In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.
The use of digital technologies for processing and diagnosing electrocardiogram signals using wavelet-analysis can significantly improve the efficiency and quality of parameter estimations of the pacemaker configuration during implantation. It is also efficient in the process of correction of functional modes of cardiac pacemaker and diagnostics to eliminate postoperative complications, etc. A special processing of complex cardio signals at a qualitatively new level is an indispensable condition for the decisive improvement of the processing of current values of diagnosed parameters, widespread use of digital instruments for sound and informed decision-making on the provision of medical care and the treatment of people with diseases of the cardiovascular system. The article discusses the approximation method. Digital technologies are implemented using MATLAB computing environment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.