Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder.

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated.

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can.

Adaptive transmit diversity with orthogonal space-time block coding for telemedicine application.

Recently an adaptive transmit eigenbeamforming with orthogonal space-time block coding (Eigen-OSTBC) has been proposed. This model was simulated over macrocell environment with a uniform linear array (ULA) at the base station (BS) for next-generation (NG) wireless/mobile network. In this paper, we introduce a telemedicine simulation framework employing the Eigen-OSTBC scheme for the investigation of communication system characteristics in the application of biological data such as electrocardiogram (ECG).