Feb. 8, 2024, 5:47 a.m. | Mingxaun Liu Jiankai Tang Haoxiang Li Jiahao Qi Siwei Li Kegang Wang Yuntao Wang Hong Chen

cs.CV updates on arXiv.org arxiv.org

Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture. To the best of our knowledge, we are …

accuracy ann anns artificial artificial neural networks challenges computing computing resources cs.cv deployment devices measuring mobile mobile devices networks neural networks rate resources transformer videos

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

ETL Developer

@ Gainwell Technologies | Bengaluru, KA, IN, 560100

Medical Radiation Technologist, Breast Imaging

@ University Health Network | Toronto, ON, Canada

Data Scientist

@ PayPal | USA - Texas - Austin - Corp - Alterra Pkwy