Design of multi-axis micromotion system in TERS and its nonlinearity and crosstalk correction method.

Tip-Enhanced Raman Spectroscopy (TERS) is an advanced analytical measurement technology combining Raman spectroscopy with Scanning Probe Microscopy, which can detect the molecular structure and chemical composition in micro-nano-scale. As an indispensable part, the micromotion system directly determines TERS spatial resolution. The existing multi-axis system is often composed of several single-axis nonlinear systems, which solves whole problems with a superposition idea of single-axis part. But the multi-axis crosstalk under an overall idea is not fully considered and will cause system uncooperative and even oscillational. Therefore, a multi-axis micromotion system in TERS and its correction method are proposed. The improved Duhem model, simple calculation without inversion, accurate matching and fast response, has been built for nonlinearity. And the feedforward decoupling method is designed for crosstalk, having a favorable multi-axis coordination, good error tracking and simplified controllers. Experimental results show that it can greatly correct the nonlinearity and crosstalk of multi-axis system simultaneously.

An effective spectral unmixing algorithm for flow cytometry based on GA and least squares.

Spectral unmixing algorithm is one of the key technologies for spectral flow cytometer in biology, chemistry and medicine. The proposed algorithm can separate the overlapping spectra automatically without the premeasured single stained or un-stained samples as the basic pure spectra. Genetic algorithm is adopted to search the optimal positions and peak sharps of the basic spectra derived from the unknown components, and then the concentration of each component can be estimated simultaneously by least squares method. Compared with conventional methods, the proposed algorithm has a wider application scope, such as the multi-stained samples with unknown components or the samples with auto-fluorescence. In the simulation, the convergence rate, accuracy and stability of the proposed algorithm are evaluated under the conditions of completely and partly unknown components. In the experiment, the flow spectra of cyanobacteria are processed, and the results demonstrate the feasibility and effectiveness of the proposed algorithm.

Fully-automatic defects classification and restoration for STM images.

The Scanning tunneling microscope (STM) is a micro instrument designed for surface morphology with nanometer precision. The restoration of the STM image defects usually needs human judgements and manual positioning because of the diversity of the morphology and the randomness of the defects. This paper provides a new fully-automatic method that combines deep convolutional neural classification network and unique restoration algorithms corresponding to different defects. Aimed at automatically processing compound defects in STM images, the method first predicts what kinds of defects a raw STM image has by a series of parallel binary classification networks, and then decides the process order according to the predicted labels, and finally restores the defects by corresponding global restoration algorithms in order. Experiment results prove the provided method can restore the STM images by self-judging, self-positioning, self-processing without any manual intervention.