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Aiming at the disadvantages of edge detail loss and false edges in conventional Canny algorithm, this paper proposes an optimized Canny edge detection algorithm for edge detection of core image by combining self-adaption smooth filtering and self-adaptive threshold value. The algorithm, which retains the advantages of high signal to noise ra- tio, accurate positioning, single edge single response and etc in conventional Canny algorithm, has stronger de-noising ca- pability and better edge-detection effect. The experiment results indicate that the improved algorithm can perform self- adaptive edge-detection on the core image and create a clear coherent detected edge of image which can display the edge feature better.

A speech signal is sampled to form a sequence of speech data. The sequence of speech data is segmented into overlapping segments. Speech coefficients are generated by fitting each segment to a nonlinear predictive coding equation. The nonlinear predictive coding equation includes a linear predictive coding equation with linear terms, and additionally includes at least one cross term that is proportional to a product of two or more of the linear terms. If the segment is voiced, a sinusoidal term is included in the nonlinear predictive coding equation and sinusoidal parameters are generated. Otherwise, a noise term is included in the nonlinear predictive coding equation. The speech coefficients, a voiced bit, and, if the segment is voiced, the sinusoidal parameters are included as compressed speech data.