Radiance field (RF) models stand out as powerful 3D scene descriptors capable of rendering realistic views from arbitrary directions. In the context of moving-camera scene change detection (SCD), RFs can be used to overcome common challenges such as parallax and occlusion, allowing different paths to be taken in reference and target scans. In this work, both neural radiance fields (NeRFs) and 3D Gaussian splatting (3DGS) RF models are shown to be effective tools in solving the reference-target frame pairing problem. Employing modern structure-from-motion methods for localization of reference and query views, RF renders are used as references in an image-space comparison SCD, with good results. Using a new moving-camera SCD dataset, tailored towards the pairing problem challenges, a simple detector using RF renders as references achieved average precision scores of $87{\%}$ and $95{\%}$ with NeRF and 3DGS models, respectively, without any previous frame pairing.
Contrast-Enhanced Computed Tomography (CECT) is extensively utilized in cardiac imaging due to its superior anatomical detail, enabling accurate segmentation of cardiac structures. However, the use of contrast agents poses risks for patients with pre-existing medical conditions such as allergies. Non-Contrast Computed Tomography (NCCT) offers a safer alternative but presents significant challenges in segmentation, particularly for small and low-contrast structures such as the left ventricle, due to inherently lower image quality and complex three-dimensional anatomical variability. To address these limitations, this study proposes an enhanced 3D U-Net architecture integrated with dual attention mechanisms, specifically Convolutional Block Attention Modules (CBAM), designed to improve feature representation and enhance segmentation accuracy of fine-grained cardiac structures in NCCT images. Benchmark results demonstrate improved NCCT segmentation performance, indicating reduced dependency on contrast agents and enhancing the clinical viability of non-contrast cardiac imaging.
This paper describes a real-time Extended Reality (XR) Heart Twin based on ECG IoT measurements from wearable devices. Recent research has highlighted the importance of non-Euclidean features, such as wavelet graphs and heart meshes, in conveying information about a user's condition and enabling effective visualization in XR interfaces. We propose an end-to-end architecture to generate an XR Heart Twin from signals acquired by wearable devices. The XR Heart Twin architecture includes IoT communication protocols and middleware for data processing, extending to a web-based XR visualization tool that presents up-to-date features characterizing the user's condition. The proposed architecture enables a scalable monitoring solution suitable for cloud deployment. Examples of outputs presented via the XR interface are shown using ECG signals from publicly available datasets, processed in real time to extract XRrelevant heart features. This approach enables continuous user monitoring through a responsive XR dashboard. Experimental results demonstrate how an XR Heart Twin based on IoT measurements can be developed for healthcare systems using automated deployment capabilities on modern cloud platforms.
Satellite data is useful for many applications in environmental monitoring, such as vegetation cover and plant health determined by calculating vegetation indices. When using satellite data, the question often arises of how this data can be evaluated. This paper presents a method that can be used to evaluate aquatic plants detection on satellite data using an onboard camera and remote sensing methods. To this end, the Aquatic Plants and Algae (APA) composite index is used to compare estimates of the amount of seaweed in a lake in Northern Germany with satellite data. Sensor data from the mowing boat, on which the amount of seaweed is recorded with the respective mowing position, is used as a comparative value. It was shown that in the areas where the mowing boat harvested seaweed, the amount of seaweed identified by the APA index decreased compared to the amount identified before the mowing process. However, further data is required for quantitative and general results.
This work addresses the challenge of efficient Extended Reality (XR) communication in 6 G networks, where signals acquired by IoT sensors are transmitted for remote XR content reconstruction. Given the energy constraints of IoT devices and the low-latency demands of XR services, lightweight and fast encoding is essential. We propose a Semantically Weighted Principal Component Analysis (SWPCA), which selects components based on their relevance to XR rendering objectives-such as preserving object shape or motion. SWPCA applies data-dependent weights that reflect the saliency of sensor signals for reconstructing XR objects from the measurements. We analytically introduce SWPCA and apply it to XR gesture representation using wearable IoT measurements. We evaluate SWPCA performances within both an ideal and a realistic 6G end-to-end communication framework. SWPCA significantly reduces transmission bandwidth while preserving critical information for accurate XR gesture reconstruction. SWPCA provides a robust and fast encoding solution for real-time XR applications in energy/bandwidth constrained environments.
