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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
Virtual VNA: Minimal-Ambiguity Scattering Matrix Estimati...
Philipp del Hougne · 2024-03-13 · via eess.SP updates on arXiv.org

We estimate the scattering matrix of an arbitrarily complex linear, passive, time-invariant system with $N$ monomodal lumped ports by inputting and outputting waves only via a fixed set of $N_\mathrm{A}<N$ ports while terminating the remaining $N_\mathrm{S}=N-N_\mathrm{A}$ "not-directly-accessible" (NDA) ports with tunable individual loads. First, we present a closed-form approach requiring at least three arbitrary, distinct, and known loads at each NDA port; sign ambiguities on off-diagonal scattering coefficients associated with NDA ports are inevitable. Being matrix-valued, our approach is ideally suited to mitigate noise sensitivity using more accessible ports. It also yields $1+2N_\mathrm{S}+N_S(N_S-1)/2$ as upper bound on the number of required measurements $N_\mathrm{cal}$ for $N_\mathrm{A}>1$ in the low-noise regime. Second, we present a gradient-descent approach using random load configurations, enabling flexible adjustments of $N_\mathrm{cal}$ to further mitigate noise. Third, we present an intensity-only gradient-descent approach, dispensing with phase-sensitive detectors at the expense of an additional blockwise phase ambiguity. Then, we discuss in what applications the inevitable remaining ambiguities are problematic and how to lift them. Finally, we experimentally validate all three approaches with an eight-port reverberation chamber and $N_\mathrm{A}=N_\mathrm{S}=4$, systematically assessing the sensitivity to noise and $N_\mathrm{cal}$. We coin our technique "virtual vector network analyzer (VNA)" because it implies that suitably tunable and characterized individual loads can essentially be interpreted as additional "virtual" VNA ports. Our method can characterize static large antenna systems with many and/or embedded ports, but also reconfigurable wave systems; it may further enable wireless sensing in indoor surveillance, non-destructive testing, and bioelectronics.