Phase unwrapping is a crucial step in deriving deformation from interferometric synthetic aperture radar data, yet challenges such as discontinuous noise in low-coherence areas and random sensor noise can significantly compromise reconstruction accuracy and efficiency. Existing methods often struggle under noisy conditions and exhibit slower processing times. To overcome these limitations, this paper introduces Phaseformer, a transformer-based model designed to predict the wrap count at each pixel directly from wrapped phase maps. Due to the limited size of the real-world dataset, a simulated dataset with diverse noise levels was developed to train the model. Phaseformer achieved the lowest RMSE of 1.88 rad, a success ratio of 95.6%, and a runtime of 0.01 s per image, outperforming both state-of-the-art methods and traditional methods. For real-world scenarios, unwrapped phase results were integrated into a post-processing workflow using MintPy to derive time-series deformation. Interferograms from Hawaii and Mexico City, generated using Sentinel-1 data and validated with GPS ground truth, revealed consistent deformation rates with minimal loop closure errors. These results highlight Phaseformer’s potential for accurate and efficient phase unwrapping in complex, real-world applications.

This study presents a framework for distributed predictive maintenance using the BaSyx platform and edge computing devices. By integrating predictive maintenance algorithms, the system can estimate remaining useful life, detect early faults, and identify anomalies without prior failure data. The real-time processing capabilities of edge devices, combined with a local server, aid in immediate data integration and decision-making to optimize maintenance scheduling and improve operational reliability. While vehicles are used as a specific example in this research, the proposed framework is adaptable and applicable to predictive maintenance across various industrial assets. This study provides a baseline for advanced predictive maintenance strategies.

It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a trade-off between the computational complexity and memory consumption. In this study, we evaluate the performance and complexity of different fusion operators in multi-modal object detection tasks. On top of that, a Poolformer-based fusion operator (PoolFuser) is proposed to enhance the accuracy of detecting targets without compromising the efficiency of the detection framework.