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Hamming codes are of primary concern in information theory and its applications. Despite a number of researches that have been conducted on such codes and their characterizations, dealing with the properties of previously introduced codes in a BCK-algebraic framework has not been considered in earlier works. This paper investigates a code constructed based on BCK-algebraic models and proposes an algorithm corresponding to the presented code. It is noticeable that the suggested rendered algorithm is also established on the basis of the elements of a BCK-algebra. In fact, both the Hamming distance and dimension, associated with the presented code, can be estimated through a BCK-algebra structure due to the mechanism of its algorithm which is heavily dependent on the parity check matrix. In addition, the way in which the codes are designed contributes substantially to classification of them and to extract greater number of their attributes compared to the previous works. The highlight of the proposed method is that the number of atoms of the BCK-algebra plays a key role in calculation of the Hamming distance and dimension of these codes. Moreover, the obtained codes possess specified and recognizable Hamming distance which are essential in performing error-correcting, error-detecting and decoding tasks. [ABSTRACT FROM AUTHOR]

In the present paper we describe innovative architecture of artificial neural network based on Hopfield structure - Self Correcting Neural Network (SCNN). It is implementation similar to dual mode Hopfield-like network for solving stereo matching problem. Considered network consists of basic layer of neurons implemented as analogue Hopfield-like network and supervising layer. Thanks to the supervising layer, there is a possibility of modification of the connection weights between the neurons in the basic layer. This enables the improvement of the network performance (accuracy). Authors propose a depth map use for image segmentation and objects auto-selection. High enough accuracy of these processes can be achieved when proposed network (SCNN) is applied. Similar idea can be applied also for images noise removal. In the present article we also describe in detail neurons dynamics in the basic and supervising layers of the SCNN. The network considered here was a subject of experimental tests using real stereo pictures as well as simulated stereo images. This enabled calculation of error and direct comparison with classic analogue Hopfield neural network. [ABSTRACT FROM AUTHOR]