No abstract

No abstract

To improve the recognition accuracy of underwater acoustic targets by artificial neural network, this study presents a new recognition method that integrates a one-dimensional convolutional neural network and a long short-term memory network. This new network framework is constructed and applied to underwater acoustic target recognition for the first time. Ship acoustic data are used as input to evaluate the network performance. A visual analysis of the recognition results is performed. The results show that this method can realize the recognition and classification of underwater acoustic targets. Compared with a single neural network, the relevant indices, such as the recognition accuracy of the joint network are considerably higher. This provides a new direction for the application of deep learning in the field of underwater acoustic target recognition.

No abstract

When an image is enhanced using the traditional Laplacian enhancement model, the enhancement effect is evident but the overshoot phenomenon occurs simultaneously as the neighborhood and weight increase. To solve the contradiction between edge preservation and noise suppression during the image enhancement process, this study proposed an improved partial differential equation image enhancement algorithm. The algorithm combined the neighborhood features of digital images, mined the relationship between the local variance and the detail information of images, and classified the image noise and detail information with local variance. On this basis, the periodic change features of the trigonometric function were used to construct anisotropic diffusion coefficients, the numerical solution method of the partial differential equation was used to calculate the numerical solution of the proposed algorithm, and simulation experiment was implemented to analyze the influence of the algorithm model parameters on enhancement effect. Lastly, the anisotropic enhancement algorithm proposed in this study was validated through comparison with the traditional Laplace algorithm. Results indicate that tangential direction parameter and the number of iterations exert influence on the image enhancement effects. That is, when tangential direction parameter is set as 0.5 and the iterative operation is conducted 5 times, the algorithm smoothens the noise and enhances the image details. When the proposed algorithm is used to process low-contrast industrial image, the signal-to-noise ratio of the enhanced image is increased by 8 db and the mean squared error is reduced by 55% compared with the enhancement effect of the traditional Laplace enhancement method. In terms of subjective visual effect, the noise of the enhanced image is filtered, whereas detailed information (e.g., numerous weak edges) is reserved. Hence, subjective and objective evaluations reflected that the proposed algorithm can effectively enhance image details and avoid over-enhancement. Moreover, the proposed algorithm is of favorable adaptability and certain reference value to low-contrast image enhancement.