Papers
1
Total Citations
31
H-Index
1
About
Yingmei Qin is a researcher whose work bridges the gap between biological neural systems and real-time hardware implementations. Her primary research areas include neuromorphic engineering, central pattern generators (CPGs), and field-programmable gate array (FPGA) design for robotics and neural control systems. Her most notable contribution is the development of a real-time FPGA implementation of a biologically inspired CPG network, published in 2017, which has garnered 31 citations. This work demonstrates how neural oscillators can be efficiently mapped onto digital hardware to generate rhythmic patterns for locomotion in robots, offering a robust, low-latency alternative to software-based approaches. By translating biological principles into practical, high-speed circuits, Qin has advanced the field of bio-inspired robotics, enabling more adaptive and energy-efficient control systems. Her research holds significant potential for applications in prosthetics, autonomous vehicles, and rehabilitation devices. With a focus on hardware-software co-design and neural modeling, Qin continues to contribute to the growing intersection of computational neuroscience and embedded systems, making her work a valuable resource for students and researchers exploring real-time neural control.
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Top Papers
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