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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 Learning Coverage- and Power-Optimal Transmitter Placement from Building Maps: A Comparative Study of Direct and Indirect Neural Approaches Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems Low-Rank Adaptation Redux for Large Models Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels How Well Can We Decode Vowels from Auditory EEG -- A Rigorous Cross-Subject Benchmark with Honest Assessment FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G What Physics do Data-Driven MoCap-to-Radar Models Learn? TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset AI-Enabled Covert Channel Detection in RF Receiver Architectures Temporal Cross-Modal Knowledge-Distillation-Based Transfer-Learning for Gas Turbine Vibration Fault Detection Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain The Existential Theory of Research: Why Discovery Is Hard Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding BioTrain: Sub-MB, Sub-50mW On-Device Fine-Tuning for Edge-AI on Biosignals Applied AI-Enhanced RF Interference Rejection From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals Diffusion-Based Generative Priors for Efficient Beam Alignment in Directional Networks WearBCI Dataset: Understanding and Benchmarking Real-World Wearable Brain-Computer Interfaces Signals An Edge-Cloud Collaborative Architecture for Proactive Elderly Care: Real-Time Risk Assessment and Three-Level Emergency Response A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops LiveSense: A Real-Time Wi-Fi Sensing Platform for Range-Doppler on COTS Laptop WST-X Series: Wavelet Scattering Transform for Interpretable Speech Deepfake Detection Real-Time Streamable Generative Speech Restoration with Flow Matching Concurrence: A dependence criterion for time series, applied to biological data PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData Feedback Lunch: Learned Feedback Codes for Secure Communications StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs Distributed Associative Memory via Online Convex Optimization Networks of Causal Abstractions: A Sheaf-theoretic Framework Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention Biased Federated Learning under Wireless Heterogeneity Drivetrain simulation using variational autoencoders Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
Reconfigurable Intelligent Surface-Enabled Gridless DoA Estimation System for NLoS Scenarios
Jiawen Yuan, Shaodan Ma, Gong Zhang, Henry Leung · 2023-04-16 · via eess.SP updates on arXiv.org

The conventional direction-of-arrival (DoA) estimation approaches only be effective when the line-of-sight (LoS) link exists, while in the case of the non-line-of-sight (NLoS) situation, the spatial angle can not be captured and thus the DoA estimation performance would be significantly degraded. To address this challenge, a novel reconfigurable intelligent surface (RIS)- enabled gridless DoA estimation approach is proposed, where the RIS can establish the virtual LoS link between the base station (BS) and the targets. For extracting the statistics of the signal, the RIS-enabled signal model in the covariance domain is proposed. Then we estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to obtain the RIS-enabled covariance matrix free of noise nuisance. Additionally, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem. To ease the calculation burden, an efficient iterative approach finally is designed to solve the ANM problem via the alternating direction method of multipliers (ADMM). Numerical experiments validate the robustness of the proposed method against the benchmark in terms of computational efficiency and multi-source DoA estimation precision.

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