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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
Design and Implementation of DC-to-5~MHz Wide-Bandwidth H...
Jinshui Zhang, Boshuo Wang, Xiaoyang Tian, Angel Peterchev, Stef · 2023-09-13 · via eess.SP updates on arXiv.org

Advances in power electronics have made it possible to achieve high power levels, e.g., reaching GW in grids, or alternatively high output bandwidths, e.g., beyond MHz in communication. Achieving both simultaneously, however, remains challenging. Various applications, ranging from efficient multichannel wireless power transfer to cutting-edge medical and neuroscience applications, are demanding both high power and wide bandwidth. Conventional inverters can achieve high power and high quality at grid or specific frequency ranges but lose their fidelity when reaching higher output frequencies. Resonant circuits can promise a high output frequency but only a narrow bandwidth. We overcome the hardware challenges by combining gallium-nitride (GaN) transistors with modular cascaded double-H bridge circuits and control that can manage typical timing and balancing issues. We developed a lightweight embedded control solution that includes an improved look-up-table digital synthesizer and a novel adaptive-bias-elimination nearest-level modulation. This solution effectively solves the conflict between a high power level and high output bandwidth and can--in contrast to previous approaches--in principle be scaled in both dimensions. Our prototype exhibits a frequency range from DC to 5 MHz with <18% total voltage distortion across the entire frequency spectrum, while achieving a power level of >5 kW. We conducted tests by sweeping the output frequency and two channel-mixing trials, which included a practical magnetogenetics-oriented stimulation pulse and an entertaining trial to reproduce the famous Arecibo message with the current spectrum.