Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human–machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.
Micro-Electro-Mechanical Systems (MEMS) accelerometers have great potentials for applications in aerospace, autonomous driving and consumer electronics. However, most of their working principles are based on capacitive and resistance types, which cannot be easily used for wireless and passive sensing, while surface acoustic waves (SAWs) are the key solution for this problem. Due to complex acoustic-electric-mechanical coupling during accelerator operations, currently, there needs an accurate, reliable, and efficient design and simulation platform to improve the structure and performance of SAW based accelerometers. In this work, we proposed an accurate, reliable, and efficient modeling platform to optimize designs of SAW accelerometers, using a double-ended cantilever beam structure as an example. This model integrated elastic acoustic effect and the coupled wave equations under both the mechanical and electrical loading using the finite element analysis, and obtained acceleration-frequency responses of the accelerators. We have systematically simulated effects of thickness of piezoelectric film, wavelengths, and structural parameters of cantilever beams, and the simulation results are well consistent with the theoretical results. Finally, using the developed model, we designed a high-G SAW accelerometer (up to 20000 g), which achieved a high sensitivity (-41.8 Hz/g) and excellent linearity (0.9999), and another high sensitivity accelerometer (3.02 KHz/g), with a good linearity (0.9999) over a 100 g acceleration range.
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