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Object-Detecting Deep Learning for Multi-Event Electrochemical Data (Case No. 2024-088)

Summary: UCLA researchers in the Department of Chemistry and Biochemistry developed a custom-designed deep learning architecture for object detection and mechanisms classification to streamline the experimental design of cyclic voltammograms. Background: Cyclic voltammetry, a widely used electrochemical test, serves as a crucial tool in understanding...

Published: 2/14/2025 | Inventor(s): Chong Liu, Quanquan Gu, Benjamin Hoar, Weitong Zhang, Yuanzhou Chen

Keywords(s): 2D ultrasound imaging, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, bioelectronics, Biomaterial, electrochemical sensors, Electronics & Semiconductors, Lithium Battery, Lithium-Ion Battery, Machine Learning, Materials, Medical Devices and Materials, Medical diagnostics, Pharmaceutical Drug, Pharmaceutical testing, Rechargeable Battery, zzenergy storage

Category(s): Electrical > Electronics & Semiconductors, Energy & Environment > Energy Efficiency, Energy & Environment > Energy Storage, Energy & Environment > Water Monitoring & Treatment, Software & Algorithms > Image Processing, Software & Algorithms > Artificial Intelligence & Machine Learning, Electrical > Signal Processing