Data mining can effectively obtain the faint spatial object’s patterns and characteristics, the universal relations and other
implicated data characteristics, the key of which is classifier construction. Faint spatial object classifier construction with
spatial data mining technology for faint spatial target detection is proposed based on theoretical analysis of design
procedures and guidelines in detail. For the one-sidedness weakness during dealing with the fuzziness and randomness
using this method, cloud modal classifier is proposed. Simulating analyzing results indicate that this method can realize
classification quickly through feature combination and effectively resolve the one-sidedness weakness problem.
Based on the real-time adaptive femtosecond pulse shaping system, the phase compensating algorithms
which can effectively compensate the output shaping waveform distortions are investigated in detail.
The simulated-annealing algorithm that can modify the output pulse temporal waveforms iteratively
toward the target shapes using the second harmonic generating frequency resolved optical gating
(SHG-FROG) measurement as feedback is proposed. Compared with the cross-correlation feedback
measurement method, the output based on the SHG-FROG measurement method is better and the
temporal chirp of the output pulse is compensated more effectively. Moreover the performance of the
SHG-FROG measurement feedback algorithm is compared to other exemplary standard approaches
such as the Genetic Algorithm based on the cross-correlation feedback measurement method, the result
is much better.