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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
Monitoring Electrostatic Adhesion Forces via Acoustic Pre...
Huacen Wang, Jiarui Zou, Zeju Zheng, Hongqiang Wang · 2025-05-22 · via eess.SP updates on arXiv.org

Electrostatic adhesion is widely used in mobile robotics, haptics, and robotic end effectors for its adaptability to diverse substrates and low energy consumption. Force sensing is important for feedback control, interaction, and monitoring in the EA system. However, EA force monitoring often relies on bulky and expensive sensors, increasing the complexity and weight of the entire system. This paper presents an acoustic-pressure-based method to monitor EA forces without contacting the adhesion pad. When the EA pad is driven by a bipolar square-wave voltage to adhere a conductive object, periodic acoustic pulses arise from the EA system. We employed a microphone to capture these acoustic pressure signals and investigate the influence of peak pressure values. Results show that the peak value of acoustic pressure increased with the mass and contact area of the adhered object, as well as with the amplitude and frequency of the driving voltage. We applied this technique to mass estimation of various objects and simultaneous monitoring of two EA systems. Then, we integrated this technique into an EA end effector that enables monitoring the change of adhered object mass during transport. The proposed technique offers a low-cost, non-contact, and multi-object monitoring solution for EA end effectors in handling tasks.