Tet Hin Yeap

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A closed-loop or recurrent neural network was taught to generate output discharges to reproduce the prototypical activations in agonist and antagonist muscles which produce the displacement of a limb about a single joint. By introducing a generalized decrease in the excitability of the pre-output layer in the network, the network made the displacement more(More)
Real-time recurrent learning (RTRL), commonly employed for training a fully connected recurrent neural network (RNN), has a drawback of slow convergence rate. In the light of this deficiency, a decision feedback recurrent neural equalizer (DFRNE) using the RTRL requires long training sequences to achieve good performance. In this paper, extended Kalman(More)
— This paper presents an approach to enhance speech feature estimation in the log spectral domain under additive noise environments. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, enforcing a state transition in the feature space and capturing the smooth time evolution of speech conditioned on(More)
— Accurate channel state information (CSI) is a prerequisite for diversity and multiplexing gains in multiple-input multiple-output (MIMO) wireless systems. More refined CSI can be attained by bootstrapping channel estimation techniques. In this paper, we propose adaptive filtering-based iterative channel estimators with the incorporation of an iterative(More)