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.

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.