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Grain yield is important in national economy so there is necessary for grain yield prediction. A novel predicting model based on spiking neural networks (SNNs) is presented for this purpose. SNNs are computationally more effective than conventional artificial neural networks. The spiking neurons act as basic elements in which information deliver from one neuron to another in forms of multiple spikes via plenty of synapses. Besides, the corresponding learning mechanism called Spikeprop is also discussed. An example, prediction of China annual grain yields as our experiment, is used to explain the principle of SNNs based method. Experimental results are demonstrated showing the feasibility and accuracy of our approach.

Biomedical image denoising algorithm based on gradient dependent energy functional often compromised the biomedical image features like textures or certain details. This paper proposes an iterative regularization denoising method based on OSV model for biomedical image denoising. By using iterative regularization, the oscillating patterns of texture and detail are added back to fit and compute the original OSV model,and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed algorithm also be proved the convergence property. Experimental results show that the proposed method can achieve a batter result in preserving not only the features of textures for biomedical image denoising but also the details for biomedical image.