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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
Sensor-Outage-Aware Spatio-Temporal Graph Reconstruction ...
Seyedeh Fatemeh Mirfakhar, Reda Snaiki · 2026-05-04 · via eess.SP updates on arXiv.org

Time-resolved facade pressure fields are essential for the wind-resistant design and aerodynamic assessment of high-rise buildings. However, dense instrumentation is costly and often impractical, and sensor outages can further reduce data availability. This study proposes a sensor-outage-aware spatio-temporal graph reconstruction framework for completing facade pressure fields from sparse measurements. The method couples temporal feature extraction with graph-based spatial propagation on a unified facade-domain representation and uses an explicit observation-availability indicator to handle temporarily unavailable sensor signals while reconstructing both missing instrumented channels and non-instrumented locations. The framework is evaluated using wind-tunnel pressure coefficient data for a high-rise building across windward, lateral, and leeward facades under multiple wind directions. The results show reliable outage-tolerant reconstruction at instrumented sensors and accurate full-field completion at non-instrumented nodes, with reconstruction generally most accurate on the windward facade and more challenging on the lateral and leeward facades. Time-domain, spectral, and spatial validations further show that the framework preserves the dominant temporal evolution, principal dynamic content, and coherent large-scale pressure-field organization, while the largest residual discrepancies remain localized in higher-frequency or intermittent components. A two-stage predictive extension is also outlined, in which future sensor signals are forecast at available instrumented locations and then mapped to future full-field pressure estimates through the proposed reconstruction model.