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This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data...
In this paper, a novel estimation method on Volterra series high-order kernels to nonlinear dynamic system to arbitrary approximation is proposed. On the theoretical basis of kernel function, by the construction of linear space, the issue of solving the Volterra series order kernels is converted to solving the projection of the output of the observation vector in the a sub-space of Hilbert space,...
In this paper, a novel method to identify Volterra kernels of nonlinear and dynamical systems is proposed, The new notions introduced here develops an implementation of accurate and efficient nonparametric algorithm for the identification of Volterra series orders up to arbitrary precision. The model of aeroturbine is constructed using Volterra series. It is demonstrated that the speed of the turbine...
Traditional dimension reduction approaches always consider the samples in a class are uni-modal. In real world, samples in a class are usually multi-modal, for instance, the manifold of the facial appearance of a person under different illumination, expression, and poses is multi-modal. Recently, dimension reduction approaches based on manifold learning are presented, the main purpose is to preserve...
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