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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
Flexible Architecture for Real-time Processing of Multipl...
Mohamed Awad, Islam T. Abougindia, Ahmed Elliethy, Hussein A. Al · 2019-12-30 · via eess.SP updates on arXiv.org

Simultaneous processing of multiple video sources requires each pixel in a frame from a video source to be processed synchronously with the pixels at the same spatial positions in corresponding frames from the other video sources. However, simultaneous processing is challenging as corresponding frames from different video signals provided by multiple sources have time-varying delay because of the electrical and mechanical restrictions inside the video sources hardware that cause deviation in the corresponding frame rates. Researchers overcome the aforementioned challenges either by utilizing ready-made video processing systems or designing and implementing a custom system tailored to their specific application. These video processing systems lack flexibility in handling different applications requirements such as the required number of video sources and outputs, video standards, or frame rates of the input/output videos. In this paper, we present a design for a flexible simultaneous video processing architecture that is suitable for various applications. The proposed architecture is upgradeable to deal with multiple video standards, scalable to process/produce a variable number of input/output videos, and compatible with most video processors. Moreover, we present in details the analog/digital mixed-signals and power distribution considerations used in designing the proposed architecture. As a case study application of the proposed flexible architecture, we utilized the architecture for a realization of a simultaneous video processing system that performs video fusion from visible and near-infrared video sources in real time. We make available the source files of the hardware design along with the bill of material (BOM) of the case study to be a reference for researchers who intend to design and implement simultaneous multi-video processing systems.