Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, often diagnosed using X-rays due to its cost-effectiveness. While Magnetic Resonance Imaging (MRI) provides superior soft tissue visualization and serves as a valuable supplementary diagnostic tool, its high cost and limited accessibility significantly restrict its widespread use. To explore the feasibility of bridging this imaging gap, we conducted a feasibility study leveraging a diffusion-based model that uses an X-ray image as conditional input, alongside target depth and additional patient-specific feature information, to generate corresponding MRI sequences. Our findings demonstrate that the MRI volumes generated by our approach are not only visually closer to real MRI scans compared with other methods but also achieve the highest quantitative performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, by increasing the number of inference steps to interpolate between slice depths, we enhance the continuity of the generated volume, achieving higher adjacent slice correlation coefficients. Through ablation studies, we further validate that integrating supplemental patient-specific information, beyond what X-rays alone can provide, enhances the accuracy and clinical relevance of the generated MRI, which underscores the potential of leveraging external patient-specific information to improve the performance of the MRI generation.

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.

Computing ambient occlusion in screen-space (SSAO) is a common technique in real-time rendering applications which use rasterization to process 3D triangle data. However, one of the most critical problems emerging in screen-space is the lack of information regarding occluded geometry which does not pass the depth test and is therefore not resident in the G-buffer. These occluded fragments may have an impact on the proximity-based shadowing outcome of the ambient occlusion pass. This not only decreases image quality but also prevents the application of SSAO on multiple layers of transparent surfaces where the shadow contribution depends on opacity. We propose a novel approach to the SSAO concept by taking advantage of per-pixel fragment lists to store multiple geometric layers of the scene in the G-buffer, thus allowing order independent transparency (OIT) in combination with high quality, opacity-based ambient occlusion (OITAO). This A-buffer concept is also used to enhance overall ambient occlusion quality by providing stable results for low-frequency details in dynamic scenes. Furthermore, a flexible compression-based optimization strategy is introduced to improve performance while maintaining high quality results.

In the efforts to fight piracy of high-valued media content, forensic digital watermarking as a passive content security scheme is a potential alternative to current, restrictive approaches like DRM. In this paper, we present a novel watermarking scheme for videos based on affine geometric transformations. Frames can be modified in an imperceptible manner by applying a small, global rotation, translation, or zooming, which can be detected later on by comparison with the originals. To compensate geometric distortions that have been introduced while a video travels down legal as well as illegal distribution chains, a spatio-temporal synchronization is performed using our video registration toolkit application. To evaluate our approach, we compare it with several other schemes regarding the robustness against common attacks, including camcorder capture.