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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
Board-level Code-Modulated Embedded Test and Calibration ...
Zhangjie Hong, Simon Schönherr, Vikas Chauhan, Brian Floyd · 2021-07-12 · via eess.SP updates on arXiv.org

We present methods for built-in test and calibration of phased arrays using code-modulated embedded test (CoMET). Our approach employs Cartesian modulation of test signals within each element using existing phase shifters, combining of these signals into an aggregate code-multiplexed response, downconversion and creation of code-modulated element-to-element "interference products" using a built-in power detector, demodulation of correlations from the digitized interference response, and extraction of amplitude and phase per element using an equation solver. Rotated-axis methodology is discussed for accurate extraction of phase near the original 0/90/180/270 degree axes. Our techniques are demonstrated at board level for both receive and transmit modes using an eight-element 8-16 GHz phased array constructed using ADAR1000 chips from ADI. At 6 GHz, CoMET-extracted gain and phase are accurate to within 0.2 dB and 3 degree compared to network-analyzer measurements. We then employ CoMET in a calibration loop to determinate optimum control settings at 6 GHz, outside the 8-16 GHz band for which the array was designed. We achieve seven-bit phase resolution with equalized gain. The root-mean squared gain and phase errors are improved from 0.8 dB and 8 degree before calibration to 0.1 dB and 1.7 degree after calibration.