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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
A Do-It-Yourself (DIY) Light-Wave Sensing and Communicati...
Sabit Ekin, John F. O'Hara, Emrah Turgut, Nicole Colston, Jeffre · 2020-07-20 · via eess.SP updates on arXiv.org

A do-it-yourself (DIY) light-wave sensing (LWS) and communication project was developed to generate interest and clarify basic electromagnetic (EM) and wireless communication concepts among students at different education levels from middle school to undergraduate. This paper demonstrates the nature of the project and its preliminary effectiveness. Wireless sensing/communication concepts are generally considered hard to comprehend being underpinned only by theoretical coursework and occasional simulations. Further, K-12 schools and small academic institutions may not have the resources necessary to produce tangible demonstrations for clarification. The consequent lack of affordable hands-on experiences fails to motivate and engage students. The DIY-LWS is intended to make wireless concepts more understandable and less esoteric by linking fundamental concepts with familiar technologies such as solar cells, visible lights, and smartphones. It is also intended to pique student interest by allowing them to personally assemble, operate, and explore a light-based wireless communication system. A preliminary assessment is used to determine the student base knowledge level and enthusiasm for wireless and related core topics. Students are instructed to assemble and test their own DIY-LWS hardware to provide a hands-on experience and stimulate further exploration. Short lectures are given to link conceptual ideas to the real-world phenomena. Finally, students are re-assessed to quantify any change in conceptual understanding. The DIY-LWS kits have been used in multiple events with students at different levels from secondary to high schools to college. Pre- and post-assessments revealed pronounced improvements (the number of correct answers doubled) in student understanding of EM concepts. Instructors observed tremendous interest and excitement among the students during and after the experiments.