The field of sensing and prediction technologies is witnessing significant advancements, driven by innovative applications of deep learning, graph modeling, and signal processing techniques. Researchers are exploring new ways to improve the accuracy and efficiency of sensing systems, including the use of multimodal fusion, attention mechanisms, and hierarchical architectures. Notably, the integration of prior knowledge and semantic information into inverse problems, such as Electrical Impedance Tomography, is showing promising results. Furthermore, the development of robust and noise-resilient prediction models for spatiotemporal data is enabling more accurate forecasting in various domains, including transportation and energy management.

Some noteworthy papers in this area include: The paper on Electromyography-Based Gesture Recognition presents a lightweight deep learning-based approach for effective sEMG-based hand gesture recognition, achieving high accuracy rates on multiple datasets. The SDEIT paper introduces a novel semantic-driven framework for Electrical Impedance Tomography, leveraging large-scale text-to-image generation models to improve structural consistency and recover fine details in reconstructions.