic diseases. The captured data is stored in a medical server through a controller node. Our work focuses on wireless communication, so sensitive patient data over a public channel might be tampered with or eavesdropped by unauthorised access. Furthermore, the leakage of health data and malfunctioning of IMDs are vital in constructing cryptographic protocols, particularly in the design of remote user authentication. In this paper, we proposed a novel secure remote user authentication scheme using a lightweight consortium blockchain for the communication network with IMDs.