In this study, a fast and fully software-based algorithm for digital phase-locked loop (PLL) is proposed via a new hybrid approach in software and hardware by using an advanced digital signal processor architecture. The proposed algorithm is robust against line disturbances such as phase-angle jump, voltage sag, third harmonic injection, multi-zero crossing and step change in frequency at the input voltage. Performance and robustness of the proposed method are investigated through experimental studies. Furthermore, it is compared with three different PLL algorithms in detail to show its superiority over existing methods.
In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen-Loéve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method.
In linear image restoration, the point spread function of the degrading system is assumed known even though this information is usually not available in real applications. As a result, both blur identification and image restoration must be performed from the observed noisy blurred image. This paper presents a computationally simple linear adaptive finite impulse response filter for blind image deconvolution. This is essentially a two-dimensional version of the Constant Modulus Algorithm that is well known in the field of blind equalization. The two-dimensional extension is shown capable of reconstructing noisy blurred images using partial a priori information about the true image and the point spread function. The method is applicable to minimum as well as mixed phase blurs. Experimental results are provided.
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