No abstract

The surface electromyography(sEMG) signal emanates when people contract their muscles. The sEMG signal contains plenty of information about muscle activity. Therefore,it can be used in activity recognition, which makes great contribution to medical devices, e.g., protheses or orthoses control systems. Here,a de-noising technique is presented which applies singular spectrum analysis(SSA) to de-noise sEMG signals. The principle of SSA is to decompose the original time series into a set of additive time series in which noise can be easily distinguished from the useful signal. Unlike transform-based algorithms, such as discrete wavelet transform, SSA is a time-series analysis algorithm which is completely driven by signal itself. This data-driven nature makes SSA very useful for sEMG signal de-noising.

No abstract

Chinese calligraphy is a unique form of art that has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to generalize to new characters. As such, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose a new virtual brush model for simulating the real dynamic writing process. Our optimization approach is taken from pseudospectral optimal control, where the proposed dynamic virtual brush model plays a key role in formulating the objective function to be optimized. We also propose a strokelevel optimization to achieve better performance compared to the character-level optimization proposed in previous work. Our methodology shows good performance in drawing aesthetically pleasing characters.