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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
CONCERTO: Characterization of analog readout electronics
2026-04-27 · via eess.SP updates on arXiv.org

CONCERTO is a millimeter-wave imaging instrument that operated on the Atacama Pathfinder Experiment (APEX) telescope from April 2021 to May 2023. Its primary scientific objectives include the study of galaxy clusters through the Sunyaev-Zel'dovich (SZ) effects, the observation of Galactic star-forming regions, and the first measurements constraining the power spectrum of dusty star-forming galaxies. The instrument consists of two detector arrays, each comprising 2400 Microwave Kinetic Inductance Detectors (MKIDs). Each of the two arrays comprises six feed-lines and is read out by six KID_READOUT electronic boards, each capable of reading out one feed-line coupled to 400 frequency-multiplexed MKIDs. As the demand for higher-resolution millimeter-wave imaging continues to grow, future instruments aim to significantly increase the pixel count, with more than 800 detectors per feed-line. However, the MKID readout electronics chain is inherently complex, making it difficult to fully understand its performance limits and optimization margins. To address this challenge, we initiated a modeling effort that first focused on the digital section of KID\_READOUT. In this phase, we developed a digital twin of the FPGA-based signal processing chain, which led to substantial performance improvements. The present paper extends this modeling strategy to the analog readout chain. It presents the characterization and behavioral modeling of all analog components, allowing us to identify the elements that limit the frequency multiplexing factor, determine the dominant noise contributors, and highlight areas for improvement in signal conditioning for the future-generation board. Together, these developments establish, to the best of our knowledge, the first consolidated digital-and-analog behavioral framework of an MKID readout architecture, implemented in a unified Python-based modeling environment.