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Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary...
We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights...
The mathematical model of nonlinear device (ND) plays an essential role in the power amplifier (PA) linearization by means of digital signal processing. In this paper we propose a model derived by the Wiener orthogonalization method. The one important feature of this method is that the resulted output decomposition depends on the statistics of the input signal, and initially it was derived by Wiener...
Many background subtraction algorithms have been proposed in the last fifteen years and an important issue is to provide a way to evaluate and compare most popular models according to criteria. This paper present a comparison among the eleven models using BMC dataset and give a guideline to choose different algorithms in different scenes by computing the F-measure, Peak Signal-Noise Ratio, Structural...
Modern cars require powerful multi- and manycore hardware platforms to fulfill the demands of upcoming computationally intensive advanced driver assistance systems. This leads to a distributed hardware/software architecture that poses an unbearable system complexity to designers. Additionally, the strict requirements of new functional safety standards make it extremely difficult to rapidly and comprehensively...
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression...
This paper describes a technical problem of obtaining a unique SDL entity identifier using SDL/SystemC co-modeling. The problem occurs during scientific research in co-modeling field as well as work under real projects. Solution consists in redefinition the standard function of SDL simulation kernel for getting access to entity name and then conversion its name to a unique identifier. The paper explains...
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