Holographic multiple-input multiple-output (HMIMO) systems deploy ultra-dense antennas in confined spaces, yet spatial degrees of freedom (DoF) fail to scale with element count. Only by harnessing evanescent waves can the potential gains of ultra-dense arrays be unlocked. However, in practical scenarios, antenna apertures are typically too small relative to communication distances to generate significant near-field effects for capturing these evanescent waves. This paper proposes a method to alter the dispersion relation via locally resonant metamaterials (LRM), thereby enabling the radiation of near-field evanescent wave components into the far field. This approach leverages the DoF gains offered by ultra-dense elements within a confined aperture. Full-wave simulations validate the effectiveness of the evanescent-propagating wave conversion method, demonstrating an increase in the spatial DoF radiated into the far field by the HMIMO system even with a limited aperture.

As more medical data become digitalized, machine learning is regarded as a promising tool for constructing medical decision support systems. Even with vast medical data volumes, machine learning is still not fully exploiting its potential because the data usually sits in data silos, and privacy and security regulations restrict their access and use. To address these issues, we built a secured and explainable machine learning framework, called explainable federated XGBoost (EXPERTS), which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data. It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals. To study the performance, we evaluate our approach by real-world datasets, and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.