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In this paper we propose to use an adaptive ensemble learning framework with different levels of diversity to handle streams of data in non-stationary scenarios in which concept drifts are present. Our adaptive system consists of two ensembles, each one with a different level of diversity (from high to low), and, therefore, with different and complementary capabilities, that are adaptively combined...
In this paper, we propose a novel localization algorithm to be used in applications where the measurement model is neither accurate nor complete. In our algorithm, we apply radial basis function (RBF) interpolation to evaluate the measurement function on the entire surveillance area and, then, estimate the target position. Since the signal function is sparse in the spatial domain, we also propose...
This paper deals with the compensation for nonlinear distortions introduced by power-efficient amplifiers on linear modulations by means of equalization. In our approach, we employ a Decision-Feedback Equalizer (DFE) based on the Generalized Cerebellar Model Arithmetic Computer. The new scheme is compared with the conventional Linear DFE, the Volterra and the Multi-Layer Perceptron-based DFE in terms...
The subject of this communication is the compensation of nonlinearities in digital radio links, where the major source of nonlinearity is caused by the High Power Amplifier (HPA), typically working close to its saturation point because of energy constraints. This paper deals with the design of CMAC-based predistorters for application in digital transmission over nonlinear channels with memory. A novel...
In this paper, we focus on the parameter estimation of dynamic state-space models using privacy-protected data. We consider an scenario with two parties: on one side, the data owner, which provides privacy-protected observations to, on the other side, an algorithm owner, that processes them to learn the system’s state vector. We combine additive homomorphic encryption and Secure Multiparty Computation...
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