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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
Sample clock frequency offset (SCFO) Resolution Team 3 (R...
Brent Carlson, Paul Boven, Kris Caputa · 2018-12-19 · via eess.SP updates on arXiv.org

This Resolution Team 3 (RT-3) report contains the results of the investigation of key aspects of the proposed Sample Clock Frequency Offset (SCFO) scheme for the Mid SKA1 telescope. This is a scheme, first proposed by one author (Carlson) at a meeting at the SKAO 25-Jan-2013, to digitize the analog signal at each antenna at a slightly different sample rate and transmit the data to the CSP Mid.CBF for subsequent digital re-sampling to a common sample clock frequency before channelization, correlation, and beamforming. The primary purpose for doing this is to cause de-correlation of sample clock-related self-interference to be able to improve correlated and beamformed signal quality. This report includes an investigation of the efficacy of the method, investigation of its implementation by SADT, DISH, and CSP, presentation of further supporting modeling results augmenting the original modeling work, a note on expansion to SKA-2, as well as possible draw-backs and concerns. Finally, there are potential additional benefits in signal quality in implementing the SCFO scheme, in particular de-correlation of aliased RFI (particularly for Nyquist Zone-2 digitized signals) as well as relaxation of signal-chain anti-aliasing filters transition band roll-off and reject band attenuation prior to digitization in the antenna.