Investment Micro-Casting 3D-Printed Multi-Metamaterial for Programmable Multimodal Biomimetic Electronics

Research on Multi-material Biomimetic Electronics Based on Investment Micro-casting 3D Printing Academic Background With the rapid development of biomimetic electronics, electronic skin (E-skin) and flexible sensors that mimic human perceptual functions have shown broad application prospects in robotics, medical devices, and human-computer interact...

An Inkjet-Printable Organic Electrochemical Transistor Array with Differentiated Ion Dynamics for Sweat Fingerprint Identification

An Inkjet-Printable Organic Electrochemical Transistor Array with Differentiated Ion Dynamics for Sweat Fingerprint Identification

Sweat Fingerprint Identification Technology Based on Ion Dynamics: Research on Inkjet-Printed Organic Electrochemical Transistor Arrays Academic Background Sweat, as a non-invasive biomarker, contains rich physiological information that can reflect human health conditions, such as hydration balance and disease markers. However, sweat has complex co...

Floating Electricity Generator for Omnidirectional Droplet Vibration Harvesting

Floating Electricity Generator for Omnidirectional Droplet Vibration Harvesting

Floating Omnidirectional Droplet Vibration Generator: Breakthrough Research Academic Background With the widespread application of Internet of Things (IoT) devices in marine environmental monitoring, how to provide stable power to these devices without relying on the power grid has become an urgent issue. Traditional renewable energy sources such a...

Deep Bayesian Active Learning Using In-Memory Computing Hardware

With the rapid development of artificial intelligence (AI) technologies, deep learning has made significant progress in complex tasks. However, the success of deep learning largely relies on massive amounts of labeled data, and the data labeling process is not only time-consuming and labor-intensive but also requires specialized domain knowledge, m...

Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment with Relational Graph Learning

Multi-agent Cooperative Navigation in Hybrid Environments: A New Reinforcement Learning Approach Based on Relational Graph Learning Mobile robotics is witnessing a surge in applications, fueled by advancements in artificial intelligence, with navigation capabilities being one of the core focus areas of research. Traditional navigation methods often...