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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
Cost-Effective Quasi-Parallel Sensing Instrumentation for...
Godwin Enemali, Rui Zhang, Hugh McCann, Chang Liu · 2020-11-21 · via eess.SP updates on arXiv.org

Chemical Species Tomography (CST) has been widely applied for imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by line-of-sight Wavelength Modulation Spectroscopy (WMS) measurements from multiple laser beams. The modulated transmission signal on each laser beam needs to be a) digitised by a high-speed analogue-to-digital converter (ADC); b) demodulated by a digital lock-in (DLI) module; and c) transferred to high-level processor for image reconstruction. Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone. In addition, the huge amount of spectral data sampled in parallel significantly burdens the communication process in industrial applications where in situ signal digitisation is distanced from the high-level data processing. To address these issues, a quasi-parallel sensing technique and electronic circuits were developed for industrial CST, in which the digitisation and demodulation of the multi-beam transmission signals are multiplexed over the high-frequency modulation within a wavelength scan. Our development not only maintains the temporal response of the fully parallel sensing scheme, but also facilitates the cost-effective implementation of industrial CST with very low complexity and reduced load on data transfer. The proposed technique is analytically proven, numerically examined by noise-contaminated CST simulations, and experimentally validated using a lab-scale CST system with 32 laser beams.