arXiv subjects

Yueying Ma

Publications and source records attributed to Yueying Ma.

3 recordsLinked to original sources

arXiv2025

Single-crystalline high-quality beta-Ga2O3 pseudo-substrate on sapphire through sputtering for epitaxial deposition

Solid-phase epitaxy (SPE) of beta-Ga2O3 thin films by radio-frequency (RF) sputtering and then crystallized through high-temperature post-deposition annealing is employed on sapphire substrates, yielding a high-quality pseudo-substrate for subsequent buffer growth via MOCVD and LPCVD. Low roughness (<0.5 nm) and sharp single-crystalline diffraction peaks corresponding to the (-201), (-402), and (-603) reflections of beta-Ga2O3 were observed in the SPE beta-Ga2O3 film and the subsequent epitaxial buffer layer. N-doped Ga2O3 film on SPE Ga2O3 film grown by LPCVD showed step-assisted growth mode with reasonable electronic behavior with 45 cm^2/V-s mobility at a bulk carrier concentration of 1.3e17 cm^-3. These results suggest that SPE Ga2O3 is a promising pathway to advance the development of beta-Ga2O3 on foreign substrates.

cond-mat.mtrl-sci↗

arXiv2022

Trading Strategies: Earning More in Investment

Gold and bitcoin are not new to us, but with limited cash and time, given only the past stream of the daily price of gold and bitcoin, it is a kind of new problem for us to develop a certain model and determine the best strategy to get the most return. Here, our team members analyzed the data provided and finally made a unified system of models to predict the price and evaluate the risk and return in our act of investment, and we name this series of models and measurements as CTP Model. This is a model which can determine and describe what transaction should the trader make each day and what is the certain maximum return he will get under different risk levels.

cs.OH↗

arXiv2022

Deep Residual Shrinkage Networks for EMG-based Gesture Identification

This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN.

eess.SP↗