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We report the first demonstration, to our knowledge, of the femtosecond laser operation by using a new alloyed Yb:GYSO crystal as the gain medium. With a 5 at. %Yb(3+)-doped sample and chirped mirrors for dispersion compensation, we obtained pulses as short as 210 fs at the center wavelength of 1093 nm. The average mode-locking power is 300 mW, and the pulse repetition frequency is 80 MHz.

A highly efficient diode-end-pumped femtosecond Yb:yttrium aluminum garnet (YAG) ceramic laser was demonstrated. Pumped by a 968 nm fiber-coupled diode laser, 1.9 W mode-locked output power at a repetition rate of 64.27 MHz was obtained with 3.5 W absorbed pump power, corresponding to a slope efficiency of 76%. Our measurement showed that the pulse duration was 418 fs with the central wavelength of 1048 nm.

AbstracztThe main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher's linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network.It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.

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