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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 Fully Screen-Printed Vanadium-Dioxide Switches Based Wi...
Yiming Yang, Mohammad Vaseem, Ruiqi Wang, Behrooz Makki, Atif Sh · 2024-04-30 · via eess.SP updates on arXiv.org

Reconfigurable Intelligent Surface (RIS) is attracting more and more research interest because of its ability to reprogram the radio environment. Designing and implementing the RIS, however, is challenging because of limitations of printed circuit board (PCB) technology related to manufacturing of large sizes as well as the cost of switches. Thus, a low-cost manufacturing process suitable for large size and volume of devices, such as screen-printing is necessary. In this paper, for the first time, a fully screen-printed reconfigurable intelligent surface (RIS) with vanadium dioxide (VO2) switches for 5G and beyond communications is proposed. A VO2 ink has been prepared and batches of switches have been printed and integrated with the resonator elements. These switches are a fraction of the cost of commercial switches. Furthermore, the printing of these switches directly on metal patterns negates the need of any minute soldering of the switches. To avoid the complications of multilayer printing and realizing the RIS without vias, the resonators and the biasing lines are realized on a single layer. However, this introduces the challenge of interference between the biasing lines and the resonators, which is tackled in this work by designing the bias lines as part of the resonator. By adjusting the unit cell periodicity and the dimension of the H-shaped resonator, we achieve a 220 to 170° phase shift from 23.5 GHz to 29.5 GHz covering both n257 and n258 bands. Inside the wide bandwidth, the maximum ON reflection magnitude is 74%, and the maximum OFF magnitude is 94%. The RIS array comprises 20x20 unit cells (4.54x4.54λ^2 at 29.5 GHz). Each column of unit cells is serially connected to a current biasing circuit. To validate the array's performance, we conduct full-wave simulations as well as near-field and far-field measurements.