Watermarking is a technique for protecting multimedia data, mainly images, from malicious attacks by adding a signature into these images. Traditional watermarking techniques, unfortunately, have drawbacks when used on sensitive images like those used in medicine. A reliable and blind watermarking method is suggested in this work to protect medical picture transferred in telemedicine. Medical picture marking enables accurate patient identification, prevents scan confusion, and minimizes the risk of diagnostic mistakes that could have negative consequences. In this method, three distinct transforms are used to acquire the frequency content of the image. The low frequency subbands are subsequently subjected to Schur decomposition. In order to incorporate the watermark bits, the resultant upper triangular matrix values are modified. The proposed approaches effectively retain a considerable quality of watermarked images and are impressively resistant against numerous conventional attacks, according to imperceptibility and robustness experimental results. The average Peak signal-to-noise ratio (PSNR) which was obtained was 44.90 dB, demonstrating that the integration procedure produces very low distortion to the original image. The observed results for robustness demonstrate that the watermark is resilient to the most of the attacks performed in watermarking, with a normalized cross correlation rate higher than 0.9.

This research provides a method that accelerates learning and avoids local minima to improve the policy gradient algorithm’s learning process. Reinforcement learning has the advantage of not requiring a model. Consequently, it can improve control performance, mainly when a model is generally unavailable, such as when an error occurs. The proposed method efficiently and expeditiously investigates the action space. First, it quantifies the resemblance between agents’ and traditional controllers’ actions. Then, the principal reward function is modified to reflect this similarity. This reward-shaping mechanism guides the agent to maximize its return via an attractive force during the gradient ascent. To validate our concept, we establish a satellite attitude control environment with a similarity subsystem. The outcomes demonstrate the effectiveness and robustness of our method.

Maintaining an accurate estimate of the satellite's attitude is essential for pointing performance to ensure the quality of products such as spatial images. Nevertheless, if sensors and actuators are affected by the severe space environment, the attitude control systems may be disrupted. The South Atlantic Anomaly (SAA) is a well-known disturbance in space whose effects are consistently monitored by space agencies such as NASA. This work provides a straightforward yet robust estimation strategy that enhances the attitude determination system's (ADS) performance when sensor-filter communication loss occurs due to the SAA. Our method utilizes the statistical properties of SAA to adapt the attitude estimator's gain following measurement restoration. This technique helps reduce the attitude innovation commonly regarded as an indicator of ADS health. We used data collected by attitude sensors on an actual satellite in low earth orbit to validate our method.

In order to increase the security of medical image sharing and transfer, we present in this work a watermarking approach to protect medical images. This approach consists of inserting hospital signature information and patient data into the medical image. The goal of our work is to integrate the watermark with as little distortion as possible to retain the medical information in the image. In this approach DWT decomposition is applied to the image which allows a very fine adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which allows storing the maximum energy of the image in a minimum of singular values. A combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures data integrity, confidentiality when sharing data, and robustness to several conventional attacks.