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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
A Microfabricated PCM-Switched Reconfigurable Intelligent...
Afsaneh Hojjati-Firoozabadi, Raafat Mansour · 2026-05-08 · via eess.SP updates on arXiv.org

This paper presents the design, fabrication, and experimental validation of a reconfigurable intelligent surface (RIS) employing electrically actuated vanadium dioxide (VO2) switches for millimeter wave beam steering. The proposed RIS is realized using a multilayer microfabrication process on an alumina substrate, enabling monolithic integration of hundreds of sub-4 micrometer VO2 switching elements within deeply subwavelength unit cells, approximately one-fifth of the wavelength. The switching-induced modulation of surface impedance is analyzed through full-wave simulations, and the resulting phase and amplitude responses are experimentally characterized using a custom WR-28 waveguide measurement setup. Based on the validated unit-cell design, a 10 x 20 RIS array integrating 200 VO2 switches is fabricated. The switches within each column are serially biased using integrated routing lines, allowing programmable control of the spatial phase distribution across the surface. Synthesized phase profiles enable dynamic beam steering, resulting in measured far-field gain enhancement of 10-20 dB over an 18 percent fractional bandwidth centered at 33 GHz, with steering angles up to 60 degrees. The measured radiation patterns are in good agreement with semi-numerical channel modeling predictions. By combining thin-film PCM switching with an integration-aware unit-cell design, this work demonstrates a scalable RIS architecture that mitigates packaging parasitics and footprint limitations inherent to conventional semiconductor-based implementations, providing a practical pathway toward higher-frequency reconfigurable surfaces.